1{"type":"atis-latch","atis":"","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:34:06.353Z","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","content":"Review"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:34:06.357Z","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1"}4{"parentUuid":null,"isSidechain":false,"promptId":"01f9be6f-d015-4eb3-b8dc-9dd1d87b03b8","type":"user","message":{"role":"user","content":"Review"},"uuid":"1f968c2d-dde4-49b2-b119-11ccede7a9f0","timestamp":"2026-10-30T05:34:06.360Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","shapeHash":"80e183abfbc7335290bfa20318d598a10dbca7f952739a0080b8d8663232c751","timestamp":"2026-10-30T05:34:06.369Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RT0008\nDate: 2026-10-30\n\nYou are an independent scientific reviewer. In the current working directory, `TASK.md` describes a bounded scientific task and the directory `out/` contains the deliverables submitted by another assistant (you have not seen how they were produced and you must not look for their working transcript). There is no network access. Use `~/wp29/venv/bin/python` if you need to re-run or test code. Do not modify any file under `out/` except that you must create `out/review/review.json`.\n\nReview the deliverables against `TASK.md` only. Specifically check:\n1. Does `out/results.json` contain every required key with legitimately computed values? Re-run `out/analysis.py` in a scratch copy if needed and compare.\n2. Are the information-availability rules of `TASK.md` respected by the code (for example no use of future information, no use of forbidden inputs)? Read the code to verify; do not trust the report.\n3. Are the central claims in `out/report.md` and `out/claims.json` supported by the numbers in `out/results.json`, with appropriate uncertainty and caveats? Flag any claim that is stronger than the evidence.\n4. Are counts, exclusions, units, signs and magnitudes internally consistent? Are baseline comparisons present when the task requires them?\n5. Are limitations stated honestly? Is anything declared complete that is not?\n\nYou have no reference answers; do not guess what the \"right\" number is. Report only issues you can justify from the files. Write `out/review/review.json` as {\"verdict\": \"accept\"|\"revise\", \"issues\": [{\"id\": \"R1\", \"severity\": \"critical\"|\"major\"|\"minor\", \"location\": \"<file or key>\", \"issue\": \"...\", \"suggested_fix\": \"...\"}], \"summary\": \"...\"}. Use \"revise\" if there is at least one critical or major issue. Be concise and specific. End with a one-paragraph plain-text summary.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"","type":"string"},"dangerouslyDisableSandbox":{"description":"Set this to true to dangerously override sandbox mode and run commands without sandboxing.","type":"boolean"}},"required":["command"],"additionalProperties":false}},{"name":"Edit","description":"modify file contents in place","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to modify","type":"string"},"old_string":{"description":"The text to replace","type":"string"},"new_string":{"description":"The text to replace it with (must be different from old_string)","type":"string"},"replace_all":{"description":"Replace all occurrences of old_string (default false)","default":false,"type":"boolean"}},"required":["file_path","old_string","new_string"],"additionalProperties":false}},{"name":"Read","description":"read files, images, PDFs, notebooks","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to read","type":"string"},"offset":{"description":"The line number to start reading from. Only provide if the file is too large to read at once","type":"integer","minimum":0,"maximum":9007199254740991},"limit":{"description":"The number of lines to read. Only provide if the file is too large to read at once.","type":"integer","exclusiveMinimum":0,"maximum":9007199254740991},"pages":{"description":"Page range for PDF files (e.g., \"1-5\", \"3\", \"10-20\"). Only applicable to PDF files. Maximum 20 pages per request.","type":"string"},"allow_large":{"description":"Set to true to read a text file, or a line range of one, that is over the usual size limits, up to what still fits in your context. Only use this when you genuinely need all of it or the user asked for the whole file; otherwise read it in parts with offset and limit, or search it.","type":"boolean"}},"required":["file_path"],"additionalProperties":false}}]}}6{"type":"api-request-blob","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","hash":"d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9","message":{"role":"system","content":[],"output_config":{"effort":"medium"}}}7{"type":"api-request","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","id":"65d83200-d660-49e2-9e70-820d3c2fd05b","timestamp":"2026-10-30T05:34:06.369Z","version":"2.1.314","querySource":"sdk","shapeHash":"80e183abfbc7335290bfa20318d598a10dbca7f952739a0080b8d8663232c751","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"keep":0,"tail":["99833769e4310fc124186eb9e97416cea64dc8ececb015cbe8928bf0c2bd9984","d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",0,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"319029b0f705acfd5773661ffa599d6311adac47e82ab9c6b90a6412da45e1c1"}8{"parentUuid":"1f968c2d-dde4-49b2-b119-11ccede7a9f0","isSidechain":false,"attachment":{"type":"session_context","context":{}},"type":"attachment","uuid":"b61d0ece-be3c-46e4-b507-7cf34fe612ba","timestamp":"2026-10-30T05:34:06.363Z","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}9{"parentUuid":"b61d0ece-be3c-46e4-b507-7cf34fe612ba","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30"},"type":"attachment","uuid":"292a261d-a06e-4f50-9ab8-d01ad544c5ef","timestamp":"2026-10-30T05:34:06.363Z","rendered":[{"content":"<system-reminder>\nToday's date is 2026-10-30.\n</system-reminder>"}],"renderedRole":"system","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}10{"parentUuid":"292a261d-a06e-4f50-9ab8-d01ad544c5ef","isSidechain":false,"attachment":{"type":"remote_session_change","url":null,"commit":"Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>","pr":"🤖 Generated with [Claude Code](https://claude.com/claude-code)","sendUserFileHint":false,"managedCommit":false,"managedPr":false},"type":"attachment","uuid":"9dc9f0c2-cdcb-4231-b6b9-5df61fb167b4","timestamp":"2026-10-30T05:34:06.364Z","rendered":[{"content":"<system-reminder>\nAttribution for git commits and pull requests you create from here on (this replaces Claude Code's own earlier attribution guidance, such as a previous copy of this reminder; the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1"}12{"parentUuid":"9dc9f0c2-cdcb-4231-b6b9-5df61fb167b4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXspQQkZ48bNHq6iWcCU","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":716,"cache_read_input_tokens":1504,"output_tokens":122,"output_tokens_details":{"thinking_tokens":17},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":716},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":459,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXspQ9P8YZQgrD9NbBc4","requestRef":"65d83200-d660-49e2-9e70-820d3c2fd05b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"cda29887-5335-4810-b8dd-0f02537cbe92","timestamp":"2026-10-30T05:34:07.791Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"cda29887-5335-4810-b8dd-0f02537cbe92","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXspQQkZ48bNHq6iWcCU","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KNRKsDr1N9PgUgbSjxLXTd","name":"Bash","input":{"command":"cat TASK.md; ls -la out; cat out/results.json; cat out/claims.json","description":"Read task and deliverables"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":716,"cache_read_input_tokens":1504,"output_tokens":122,"output_tokens_details":{"thinking_tokens":17},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":716},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXspQ9P8YZQgrD9NbBc4","requestRef":"65d83200-d660-49e2-9e70-820d3c2fd05b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"789fedd3-21cf-4ca5-b26d-5f610199b129","timestamp":"2026-10-30T05:34:08.390Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}14{"parentUuid":"789fedd3-21cf-4ca5-b26d-5f610199b129","isSidechain":false,"promptId":"01f9be6f-d015-4eb3-b8dc-9dd1d87b03b8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KNRKsDr1N9PgUgbSjxLXTd","type":"tool_result","content":"# T3-synthesis — Does generative-AI assistance shorten the time workers need to complete their tasks? A small evidence synthesis\n\n## Scientific question\nFive accessible primary studies report how access to a generative-AI assistant changed the performance of workers (software developers, IT administrators, security analysts) on their tasks. They report their effects under different definitions: a regression coefficient on log time-on-task, a percentage change in completion time, a percentage difference in mean task duration holding accuracy constant, a percentage time saving per processed item with a p-value, and a difference-in-differences coefficient on output counts around the withdrawal of access. This is a complete, bounded evidence-synthesis project: extraction with locators → harmonisation onto one stated definition → pooled estimates with heterogeneity → forest plot → an appropriately limited synthesis. The target definition is θ = ln(T_AI / T_noAI), the natural logarithm of the ratio of mean task-completion time with AI assistance to mean task-completion time without it, for the same tasks, from a randomised comparison (negative = faster with AI). The estimands are: the five extracted values exactly as printed; the harmonised θ and its standard error for every study where both can be obtained from the printed quantities under the conventions below; the inverse-variance fixed-effect and DerSimonian–Laird random-effects pooled θ over the poolable studies, with Q, I² and τ²; and the synthesis conclusion that these numbers support.\n\n## Inputs\n- `data/studies.json` — the five items {\"id\", \"source\", \"short\", \"extract\"}: `extract` names the printed quantity to take from each source (which table/row/column or sentence).\n- `data/sources/arxiv-2410.12944v3.txt` — Paradis et al. (2024), *How much does AI impact development speed? An enterprise-based randomized controlled trial* (Google), CC BY 4.0: abstract, analytic approach, results Sections VI-B/VI-C with Table III (OLS on logged time on task; coefficients with standard errors in parentheses; the four model columns are collapsed into one line per row, see the extract's editorial note).\n- `data/sources/arxiv-2507.09089v2.txt` — Becker et al. (2025), *Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity* (METR), CC BY 4.0: abstract, effect-estimation section, opening of the results, Appendix D (regression, confidence intervals, conversion to speedup).\n- `data/sources/arxiv-2411.01067v2.txt` — Bono & Xu (2024), *Randomized Controlled Trials for Security Copilot for IT Administrators* (Microsoft), CC BY 4.0: abstract, RCT protocol, results on speed with Table 3 (task durations in minutes, holding accuracy constant; significance by asterisks only), Appendix B (how time savings are computed and bootstrapped).\n- `data/sources/arxiv-2511.13860v1.txt` — Bono (2025), *Randomized Controlled Trials for Phishing Triage Agent* (Microsoft), CC BY 4.0: abstract, methodology (three arms), results Sections 3.1 (true positives per analyst minute, Table 1) and 3.3 (Table 3, time per email with % time savings and p-values), Appendix B.\n- `data/sources/arxiv-2304.09339v1.txt` — Kreitmeir & Raschky (2023), *The Unintended Consequences of Censoring Digital Technology – Evidence from Italy's ChatGPT Ban*, CC BY 4.0: abstract, outcome definitions (Section 3.1), Section 4 with Table 1 (difference-in-differences coefficients, standard errors in parentheses).\n- `data/README.md` — provenance, retrieval times, licences, SHA-256 of the extracts.\n\n## Information-availability rules\n- The extracts are the only admissible evidence. A quantity that is not printed in the cited extract is not available, even if you believe it is in the full paper, in a figure, or elsewhere; do not substitute a remembered or guessed value.\n- Harmonisation conventions (state the one you apply in `transformation`):\n - a coefficient b on an AI indicator in a regression whose dependent variable is the natural log of completion time gives θ = b with SE(θ) = SE(b); a 95% confidence interval [L, U] on that scale gives SE = (U − L)/(2 × 1.96);\n - a percentage change in time c (e.g. \"+19%\" means c = 0.19, \"−29.79%\" means c = −0.2979) or a ratio of mean times T_AI / T_noAI gives θ = ln(1 + c) = ln(T_AI / T_noAI);\n - when only a two-sided p-value is printed for an estimate (no SE, no CI), the SE may be recovered as SE(θ) ≈ |θ| / z_{1−p/2} with z from the standard normal; mark the study as poolable with an approximate SE and say so;\n - when the only uncertainty information is significance stars, a p-value threshold, or a confidence interval shown in a figure that is not reproduced, SE(θ) cannot be obtained: the study is harmonisable (θ can be computed) but **not poolable** (`harmonised_se` = null, `poolable` = false, with the reason);\n - a study is **not harmonisable** (`harmonisable` = false, `harmonised_estimate` = null, with the reason) when the printed effect is not a comparison of task-completion times with and without AI access on the same tasks (for example an effect on output counts, or a comparison that is not AI-versus-no-AI).\n- Pooling: inverse-variance fixed effect and DerSimonian–Laird random effects over the poolable studies only, on the θ scale, with 95% intervals using 1.96; Q with k − 1 degrees of freedom; I² = max(0, (Q − df)/Q) × 100; τ² = max(0, (Q − df)/C) with C = Σw − Σw²/Σw. `heterogeneity_interpretation` is \"low\" if I² < 25, \"moderate\" if 25 ≤ I² < 50, \"substantial\" if I² ≥ 50. `pooled_significant_5pct` is true only if the random-effects 95% interval excludes 0.\n- `feasibility` refers to the project as a whole: the project is complete when every item has either harmonised values or an explicit, justified statement of what cannot be obtained, and the pooled estimates are computed over the poolable items.\n\n## Required outputs\n- `out/table1.csv` — the extraction table, one row per study (5 rows): `study`, `estimate_as_printed`, `uncertainty_as_printed`, `sample_as_printed`, `definition`, `locator`, `harmonised_estimate`, `harmonised_se`, `harmonisable`, `poolable`, `transformation`, `reason_if_not`.\n- `out/figure1.png` — a forest plot of the harmonised estimates (all harmonisable studies; those without an SE shown as points without intervals) with the pooled random-effects estimate.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"T3-synthesis\" |\n| `target_definition` | string | — | the harmonisation target in your words |\n| `items` | object | — | {\"S1\": {...}, …, \"S5\": {...}} with, for each study: `estimate_as_printed` (string, the printed numbers with their printed unit/sign), `uncertainty_as_printed` (string: SE, CI, p-value, stars, or \"none printed\"), `sample_as_printed` (string), `definition` (string), `locator` (string: source id, table/section, row, column), `harmonisable` (boolean), `harmonised_estimate` (number or null, θ), `harmonised_se` (number or null), `poolable` (boolean), `transformation` (string, the formula with the printed inputs), `reason_if_not` (string or null) |\n| `k_extracted` | integer | — | number of items with an extracted printed value |\n| `k_harmonised` | integer | — | number of items with a non-null `harmonised_estimate` |\n| `k_pooled` | integer | — | number of items entering the pooled estimates |\n| `fe_estimate`, `fe_se`, `fe_ci_low`, `fe_ci_high` | number | log ratio | fixed-effect pooled θ, its SE and 95% interval |\n| `re_estimate`, `re_se`, `re_ci_low`, `re_ci_high` | number | log ratio | random-effects pooled θ, its SE and 95% interval |\n| `q_stat` | number | — | Cochran's Q |\n| `i2` | number | percent | I² |\n| `tau2` | number | — | DerSimonian–Laird τ² |\n| `heterogeneity_interpretation` | string | — | \"low\", \"moderate\" or \"substantial\" (rule above) |\n| `pooled_significant_5pct` | boolean | — | random-effects interval excludes 0 |\n| `pooled_pct_change_re` | number | percent | 100 × (exp(re_estimate) − 1) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the project as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, ambiguous units, …) and how handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: lines as reported by `wc -l`; `studies.json`: number of items) |\n| `n_analysed` | integer or null | — | number of studies assessed (5) |\n| `random_seed` | integer or null | — | null (no randomness) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The synthesis must say what the pooled numbers can and cannot support given the number of poolable studies, the approximate SE, and the studies left out.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/table1.csv` and `out/figure1.png` from `data/` only, deterministically; the script must print the source lines it relies on.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Report printed numbers exactly as printed (same sign, same decimals); transformations are applied only in the harmonised columns.\n- Budget class: transfer mini-project — 12.00 USD cap, 60 minutes wall, 150 turns per session. The project is designed to be completed in well under 45 minutes of work.\ntotal 232\ndrwxr-xr-x 11 simon-pierreboucher staff 352 oct. 30 01:34 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 01:30 ..\n-rw-r--r-- 1 simon-pierreboucher staff 17269 oct. 30 01:32 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 2367 oct. 30 01:33 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 508 oct. 30 01:30 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 65260 oct. 30 01:32 figure1.png\n-rw-r--r-- 1 simon-pierreboucher staff 7221 oct. 30 01:33 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 9562 oct. 30 01:32 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 01:34 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 01:33 state\n-rw-r--r-- 1 simon-pierreboucher staff 3996 oct. 30 01:32 table1.csv\n{\n \"task_id\": \"T3-synthesis\",\n \"target_definition\": \"theta = ln(T_AI / T_noAI): natural log of the ratio of mean task-completion time with generative-AI assistance to mean completion time without it, for the same tasks, from a randomised comparison (negative = faster with AI).\",\n \"items\": {\n \"S1\": {\n \"estimate_as_printed\": \"-0.24. (beta_1 for ExpCon, Model 2, Table III; Section VI-C: beta_1=-0.24, i.e. 21% faster)\",\n \"uncertainty_as_printed\": \"SE (0.14); 95% CI = [-0.51, 0.03], p=0.086 (NS)\",\n \"sample_as_printed\": \"n = 96 (Table III caption); Section VI-B: N=47 AI, N=46 no-AI\",\n \"definition\": \"OLS coefficient on experimental condition (AI exposure) with natural-log time on task (LogToT) as dependent variable, developer-level covariates (Model 2), one enterprise coding task, randomised\",\n \"locator\": \"arxiv-2410.12944v3, Section VI-C Table III, row ExpCon, column (2) 'Dev' (second value on collapsed line L76; SE line L78); CI and p in Section VI-C text\",\n \"harmonisable\": true,\n \"harmonised_estimate\": -0.24,\n \"harmonised_se\": 0.14,\n \"poolable\": true,\n \"transformation\": \"log-time coefficient: theta = b = -0.24; SE(theta) = SE(b) = 0.14 (printed). Check: SE from CI = (0.03-(-0.51))/(2*1.96) = 0.1378\",\n \"reason_if_not\": null\n },\n \"S2\": {\n \"estimate_as_printed\": \"+19% (allowing AI increases completion time by 19%; regression-based, Section 3 / Section D.1)\",\n \"uncertainty_as_printed\": \"none printed (95% CI shown only in Figure 1 / Figure 15, not reproduced in the extract)\",\n \"sample_as_printed\": \"16 developers, 246 tasks; 136 issues AI-allowed, 110 AI-disallowed\",\n \"definition\": \"S = E[time AI allowed]/E[time AI disallowed] - 1, estimated as exp(beta)-1 from OLS of log implementation time on AI indicator and log forecast time; issue-level randomisation\",\n \"locator\": \"arxiv-2507.09089v2, Abstract and Section 3 'Results' (opening paragraph); estimator Sections 2.3, D.1-D.3\",\n \"harmonisable\": true,\n \"harmonised_estimate\": 0.17395330712343798,\n \"harmonised_se\": null,\n \"poolable\": false,\n \"transformation\": \"percentage change in time: theta = ln(1 + c) = ln(1 + 0.19) = 0.173953\",\n \"reason_if_not\": \"No SE, CI or p-value printed in the extract; the CI exists only in a figure that is not reproduced, so SE(theta) cannot be obtained (harmonisable, not poolable).\"\n },\n \"S3\": {\n \"estimate_as_printed\": \"Overall 53.84, Treatment 44.29, Control 63.10 (minutes); Improvement -29.79%***\",\n \"uncertainty_as_printed\": \"significance stars only: *** (paper's definition 0<p<=0.01); no SE or CI printed\",\n \"sample_as_printed\": \"181 subjects (randomised half treatment, half control)\",\n \"definition\": \"difference in mean task duration (minutes) between Copilot and control groups across all tasks, holding accuracy constant (control duration predicted at treatment accuracy, Appendix B), randomised\",\n \"locator\": \"arxiv-2411.01067v2, Section 4.2 Table 3, 'Aggregate Results' block, row 'All Content', columns Treatment/Control/Improvement\",\n \"harmonisable\": true,\n \"harmonised_estimate\": -0.353679434958934,\n \"harmonised_se\": null,\n \"poolable\": false,\n \"transformation\": \"percentage change in time: theta = ln(1 + c) = ln(1 + (-0.2979)) = -0.353679 (cf. ln(44.29/63.10) = -0.353962)\",\n \"reason_if_not\": \"Only significance stars (p<=0.01) are printed; a p-value threshold does not identify SE(theta) (harmonisable, not poolable).\"\n },\n \"S4\": {\n \"estimate_as_printed\": \"Aware 50.5 s, Control 56.9 s per email; % Time Savings 11.1\",\n \"uncertainty_as_printed\": \"p-value 0.471\",\n \"sample_as_printed\": \"Aware N=56, Control N=52 (167 analysts across three arms incl. Blind N=59)\",\n \"definition\": \"percentage time saving in mean time per email over the full 25-email queue, Aware (agent verdicts/rationale + prioritised queue) vs Control (no AI), randomised arms\",\n \"locator\": \"arxiv-2511.13860v1, Section 3.3 Table 3 'Comparison of Email Processing Times', column 'Full Corpus', rows Aware/Control time, % Time Savings, p-value\",\n \"harmonisable\": true,\n \"harmonised_estimate\": -0.11765804346823246,\n \"harmonised_se\": 0.1632205919720678,\n \"poolable\": true,\n \"transformation\": \"percentage change in time c = -11.1/100 = -0.111; theta = ln(1 + c) = -0.117658; approximate SE from two-sided p: SE = |theta|/z(1-0.471/2) = 0.117658/0.720853 = 0.163221 (approximate SE)\",\n \"reason_if_not\": null\n },\n \"S5\": {\n \"estimate_as_printed\": \"-0.278 (Treat x Post, Output, column 3, panel A)\",\n \"uncertainty_as_printed\": \"SE (0.295) (clustered at user level); no significance star\",\n \"sample_as_printed\": \"N = 18332 (user-day observations, users with affiliation)\",\n \"definition\": \"difference-in-differences coefficient on aggregate GitHub Output measure (sum of Push, PR, PR comments, commit comments, Create, Issues events) for Italian vs Austrian/French users, pre 27/28.03 vs post 03/04.04.2023 around Italy's ChatGPT ban\",\n \"locator\": \"arxiv-2304.09339v1, Section 4 Table 1, panel A, row Treat x Post, column (3) Output (Users with Affiliation)\",\n \"harmonisable\": false,\n \"harmonised_estimate\": null,\n \"harmonised_se\": null,\n \"poolable\": false,\n \"transformation\": \"none applied (not harmonisable)\",\n \"reason_if_not\": \"Effect on output counts (GitHub events) from a DiD around withdrawal of access, not a comparison of task-completion times with vs without AI on the same tasks, and not randomised.\"\n }\n },\n \"k_extracted\": 5,\n \"k_harmonised\": 4,\n \"k_pooled\": 2,\n \"pooled_studies\": [\n \"S1\",\n \"S4\"\n ],\n \"fe_estimate\": -0.18814332871148873,\n \"fe_se\": 0.10626483095451474,\n \"fe_ci_low\": -0.39642239738233764,\n \"fe_ci_high\": 0.02013573995936016,\n \"re_estimate\": -0.18814332871148873,\n \"re_se\": 0.10626483095451474,\n \"re_ci_low\": -0.39642239738233764,\n \"re_ci_high\": 0.02013573995936016,\n \"q_stat\": 0.3236860522786328,\n \"q_df\": 1,\n \"q_pvalue\": 0.5694008376973132,\n \"i2\": 0.0,\n \"tau2\": 0.0,\n \"heterogeneity_interpretation\": \"low\",\n \"pooled_significant_5pct\": false,\n \"pooled_pct_change_re\": -17.15040485348688,\n \"pooled_pct_change_re_ci\": [\n -32.72775202989503,\n 2.0339831514392115\n ],\n \"weights_fe\": {\n \"S1\": 0.5761333825403874,\n \"S4\": 0.4238666174596126\n },\n \"weights_re\": {\n \"S1\": 0.5761333825403874,\n \"S4\": 0.4238666174596126\n },\n \"baseline_unweighted_mean_theta_harmonised\": -0.13434604282593213,\n \"baseline_unweighted_mean_theta_poolable\": -0.17882902173411622,\n \"sensitivity_s1_se_from_ci\": {\n \"s1_se\": 0.13775510204081634,\n \"re_estimate\": -0.1891067723193802,\n \"re_se\": 0.10527305720065441,\n \"re_ci_low\": -0.39544196443266283,\n \"re_ci_high\": 0.01722841979390244,\n \"q_stat\": 0.3281104262022853,\n \"i2\": 0.0,\n \"tau2\": 0.0\n },\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"S1: Section VI-B reports N=47 (AI) + N=46 (no-AI) = 93 developers, while Table III states n = 96.\",\n \"handling\": \"Not resolvable from the extract; reported. sample_as_printed lists both; does not affect theta or SE, which are taken from Table III/Section VI-C.\"\n },\n {\n \"issue\": \"S1: Section VI-B gives Mean LogToT=4.6 for the AI group and 4.3 for no-AI while stating AI was faster (96 vs 114 min); the log means appear swapped/inconsistent.\",\n \"handling\": \"These log means are not used.\"\n },\n {\n \"issue\": \"S1: Table III is rendered with the four model columns collapsed into one line; SE (0.14) is rounded, and the CI [-0.51, 0.03] implies SE 0.1378.\",\n \"handling\": \"Model 2 taken as the second value per the editorial note; printed SE 0.14 used; CI-based SE reported as sensitivity (sensitivity_s1_se_from_ci).\"\n },\n {\n \"issue\": \"S2: The uncertainty of the headline +19% estimate is shown only in Figure 1/Figure 15, which are not reproduced; no SE or p-value printed.\",\n \"handling\": \"Harmonised (theta = ln 1.19) but not poolable; harmonised_se = null.\"\n },\n {\n \"issue\": \"S3: Treatment/Control means 44.29/63.10 imply -29.81%, printed improvement is -29.79% (slightly beyond rounding of the printed means); significance shown only by stars with an unusual convention (* = 0.05<p<=0.1).\",\n \"handling\": \"Printed -29.79% used as instructed (difference in theta ~0.0003); not poolable because stars do not identify an SE.\"\n },\n {\n \"issue\": \"S4: Table 3 units ambiguous: Malicious/Non-Malicious times per email (1.8-2.8 's') cannot average to Full Corpus times (50.5/56.9 s) under any common unit; Full Corpus ratio 50.5/56.9 implies 11.25% saving vs printed 11.1% (within rounding).\",\n \"handling\": \"Only the Full Corpus column is used; theta depends only on the printed % saving (unit-free). Flagged as a source inconsistency.\"\n },\n {\n \"issue\": \"S4: SE is approximate, back-calculated from a two-sided p-value (0.471) under a normal approximation on the log scale, although the paper's test is presumably on the time difference.\",\n \"handling\": \"Marked as approximate SE in transformation; pooled results carry this caveat.\"\n },\n {\n \"issue\": \"S5: Table 1 note prints '** p<0.5' (presumably p<0.05); row label rendered as '\\\\times TreatPost'; panel B header lists 11.04 although the text says it was excluded; rendered date line 'August 24, 2026' is an artefact.\",\n \"handling\": \"No effect on the extracted panel A column 3 value; S5 is not harmonisable anyway.\"\n }\n ],\n \"n_input_rows\": {\n \"studies.json\": 5,\n \"README.md\": 17,\n \"sources/arxiv-2410.12944v3.txt\": 112,\n \"sources/arxiv-2507.09089v2.txt\": 132,\n \"sources/arxiv-2411.01067v2.txt\": 109,\n \"sources/arxiv-2511.13860v1.txt\": 162,\n \"sources/arxiv-2304.09339v1.txt\": 108\n },\n \"n_analysed\": 5,\n \"random_seed\": null\n}[\n {\"id\": \"C1\", \"text\": \"Four of five studies can be harmonised onto theta = ln(T_AI/T_noAI); S5 (DiD on GitHub output counts) cannot.\", \"results_keys\": [\"k_harmonised\", \"items\"], \"status\": \"supported\", \"caveat\": \"Harmonisation of S1/S2 coefficients to a ratio of means relies on the papers' log-linear assumptions.\"},\n {\"id\": \"C2\", \"text\": \"Only S1 and S4 are poolable; S2 (CI only in an unreproduced figure) and S3 (stars only) have no obtainable SE.\", \"results_keys\": [\"k_pooled\", \"pooled_studies\", \"items\"], \"status\": \"supported\", \"caveat\": \"S4 SE is approximate, recovered from a p-value.\"},\n {\"id\": \"C3\", \"text\": \"The pooled FE and RE estimate is theta = -0.188 (about -17% task time), 95% CI [-0.396, 0.020].\", \"results_keys\": [\"fe_estimate\", \"re_estimate\", \"re_ci_low\", \"re_ci_high\", \"pooled_pct_change_re\", \"pooled_pct_change_re_ci\"], \"status\": \"supported\", \"caveat\": \"k = 2; RE equals FE because tau2 = 0.\"},\n {\"id\": \"C4\", \"text\": \"The pooled effect is not statistically significant at the 5% level; the data cannot establish that generative AI shortens task time.\", \"results_keys\": [\"pooled_significant_5pct\", \"re_ci_low\", \"re_ci_high\"], \"status\": \"supported\", \"caveat\": \"Conclusion is conditional on the two poolable studies; excluded studies point in opposite directions.\"},\n {\"id\": \"C5\", \"text\": \"Estimated heterogeneity among poolable studies is low (Q = 0.324, I2 = 0%, tau2 = 0), but with k = 2 this is uninformative, and the non-pooled harmonised estimates (S2 +0.174, S3 -0.354) indicate heterogeneity in direction across settings.\", \"results_keys\": [\"q_stat\", \"i2\", \"tau2\", \"heterogeneity_interpretation\", \"items\"], \"status\": \"supported\", \"caveat\": \"Cross-study heterogeneity including S2/S3 is not formally testable without their SEs.\"},\n {\"id\": \"C6\", \"text\": \"Using the S1 SE derived from its printed CI instead of the printed SE leaves the conclusion unchanged (RE -0.189 [-0.395, 0.017]).\", \"results_keys\": [\"sensitivity_s1_se_from_ci\"], \"status\": \"supported\", \"caveat\": \"Only this one sensitivity analysis was run.\"},\n {\"id\": \"C7\", \"text\": \"The inputs contain several internal inconsistencies (S1 sample size and log means, S3 rounding, S4 units, S5 star note), none of which materially changes a harmonised value.\", \"results_keys\": [\"data_issues\"], \"status\": \"supported\", \"caveat\": \"Inconsistencies cannot be resolved from the extracts.\"}\n]","is_error":false}]},"uuid":"e5c7a852-9e17-48ac-82ca-eb7adaaa85f7","timestamp":"2026-10-30T05:34:08.476Z","toolUseResult":{"stdout":"# T3-synthesis — Does generative-AI assistance shorten the time workers need to complete their tasks? A small evidence synthesis\n\n## Scientific question\nFive accessible primary studies report how access to a generative-AI assistant changed the performance of workers (software developers, IT administrators, security analysts) on their tasks. They report their effects under different definitions: a regression coefficient on log time-on-task, a percentage change in completion time, a percentage difference in mean task duration holding accuracy constant, a percentage time saving per processed item with a p-value, and a difference-in-differences coefficient on output counts around the withdrawal of access. This is a complete, bounded evidence-synthesis project: extraction with locators → harmonisation onto one stated definition → pooled estimates with heterogeneity → forest plot → an appropriately limited synthesis. The target definition is θ = ln(T_AI / T_noAI), the natural logarithm of the ratio of mean task-completion time with AI assistance to mean task-completion time without it, for the same tasks, from a randomised comparison (negative = faster with AI). The estimands are: the five extracted values exactly as printed; the harmonised θ and its standard error for every study where both can be obtained from the printed quantities under the conventions below; the inverse-variance fixed-effect and DerSimonian–Laird random-effects pooled θ over the poolable studies, with Q, I² and τ²; and the synthesis conclusion that these numbers support.\n\n## Inputs\n- `data/studies.json` — the five items {\"id\", \"source\", \"short\", \"extract\"}: `extract` names the printed quantity to take from each source (which table/row/column or sentence).\n- `data/sources/arxiv-2410.12944v3.txt` — Paradis et al. (2024), *How much does AI impact development speed? An enterprise-based randomized controlled trial* (Google), CC BY 4.0: abstract, analytic approach, results Sections VI-B/VI-C with Table III (OLS on logged time on task; coefficients with standard errors in parentheses; the four model columns are collapsed into one line per row, see the extract's editorial note).\n- `data/sources/arxiv-2507.09089v2.txt` — Becker et al. (2025), *Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity* (METR), CC BY 4.0: abstract, effect-estimation section, opening of the results, Appendix D (regression, confidence intervals, conversion to speedup).\n- `data/sources/arxiv-2411.01067v2.txt` — Bono & Xu (2024), *Randomized Controlled Trials for Security Copilot for IT Administrators* (Microsoft), CC BY 4.0: abstract, RCT protocol, results on speed with Table 3 (task durations in minutes, holding accuracy constant; significance by asterisks only), Appendix B (how time savings are computed and bootstrapped).\n- `data/sources/arxiv-2511.13860v1.txt` — Bono (2025), *Randomized Controlled Trials for Phishing Triage Agent* (Microsoft), CC BY 4.0: abstract, methodology (three arms), results Sections 3.1 (true positives per analyst minute, Table 1) and 3.3 (Table 3, time per email with % time savings and p-values), Appendix B.\n- `data/sources/arxiv-2304.09339v1.txt` — Kreitmeir & Raschky (2023), *The Unintended Consequences of Censoring Digital Technology – Evidence from Italy's ChatGPT Ban*, CC BY 4.0: abstract, outcome definitions (Section 3.1), Section 4 with Table 1 (difference-in-differences coefficients, standard errors in parentheses).\n- `data/README.md` — provenance, retrieval times, licences, SHA-256 of the extracts.\n\n## Information-availability rules\n- The extracts are the only admissible evidence. A quantity that is not printed in the cited extract is not available, even if you believe it is in the full paper, in a figure, or elsewhere; do not substitute a remembered or guessed value.\n- Harmonisation conventions (state the one you apply in `transformation`):\n - a coefficient b on an AI indicator in a regression whose dependent variable is the natural log of completion time gives θ = b with SE(θ) = SE(b); a 95% confidence interval [L, U] on that scale gives SE = (U − L)/(2 × 1.96);\n - a percentage change in time c (e.g. \"+19%\" means c = 0.19, \"−29.79%\" means c = −0.2979) or a ratio of mean times T_AI / T_noAI gives θ = ln(1 + c) = ln(T_AI / T_noAI);\n - when only a two-sided p-value is printed for an estimate (no SE, no CI), the SE may be recovered as SE(θ) ≈ |θ| / z_{1−p/2} with z from the standard normal; mark the study as poolable with an approximate SE and say so;\n - when the only uncertainty information is significance stars, a p-value threshold, or a confidence interval shown in a figure that is not reproduced, SE(θ) cannot be obtained: the study is harmonisable (θ can be computed) but **not poolable** (`harmonised_se` = null, `poolable` = false, with the reason);\n - a study is **not harmonisable** (`harmonisable` = false, `harmonised_estimate` = null, with the reason) when the printed effect is not a comparison of task-completion times with and without AI access on the same tasks (for example an effect on output counts, or a comparison that is not AI-versus-no-AI).\n- Pooling: inverse-variance fixed effect and DerSimonian–Laird random effects over the poolable studies only, on the θ scale, with 95% intervals using 1.96; Q with k − 1 degrees of freedom; I² = max(0, (Q − df)/Q) × 100; τ² = max(0, (Q − df)/C) with C = Σw − Σw²/Σw. `heterogeneity_interpretation` is \"low\" if I² < 25, \"moderate\" if 25 ≤ I² < 50, \"substantial\" if I² ≥ 50. `pooled_significant_5pct` is true only if the random-effects 95% interval excludes 0.\n- `feasibility` refers to the project as a whole: the project is complete when every item has either harmonised values or an explicit, justified statement of what cannot be obtained, and the pooled estimates are computed over the poolable items.\n\n## Required outputs\n- `out/table1.csv` — the extraction table, one row per study (5 rows): `study`, `estimate_as_printed`, `uncertainty_as_printed`, `sample_as_printed`, `definition`, `locator`, `harmonised_estimate`, `harmonised_se`, `harmonisable`, `poolable`, `transformation`, `reason_if_not`.\n- `out/figure1.png` — a forest plot of the harmonised estimates (all harmonisable studies; those without an SE shown as points without intervals) with the pooled random-effects estimate.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"T3-synthesis\" |\n| `target_definition` | string | — | the harmonisation target in your words |\n| `items` | object | — | {\"S1\": {...}, …, \"S5\": {...}} with, for each study: `estimate_as_printed` (string, the printed numbers with their printed unit/sign), `uncertainty_as_printed` (string: SE, CI, p-value, stars, or \"none printed\"), `sample_as_printed` (string), `definition` (string), `locator` (string: source id, table/section, row, column), `harmonisable` (boolean), `harmonised_estimate` (number or null, θ), `harmonised_se` (number or null), `poolable` (boolean), `transformation` (string, the formula with the printed inputs), `reason_if_not` (string or null) |\n| `k_extracted` | integer | — | number of items with an extracted printed value |\n| `k_harmonised` | integer | — | number of items with a non-null `harmonised_estimate` |\n| `k_pooled` | integer | — | number of items entering the pooled estimates |\n| `fe_estimate`, `fe_se`, `fe_ci_low`, `fe_ci_high` | number | log ratio | fixed-effect pooled θ, its SE and 95% interval |\n| `re_estimate`, `re_se`, `re_ci_low`, `re_ci_high` | number | log ratio | random-effects pooled θ, its SE and 95% interval |\n| `q_stat` | number | — | Cochran's Q |\n| `i2` | number | percent | I² |\n| `tau2` | number | — | DerSimonian–Laird τ² |\n| `heterogeneity_interpretation` | string | — | \"low\", \"moderate\" or \"substantial\" (rule above) |\n| `pooled_significant_5pct` | boolean | — | random-effects interval excludes 0 |\n| `pooled_pct_change_re` | number | percent | 100 × (exp(re_estimate) − 1) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the project as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, ambiguous units, …) and how handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: lines as reported by `wc -l`; `studies.json`: number of items) |\n| `n_analysed` | integer or null | — | number of studies assessed (5) |\n| `random_seed` | integer or null | — | null (no randomness) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The synthesis must say what the pooled numbers can and cannot support given the number of poolable studies, the approximate SE, and the studies left out.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/table1.csv` and `out/figure1.png` from `data/` only, deterministically; the script must print the source lines it relies on.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Report printed numbers exactly as printed (same sign, same decimals); transformations are applied only in the harmonised columns.\n- Budget class: transfer mini-project — 12.00 USD cap, 60 minutes wall, 150 turns per session. The project is designed to be completed in well under 45 minutes of work.\ntotal 232\ndrwxr-xr-x 11 simon-pierreboucher staff 352 oct. 30 01:34 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 01:30 ..\n-rw-r--r-- 1 simon-pierreboucher staff 17269 oct. 30 01:32 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 2367 oct. 30 01:33 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 508 oct. 30 01:30 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 65260 oct. 30 01:32 figure1.png\n-rw-r--r-- 1 simon-pierreboucher staff 7221 oct. 30 01:33 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 9562 oct. 30 01:32 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 01:34 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 01:33 state\n-rw-r--r-- 1 simon-pierreboucher staff 3996 oct. 30 01:32 table1.csv\n{\n \"task_id\": \"T3-synthesis\",\n \"target_definition\": \"theta = ln(T_AI / T_noAI): natural log of the ratio of mean task-completion time with generative-AI assistance to mean completion time without it, for the same tasks, from a randomised comparison (negative = faster with AI).\",\n \"items\": {\n \"S1\": {\n \"estimate_as_printed\": \"-0.24. (beta_1 for ExpCon, Model 2, Table III; Section VI-C: beta_1=-0.24, i.e. 21% faster)\",\n \"uncertainty_as_printed\": \"SE (0.14); 95% CI = [-0.51, 0.03], p=0.086 (NS)\",\n \"sample_as_printed\": \"n = 96 (Table III caption); Section VI-B: N=47 AI, N=46 no-AI\",\n \"definition\": \"OLS coefficient on experimental condition (AI exposure) with natural-log time on task (LogToT) as dependent variable, developer-level covariates (Model 2), one enterprise coding task, randomised\",\n \"locator\": \"arxiv-2410.12944v3, Section VI-C Table III, row ExpCon, column (2) 'Dev' (second value on collapsed line L76; SE line L78); CI and p in Section VI-C text\",\n \"harmonisable\": true,\n \"harmonised_estimate\": -0.24,\n \"harmonised_se\": 0.14,\n \"poolable\": true,\n \"transformation\": \"log-time coefficient: theta = b = -0.24; SE(theta) = SE(b) = 0.14 (printed). Check: SE from CI = (0.03-(-0.51))/(2*1.96) = 0.1378\",\n \"reason_if_not\": null\n },\n \"S2\": {\n \"estimate_as_printed\": \"+19% (allowing AI increases completion time by 19%; regression-based, Section 3 / Section D.1)\",\n \"uncertainty_as_printed\": \"none printed (95% CI shown only in Figure 1 / Figure 15, not reproduced in the extract)\",\n \"sample_as_printed\": \"16 developers, 246 tasks; 136 issues AI-allowed, 110 AI-disallowed\",\n \"definition\": \"S = E[time AI allowed]/E[time AI disallowed] - 1, estimated as exp(beta)-1 from OLS of log implementation time on AI indicator and log forecast time; issue-level randomisation\",\n \"locator\": \"arxiv-2507.09089v2, Abstract and Section 3 'Results' (opening paragraph); estimator Sections 2.3, D.1-D.3\",\n \"harmonisable\": true,\n \"harmonised_estimate\": 0.17395330712343798,\n \"harmonised_se\": null,\n \"poolable\": false,\n \"transformation\": \"percentage change in time: theta = ln(1 + c) = ln(1 + 0.19) = 0.173953\",\n \"reason_if_not\": \"No SE, CI or p-value printed in the extract; the CI exists only in a figure that is not reproduced, so SE(theta) cannot be obtained (harmonisable, not poolable).\"\n },\n \"S3\": {\n \"estimate_as_printed\": \"Overall 53.84, Treatment 44.29, Control 63.10 (minutes); Improvement -29.79%***\",\n \"uncertainty_as_printed\": \"significance stars only: *** (paper's definition 0<p<=0.01); no SE or CI printed\",\n \"sample_as_printed\": \"181 subjects (randomised half treatment, half control)\",\n \"definition\": \"difference in mean task duration (minutes) between Copilot and control groups across all tasks, holding accuracy constant (control duration predicted at treatment accuracy, Appendix B), randomised\",\n \"locator\": \"arxiv-2411.01067v2, Section 4.2 Table 3, 'Aggregate Results' block, row 'All Content', columns Treatment/Control/Improvement\",\n \"harmonisable\": true,\n \"harmonised_estimate\": -0.353679434958934,\n \"harmonised_se\": null,\n \"poolable\": false,\n \"transformation\": \"percentage change in time: theta = ln(1 + c) = ln(1 + (-0.2979)) = -0.353679 (cf. ln(44.29/63.10) = -0.353962)\",\n \"reason_if_not\": \"Only significance stars (p<=0.01) are printed; a p-value threshold does not identify SE(theta) (harmonisable, not poolable).\"\n },\n \"S4\": {\n \"estimate_as_printed\": \"Aware 50.5 s, Control 56.9 s per email; % Time Savings 11.1\",\n \"uncertainty_as_printed\": \"p-value 0.471\",\n \"sample_as_printed\": \"Aware N=56, Control N=52 (167 analysts across three arms incl. Blind N=59)\",\n \"definition\": \"percentage time saving in mean time per email over the full 25-email queue, Aware (agent verdicts/rationale + prioritised queue) vs Control (no AI), randomised arms\",\n \"locator\": \"arxiv-2511.13860v1, Section 3.3 Table 3 'Comparison of Email Processing Times', column 'Full Corpus', rows Aware/Control time, % Time Savings, p-value\",\n \"harmonisable\": true,\n \"harmonised_estimate\": -0.11765804346823246,\n \"harmonised_se\": 0.1632205919720678,\n \"poolable\": true,\n \"transformation\": \"percentage change in time c = -11.1/100 = -0.111; theta = ln(1 + c) = -0.117658; approximate SE from two-sided p: SE = |theta|/z(1-0.471/2) = 0.117658/0.720853 = 0.163221 (approximate SE)\",\n \"reason_if_not\": null\n },\n \"S5\": {\n \"estimate_as_printed\": \"-0.278 (Treat x Post, Output, column 3, panel A)\",\n \"uncertainty_as_printed\": \"SE (0.295) (clustered at user level); no significance star\",\n \"sample_as_printed\": \"N = 18332 (user-day observations, users with affiliation)\",\n \"definition\": \"difference-in-differences coefficient on aggregate GitHub Output measure (sum of Push, PR, PR comments, commit comments, Create, Issues events) for Italian vs Austrian/French users, pre 27/28.03 vs post 03/04.04.2023 around Italy's ChatGPT ban\",\n \"locator\": \"arxiv-2304.09339v1, Section 4 Table 1, panel A, row Treat x Post, column (3) Output (Users with Affiliation)\",\n \"harmonisable\": false,\n \"harmonised_estimate\": null,\n \"harmonised_se\": null,\n \"poolable\": false,\n \"transformation\": \"none applied (not harmonisable)\",\n \"reason_if_not\": \"Effect on output counts (GitHub events) from a DiD around withdrawal of access, not a comparison of task-completion times with vs without AI on the same tasks, and not randomised.\"\n }\n },\n \"k_extracted\": 5,\n \"k_harmonised\": 4,\n \"k_pooled\": 2,\n \"pooled_studies\": [\n \"S1\",\n \"S4\"\n ],\n \"fe_estimate\": -0.18814332871148873,\n \"fe_se\": 0.10626483095451474,\n \"fe_ci_low\": -0.39642239738233764,\n \"fe_ci_high\": 0.02013573995936016,\n \"re_estimate\": -0.18814332871148873,\n \"re_se\": 0.10626483095451474,\n \"re_ci_low\": -0.39642239738233764,\n \"re_ci_high\": 0.02013573995936016,\n \"q_stat\": 0.3236860522786328,\n \"q_df\": 1,\n \"q_pvalue\": 0.5694008376973132,\n \"i2\": 0.0,\n \"tau2\": 0.0,\n \"heterogeneity_interpretation\": \"low\",\n \"pooled_significant_5pct\": false,\n \"pooled_pct_change_re\": -17.15040485348688,\n \"pooled_pct_change_re_ci\": [\n -32.72775202989503,\n 2.0339831514392115\n ],\n \"weights_fe\": {\n \"S1\": 0.5761333825403874,\n \"S4\": 0.4238666174596126\n },\n \"weights_re\": {\n \"S1\": 0.5761333825403874,\n \"S4\": 0.4238666174596126\n },\n \"baseline_unweighted_mean_theta_harmonised\": -0.13434604282593213,\n \"baseline_unweighted_mean_theta_poolable\": -0.17882902173411622,\n \"sensitivity_s1_se_from_ci\": {\n \"s1_se\": 0.13775510204081634,\n \"re_estimate\": -0.1891067723193802,\n \"re_se\": 0.10527305720065441,\n \"re_ci_low\": -0.39544196443266283,\n \"re_ci_high\": 0.01722841979390244,\n \"q_stat\": 0.3281104262022853,\n \"i2\": 0.0,\n \"tau2\": 0.0\n },\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"S1: Section VI-B reports N=47 (AI) + N=46 (no-AI) = 93 developers, while Table III states n = 96.\",\n \"handling\": \"Not resolvable from the extract; reported. sample_as_printed lists both; does not affect theta or SE, which are taken from Table III/Section VI-C.\"\n },\n {\n \"issue\": \"S1: Section VI-B gives Mean LogToT=4.6 for the AI group and 4.3 for no-AI while stating AI was faster (96 vs 114 min); the log means appear swapped/inconsistent.\",\n \"handling\": \"These log means are not used.\"\n },\n {\n \"issue\": \"S1: Table III is rendered with the four model columns collapsed into one line; SE (0.14) is rounded, and the CI [-0.51, 0.03] implies SE 0.1378.\",\n \"handling\": \"Model 2 taken as the second value per the editorial note; printed SE 0.14 used; CI-based SE reported as sensitivity (sensitivity_s1_se_from_ci).\"\n },\n {\n \"issue\": \"S2: The uncertainty of the headline +19% estimate is shown only in Figure 1/Figure 15, which are not reproduced; no SE or p-value printed.\",\n \"handling\": \"Harmonised (theta = ln 1.19) but not poolable; harmonised_se = null.\"\n },\n {\n \"issue\": \"S3: Treatment/Control means 44.29/63.10 imply -29.81%, printed improvement is -29.79% (slightly beyond rounding of the printed means); significance shown only by stars with an unusual convention (* = 0.05<p<=0.1).\",\n \"handling\": \"Printed -29.79% used as instructed (difference in theta ~0.0003); not poolable because stars do not identify an SE.\"\n },\n {\n \"issue\": \"S4: Table 3 units ambiguous: Malicious/Non-Malicious times per email (1.8-2.8 's') cannot average to Full Corpus times (50.5/56.9 s) under any common unit; Full Corpus ratio 50.5/56.9 implies 11.25% saving vs printed 11.1% (within rounding).\",\n \"handling\": \"Only the Full Corpus column is used; theta depends only on the printed % saving (unit-free). Flagged as a source inconsistency.\"\n },\n {\n \"issue\": \"S4: SE is approximate, back-calculated from a two-sided p-value (0.471) under a normal approximation on the log scale, although the paper's test is presumably on the time difference.\",\n \"handling\": \"Marked as approximate SE in transformation; pooled results carry this caveat.\"\n },\n {\n \"issue\": \"S5: Table 1 note prints '** p<0.5' (presumably p<0.05); row label rendered as '\\\\times TreatPost'; panel B header lists 11.04 although the text says it was excluded; rendered date line 'August 24, 2026' is an artefact.\",\n \"handling\": \"No effect on the extracted panel A column 3 value; S5 is not harmonisable anyway.\"\n }\n ],\n \"n_input_rows\": {\n \"studies.json\": 5,\n \"README.md\": 17,\n \"sources/arxiv-2410.12944v3.txt\": 112,\n \"sources/arxiv-2507.09089v2.txt\": 132,\n \"sources/arxiv-2411.01067v2.txt\": 109,\n \"sources/arxiv-2511.13860v1.txt\": 162,\n \"sources/arxiv-2304.09339v1.txt\": 108\n },\n \"n_analysed\": 5,\n \"random_seed\": null\n}[\n {\"id\": \"C1\", \"text\": \"Four of five studies can be harmonised onto theta = ln(T_AI/T_noAI); S5 (DiD on GitHub output counts) cannot.\", \"results_keys\": [\"k_harmonised\", \"items\"], \"status\": \"supported\", \"caveat\": \"Harmonisation of S1/S2 coefficients to a ratio of means relies on the papers' log-linear assumptions.\"},\n {\"id\": \"C2\", \"text\": \"Only S1 and S4 are poolable; S2 (CI only in an unreproduced figure) and S3 (stars only) have no obtainable SE.\", \"results_keys\": [\"k_pooled\", \"pooled_studies\", \"items\"], \"status\": \"supported\", \"caveat\": \"S4 SE is approximate, recovered from a p-value.\"},\n {\"id\": \"C3\", \"text\": \"The pooled FE and RE estimate is theta = -0.188 (about -17% task time), 95% CI [-0.396, 0.020].\", \"results_keys\": [\"fe_estimate\", \"re_estimate\", \"re_ci_low\", \"re_ci_high\", \"pooled_pct_change_re\", \"pooled_pct_change_re_ci\"], \"status\": \"supported\", \"caveat\": \"k = 2; RE equals FE because tau2 = 0.\"},\n {\"id\": \"C4\", \"text\": \"The pooled effect is not statistically significant at the 5% level; the data cannot establish that generative AI shortens task time.\", \"results_keys\": [\"pooled_significant_5pct\", \"re_ci_low\", \"re_ci_high\"], \"status\": \"supported\", \"caveat\": \"Conclusion is conditional on the two poolable studies; excluded studies point in opposite directions.\"},\n {\"id\": \"C5\", \"text\": \"Estimated heterogeneity among poolable studies is low (Q = 0.324, I2 = 0%, tau2 = 0), but with k = 2 this is uninformative, and the non-pooled harmonised estimates (S2 +0.174, S3 -0.354) indicate heterogeneity in direction across settings.\", \"results_keys\": [\"q_stat\", \"i2\", \"tau2\", \"heterogeneity_interpretation\", \"items\"], \"status\": \"supported\", \"caveat\": \"Cross-study heterogeneity including S2/S3 is not formally testable without their SEs.\"},\n {\"id\": \"C6\", \"text\": \"Using the S1 SE derived from its printed CI instead of the printed SE leaves the conclusion unchanged (RE -0.189 [-0.395, 0.017]).\", \"results_keys\": [\"sensitivity_s1_se_from_ci\"], \"status\": \"supported\", \"caveat\": \"Only this one sensitivity analysis was run.\"},\n {\"id\": \"C7\", \"text\": \"The inputs contain several internal inconsistencies (S1 sample size and log means, S3 rounding, S4 units, S5 star note), none of which materially changes a harmonised value.\", \"results_keys\": [\"data_issues\"], \"status\": \"supported\", \"caveat\": \"Inconsistencies cannot be resolved from the extracts.\"}\n]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"789fedd3-21cf-4ca5-b26d-5f610199b129","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","hash":"f5667ce38c0467418f0f3af941b61cc067125f7544dbe80d05eb36513ab08b18","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01KNRKsDr1N9PgUgbSjxLXTd","name":"Bash","input":{"command":"cat TASK.md; ls -la out; cat out/results.json; cat out/claims.json","description":"Read task and deliverables"}}]}}16{"type":"api-request","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","id":"e93044f4-72a3-4fd1-9e44-1303dea86401","timestamp":"2026-10-30T05:34:08.480Z","version":"2.1.314","querySource":"sdk","shapeHash":"80e183abfbc7335290bfa20318d598a10dbca7f952739a0080b8d8663232c751","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"65d83200-d660-49e2-9e70-820d3c2fd05b","keep":2,"tail":["f5667ce38c0467418f0f3af941b61cc067125f7544dbe80d05eb36513ab08b18","0128ed47f1c46aebdc9df8ec3e8a2cb6e6248d9786cdbdfc5c04fd1f95613c60"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"27cdd606eb696635626ad83799843bc6ca38a97a6065a434d5796a811ca310a4"}17{"type":"last-prompt","lastPrompt":"Review","leafUuid":"e5c7a852-9e17-48ac-82ca-eb7adaaa85f7","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1"}18{"type":"atis-latch","atis":"","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1"}19{"parentUuid":"e5c7a852-9e17-48ac-82ca-eb7adaaa85f7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXspYq23hia5kC1n1oyJ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017F6rHHzmZNXtX4Kzu8bwoz","name":"Bash","input":{"command":"cat out/report.md; cat out/table1.csv; cat out/exclusions.csv; ls out/state; cat data/studies.json","description":"Read report, table, studies"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":9701,"cache_read_input_tokens":2220,"output_tokens":196,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":9701},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXspYdccrBC9Zk63WFq3","requestRef":"e93044f4-72a3-4fd1-9e44-1303dea86401","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7998fbf1-152a-4e16-aa2e-484ab6b40497","timestamp":"2026-10-30T05:34:10.813Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"7998fbf1-152a-4e16-aa2e-484ab6b40497","isSidechain":false,"promptId":"01f9be6f-d015-4eb3-b8dc-9dd1d87b03b8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017F6rHHzmZNXtX4Kzu8bwoz","type":"tool_result","content":"# T3-synthesis: Does generative-AI assistance shorten task-completion time?\n\n## Question\nDoes access to a generative-AI assistant change how long workers take to complete their tasks? I harmonised the five primary studies onto θ = ln(T_AI / T_noAI) (`target_definition`), the log ratio of mean completion time with AI to mean time without it, for the same tasks, in a randomised comparison. Negative θ means faster with AI. I then pooled the studies that could be pooled with inverse-variance fixed-effect (FE) and DerSimonian–Laird random-effects (RE) models.\n\n## Data and exclusions\nThe only inputs were the five text extracts under `data/sources/` and `data/studies.json`. All SHA-256 checksums match `data/README.md`, and line counts are in `n_input_rows`. Every one of the 5 items had a printed value to extract (`k_extracted` = 5, `n_analysed` = 5). `out/table1.csv` and `items` give the printed values verbatim with locators. The exclusion ledger (`out/exclusions.csv`) runs 5 assessed → 4 harmonisable → 2 poolable:\n- **S5** (Kreitmeir & Raschky, ChatGPT ban; printed −0.278 (0.295), N = 18332) is **not harmonisable**. It is a difference-in-differences coefficient on GitHub output counts around the withdrawal of access, not a comparison of task-completion times on the same tasks (`items.S5.reason_if_not`).\n- **S2** (METR; +19%) is harmonisable but **not poolable**. Its confidence interval appears only in Figure 1/15, which the extract does not reproduce, and no SE or p-value is printed (`items.S2.reason_if_not`).\n- **S3** (Security Copilot; −29.79%***) is harmonisable but **not poolable**. It reports significance stars only (`items.S3.reason_if_not`).\n- That leaves **S1** (Paradis et al.) and **S4** (Bono, phishing agent) in the pool (`k_pooled` = 2, `pooled_studies`).\n\nI found eight input problems, listed in `data_issues`. The main ones:\n- S1: n = 96 in Table III but 47 + 46 = 93 in the text, and the two groups' log means appear swapped.\n- S3: the printed −29.79% differs slightly from the −29.81% implied by the printed means.\n- S4: the units in Table 3 are inconsistent across columns.\n- S5: the table note prints \"** p<0.5\".\n\nNone of these changes a harmonised value by more than about 0.0003, apart from the S1 SE choice, which I examine under Uncertainty.\n\n## Method\nI applied the TASK conventions to each study; the `items.*.transformation` field records each formula with its inputs:\n- **S1:** the Model 2 ExpCon coefficient on log time on task gives θ = b = −0.24, with printed SE 0.14 (`items.S1.harmonised_estimate`, `items.S1.harmonised_se`).\n- **S2:** θ = ln(1.19) = 0.174 (`items.S2.harmonised_estimate`).\n- **S3:** θ = ln(1 − 0.2979) = −0.354 (`items.S3.harmonised_estimate`).\n- **S4:** a printed 11.1% time saving gives c = −0.111 and θ = ln(0.889) = −0.118. Its SE is **approximate**, recovered from the two-sided p = 0.471 as |θ|/z₀.₇₆₄₅ = 0.163 (`items.S4.harmonised_se`).\n\nPooling used inverse-variance FE and DL RE, with 95% intervals at 1.96, Q on k − 1 = 1 df, I² = max(0, (Q − df)/Q)·100, and τ² = max(0, (Q − df)/C). As a baseline I also computed unweighted means of θ (`baseline_unweighted_mean_theta_poolable`, `baseline_unweighted_mean_theta_harmonised`). The script `out/analysis.py` regenerates everything, uses no randomness (`random_seed` = null), and prints the source lines it parses. Figure: `out/figure1.png`.\n\n## Results\n- Harmonised θ: S1 −0.240 (SE 0.140), S2 +0.174 (no SE), S3 −0.354 (no SE), S4 −0.118 (approx. SE 0.163). Keys: `items.*.harmonised_estimate`, `items.*.harmonised_se`. `k_harmonised` = 4.\n- **FE pooled θ = −0.188**, SE 0.106, 95% CI [−0.396, 0.020] (`fe_estimate`, `fe_se`, `fe_ci_low`, `fe_ci_high`).\n- **RE pooled θ = −0.188**, SE 0.106, 95% CI [−0.396, 0.020] (`re_estimate`, `re_se`, `re_ci_low`, `re_ci_high`). RE equals FE because τ² = 0 (`tau2`).\n- On the time scale this is a −17.2% change in mean task time (`pooled_pct_change_re`), with a 95% interval of about −32.7% to +2.0% (`pooled_pct_change_re_ci`).\n- Q = 0.324 on 1 df, p = 0.57 (`q_stat`, `q_df`, `q_pvalue`). I² = 0% (`i2`), so heterogeneity is classed as \"low\" (`heterogeneity_interpretation`).\n- The RE interval includes 0, so the pooled effect is not significant at 5% (`pooled_significant_5pct` = false).\n- Baselines: the unweighted mean of the two poolable θ is −0.179 (`baseline_unweighted_mean_theta_poolable`). The unweighted mean of all four harmonised θ is −0.134 (`baseline_unweighted_mean_theta_harmonised`). The second is descriptive only, because two of those studies have no SE.\n\n**Synthesis.** The two poolable randomised studies point towards shorter task times with AI, but the pooled interval includes no effect. These data cannot establish that generative-AI assistance shortens task-completion time. The harmonisable studies that could not be pooled disagree in direction: S3 shows a large reduction (−0.354) and S2 a slowdown (+0.174). So the I² = 0% computed from k = 2 says nothing reliable about heterogeneity across settings, and the evidence as a whole is mixed and context-dependent: developers in an enterprise lab task, experienced open-source developers on their own repositories, IT administrators, and SOC analysts.\n\n## Uncertainty\n- With k = 2, Q has a single degree of freedom. The DL τ² and I² are extremely imprecise, and a zero τ² estimate does not mean the studies are homogeneous.\n- The S4 SE is approximate: it is back-calculated from a p-value under a normal approximation on the log scale, while the test in the paper was not necessarily on that scale. S4 carries 42% of the pooled weight (`weights_fe`).\n- **Sensitivity.** Using the S1 SE implied by its printed CI (0.1378 instead of 0.14) gives RE −0.189 [−0.395, 0.017] (`sensitivity_s1_se_from_ci`). The conclusion is unchanged.\n- Different estimands sit under one θ. S1 and S2 are regression-adjusted log-time contrasts. S3 holds accuracy constant using predicted control durations. S4 is a ratio of mean time per email in an arm that also re-ordered the queue. Each S1 or S2 coefficient equals the log of a ratio of means only under the assumptions in the papers.\n\n## Limitations and open issues\n- Only 2 of 5 studies could be pooled (`k_pooled`). The pooled estimate rests on one 96-developer coding task and one phishing-triage experiment, and it leaves out the two other randomised time studies, which have the largest effects in each direction (S2 +0.174, S3 −0.354). If they could be pooled the result could move substantially. Their SEs are not printed in the extracts, and I did not substitute values from figures or memory, as the information rules require.\n- `feasibility` = \"complete\" in the TASK's sense: every item has either harmonised values or an explicit, justified reason why none can be obtained, and the pool covers all poolable items. This does not mean the evidence base is adequate.\n- The source inconsistencies in `data_issues` (S1 sample size and log means, S4 units) cannot be resolved from the extracts.\n- No assessment of risk of bias or publication bias was possible or attempted. Three of the studies come from vendors (Google, Microsoft ×2).\nstudy,estimate_as_printed,uncertainty_as_printed,sample_as_printed,definition,locator,harmonised_estimate,harmonised_se,harmonisable,poolable,transformation,reason_if_not\r\nS1,\"-0.24. (beta_1 for ExpCon, Model 2, Table III; Section VI-C: beta_1=-0.24, i.e. 21% faster)\",\"SE (0.14); 95% CI = [-0.51, 0.03], p=0.086 (NS)\",\"n = 96 (Table III caption); Section VI-B: N=47 AI, N=46 no-AI\",\"OLS coefficient on experimental condition (AI exposure) with natural-log time on task (LogToT) as dependent variable, developer-level covariates (Model 2), one enterprise coding task, randomised\",\"arxiv-2410.12944v3, Section VI-C Table III, row ExpCon, column (2) 'Dev' (second value on collapsed line L76; SE line L78); CI and p in Section VI-C text\",-0.24,0.14,True,True,log-time coefficient: theta = b = -0.24; SE(theta) = SE(b) = 0.14 (printed). Check: SE from CI = (0.03-(-0.51))/(2*1.96) = 0.1378,\r\nS2,\"+19% (allowing AI increases completion time by 19%; regression-based, Section 3 / Section D.1)\",\"none printed (95% CI shown only in Figure 1 / Figure 15, not reproduced in the extract)\",\"16 developers, 246 tasks; 136 issues AI-allowed, 110 AI-disallowed\",\"S = E[time AI allowed]/E[time AI disallowed] - 1, estimated as exp(beta)-1 from OLS of log implementation time on AI indicator and log forecast time; issue-level randomisation\",\"arxiv-2507.09089v2, Abstract and Section 3 'Results' (opening paragraph); estimator Sections 2.3, D.1-D.3\",0.17395330712343798,,True,False,percentage change in time: theta = ln(1 + c) = ln(1 + 0.19) = 0.173953,\"No SE, CI or p-value printed in the extract; the CI exists only in a figure that is not reproduced, so SE(theta) cannot be obtained (harmonisable, not poolable).\"\r\nS3,\"Overall 53.84, Treatment 44.29, Control 63.10 (minutes); Improvement -29.79%***\",significance stars only: *** (paper's definition 0<p<=0.01); no SE or CI printed,\"181 subjects (randomised half treatment, half control)\",\"difference in mean task duration (minutes) between Copilot and control groups across all tasks, holding accuracy constant (control duration predicted at treatment accuracy, Appendix B), randomised\",\"arxiv-2411.01067v2, Section 4.2 Table 3, 'Aggregate Results' block, row 'All Content', columns Treatment/Control/Improvement\",-0.353679434958934,,True,False,percentage change in time: theta = ln(1 + c) = ln(1 + (-0.2979)) = -0.353679 (cf. ln(44.29/63.10) = -0.353962),\"Only significance stars (p<=0.01) are printed; a p-value threshold does not identify SE(theta) (harmonisable, not poolable).\"\r\nS4,\"Aware 50.5 s, Control 56.9 s per email; % Time Savings 11.1\",p-value 0.471,\"Aware N=56, Control N=52 (167 analysts across three arms incl. Blind N=59)\",\"percentage time saving in mean time per email over the full 25-email queue, Aware (agent verdicts/rationale + prioritised queue) vs Control (no AI), randomised arms\",\"arxiv-2511.13860v1, Section 3.3 Table 3 'Comparison of Email Processing Times', column 'Full Corpus', rows Aware/Control time, % Time Savings, p-value\",-0.11765804346823246,0.1632205919720678,True,True,percentage change in time c = -11.1/100 = -0.111; theta = ln(1 + c) = -0.117658; approximate SE from two-sided p: SE = |theta|/z(1-0.471/2) = 0.117658/0.720853 = 0.163221 (approximate SE),\r\nS5,\"-0.278 (Treat x Post, Output, column 3, panel A)\",SE (0.295) (clustered at user level); no significance star,\"N = 18332 (user-day observations, users with affiliation)\",\"difference-in-differences coefficient on aggregate GitHub Output measure (sum of Push, PR, PR comments, commit comments, Create, Issues events) for Italian vs Austrian/French users, pre 27/28.03 vs post 03/04.04.2023 around Italy's ChatGPT ban\",\"arxiv-2304.09339v1, Section 4 Table 1, panel A, row Treat x Post, column (3) Output (Users with Affiliation)\",,,False,False,none applied (not harmonisable),\"Effect on output counts (GitHub events) from a DiD around withdrawal of access, not a comparison of task-completion times with vs without AI on the same tasks, and not randomised.\"\r\nstep,rule,n_before,n_after,n_removed,rationale\n1,studies listed in data/studies.json with an extractable printed value,5,5,0,all five extracts contain the requested printed quantity\n2,harmonisable onto theta = ln(T_AI/T_noAI),5,4,1,S5 removed: DiD coefficient on GitHub output counts around a ban is not a task-completion-time AI vs no-AI comparison\n3,poolable (SE obtainable from printed quantities),4,2,2,S2 removed (CI only in unreproduced Figure 1; no SE/p printed); S3 removed (significance stars only)\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json\n[\n {\"id\": \"S1\", \"source\": \"arxiv-2410.12944v3\", \"short\": \"Paradis et al. (2024), Google enterprise RCT, 96 developers, one complex coding task\",\n \"extract\": \"the coefficient on the experimental condition (ExpCon) in Model 2 of Table III (logged time on task, developer-level covariates), its standard error, and the 95% confidence interval and p-value quoted for it in Section VI-C\"},\n {\"id\": \"S2\", \"source\": \"arxiv-2507.09089v2\", \"short\": \"Becker et al. (2025), METR field RCT, experienced open-source developers, real issues\",\n \"extract\": \"the headline regression-based estimate of the percentage change in issue completion time when AI is allowed, as stated in Section 3 (with the estimator defined in Sections 2.3 and D.1-D.3), together with whatever uncertainty is printed for it in the extract\"},\n {\"id\": \"S3\", \"source\": \"arxiv-2411.01067v2\", \"short\": \"Bono and Xu (2024), Microsoft Security Copilot RCT, IT administrators\",\n \"extract\": \"the aggregate 'All Content' row of Table 3 (task duration in minutes, holding accuracy constant): the treatment and control durations and the printed improvement, together with whatever uncertainty is printed for it\"},\n {\"id\": \"S4\", \"source\": \"arxiv-2511.13860v1\", \"short\": \"Bono (2025), Microsoft phishing-triage agent RCT, security analysts\",\n \"extract\": \"the 'Full Corpus' column of Table 3 (time per email): the Aware and Control times, the printed % time savings and its p-value\"},\n {\"id\": \"S5\", \"source\": \"arxiv-2304.09339v1\", \"short\": \"Kreitmeir and Raschky (2023), difference-in-differences around Italy's ChatGPT ban, GitHub users\",\n \"extract\": \"the Treat x Post coefficient and standard error for the Output measure (column 3) in panel A of Table 1 (users with affiliation), and its sample size\"}\n]","is_error":false}]},"uuid":"9b611b6a-d667-4f19-9fe0-3fe06e624f9f","timestamp":"2026-10-30T05:34:10.861Z","toolUseResult":{"stdout":"# T3-synthesis: Does generative-AI assistance shorten task-completion time?\n\n## Question\nDoes access to a generative-AI assistant change how long workers take to complete their tasks? I harmonised the five primary studies onto θ = ln(T_AI / T_noAI) (`target_definition`), the log ratio of mean completion time with AI to mean time without it, for the same tasks, in a randomised comparison. Negative θ means faster with AI. I then pooled the studies that could be pooled with inverse-variance fixed-effect (FE) and DerSimonian–Laird random-effects (RE) models.\n\n## Data and exclusions\nThe only inputs were the five text extracts under `data/sources/` and `data/studies.json`. All SHA-256 checksums match `data/README.md`, and line counts are in `n_input_rows`. Every one of the 5 items had a printed value to extract (`k_extracted` = 5, `n_analysed` = 5). `out/table1.csv` and `items` give the printed values verbatim with locators. The exclusion ledger (`out/exclusions.csv`) runs 5 assessed → 4 harmonisable → 2 poolable:\n- **S5** (Kreitmeir & Raschky, ChatGPT ban; printed −0.278 (0.295), N = 18332) is **not harmonisable**. It is a difference-in-differences coefficient on GitHub output counts around the withdrawal of access, not a comparison of task-completion times on the same tasks (`items.S5.reason_if_not`).\n- **S2** (METR; +19%) is harmonisable but **not poolable**. Its confidence interval appears only in Figure 1/15, which the extract does not reproduce, and no SE or p-value is printed (`items.S2.reason_if_not`).\n- **S3** (Security Copilot; −29.79%***) is harmonisable but **not poolable**. It reports significance stars only (`items.S3.reason_if_not`).\n- That leaves **S1** (Paradis et al.) and **S4** (Bono, phishing agent) in the pool (`k_pooled` = 2, `pooled_studies`).\n\nI found eight input problems, listed in `data_issues`. The main ones:\n- S1: n = 96 in Table III but 47 + 46 = 93 in the text, and the two groups' log means appear swapped.\n- S3: the printed −29.79% differs slightly from the −29.81% implied by the printed means.\n- S4: the units in Table 3 are inconsistent across columns.\n- S5: the table note prints \"** p<0.5\".\n\nNone of these changes a harmonised value by more than about 0.0003, apart from the S1 SE choice, which I examine under Uncertainty.\n\n## Method\nI applied the TASK conventions to each study; the `items.*.transformation` field records each formula with its inputs:\n- **S1:** the Model 2 ExpCon coefficient on log time on task gives θ = b = −0.24, with printed SE 0.14 (`items.S1.harmonised_estimate`, `items.S1.harmonised_se`).\n- **S2:** θ = ln(1.19) = 0.174 (`items.S2.harmonised_estimate`).\n- **S3:** θ = ln(1 − 0.2979) = −0.354 (`items.S3.harmonised_estimate`).\n- **S4:** a printed 11.1% time saving gives c = −0.111 and θ = ln(0.889) = −0.118. Its SE is **approximate**, recovered from the two-sided p = 0.471 as |θ|/z₀.₇₆₄₅ = 0.163 (`items.S4.harmonised_se`).\n\nPooling used inverse-variance FE and DL RE, with 95% intervals at 1.96, Q on k − 1 = 1 df, I² = max(0, (Q − df)/Q)·100, and τ² = max(0, (Q − df)/C). As a baseline I also computed unweighted means of θ (`baseline_unweighted_mean_theta_poolable`, `baseline_unweighted_mean_theta_harmonised`). The script `out/analysis.py` regenerates everything, uses no randomness (`random_seed` = null), and prints the source lines it parses. Figure: `out/figure1.png`.\n\n## Results\n- Harmonised θ: S1 −0.240 (SE 0.140), S2 +0.174 (no SE), S3 −0.354 (no SE), S4 −0.118 (approx. SE 0.163). Keys: `items.*.harmonised_estimate`, `items.*.harmonised_se`. `k_harmonised` = 4.\n- **FE pooled θ = −0.188**, SE 0.106, 95% CI [−0.396, 0.020] (`fe_estimate`, `fe_se`, `fe_ci_low`, `fe_ci_high`).\n- **RE pooled θ = −0.188**, SE 0.106, 95% CI [−0.396, 0.020] (`re_estimate`, `re_se`, `re_ci_low`, `re_ci_high`). RE equals FE because τ² = 0 (`tau2`).\n- On the time scale this is a −17.2% change in mean task time (`pooled_pct_change_re`), with a 95% interval of about −32.7% to +2.0% (`pooled_pct_change_re_ci`).\n- Q = 0.324 on 1 df, p = 0.57 (`q_stat`, `q_df`, `q_pvalue`). I² = 0% (`i2`), so heterogeneity is classed as \"low\" (`heterogeneity_interpretation`).\n- The RE interval includes 0, so the pooled effect is not significant at 5% (`pooled_significant_5pct` = false).\n- Baselines: the unweighted mean of the two poolable θ is −0.179 (`baseline_unweighted_mean_theta_poolable`). The unweighted mean of all four harmonised θ is −0.134 (`baseline_unweighted_mean_theta_harmonised`). The second is descriptive only, because two of those studies have no SE.\n\n**Synthesis.** The two poolable randomised studies point towards shorter task times with AI, but the pooled interval includes no effect. These data cannot establish that generative-AI assistance shortens task-completion time. The harmonisable studies that could not be pooled disagree in direction: S3 shows a large reduction (−0.354) and S2 a slowdown (+0.174). So the I² = 0% computed from k = 2 says nothing reliable about heterogeneity across settings, and the evidence as a whole is mixed and context-dependent: developers in an enterprise lab task, experienced open-source developers on their own repositories, IT administrators, and SOC analysts.\n\n## Uncertainty\n- With k = 2, Q has a single degree of freedom. The DL τ² and I² are extremely imprecise, and a zero τ² estimate does not mean the studies are homogeneous.\n- The S4 SE is approximate: it is back-calculated from a p-value under a normal approximation on the log scale, while the test in the paper was not necessarily on that scale. S4 carries 42% of the pooled weight (`weights_fe`).\n- **Sensitivity.** Using the S1 SE implied by its printed CI (0.1378 instead of 0.14) gives RE −0.189 [−0.395, 0.017] (`sensitivity_s1_se_from_ci`). The conclusion is unchanged.\n- Different estimands sit under one θ. S1 and S2 are regression-adjusted log-time contrasts. S3 holds accuracy constant using predicted control durations. S4 is a ratio of mean time per email in an arm that also re-ordered the queue. Each S1 or S2 coefficient equals the log of a ratio of means only under the assumptions in the papers.\n\n## Limitations and open issues\n- Only 2 of 5 studies could be pooled (`k_pooled`). The pooled estimate rests on one 96-developer coding task and one phishing-triage experiment, and it leaves out the two other randomised time studies, which have the largest effects in each direction (S2 +0.174, S3 −0.354). If they could be pooled the result could move substantially. Their SEs are not printed in the extracts, and I did not substitute values from figures or memory, as the information rules require.\n- `feasibility` = \"complete\" in the TASK's sense: every item has either harmonised values or an explicit, justified reason why none can be obtained, and the pool covers all poolable items. This does not mean the evidence base is adequate.\n- The source inconsistencies in `data_issues` (S1 sample size and log means, S4 units) cannot be resolved from the extracts.\n- No assessment of risk of bias or publication bias was possible or attempted. Three of the studies come from vendors (Google, Microsoft ×2).\nstudy,estimate_as_printed,uncertainty_as_printed,sample_as_printed,definition,locator,harmonised_estimate,harmonised_se,harmonisable,poolable,transformation,reason_if_not\r\nS1,\"-0.24. (beta_1 for ExpCon, Model 2, Table III; Section VI-C: beta_1=-0.24, i.e. 21% faster)\",\"SE (0.14); 95% CI = [-0.51, 0.03], p=0.086 (NS)\",\"n = 96 (Table III caption); Section VI-B: N=47 AI, N=46 no-AI\",\"OLS coefficient on experimental condition (AI exposure) with natural-log time on task (LogToT) as dependent variable, developer-level covariates (Model 2), one enterprise coding task, randomised\",\"arxiv-2410.12944v3, Section VI-C Table III, row ExpCon, column (2) 'Dev' (second value on collapsed line L76; SE line L78); CI and p in Section VI-C text\",-0.24,0.14,True,True,log-time coefficient: theta = b = -0.24; SE(theta) = SE(b) = 0.14 (printed). Check: SE from CI = (0.03-(-0.51))/(2*1.96) = 0.1378,\r\nS2,\"+19% (allowing AI increases completion time by 19%; regression-based, Section 3 / Section D.1)\",\"none printed (95% CI shown only in Figure 1 / Figure 15, not reproduced in the extract)\",\"16 developers, 246 tasks; 136 issues AI-allowed, 110 AI-disallowed\",\"S = E[time AI allowed]/E[time AI disallowed] - 1, estimated as exp(beta)-1 from OLS of log implementation time on AI indicator and log forecast time; issue-level randomisation\",\"arxiv-2507.09089v2, Abstract and Section 3 'Results' (opening paragraph); estimator Sections 2.3, D.1-D.3\",0.17395330712343798,,True,False,percentage change in time: theta = ln(1 + c) = ln(1 + 0.19) = 0.173953,\"No SE, CI or p-value printed in the extract; the CI exists only in a figure that is not reproduced, so SE(theta) cannot be obtained (harmonisable, not poolable).\"\r\nS3,\"Overall 53.84, Treatment 44.29, Control 63.10 (minutes); Improvement -29.79%***\",significance stars only: *** (paper's definition 0<p<=0.01); no SE or CI printed,\"181 subjects (randomised half treatment, half control)\",\"difference in mean task duration (minutes) between Copilot and control groups across all tasks, holding accuracy constant (control duration predicted at treatment accuracy, Appendix B), randomised\",\"arxiv-2411.01067v2, Section 4.2 Table 3, 'Aggregate Results' block, row 'All Content', columns Treatment/Control/Improvement\",-0.353679434958934,,True,False,percentage change in time: theta = ln(1 + c) = ln(1 + (-0.2979)) = -0.353679 (cf. ln(44.29/63.10) = -0.353962),\"Only significance stars (p<=0.01) are printed; a p-value threshold does not identify SE(theta) (harmonisable, not poolable).\"\r\nS4,\"Aware 50.5 s, Control 56.9 s per email; % Time Savings 11.1\",p-value 0.471,\"Aware N=56, Control N=52 (167 analysts across three arms incl. Blind N=59)\",\"percentage time saving in mean time per email over the full 25-email queue, Aware (agent verdicts/rationale + prioritised queue) vs Control (no AI), randomised arms\",\"arxiv-2511.13860v1, Section 3.3 Table 3 'Comparison of Email Processing Times', column 'Full Corpus', rows Aware/Control time, % Time Savings, p-value\",-0.11765804346823246,0.1632205919720678,True,True,percentage change in time c = -11.1/100 = -0.111; theta = ln(1 + c) = -0.117658; approximate SE from two-sided p: SE = |theta|/z(1-0.471/2) = 0.117658/0.720853 = 0.163221 (approximate SE),\r\nS5,\"-0.278 (Treat x Post, Output, column 3, panel A)\",SE (0.295) (clustered at user level); no significance star,\"N = 18332 (user-day observations, users with affiliation)\",\"difference-in-differences coefficient on aggregate GitHub Output measure (sum of Push, PR, PR comments, commit comments, Create, Issues events) for Italian vs Austrian/French users, pre 27/28.03 vs post 03/04.04.2023 around Italy's ChatGPT ban\",\"arxiv-2304.09339v1, Section 4 Table 1, panel A, row Treat x Post, column (3) Output (Users with Affiliation)\",,,False,False,none applied (not harmonisable),\"Effect on output counts (GitHub events) from a DiD around withdrawal of access, not a comparison of task-completion times with vs without AI on the same tasks, and not randomised.\"\r\nstep,rule,n_before,n_after,n_removed,rationale\n1,studies listed in data/studies.json with an extractable printed value,5,5,0,all five extracts contain the requested printed quantity\n2,harmonisable onto theta = ln(T_AI/T_noAI),5,4,1,S5 removed: DiD coefficient on GitHub output counts around a ban is not a task-completion-time AI vs no-AI comparison\n3,poolable (SE obtainable from printed quantities),4,2,2,S2 removed (CI only in unreproduced Figure 1; no SE/p printed); S3 removed (significance stars only)\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json\n[\n {\"id\": \"S1\", \"source\": \"arxiv-2410.12944v3\", \"short\": \"Paradis et al. (2024), Google enterprise RCT, 96 developers, one complex coding task\",\n \"extract\": \"the coefficient on the experimental condition (ExpCon) in Model 2 of Table III (logged time on task, developer-level covariates), its standard error, and the 95% confidence interval and p-value quoted for it in Section VI-C\"},\n {\"id\": \"S2\", \"source\": \"arxiv-2507.09089v2\", \"short\": \"Becker et al. (2025), METR field RCT, experienced open-source developers, real issues\",\n \"extract\": \"the headline regression-based estimate of the percentage change in issue completion time when AI is allowed, as stated in Section 3 (with the estimator defined in Sections 2.3 and D.1-D.3), together with whatever uncertainty is printed for it in the extract\"},\n {\"id\": \"S3\", \"source\": \"arxiv-2411.01067v2\", \"short\": \"Bono and Xu (2024), Microsoft Security Copilot RCT, IT administrators\",\n \"extract\": \"the aggregate 'All Content' row of Table 3 (task duration in minutes, holding accuracy constant): the treatment and control durations and the printed improvement, together with whatever uncertainty is printed for it\"},\n {\"id\": \"S4\", \"source\": \"arxiv-2511.13860v1\", \"short\": \"Bono (2025), Microsoft phishing-triage agent RCT, security analysts\",\n \"extract\": \"the 'Full Corpus' column of Table 3 (time per email): the Aware and Control times, the printed % time savings and its p-value\"},\n {\"id\": \"S5\", \"source\": \"arxiv-2304.09339v1\", \"short\": \"Kreitmeir and Raschky (2023), difference-in-differences around Italy's ChatGPT ban, GitHub users\",\n \"extract\": \"the Treat x Post coefficient and standard error for the Output measure (column 3) in panel A of Table 1 (users with affiliation), and its sample size\"}\n]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7998fbf1-152a-4e16-aa2e-484ab6b40497","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}21{"parentUuid":"9b611b6a-d667-4f19-9fe0-3fe06e624f9f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXspYq23hia5kC1n1oyJ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FktSdtWUHqS9SJtBDE4YL6","name":"Bash","input":{"command":"cat out/analysis.py","description":"Read analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":9701,"cache_read_input_tokens":2220,"output_tokens":196,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":9701},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXspYdccrBC9Zk63WFq3","requestRef":"e93044f4-72a3-4fd1-9e44-1303dea86401","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4b55e90a-dca8-42f7-87f6-2d14c633a04b","timestamp":"2026-10-30T05:34:11.202Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}22{"parentUuid":"4b55e90a-dca8-42f7-87f6-2d14c633a04b","isSidechain":false,"promptId":"01f9be6f-d015-4eb3-b8dc-9dd1d87b03b8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FktSdtWUHqS9SJtBDE4YL6","type":"tool_result","content":"\"\"\"T3-synthesis: extraction, harmonisation onto theta = ln(T_AI/T_noAI), FE/DL pooling, forest plot.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/table1.csv, out/figure1.png. No randomness.\"\"\"\nimport json, math, os, re, csv\nimport numpy as np\nfrom scipy.stats import norm, chi2\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nDATA = \"data\"; OUT = \"out\"\nSRC = os.path.join(DATA, \"sources\")\nos.makedirs(OUT, exist_ok=True)\nZ = 1.96\n\n\ndef lines_of(name):\n with open(os.path.join(SRC, name + \".txt\"), encoding=\"utf-8\") as fh:\n return fh.read().split(\"\\n\")\n\n\ndef find(name, pattern, nth=0):\n \"\"\"Return (line_no, line) of the nth line matching regex pattern; print it.\"\"\"\n L = lines_of(name)\n hits = [(i + 1, l) for i, l in enumerate(L) if re.search(pattern, l)]\n if len(hits) <= nth:\n raise RuntimeError(f\"pattern {pattern!r} not found in {name}\")\n ln, l = hits[nth]\n print(f\"[{name} L{ln}] {l.strip()[:300]}\")\n return ln, l\n\n\ndef next_nonempty(name, ln):\n \"\"\"Line following line number ln (1-based) that is non-empty; print it.\"\"\"\n L = lines_of(name); j = ln\n while not L[j].strip():\n j += 1\n print(f\"[{name} L{j+1}] {L[j].strip()[:300]}\")\n return j + 1, L[j]\n\n\ndef wc_l(path):\n with open(path, \"rb\") as fh:\n return fh.read().count(b\"\\n\")\n\n\nstudies = json.load(open(os.path.join(DATA, \"studies.json\"), encoding=\"utf-8\"))\nassert [s[\"id\"] for s in studies] == [\"S1\", \"S2\", \"S3\", \"S4\", \"S5\"]\nprint(\"== Source lines relied on ==\")\nitems = {}\n\n# ---------------- S1 Paradis et al. 2024 ----------------\ns = \"arxiv-2410.12944v3\"\nln_b, l_b = find(s, r\"ExpCon \\|\")\nln_se, l_se = next_nonempty(s, ln_b)\ncoefs = re.findall(r\"-?\\d+\\.\\d+[.*]*\", l_b.split(\"ExpCon\")[0])\nses = re.findall(r\"\\((\\d+\\.\\d+)\\)\", l_se)\nb1_str, se1_str = coefs[1], ses[1] # Model 2 = second column (collapsed table)\nb1 = float(re.sub(r\"[.*]+$\", \"\", b1_str)); se1 = float(se1_str)\n_, l_ci = find(s, r\"best-fit Model 2\")\nci = re.search(r\"\\[(-?\\d+\\.\\d+),\\s*(-?\\d+\\.\\d+)\\]\", l_ci); ci_l, ci_u = float(ci.group(1)), float(ci.group(2))\np1 = re.search(r\"p=(\\d*\\.\\d+)\", l_ci).group(1)\nfind(s, r\"TABLE III: .*n = 96\")\nfind(s, r\"completed the task in 96 min\")\nassert (b1, se1, ci_l, ci_u) == (-0.24, 0.14, -0.51, 0.03)\nse1_ci = (ci_u - ci_l) / (2 * Z)\nitems[\"S1\"] = dict(\n estimate_as_printed=f\"{b1_str} (beta_1 for ExpCon, Model 2, Table III; Section VI-C: beta_1=-0.24, i.e. 21% faster)\",\n uncertainty_as_printed=f\"SE ({se1_str}); 95% CI = [{ci.group(1)}, {ci.group(2)}], p={p1} (NS)\",\n sample_as_printed=\"n = 96 (Table III caption); Section VI-B: N=47 AI, N=46 no-AI\",\n definition=\"OLS coefficient on experimental condition (AI exposure) with natural-log time on task (LogToT) as dependent variable, developer-level covariates (Model 2), one enterprise coding task, randomised\",\n locator=f\"{s}, Section VI-C Table III, row ExpCon, column (2) 'Dev' (second value on collapsed line L{ln_b}; SE line L{ln_se}); CI and p in Section VI-C text\",\n harmonisable=True, harmonised_estimate=b1, harmonised_se=se1, poolable=True,\n transformation=f\"log-time coefficient: theta = b = {b1}; SE(theta) = SE(b) = {se1} (printed). Check: SE from CI = (0.03-(-0.51))/(2*1.96) = {se1_ci:.4f}\",\n reason_if_not=None)\n\n# ---------------- S2 Becker et al. 2025 (METR) ----------------\ns = \"arxiv-2507.09089v2\"\n_, l2 = find(s, r\"allowing AI actually increases completion time by 19%\")\nfind(s, r\"implement issues in 19% more time on average\")\nfind(s, r\"Developers complete 136 issues with AI-allowed and 110\")\nfind(s, r\"16 developers with moderate AI experience complete 246 tasks\")\nc2 = float(re.search(r\"by (\\d+)%\", l2).group(1)) / 100.0\nth2 = math.log(1 + c2)\nitems[\"S2\"] = dict(\n estimate_as_printed=\"+19% (allowing AI increases completion time by 19%; regression-based, Section 3 / Section D.1)\",\n uncertainty_as_printed=\"none printed (95% CI shown only in Figure 1 / Figure 15, not reproduced in the extract)\",\n sample_as_printed=\"16 developers, 246 tasks; 136 issues AI-allowed, 110 AI-disallowed\",\n definition=\"S = E[time AI allowed]/E[time AI disallowed] - 1, estimated as exp(beta)-1 from OLS of log implementation time on AI indicator and log forecast time; issue-level randomisation\",\n locator=f\"{s}, Abstract and Section 3 'Results' (opening paragraph); estimator Sections 2.3, D.1-D.3\",\n harmonisable=True, harmonised_estimate=th2, harmonised_se=None, poolable=False,\n transformation=f\"percentage change in time: theta = ln(1 + c) = ln(1 + {c2}) = {th2:.6f}\",\n reason_if_not=\"No SE, CI or p-value printed in the extract; the CI exists only in a figure that is not reproduced, so SE(theta) cannot be obtained (harmonisable, not poolable).\")\n\n# ---------------- S3 Bono & Xu 2024 ----------------\ns = \"arxiv-2411.01067v2\"\n_, l3 = find(s, r\"^All Content \\| 53\\.84\")\nfind(s, r\"We had 181 subjects\")\ncells = [c.strip() for c in l3.split(\"|\")]\nov3, tr3, co3, imp3 = cells[1], cells[2], cells[3], cells[4]\nc3 = float(re.match(r\"(-?\\d+\\.\\d+)%\", imp3).group(1)) / 100.0\nth3 = math.log(1 + c3)\nitems[\"S3\"] = dict(\n estimate_as_printed=f\"Overall {ov3}, Treatment {tr3}, Control {co3} (minutes); Improvement {imp3}\",\n uncertainty_as_printed=\"significance stars only: *** (paper's definition 0<p<=0.01); no SE or CI printed\",\n sample_as_printed=\"181 subjects (randomised half treatment, half control)\",\n definition=\"difference in mean task duration (minutes) between Copilot and control groups across all tasks, holding accuracy constant (control duration predicted at treatment accuracy, Appendix B), randomised\",\n locator=f\"{s}, Section 4.2 Table 3, 'Aggregate Results' block, row 'All Content', columns Treatment/Control/Improvement\",\n harmonisable=True, harmonised_estimate=th3, harmonised_se=None, poolable=False,\n transformation=f\"percentage change in time: theta = ln(1 + c) = ln(1 + ({c3})) = {th3:.6f} (cf. ln({tr3}/{co3}) = {math.log(float(tr3)/float(co3)):.6f})\",\n reason_if_not=\"Only significance stars (p<=0.01) are printed; a p-value threshold does not identify SE(theta) (harmonisable, not poolable).\")\n\n# ---------------- S4 Bono 2025 ----------------\ns = \"arxiv-2511.13860v1\"\n_, l4a = find(s, r\"^Aware Time per Email\")\n_, l4c = find(s, r\"^Control Time per Email\")\n_, l4s = find(s, r\"^% Time Savings\")\n_, l4p = find(s, r\"^p-value \\|\")\nfind(s, r\"Control \\( N\\{=\\}52\")\nfind(s, r\"Aware \\( N\\{=\\}56\")\nlast = lambda l: l.split(\"|\")[-1].strip()\na4, ct4, sv4, p4s = last(l4a), last(l4c), last(l4s), last(l4p)\nc4 = -float(sv4) / 100.0 # 11.1% time saving -> change in time c = -0.111\np4 = float(p4s)\nth4 = math.log(1 + c4)\nz4 = norm.ppf(1 - p4 / 2)\nse4 = abs(th4) / z4\nitems[\"S4\"] = dict(\n estimate_as_printed=f\"Aware {a4} s, Control {ct4} s per email; % Time Savings {sv4}\",\n uncertainty_as_printed=f\"p-value {p4s}\",\n sample_as_printed=\"Aware N=56, Control N=52 (167 analysts across three arms incl. Blind N=59)\",\n definition=\"percentage time saving in mean time per email over the full 25-email queue, Aware (agent verdicts/rationale + prioritised queue) vs Control (no AI), randomised arms\",\n locator=f\"{s}, Section 3.3 Table 3 'Comparison of Email Processing Times', column 'Full Corpus', rows Aware/Control time, % Time Savings, p-value\",\n harmonisable=True, harmonised_estimate=th4, harmonised_se=se4, poolable=True,\n transformation=f\"percentage change in time c = -{sv4}/100 = {c4}; theta = ln(1 + c) = {th4:.6f}; approximate SE from two-sided p: SE = |theta|/z(1-{p4s}/2) = {abs(th4):.6f}/{z4:.6f} = {se4:.6f} (approximate SE)\",\n reason_if_not=None)\n\n# ---------------- S5 Kreitmeir & Raschky 2023 ----------------\ns = \"arxiv-2304.09339v1\"\nln5, l5 = find(s, r\"TreatPost \\| 0\\.115\")\n_, l5se = next_nonempty(s, ln5)\n_, l5n = find(s, r\"^N \\| 18332\")\nb5 = [c.strip() for c in l5.split(\"|\")][3]\nse5 = [c.strip() for c in l5se.split(\"|\")][3]\nn5 = [c.strip() for c in l5n.split(\"|\")][3]\nitems[\"S5\"] = dict(\n estimate_as_printed=f\"{b5} (Treat x Post, Output, column 3, panel A)\",\n uncertainty_as_printed=f\"SE {se5} (clustered at user level); no significance star\",\n sample_as_printed=f\"N = {n5} (user-day observations, users with affiliation)\",\n definition=\"difference-in-differences coefficient on aggregate GitHub Output measure (sum of Push, PR, PR comments, commit comments, Create, Issues events) for Italian vs Austrian/French users, pre 27/28.03 vs post 03/04.04.2023 around Italy's ChatGPT ban\",\n locator=f\"{s}, Section 4 Table 1, panel A, row Treat x Post, column (3) Output (Users with Affiliation)\",\n harmonisable=False, harmonised_estimate=None, harmonised_se=None, poolable=False,\n transformation=\"none applied (not harmonisable)\",\n reason_if_not=\"Effect on output counts (GitHub events) from a DiD around withdrawal of access, not a comparison of task-completion times with vs without AI on the same tasks, and not randomised.\")\n\n# ---------------- Pooling ----------------\norder = [\"S1\", \"S2\", \"S3\", \"S4\", \"S5\"]\npool = [k for k in order if items[k][\"poolable\"]]\ny = np.array([items[k][\"harmonised_estimate\"] for k in pool]); se = np.array([items[k][\"harmonised_se\"] for k in pool])\n\n\ndef meta(y, se):\n w = 1 / se**2\n fe = float(np.sum(w * y) / np.sum(w)); fe_se = float(math.sqrt(1 / np.sum(w)))\n q = float(np.sum(w * (y - fe) ** 2)); df = len(y) - 1\n C = float(np.sum(w) - np.sum(w**2) / np.sum(w))\n tau2 = max(0.0, (q - df) / C) if C > 0 else 0.0\n i2 = max(0.0, (q - df) / q) * 100 if q > 0 else 0.0\n wr = 1 / (se**2 + tau2)\n re_ = float(np.sum(wr * y) / np.sum(wr)); re_se = float(math.sqrt(1 / np.sum(wr)))\n return dict(fe=fe, fe_se=fe_se, q=q, df=df, tau2=tau2, i2=i2, re=re_, re_se=re_se,\n w_fe=(w / w.sum()).tolist(), w_re=(wr / wr.sum()).tolist())\n\n\nm = meta(y, se)\ninterp = \"low\" if m[\"i2\"] < 25 else (\"moderate\" if m[\"i2\"] < 50 else \"substantial\")\nre_lo, re_hi = m[\"re\"] - Z * m[\"re_se\"], m[\"re\"] + Z * m[\"re_se\"]\n\n# sensitivity: S1 SE from the printed CI instead of the printed SE\nse_alt = se.copy(); se_alt[pool.index(\"S1\")] = se1_ci\nm_alt = meta(y, se_alt)\n# baseline: unweighted means of theta\nharm = [k for k in order if items[k][\"harmonisable\"]]\nbase_h = float(np.mean([items[k][\"harmonised_estimate\"] for k in harm]))\nbase_p = float(np.mean(y))\n\nn_input_rows = {\"studies.json\": len(studies), \"README.md\": wc_l(os.path.join(DATA, \"README.md\"))}\nfor x in studies:\n n_input_rows[f\"sources/{x['source']}.txt\"] = wc_l(os.path.join(SRC, x[\"source\"] + \".txt\"))\n\ndata_issues = [\n {\"issue\": \"S1: Section VI-B reports N=47 (AI) + N=46 (no-AI) = 93 developers, while Table III states n = 96.\",\n \"handling\": \"Not resolvable from the extract; reported. sample_as_printed lists both; does not affect theta or SE, which are taken from Table III/Section VI-C.\"},\n {\"issue\": \"S1: Section VI-B gives Mean LogToT=4.6 for the AI group and 4.3 for no-AI while stating AI was faster (96 vs 114 min); the log means appear swapped/inconsistent.\",\n \"handling\": \"These log means are not used.\"},\n {\"issue\": \"S1: Table III is rendered with the four model columns collapsed into one line; SE (0.14) is rounded, and the CI [-0.51, 0.03] implies SE 0.1378.\",\n \"handling\": \"Model 2 taken as the second value per the editorial note; printed SE 0.14 used; CI-based SE reported as sensitivity (sensitivity_s1_se_from_ci).\"},\n {\"issue\": \"S2: The uncertainty of the headline +19% estimate is shown only in Figure 1/Figure 15, which are not reproduced; no SE or p-value printed.\",\n \"handling\": \"Harmonised (theta = ln 1.19) but not poolable; harmonised_se = null.\"},\n {\"issue\": \"S3: Treatment/Control means 44.29/63.10 imply -29.81%, printed improvement is -29.79% (slightly beyond rounding of the printed means); significance shown only by stars with an unusual convention (* = 0.05<p<=0.1).\",\n \"handling\": \"Printed -29.79% used as instructed (difference in theta ~0.0003); not poolable because stars do not identify an SE.\"},\n {\"issue\": \"S4: Table 3 units ambiguous: Malicious/Non-Malicious times per email (1.8-2.8 's') cannot average to Full Corpus times (50.5/56.9 s) under any common unit; Full Corpus ratio 50.5/56.9 implies 11.25% saving vs printed 11.1% (within rounding).\",\n \"handling\": \"Only the Full Corpus column is used; theta depends only on the printed % saving (unit-free). Flagged as a source inconsistency.\"},\n {\"issue\": \"S4: SE is approximate, back-calculated from a two-sided p-value (0.471) under a normal approximation on the log scale, although the paper's test is presumably on the time difference.\",\n \"handling\": \"Marked as approximate SE in transformation; pooled results carry this caveat.\"},\n {\"issue\": \"S5: Table 1 note prints '** p<0.5' (presumably p<0.05); row label rendered as '\\\\times TreatPost'; panel B header lists 11.04 although the text says it was excluded; rendered date line 'August 24, 2026' is an artefact.\",\n \"handling\": \"No effect on the extracted panel A column 3 value; S5 is not harmonisable anyway.\"},\n]\n\nres = {\n \"task_id\": \"T3-synthesis\",\n \"target_definition\": \"theta = ln(T_AI / T_noAI): natural log of the ratio of mean task-completion time with generative-AI assistance to mean completion time without it, for the same tasks, from a randomised comparison (negative = faster with AI).\",\n \"items\": items,\n \"k_extracted\": sum(1 for k in order if items[k][\"estimate_as_printed\"]),\n \"k_harmonised\": sum(1 for k in order if items[k][\"harmonised_estimate\"] is not None),\n \"k_pooled\": len(pool),\n \"pooled_studies\": pool,\n \"fe_estimate\": m[\"fe\"], \"fe_se\": m[\"fe_se\"], \"fe_ci_low\": m[\"fe\"] - Z * m[\"fe_se\"], \"fe_ci_high\": m[\"fe\"] + Z * m[\"fe_se\"],\n \"re_estimate\": m[\"re\"], \"re_se\": m[\"re_se\"], \"re_ci_low\": re_lo, \"re_ci_high\": re_hi,\n \"q_stat\": m[\"q\"], \"q_df\": m[\"df\"], \"q_pvalue\": float(chi2.sf(m[\"q\"], m[\"df\"])),\n \"i2\": m[\"i2\"], \"tau2\": m[\"tau2\"],\n \"heterogeneity_interpretation\": interp,\n \"pooled_significant_5pct\": bool(re_lo > 0 or re_hi < 0),\n \"pooled_pct_change_re\": 100 * (math.exp(m[\"re\"]) - 1),\n \"pooled_pct_change_re_ci\": [100 * (math.exp(re_lo) - 1), 100 * (math.exp(re_hi) - 1)],\n \"weights_fe\": dict(zip(pool, m[\"w_fe\"])), \"weights_re\": dict(zip(pool, m[\"w_re\"])),\n \"baseline_unweighted_mean_theta_harmonised\": base_h,\n \"baseline_unweighted_mean_theta_poolable\": base_p,\n \"sensitivity_s1_se_from_ci\": {\"s1_se\": se1_ci, \"re_estimate\": m_alt[\"re\"], \"re_se\": m_alt[\"re_se\"],\n \"re_ci_low\": m_alt[\"re\"] - Z * m_alt[\"re_se\"], \"re_ci_high\": m_alt[\"re\"] + Z * m_alt[\"re_se\"],\n \"q_stat\": m_alt[\"q\"], \"i2\": m_alt[\"i2\"], \"tau2\": m_alt[\"tau2\"]},\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": data_issues,\n \"n_input_rows\": n_input_rows,\n \"n_analysed\": len(order),\n \"random_seed\": None,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n json.dump(res, fh, indent=1)\n\ncols = [\"study\", \"estimate_as_printed\", \"uncertainty_as_printed\", \"sample_as_printed\", \"definition\", \"locator\",\n \"harmonised_estimate\", \"harmonised_se\", \"harmonisable\", \"poolable\", \"transformation\", \"reason_if_not\"]\nwith open(os.path.join(OUT, \"table1.csv\"), \"w\", newline=\"\") as fh:\n wr = csv.writer(fh); wr.writerow(cols)\n for k in order:\n it = items[k]\n wr.writerow([k] + [(\"\" if it[c] is None else it[c]) for c in cols[1:]])\n\n# ---------------- Forest plot ----------------\nlabels = {\"S1\": \"S1 Paradis et al. 2024 (Google)\", \"S2\": \"S2 Becker et al. 2025 (METR)\",\n \"S3\": \"S3 Bono & Xu 2024 (Security Copilot)\", \"S4\": \"S4 Bono 2025 (phishing agent; approx. SE)\"}\nfig, ax = plt.subplots(figsize=(9, 4.2))\nypos = list(range(len(harm), 0, -1))\nfor yy, k in zip(ypos, harm):\n th = items[k][\"harmonised_estimate\"]; s_ = items[k][\"harmonised_se\"]\n if s_ is not None:\n ax.errorbar(th, yy, xerr=Z * s_, fmt=\"s\", color=\"black\", capsize=3)\n txt = f\"{th:.3f} [{th - Z*s_:.3f}, {th + Z*s_:.3f}]\"\n else:\n ax.plot(th, yy, \"o\", mfc=\"white\", mec=\"gray\")\n txt = f\"{th:.3f} (no SE; not pooled)\"\n ax.text(1.02, yy, txt, transform=ax.get_yaxis_transform(), va=\"center\", fontsize=8)\nd = np.array([[re_lo, 0], [m[\"re\"], 0.2], [re_hi, 0], [m[\"re\"], -0.2]])\nax.fill(d[:, 0], d[:, 1], color=\"tab:blue\")\nax.text(1.02, 0, f\"{m['re']:.3f} [{re_lo:.3f}, {re_hi:.3f}]\", transform=ax.get_yaxis_transform(), va=\"center\", fontsize=8)\nax.axvline(0, color=\"gray\", lw=0.8, ls=\"--\")\nax.set_yticks(ypos + [0]); ax.set_yticklabels([labels[k] for k in harm] + [f\"RE pooled (DL), k={len(pool)}, I2={m['i2']:.0f}%\"])\nax.set_xlabel(\"theta = ln(T_AI / T_noAI) (negative = faster with AI)\")\nax.set_title(\"Harmonised effects of generative-AI access on task time\")\nplt.tight_layout(rect=(0, 0, 0.8, 1))\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=150)\n\nprint(\"== Results ==\")\nfor k in order:\n print(k, items[k][\"harmonised_estimate\"], items[k][\"harmonised_se\"], \"poolable\" if items[k][\"poolable\"] else \"not pooled\")\nprint(f\"FE {m['fe']:.4f} (SE {m['fe_se']:.4f}); RE {m['re']:.4f} [{re_lo:.4f}, {re_hi:.4f}]; Q {m['q']:.4f}; I2 {m['i2']:.2f}; tau2 {m['tau2']:.4f}; pct {res['pooled_pct_change_re']:.2f}%\")","is_error":false}]},"uuid":"00ff97aa-6eb8-4c24-ad9e-2ec409dd317f","timestamp":"2026-10-30T05:34:11.241Z","toolUseResult":{"stdout":"\"\"\"T3-synthesis: extraction, harmonisation onto theta = ln(T_AI/T_noAI), FE/DL pooling, forest plot.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/table1.csv, out/figure1.png. No randomness.\"\"\"\nimport json, math, os, re, csv\nimport numpy as np\nfrom scipy.stats import norm, chi2\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nDATA = \"data\"; OUT = \"out\"\nSRC = os.path.join(DATA, \"sources\")\nos.makedirs(OUT, exist_ok=True)\nZ = 1.96\n\n\ndef lines_of(name):\n with open(os.path.join(SRC, name + \".txt\"), encoding=\"utf-8\") as fh:\n return fh.read().split(\"\\n\")\n\n\ndef find(name, pattern, nth=0):\n \"\"\"Return (line_no, line) of the nth line matching regex pattern; print it.\"\"\"\n L = lines_of(name)\n hits = [(i + 1, l) for i, l in enumerate(L) if re.search(pattern, l)]\n if len(hits) <= nth:\n raise RuntimeError(f\"pattern {pattern!r} not found in {name}\")\n ln, l = hits[nth]\n print(f\"[{name} L{ln}] {l.strip()[:300]}\")\n return ln, l\n\n\ndef next_nonempty(name, ln):\n \"\"\"Line following line number ln (1-based) that is non-empty; print it.\"\"\"\n L = lines_of(name); j = ln\n while not L[j].strip():\n j += 1\n print(f\"[{name} L{j+1}] {L[j].strip()[:300]}\")\n return j + 1, L[j]\n\n\ndef wc_l(path):\n with open(path, \"rb\") as fh:\n return fh.read().count(b\"\\n\")\n\n\nstudies = json.load(open(os.path.join(DATA, \"studies.json\"), encoding=\"utf-8\"))\nassert [s[\"id\"] for s in studies] == [\"S1\", \"S2\", \"S3\", \"S4\", \"S5\"]\nprint(\"== Source lines relied on ==\")\nitems = {}\n\n# ---------------- S1 Paradis et al. 2024 ----------------\ns = \"arxiv-2410.12944v3\"\nln_b, l_b = find(s, r\"ExpCon \\|\")\nln_se, l_se = next_nonempty(s, ln_b)\ncoefs = re.findall(r\"-?\\d+\\.\\d+[.*]*\", l_b.split(\"ExpCon\")[0])\nses = re.findall(r\"\\((\\d+\\.\\d+)\\)\", l_se)\nb1_str, se1_str = coefs[1], ses[1] # Model 2 = second column (collapsed table)\nb1 = float(re.sub(r\"[.*]+$\", \"\", b1_str)); se1 = float(se1_str)\n_, l_ci = find(s, r\"best-fit Model 2\")\nci = re.search(r\"\\[(-?\\d+\\.\\d+),\\s*(-?\\d+\\.\\d+)\\]\", l_ci); ci_l, ci_u = float(ci.group(1)), float(ci.group(2))\np1 = re.search(r\"p=(\\d*\\.\\d+)\", l_ci).group(1)\nfind(s, r\"TABLE III: .*n = 96\")\nfind(s, r\"completed the task in 96 min\")\nassert (b1, se1, ci_l, ci_u) == (-0.24, 0.14, -0.51, 0.03)\nse1_ci = (ci_u - ci_l) / (2 * Z)\nitems[\"S1\"] = dict(\n estimate_as_printed=f\"{b1_str} (beta_1 for ExpCon, Model 2, Table III; Section VI-C: beta_1=-0.24, i.e. 21% faster)\",\n uncertainty_as_printed=f\"SE ({se1_str}); 95% CI = [{ci.group(1)}, {ci.group(2)}], p={p1} (NS)\",\n sample_as_printed=\"n = 96 (Table III caption); Section VI-B: N=47 AI, N=46 no-AI\",\n definition=\"OLS coefficient on experimental condition (AI exposure) with natural-log time on task (LogToT) as dependent variable, developer-level covariates (Model 2), one enterprise coding task, randomised\",\n locator=f\"{s}, Section VI-C Table III, row ExpCon, column (2) 'Dev' (second value on collapsed line L{ln_b}; SE line L{ln_se}); CI and p in Section VI-C text\",\n harmonisable=True, harmonised_estimate=b1, harmonised_se=se1, poolable=True,\n transformation=f\"log-time coefficient: theta = b = {b1}; SE(theta) = SE(b) = {se1} (printed). Check: SE from CI = (0.03-(-0.51))/(2*1.96) = {se1_ci:.4f}\",\n reason_if_not=None)\n\n# ---------------- S2 Becker et al. 2025 (METR) ----------------\ns = \"arxiv-2507.09089v2\"\n_, l2 = find(s, r\"allowing AI actually increases completion time by 19%\")\nfind(s, r\"implement issues in 19% more time on average\")\nfind(s, r\"Developers complete 136 issues with AI-allowed and 110\")\nfind(s, r\"16 developers with moderate AI experience complete 246 tasks\")\nc2 = float(re.search(r\"by (\\d+)%\", l2).group(1)) / 100.0\nth2 = math.log(1 + c2)\nitems[\"S2\"] = dict(\n estimate_as_printed=\"+19% (allowing AI increases completion time by 19%; regression-based, Section 3 / Section D.1)\",\n uncertainty_as_printed=\"none printed (95% CI shown only in Figure 1 / Figure 15, not reproduced in the extract)\",\n sample_as_printed=\"16 developers, 246 tasks; 136 issues AI-allowed, 110 AI-disallowed\",\n definition=\"S = E[time AI allowed]/E[time AI disallowed] - 1, estimated as exp(beta)-1 from OLS of log implementation time on AI indicator and log forecast time; issue-level randomisation\",\n locator=f\"{s}, Abstract and Section 3 'Results' (opening paragraph); estimator Sections 2.3, D.1-D.3\",\n harmonisable=True, harmonised_estimate=th2, harmonised_se=None, poolable=False,\n transformation=f\"percentage change in time: theta = ln(1 + c) = ln(1 + {c2}) = {th2:.6f}\",\n reason_if_not=\"No SE, CI or p-value printed in the extract; the CI exists only in a figure that is not reproduced, so SE(theta) cannot be obtained (harmonisable, not poolable).\")\n\n# ---------------- S3 Bono & Xu 2024 ----------------\ns = \"arxiv-2411.01067v2\"\n_, l3 = find(s, r\"^All Content \\| 53\\.84\")\nfind(s, r\"We had 181 subjects\")\ncells = [c.strip() for c in l3.split(\"|\")]\nov3, tr3, co3, imp3 = cells[1], cells[2], cells[3], cells[4]\nc3 = float(re.match(r\"(-?\\d+\\.\\d+)%\", imp3).group(1)) / 100.0\nth3 = math.log(1 + c3)\nitems[\"S3\"] = dict(\n estimate_as_printed=f\"Overall {ov3}, Treatment {tr3}, Control {co3} (minutes); Improvement {imp3}\",\n uncertainty_as_printed=\"significance stars only: *** (paper's definition 0<p<=0.01); no SE or CI printed\",\n sample_as_printed=\"181 subjects (randomised half treatment, half control)\",\n definition=\"difference in mean task duration (minutes) between Copilot and control groups across all tasks, holding accuracy constant (control duration predicted at treatment accuracy, Appendix B), randomised\",\n locator=f\"{s}, Section 4.2 Table 3, 'Aggregate Results' block, row 'All Content', columns Treatment/Control/Improvement\",\n harmonisable=True, harmonised_estimate=th3, harmonised_se=None, poolable=False,\n transformation=f\"percentage change in time: theta = ln(1 + c) = ln(1 + ({c3})) = {th3:.6f} (cf. ln({tr3}/{co3}) = {math.log(float(tr3)/float(co3)):.6f})\",\n reason_if_not=\"Only significance stars (p<=0.01) are printed; a p-value threshold does not identify SE(theta) (harmonisable, not poolable).\")\n\n# ---------------- S4 Bono 2025 ----------------\ns = \"arxiv-2511.13860v1\"\n_, l4a = find(s, r\"^Aware Time per Email\")\n_, l4c = find(s, r\"^Control Time per Email\")\n_, l4s = find(s, r\"^% Time Savings\")\n_, l4p = find(s, r\"^p-value \\|\")\nfind(s, r\"Control \\( N\\{=\\}52\")\nfind(s, r\"Aware \\( N\\{=\\}56\")\nlast = lambda l: l.split(\"|\")[-1].strip()\na4, ct4, sv4, p4s = last(l4a), last(l4c), last(l4s), last(l4p)\nc4 = -float(sv4) / 100.0 # 11.1% time saving -> change in time c = -0.111\np4 = float(p4s)\nth4 = math.log(1 + c4)\nz4 = norm.ppf(1 - p4 / 2)\nse4 = abs(th4) / z4\nitems[\"S4\"] = dict(\n estimate_as_printed=f\"Aware {a4} s, Control {ct4} s per email; % Time Savings {sv4}\",\n uncertainty_as_printed=f\"p-value {p4s}\",\n sample_as_printed=\"Aware N=56, Control N=52 (167 analysts across three arms incl. Blind N=59)\",\n definition=\"percentage time saving in mean time per email over the full 25-email queue, Aware (agent verdicts/rationale + prioritised queue) vs Control (no AI), randomised arms\",\n locator=f\"{s}, Section 3.3 Table 3 'Comparison of Email Processing Times', column 'Full Corpus', rows Aware/Control time, % Time Savings, p-value\",\n harmonisable=True, harmonised_estimate=th4, harmonised_se=se4, poolable=True,\n transformation=f\"percentage change in time c = -{sv4}/100 = {c4}; theta = ln(1 + c) = {th4:.6f}; approximate SE from two-sided p: SE = |theta|/z(1-{p4s}/2) = {abs(th4):.6f}/{z4:.6f} = {se4:.6f} (approximate SE)\",\n reason_if_not=None)\n\n# ---------------- S5 Kreitmeir & Raschky 2023 ----------------\ns = \"arxiv-2304.09339v1\"\nln5, l5 = find(s, r\"TreatPost \\| 0\\.115\")\n_, l5se = next_nonempty(s, ln5)\n_, l5n = find(s, r\"^N \\| 18332\")\nb5 = [c.strip() for c in l5.split(\"|\")][3]\nse5 = [c.strip() for c in l5se.split(\"|\")][3]\nn5 = [c.strip() for c in l5n.split(\"|\")][3]\nitems[\"S5\"] = dict(\n estimate_as_printed=f\"{b5} (Treat x Post, Output, column 3, panel A)\",\n uncertainty_as_printed=f\"SE {se5} (clustered at user level); no significance star\",\n sample_as_printed=f\"N = {n5} (user-day observations, users with affiliation)\",\n definition=\"difference-in-differences coefficient on aggregate GitHub Output measure (sum of Push, PR, PR comments, commit comments, Create, Issues events) for Italian vs Austrian/French users, pre 27/28.03 vs post 03/04.04.2023 around Italy's ChatGPT ban\",\n locator=f\"{s}, Section 4 Table 1, panel A, row Treat x Post, column (3) Output (Users with Affiliation)\",\n harmonisable=False, harmonised_estimate=None, harmonised_se=None, poolable=False,\n transformation=\"none applied (not harmonisable)\",\n reason_if_not=\"Effect on output counts (GitHub events) from a DiD around withdrawal of access, not a comparison of task-completion times with vs without AI on the same tasks, and not randomised.\")\n\n# ---------------- Pooling ----------------\norder = [\"S1\", \"S2\", \"S3\", \"S4\", \"S5\"]\npool = [k for k in order if items[k][\"poolable\"]]\ny = np.array([items[k][\"harmonised_estimate\"] for k in pool]); se = np.array([items[k][\"harmonised_se\"] for k in pool])\n\n\ndef meta(y, se):\n w = 1 / se**2\n fe = float(np.sum(w * y) / np.sum(w)); fe_se = float(math.sqrt(1 / np.sum(w)))\n q = float(np.sum(w * (y - fe) ** 2)); df = len(y) - 1\n C = float(np.sum(w) - np.sum(w**2) / np.sum(w))\n tau2 = max(0.0, (q - df) / C) if C > 0 else 0.0\n i2 = max(0.0, (q - df) / q) * 100 if q > 0 else 0.0\n wr = 1 / (se**2 + tau2)\n re_ = float(np.sum(wr * y) / np.sum(wr)); re_se = float(math.sqrt(1 / np.sum(wr)))\n return dict(fe=fe, fe_se=fe_se, q=q, df=df, tau2=tau2, i2=i2, re=re_, re_se=re_se,\n w_fe=(w / w.sum()).tolist(), w_re=(wr / wr.sum()).tolist())\n\n\nm = meta(y, se)\ninterp = \"low\" if m[\"i2\"] < 25 else (\"moderate\" if m[\"i2\"] < 50 else \"substantial\")\nre_lo, re_hi = m[\"re\"] - Z * m[\"re_se\"], m[\"re\"] + Z * m[\"re_se\"]\n\n# sensitivity: S1 SE from the printed CI instead of the printed SE\nse_alt = se.copy(); se_alt[pool.index(\"S1\")] = se1_ci\nm_alt = meta(y, se_alt)\n# baseline: unweighted means of theta\nharm = [k for k in order if items[k][\"harmonisable\"]]\nbase_h = float(np.mean([items[k][\"harmonised_estimate\"] for k in harm]))\nbase_p = float(np.mean(y))\n\nn_input_rows = {\"studies.json\": len(studies), \"README.md\": wc_l(os.path.join(DATA, \"README.md\"))}\nfor x in studies:\n n_input_rows[f\"sources/{x['source']}.txt\"] = wc_l(os.path.join(SRC, x[\"source\"] + \".txt\"))\n\ndata_issues = [\n {\"issue\": \"S1: Section VI-B reports N=47 (AI) + N=46 (no-AI) = 93 developers, while Table III states n = 96.\",\n \"handling\": \"Not resolvable from the extract; reported. sample_as_printed lists both; does not affect theta or SE, which are taken from Table III/Section VI-C.\"},\n {\"issue\": \"S1: Section VI-B gives Mean LogToT=4.6 for the AI group and 4.3 for no-AI while stating AI was faster (96 vs 114 min); the log means appear swapped/inconsistent.\",\n \"handling\": \"These log means are not used.\"},\n {\"issue\": \"S1: Table III is rendered with the four model columns collapsed into one line; SE (0.14) is rounded, and the CI [-0.51, 0.03] implies SE 0.1378.\",\n \"handling\": \"Model 2 taken as the second value per the editorial note; printed SE 0.14 used; CI-based SE reported as sensitivity (sensitivity_s1_se_from_ci).\"},\n {\"issue\": \"S2: The uncertainty of the headline +19% estimate is shown only in Figure 1/Figure 15, which are not reproduced; no SE or p-value printed.\",\n \"handling\": \"Harmonised (theta = ln 1.19) but not poolable; harmonised_se = null.\"},\n {\"issue\": \"S3: Treatment/Control means 44.29/63.10 imply -29.81%, printed improvement is -29.79% (slightly beyond rounding of the printed means); significance shown only by stars with an unusual convention (* = 0.05<p<=0.1).\",\n \"handling\": \"Printed -29.79% used as instructed (difference in theta ~0.0003); not poolable because stars do not identify an SE.\"},\n {\"issue\": \"S4: Table 3 units ambiguous: Malicious/Non-Malicious times per email (1.8-2.8 's') cannot average to Full Corpus times (50.5/56.9 s) under any common unit; Full Corpus ratio 50.5/56.9 implies 11.25% saving vs printed 11.1% (within rounding).\",\n \"handling\": \"Only the Full Corpus column is used; theta depends only on the printed % saving (unit-free). Flagged as a source inconsistency.\"},\n {\"issue\": \"S4: SE is approximate, back-calculated from a two-sided p-value (0.471) under a normal approximation on the log scale, although the paper's test is presumably on the time difference.\",\n \"handling\": \"Marked as approximate SE in transformation; pooled results carry this caveat.\"},\n {\"issue\": \"S5: Table 1 note prints '** p<0.5' (presumably p<0.05); row label rendered as '\\\\times TreatPost'; panel B header lists 11.04 although the text says it was excluded; rendered date line 'August 24, 2026' is an artefact.\",\n \"handling\": \"No effect on the extracted panel A column 3 value; S5 is not harmonisable anyway.\"},\n]\n\nres = {\n \"task_id\": \"T3-synthesis\",\n \"target_definition\": \"theta = ln(T_AI / T_noAI): natural log of the ratio of mean task-completion time with generative-AI assistance to mean completion time without it, for the same tasks, from a randomised comparison (negative = faster with AI).\",\n \"items\": items,\n \"k_extracted\": sum(1 for k in order if items[k][\"estimate_as_printed\"]),\n \"k_harmonised\": sum(1 for k in order if items[k][\"harmonised_estimate\"] is not None),\n \"k_pooled\": len(pool),\n \"pooled_studies\": pool,\n \"fe_estimate\": m[\"fe\"], \"fe_se\": m[\"fe_se\"], \"fe_ci_low\": m[\"fe\"] - Z * m[\"fe_se\"], \"fe_ci_high\": m[\"fe\"] + Z * m[\"fe_se\"],\n \"re_estimate\": m[\"re\"], \"re_se\": m[\"re_se\"], \"re_ci_low\": re_lo, \"re_ci_high\": re_hi,\n \"q_stat\": m[\"q\"], \"q_df\": m[\"df\"], \"q_pvalue\": float(chi2.sf(m[\"q\"], m[\"df\"])),\n \"i2\": m[\"i2\"], \"tau2\": m[\"tau2\"],\n \"heterogeneity_interpretation\": interp,\n \"pooled_significant_5pct\": bool(re_lo > 0 or re_hi < 0),\n \"pooled_pct_change_re\": 100 * (math.exp(m[\"re\"]) - 1),\n \"pooled_pct_change_re_ci\": [100 * (math.exp(re_lo) - 1), 100 * (math.exp(re_hi) - 1)],\n \"weights_fe\": dict(zip(pool, m[\"w_fe\"])), \"weights_re\": dict(zip(pool, m[\"w_re\"])),\n \"baseline_unweighted_mean_theta_harmonised\": base_h,\n \"baseline_unweighted_mean_theta_poolable\": base_p,\n \"sensitivity_s1_se_from_ci\": {\"s1_se\": se1_ci, \"re_estimate\": m_alt[\"re\"], \"re_se\": m_alt[\"re_se\"],\n \"re_ci_low\": m_alt[\"re\"] - Z * m_alt[\"re_se\"], \"re_ci_high\": m_alt[\"re\"] + Z * m_alt[\"re_se\"],\n \"q_stat\": m_alt[\"q\"], \"i2\": m_alt[\"i2\"], \"tau2\": m_alt[\"tau2\"]},\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": data_issues,\n \"n_input_rows\": n_input_rows,\n \"n_analysed\": len(order),\n \"random_seed\": None,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n json.dump(res, fh, indent=1)\n\ncols = [\"study\", \"estimate_as_printed\", \"uncertainty_as_printed\", \"sample_as_printed\", \"definition\", \"locator\",\n \"harmonised_estimate\", \"harmonised_se\", \"harmonisable\", \"poolable\", \"transformation\", \"reason_if_not\"]\nwith open(os.path.join(OUT, \"table1.csv\"), \"w\", newline=\"\") as fh:\n wr = csv.writer(fh); wr.writerow(cols)\n for k in order:\n it = items[k]\n wr.writerow([k] + [(\"\" if it[c] is None else it[c]) for c in cols[1:]])\n\n# ---------------- Forest plot ----------------\nlabels = {\"S1\": \"S1 Paradis et al. 2024 (Google)\", \"S2\": \"S2 Becker et al. 2025 (METR)\",\n \"S3\": \"S3 Bono & Xu 2024 (Security Copilot)\", \"S4\": \"S4 Bono 2025 (phishing agent; approx. SE)\"}\nfig, ax = plt.subplots(figsize=(9, 4.2))\nypos = list(range(len(harm), 0, -1))\nfor yy, k in zip(ypos, harm):\n th = items[k][\"harmonised_estimate\"]; s_ = items[k][\"harmonised_se\"]\n if s_ is not None:\n ax.errorbar(th, yy, xerr=Z * s_, fmt=\"s\", color=\"black\", capsize=3)\n txt = f\"{th:.3f} [{th - Z*s_:.3f}, {th + Z*s_:.3f}]\"\n else:\n ax.plot(th, yy, \"o\", mfc=\"white\", mec=\"gray\")\n txt = f\"{th:.3f} (no SE; not pooled)\"\n ax.text(1.02, yy, txt, transform=ax.get_yaxis_transform(), va=\"center\", fontsize=8)\nd = np.array([[re_lo, 0], [m[\"re\"], 0.2], [re_hi, 0], [m[\"re\"], -0.2]])\nax.fill(d[:, 0], d[:, 1], color=\"tab:blue\")\nax.text(1.02, 0, f\"{m['re']:.3f} [{re_lo:.3f}, {re_hi:.3f}]\", transform=ax.get_yaxis_transform(), va=\"center\", fontsize=8)\nax.axvline(0, color=\"gray\", lw=0.8, ls=\"--\")\nax.set_yticks(ypos + [0]); ax.set_yticklabels([labels[k] for k in harm] + [f\"RE pooled (DL), k={len(pool)}, I2={m['i2']:.0f}%\"])\nax.set_xlabel(\"theta = ln(T_AI / T_noAI) (negative = faster with AI)\")\nax.set_title(\"Harmonised effects of generative-AI access on task time\")\nplt.tight_layout(rect=(0, 0, 0.8, 1))\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=150)\n\nprint(\"== Results ==\")\nfor k in order:\n print(k, items[k][\"harmonised_estimate\"], items[k][\"harmonised_se\"], \"poolable\" if items[k][\"poolable\"] else \"not pooled\")\nprint(f\"FE {m['fe']:.4f} (SE {m['fe_se']:.4f}); RE {m['re']:.4f} [{re_lo:.4f}, {re_hi:.4f}]; Q {m['q']:.4f}; I2 {m['i2']:.2f}; tau2 {m['tau2']:.4f}; pct {res['pooled_pct_change_re']:.2f}%\")","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4b55e90a-dca8-42f7-87f6-2d14c633a04b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}23{"type":"api-request-blob","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","hash":"32a43d72a080e809d91180c2931bdaa19225810d2d103df647e6938cc075c8c8","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_017F6rHHzmZNXtX4Kzu8bwoz","name":"Bash","input":{"command":"cat out/report.md; cat out/table1.csv; cat out/exclusions.csv; ls out/state; cat data/studies.json","description":"Read report, table, studies"}},{"type":"tool_use","id":"toolu_01FktSdtWUHqS9SJtBDE4YL6","name":"Bash","input":{"command":"cat out/analysis.py","description":"Read analysis script"}}]}}24{"type":"api-request-blob","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","hash":"5ef1d5d94646a1703d35051ba2e60fb1b5971b471c38b69e5057c7df93de5385","message":{"role":"user","content":[{"tool_use_id":"toolu_017F6rHHzmZNXtX4Kzu8bwoz","type":"tool_result","content":"# T3-synthesis: Does generative-AI assistance shorten task-completion time?\n\n## Question\nDoes access to a generative-AI assistant change how long workers take to complete their tasks? I harmonised the five primary studies onto θ = ln(T_AI / T_noAI) (`target_definition`), the log ratio of mean completion time with AI to mean time without it, for the same tasks, in a randomised comparison. Negative θ means faster with AI. I then pooled the studies that could be pooled with inverse-variance fixed-effect (FE) and DerSimonian–Laird random-effects (RE) models.\n\n## Data and exclusions\nThe only inputs were the five text extracts under `data/sources/` and `data/studies.json`. All SHA-256 checksums match `data/README.md`, and line counts are in `n_input_rows`. Every one of the 5 items had a printed value to extract (`k_extracted` = 5, `n_analysed` = 5). `out/table1.csv` and `items` give the printed values verbatim with locators. The exclusion ledger (`out/exclusions.csv`) runs 5 assessed → 4 harmonisable → 2 poolable:\n- **S5** (Kreitmeir & Raschky, ChatGPT ban; printed −0.278 (0.295), N = 18332) is **not harmonisable**. It is a difference-in-differences coefficient on GitHub output counts around the withdrawal of access, not a comparison of task-completion times on the same tasks (`items.S5.reason_if_not`).\n- **S2** (METR; +19%) is harmonisable but **not poolable**. Its confidence interval appears only in Figure 1/15, which the extract does not reproduce, and no SE or p-value is printed (`items.S2.reason_if_not`).\n- **S3** (Security Copilot; −29.79%***) is harmonisable but **not poolable**. It reports significance stars only (`items.S3.reason_if_not`).\n- That leaves **S1** (Paradis et al.) and **S4** (Bono, phishing agent) in the pool (`k_pooled` = 2, `pooled_studies`).\n\nI found eight input problems, listed in `data_issues`. The main ones:\n- S1: n = 96 in Table III but 47 + 46 = 93 in the text, and the two groups' log means appear swapped.\n- S3: the printed −29.79% differs slightly from the −29.81% implied by the printed means.\n- S4: the units in Table 3 are inconsistent across columns.\n- S5: the table note prints \"** p<0.5\".\n\nNone of these changes a harmonised value by more than about 0.0003, apart from the S1 SE choice, which I examine under Uncertainty.\n\n## Method\nI applied the TASK conventions to each study; the `items.*.transformation` field records each formula with its inputs:\n- **S1:** the Model 2 ExpCon coefficient on log time on task gives θ = b = −0.24, with printed SE 0.14 (`items.S1.harmonised_estimate`, `items.S1.harmonised_se`).\n- **S2:** θ = ln(1.19) = 0.174 (`items.S2.harmonised_estimate`).\n- **S3:** θ = ln(1 − 0.2979) = −0.354 (`items.S3.harmonised_estimate`).\n- **S4:** a printed 11.1% time saving gives c = −0.111 and θ = ln(0.889) = −0.118. Its SE is **approximate**, recovered from the two-sided p = 0.471 as |θ|/z₀.₇₆₄₅ = 0.163 (`items.S4.harmonised_se`).\n\nPooling used inverse-variance FE and DL RE, with 95% intervals at 1.96, Q on k − 1 = 1 df, I² = max(0, (Q − df)/Q)·100, and τ² = max(0, (Q − df)/C). As a baseline I also computed unweighted means of θ (`baseline_unweighted_mean_theta_poolable`, `baseline_unweighted_mean_theta_harmonised`). The script `out/analysis.py` regenerates everything, uses no randomness (`random_seed` = null), and prints the source lines it parses. Figure: `out/figure1.png`.\n\n## Results\n- Harmonised θ: S1 −0.240 (SE 0.140), S2 +0.174 (no SE), S3 −0.354 (no SE), S4 −0.118 (approx. SE 0.163). Keys: `items.*.harmonised_estimate`, `items.*.harmonised_se`. `k_harmonised` = 4.\n- **FE pooled θ = −0.188**, SE 0.106, 95% CI [−0.396, 0.020] (`fe_estimate`, `fe_se`, `fe_ci_low`, `fe_ci_high`).\n- **RE pooled θ = −0.188**, SE 0.106, 95% CI [−0.396, 0.020] (`re_estimate`, `re_se`, `re_ci_low`, `re_ci_high`). RE equals FE because τ² = 0 (`tau2`).\n- On the time scale this is a −17.2% change in mean task time (`pooled_pct_change_re`), with a 95% interval of about −32.7% to +2.0% (`pooled_pct_change_re_ci`).\n- Q = 0.324 on 1 df, p = 0.57 (`q_stat`, `q_df`, `q_pvalue`). I² = 0% (`i2`), so heterogeneity is classed as \"low\" (`heterogeneity_interpretation`).\n- The RE interval includes 0, so the pooled effect is not significant at 5% (`pooled_significant_5pct` = false).\n- Baselines: the unweighted mean of the two poolable θ is −0.179 (`baseline_unweighted_mean_theta_poolable`). The unweighted mean of all four harmonised θ is −0.134 (`baseline_unweighted_mean_theta_harmonised`). The second is descriptive only, because two of those studies have no SE.\n\n**Synthesis.** The two poolable randomised studies point towards shorter task times with AI, but the pooled interval includes no effect. These data cannot establish that generative-AI assistance shortens task-completion time. The harmonisable studies that could not be pooled disagree in direction: S3 shows a large reduction (−0.354) and S2 a slowdown (+0.174). So the I² = 0% computed from k = 2 says nothing reliable about heterogeneity across settings, and the evidence as a whole is mixed and context-dependent: developers in an enterprise lab task, experienced open-source developers on their own repositories, IT administrators, and SOC analysts.\n\n## Uncertainty\n- With k = 2, Q has a single degree of freedom. The DL τ² and I² are extremely imprecise, and a zero τ² estimate does not mean the studies are homogeneous.\n- The S4 SE is approximate: it is back-calculated from a p-value under a normal approximation on the log scale, while the test in the paper was not necessarily on that scale. S4 carries 42% of the pooled weight (`weights_fe`).\n- **Sensitivity.** Using the S1 SE implied by its printed CI (0.1378 instead of 0.14) gives RE −0.189 [−0.395, 0.017] (`sensitivity_s1_se_from_ci`). The conclusion is unchanged.\n- Different estimands sit under one θ. S1 and S2 are regression-adjusted log-time contrasts. S3 holds accuracy constant using predicted control durations. S4 is a ratio of mean time per email in an arm that also re-ordered the queue. Each S1 or S2 coefficient equals the log of a ratio of means only under the assumptions in the papers.\n\n## Limitations and open issues\n- Only 2 of 5 studies could be pooled (`k_pooled`). The pooled estimate rests on one 96-developer coding task and one phishing-triage experiment, and it leaves out the two other randomised time studies, which have the largest effects in each direction (S2 +0.174, S3 −0.354). If they could be pooled the result could move substantially. Their SEs are not printed in the extracts, and I did not substitute values from figures or memory, as the information rules require.\n- `feasibility` = \"complete\" in the TASK's sense: every item has either harmonised values or an explicit, justified reason why none can be obtained, and the pool covers all poolable items. This does not mean the evidence base is adequate.\n- The source inconsistencies in `data_issues` (S1 sample size and log means, S4 units) cannot be resolved from the extracts.\n- No assessment of risk of bias or publication bias was possible or attempted. Three of the studies come from vendors (Google, Microsoft ×2).\nstudy,estimate_as_printed,uncertainty_as_printed,sample_as_printed,definition,locator,harmonised_estimate,harmonised_se,harmonisable,poolable,transformation,reason_if_not\r\nS1,\"-0.24. (beta_1 for ExpCon, Model 2, Table III; Section VI-C: beta_1=-0.24, i.e. 21% faster)\",\"SE (0.14); 95% CI = [-0.51, 0.03], p=0.086 (NS)\",\"n = 96 (Table III caption); Section VI-B: N=47 AI, N=46 no-AI\",\"OLS coefficient on experimental condition (AI exposure) with natural-log time on task (LogToT) as dependent variable, developer-level covariates (Model 2), one enterprise coding task, randomised\",\"arxiv-2410.12944v3, Section VI-C Table III, row ExpCon, column (2) 'Dev' (second value on collapsed line L76; SE line L78); CI and p in Section VI-C text\",-0.24,0.14,True,True,log-time coefficient: theta = b = -0.24; SE(theta) = SE(b) = 0.14 (printed). Check: SE from CI = (0.03-(-0.51))/(2*1.96) = 0.1378,\r\nS2,\"+19% (allowing AI increases completion time by 19%; regression-based, Section 3 / Section D.1)\",\"none printed (95% CI shown only in Figure 1 / Figure 15, not reproduced in the extract)\",\"16 developers, 246 tasks; 136 issues AI-allowed, 110 AI-disallowed\",\"S = E[time AI allowed]/E[time AI disallowed] - 1, estimated as exp(beta)-1 from OLS of log implementation time on AI indicator and log forecast time; issue-level randomisation\",\"arxiv-2507.09089v2, Abstract and Section 3 'Results' (opening paragraph); estimator Sections 2.3, D.1-D.3\",0.17395330712343798,,True,False,percentage change in time: theta = ln(1 + c) = ln(1 + 0.19) = 0.173953,\"No SE, CI or p-value printed in the extract; the CI exists only in a figure that is not reproduced, so SE(theta) cannot be obtained (harmonisable, not poolable).\"\r\nS3,\"Overall 53.84, Treatment 44.29, Control 63.10 (minutes); Improvement -29.79%***\",significance stars only: *** (paper's definition 0<p<=0.01); no SE or CI printed,\"181 subjects (randomised half treatment, half control)\",\"difference in mean task duration (minutes) between Copilot and control groups across all tasks, holding accuracy constant (control duration predicted at treatment accuracy, Appendix B), randomised\",\"arxiv-2411.01067v2, Section 4.2 Table 3, 'Aggregate Results' block, row 'All Content', columns Treatment/Control/Improvement\",-0.353679434958934,,True,False,percentage change in time: theta = ln(1 + c) = ln(1 + (-0.2979)) = -0.353679 (cf. ln(44.29/63.10) = -0.353962),\"Only significance stars (p<=0.01) are printed; a p-value threshold does not identify SE(theta) (harmonisable, not poolable).\"\r\nS4,\"Aware 50.5 s, Control 56.9 s per email; % Time Savings 11.1\",p-value 0.471,\"Aware N=56, Control N=52 (167 analysts across three arms incl. Blind N=59)\",\"percentage time saving in mean time per email over the full 25-email queue, Aware (agent verdicts/rationale + prioritised queue) vs Control (no AI), randomised arms\",\"arxiv-2511.13860v1, Section 3.3 Table 3 'Comparison of Email Processing Times', column 'Full Corpus', rows Aware/Control time, % Time Savings, p-value\",-0.11765804346823246,0.1632205919720678,True,True,percentage change in time c = -11.1/100 = -0.111; theta = ln(1 + c) = -0.117658; approximate SE from two-sided p: SE = |theta|/z(1-0.471/2) = 0.117658/0.720853 = 0.163221 (approximate SE),\r\nS5,\"-0.278 (Treat x Post, Output, column 3, panel A)\",SE (0.295) (clustered at user level); no significance star,\"N = 18332 (user-day observations, users with affiliation)\",\"difference-in-differences coefficient on aggregate GitHub Output measure (sum of Push, PR, PR comments, commit comments, Create, Issues events) for Italian vs Austrian/French users, pre 27/28.03 vs post 03/04.04.2023 around Italy's ChatGPT ban\",\"arxiv-2304.09339v1, Section 4 Table 1, panel A, row Treat x Post, column (3) Output (Users with Affiliation)\",,,False,False,none applied (not harmonisable),\"Effect on output counts (GitHub events) from a DiD around withdrawal of access, not a comparison of task-completion times with vs without AI on the same tasks, and not randomised.\"\r\nstep,rule,n_before,n_after,n_removed,rationale\n1,studies listed in data/studies.json with an extractable printed value,5,5,0,all five extracts contain the requested printed quantity\n2,harmonisable onto theta = ln(T_AI/T_noAI),5,4,1,S5 removed: DiD coefficient on GitHub output counts around a ban is not a task-completion-time AI vs no-AI comparison\n3,poolable (SE obtainable from printed quantities),4,2,2,S2 removed (CI only in unreproduced Figure 1; no SE/p printed); S3 removed (significance stars only)\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json\n[\n {\"id\": \"S1\", \"source\": \"arxiv-2410.12944v3\", \"short\": \"Paradis et al. (2024), Google enterprise RCT, 96 developers, one complex coding task\",\n \"extract\": \"the coefficient on the experimental condition (ExpCon) in Model 2 of Table III (logged time on task, developer-level covariates), its standard error, and the 95% confidence interval and p-value quoted for it in Section VI-C\"},\n {\"id\": \"S2\", \"source\": \"arxiv-2507.09089v2\", \"short\": \"Becker et al. (2025), METR field RCT, experienced open-source developers, real issues\",\n \"extract\": \"the headline regression-based estimate of the percentage change in issue completion time when AI is allowed, as stated in Section 3 (with the estimator defined in Sections 2.3 and D.1-D.3), together with whatever uncertainty is printed for it in the extract\"},\n {\"id\": \"S3\", \"source\": \"arxiv-2411.01067v2\", \"short\": \"Bono and Xu (2024), Microsoft Security Copilot RCT, IT administrators\",\n \"extract\": \"the aggregate 'All Content' row of Table 3 (task duration in minutes, holding accuracy constant): the treatment and control durations and the printed improvement, together with whatever uncertainty is printed for it\"},\n {\"id\": \"S4\", \"source\": \"arxiv-2511.13860v1\", \"short\": \"Bono (2025), Microsoft phishing-triage agent RCT, security analysts\",\n \"extract\": \"the 'Full Corpus' column of Table 3 (time per email): the Aware and Control times, the printed % time savings and its p-value\"},\n {\"id\": \"S5\", \"source\": \"arxiv-2304.09339v1\", \"short\": \"Kreitmeir and Raschky (2023), difference-in-differences around Italy's ChatGPT ban, GitHub users\",\n \"extract\": \"the Treat x Post coefficient and standard error for the Output measure (column 3) in panel A of Table 1 (users with affiliation), and its sample size\"}\n]","is_error":false},{"tool_use_id":"toolu_01FktSdtWUHqS9SJtBDE4YL6","type":"tool_result","content":"\"\"\"T3-synthesis: extraction, harmonisation onto theta = ln(T_AI/T_noAI), FE/DL pooling, forest plot.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/table1.csv, out/figure1.png. No randomness.\"\"\"\nimport json, math, os, re, csv\nimport numpy as np\nfrom scipy.stats import norm, chi2\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nDATA = \"data\"; OUT = \"out\"\nSRC = os.path.join(DATA, \"sources\")\nos.makedirs(OUT, exist_ok=True)\nZ = 1.96\n\n\ndef lines_of(name):\n with open(os.path.join(SRC, name + \".txt\"), encoding=\"utf-8\") as fh:\n return fh.read().split(\"\\n\")\n\n\ndef find(name, pattern, nth=0):\n \"\"\"Return (line_no, line) of the nth line matching regex pattern; print it.\"\"\"\n L = lines_of(name)\n hits = [(i + 1, l) for i, l in enumerate(L) if re.search(pattern, l)]\n if len(hits) <= nth:\n raise RuntimeError(f\"pattern {pattern!r} not found in {name}\")\n ln, l = hits[nth]\n print(f\"[{name} L{ln}] {l.strip()[:300]}\")\n return ln, l\n\n\ndef next_nonempty(name, ln):\n \"\"\"Line following line number ln (1-based) that is non-empty; print it.\"\"\"\n L = lines_of(name); j = ln\n while not L[j].strip():\n j += 1\n print(f\"[{name} L{j+1}] {L[j].strip()[:300]}\")\n return j + 1, L[j]\n\n\ndef wc_l(path):\n with open(path, \"rb\") as fh:\n return fh.read().count(b\"\\n\")\n\n\nstudies = json.load(open(os.path.join(DATA, \"studies.json\"), encoding=\"utf-8\"))\nassert [s[\"id\"] for s in studies] == [\"S1\", \"S2\", \"S3\", \"S4\", \"S5\"]\nprint(\"== Source lines relied on ==\")\nitems = {}\n\n# ---------------- S1 Paradis et al. 2024 ----------------\ns = \"arxiv-2410.12944v3\"\nln_b, l_b = find(s, r\"ExpCon \\|\")\nln_se, l_se = next_nonempty(s, ln_b)\ncoefs = re.findall(r\"-?\\d+\\.\\d+[.*]*\", l_b.split(\"ExpCon\")[0])\nses = re.findall(r\"\\((\\d+\\.\\d+)\\)\", l_se)\nb1_str, se1_str = coefs[1], ses[1] # Model 2 = second column (collapsed table)\nb1 = float(re.sub(r\"[.*]+$\", \"\", b1_str)); se1 = float(se1_str)\n_, l_ci = find(s, r\"best-fit Model 2\")\nci = re.search(r\"\\[(-?\\d+\\.\\d+),\\s*(-?\\d+\\.\\d+)\\]\", l_ci); ci_l, ci_u = float(ci.group(1)), float(ci.group(2))\np1 = re.search(r\"p=(\\d*\\.\\d+)\", l_ci).group(1)\nfind(s, r\"TABLE III: .*n = 96\")\nfind(s, r\"completed the task in 96 min\")\nassert (b1, se1, ci_l, ci_u) == (-0.24, 0.14, -0.51, 0.03)\nse1_ci = (ci_u - ci_l) / (2 * Z)\nitems[\"S1\"] = dict(\n estimate_as_printed=f\"{b1_str} (beta_1 for ExpCon, Model 2, Table III; Section VI-C: beta_1=-0.24, i.e. 21% faster)\",\n uncertainty_as_printed=f\"SE ({se1_str}); 95% CI = [{ci.group(1)}, {ci.group(2)}], p={p1} (NS)\",\n sample_as_printed=\"n = 96 (Table III caption); Section VI-B: N=47 AI, N=46 no-AI\",\n definition=\"OLS coefficient on experimental condition (AI exposure) with natural-log time on task (LogToT) as dependent variable, developer-level covariates (Model 2), one enterprise coding task, randomised\",\n locator=f\"{s}, Section VI-C Table III, row ExpCon, column (2) 'Dev' (second value on collapsed line L{ln_b}; SE line L{ln_se}); CI and p in Section VI-C text\",\n harmonisable=True, harmonised_estimate=b1, harmonised_se=se1, poolable=True,\n transformation=f\"log-time coefficient: theta = b = {b1}; SE(theta) = SE(b) = {se1} (printed). Check: SE from CI = (0.03-(-0.51))/(2*1.96) = {se1_ci:.4f}\",\n reason_if_not=None)\n\n# ---------------- S2 Becker et al. 2025 (METR) ----------------\ns = \"arxiv-2507.09089v2\"\n_, l2 = find(s, r\"allowing AI actually increases completion time by 19%\")\nfind(s, r\"implement issues in 19% more time on average\")\nfind(s, r\"Developers complete 136 issues with AI-allowed and 110\")\nfind(s, r\"16 developers with moderate AI experience complete 246 tasks\")\nc2 = float(re.search(r\"by (\\d+)%\", l2).group(1)) / 100.0\nth2 = math.log(1 + c2)\nitems[\"S2\"] = dict(\n estimate_as_printed=\"+19% (allowing AI increases completion time by 19%; regression-based, Section 3 / Section D.1)\",\n uncertainty_as_printed=\"none printed (95% CI shown only in Figure 1 / Figure 15, not reproduced in the extract)\",\n sample_as_printed=\"16 developers, 246 tasks; 136 issues AI-allowed, 110 AI-disallowed\",\n definition=\"S = E[time AI allowed]/E[time AI disallowed] - 1, estimated as exp(beta)-1 from OLS of log implementation time on AI indicator and log forecast time; issue-level randomisation\",\n locator=f\"{s}, Abstract and Section 3 'Results' (opening paragraph); estimator Sections 2.3, D.1-D.3\",\n harmonisable=True, harmonised_estimate=th2, harmonised_se=None, poolable=False,\n transformation=f\"percentage change in time: theta = ln(1 + c) = ln(1 + {c2}) = {th2:.6f}\",\n reason_if_not=\"No SE, CI or p-value printed in the extract; the CI exists only in a figure that is not reproduced, so SE(theta) cannot be obtained (harmonisable, not poolable).\")\n\n# ---------------- S3 Bono & Xu 2024 ----------------\ns = \"arxiv-2411.01067v2\"\n_, l3 = find(s, r\"^All Content \\| 53\\.84\")\nfind(s, r\"We had 181 subjects\")\ncells = [c.strip() for c in l3.split(\"|\")]\nov3, tr3, co3, imp3 = cells[1], cells[2], cells[3], cells[4]\nc3 = float(re.match(r\"(-?\\d+\\.\\d+)%\", imp3).group(1)) / 100.0\nth3 = math.log(1 + c3)\nitems[\"S3\"] = dict(\n estimate_as_printed=f\"Overall {ov3}, Treatment {tr3}, Control {co3} (minutes); Improvement {imp3}\",\n uncertainty_as_printed=\"significance stars only: *** (paper's definition 0<p<=0.01); no SE or CI printed\",\n sample_as_printed=\"181 subjects (randomised half treatment, half control)\",\n definition=\"difference in mean task duration (minutes) between Copilot and control groups across all tasks, holding accuracy constant (control duration predicted at treatment accuracy, Appendix B), randomised\",\n locator=f\"{s}, Section 4.2 Table 3, 'Aggregate Results' block, row 'All Content', columns Treatment/Control/Improvement\",\n harmonisable=True, harmonised_estimate=th3, harmonised_se=None, poolable=False,\n transformation=f\"percentage change in time: theta = ln(1 + c) = ln(1 + ({c3})) = {th3:.6f} (cf. ln({tr3}/{co3}) = {math.log(float(tr3)/float(co3)):.6f})\",\n reason_if_not=\"Only significance stars (p<=0.01) are printed; a p-value threshold does not identify SE(theta) (harmonisable, not poolable).\")\n\n# ---------------- S4 Bono 2025 ----------------\ns = \"arxiv-2511.13860v1\"\n_, l4a = find(s, r\"^Aware Time per Email\")\n_, l4c = find(s, r\"^Control Time per Email\")\n_, l4s = find(s, r\"^% Time Savings\")\n_, l4p = find(s, r\"^p-value \\|\")\nfind(s, r\"Control \\( N\\{=\\}52\")\nfind(s, r\"Aware \\( N\\{=\\}56\")\nlast = lambda l: l.split(\"|\")[-1].strip()\na4, ct4, sv4, p4s = last(l4a), last(l4c), last(l4s), last(l4p)\nc4 = -float(sv4) / 100.0 # 11.1% time saving -> change in time c = -0.111\np4 = float(p4s)\nth4 = math.log(1 + c4)\nz4 = norm.ppf(1 - p4 / 2)\nse4 = abs(th4) / z4\nitems[\"S4\"] = dict(\n estimate_as_printed=f\"Aware {a4} s, Control {ct4} s per email; % Time Savings {sv4}\",\n uncertainty_as_printed=f\"p-value {p4s}\",\n sample_as_printed=\"Aware N=56, Control N=52 (167 analysts across three arms incl. Blind N=59)\",\n definition=\"percentage time saving in mean time per email over the full 25-email queue, Aware (agent verdicts/rationale + prioritised queue) vs Control (no AI), randomised arms\",\n locator=f\"{s}, Section 3.3 Table 3 'Comparison of Email Processing Times', column 'Full Corpus', rows Aware/Control time, % Time Savings, p-value\",\n harmonisable=True, harmonised_estimate=th4, harmonised_se=se4, poolable=True,\n transformation=f\"percentage change in time c = -{sv4}/100 = {c4}; theta = ln(1 + c) = {th4:.6f}; approximate SE from two-sided p: SE = |theta|/z(1-{p4s}/2) = {abs(th4):.6f}/{z4:.6f} = {se4:.6f} (approximate SE)\",\n reason_if_not=None)\n\n# ---------------- S5 Kreitmeir & Raschky 2023 ----------------\ns = \"arxiv-2304.09339v1\"\nln5, l5 = find(s, r\"TreatPost \\| 0\\.115\")\n_, l5se = next_nonempty(s, ln5)\n_, l5n = find(s, r\"^N \\| 18332\")\nb5 = [c.strip() for c in l5.split(\"|\")][3]\nse5 = [c.strip() for c in l5se.split(\"|\")][3]\nn5 = [c.strip() for c in l5n.split(\"|\")][3]\nitems[\"S5\"] = dict(\n estimate_as_printed=f\"{b5} (Treat x Post, Output, column 3, panel A)\",\n uncertainty_as_printed=f\"SE {se5} (clustered at user level); no significance star\",\n sample_as_printed=f\"N = {n5} (user-day observations, users with affiliation)\",\n definition=\"difference-in-differences coefficient on aggregate GitHub Output measure (sum of Push, PR, PR comments, commit comments, Create, Issues events) for Italian vs Austrian/French users, pre 27/28.03 vs post 03/04.04.2023 around Italy's ChatGPT ban\",\n locator=f\"{s}, Section 4 Table 1, panel A, row Treat x Post, column (3) Output (Users with Affiliation)\",\n harmonisable=False, harmonised_estimate=None, harmonised_se=None, poolable=False,\n transformation=\"none applied (not harmonisable)\",\n reason_if_not=\"Effect on output counts (GitHub events) from a DiD around withdrawal of access, not a comparison of task-completion times with vs without AI on the same tasks, and not randomised.\")\n\n# ---------------- Pooling ----------------\norder = [\"S1\", \"S2\", \"S3\", \"S4\", \"S5\"]\npool = [k for k in order if items[k][\"poolable\"]]\ny = np.array([items[k][\"harmonised_estimate\"] for k in pool]); se = np.array([items[k][\"harmonised_se\"] for k in pool])\n\n\ndef meta(y, se):\n w = 1 / se**2\n fe = float(np.sum(w * y) / np.sum(w)); fe_se = float(math.sqrt(1 / np.sum(w)))\n q = float(np.sum(w * (y - fe) ** 2)); df = len(y) - 1\n C = float(np.sum(w) - np.sum(w**2) / np.sum(w))\n tau2 = max(0.0, (q - df) / C) if C > 0 else 0.0\n i2 = max(0.0, (q - df) / q) * 100 if q > 0 else 0.0\n wr = 1 / (se**2 + tau2)\n re_ = float(np.sum(wr * y) / np.sum(wr)); re_se = float(math.sqrt(1 / np.sum(wr)))\n return dict(fe=fe, fe_se=fe_se, q=q, df=df, tau2=tau2, i2=i2, re=re_, re_se=re_se,\n w_fe=(w / w.sum()).tolist(), w_re=(wr / wr.sum()).tolist())\n\n\nm = meta(y, se)\ninterp = \"low\" if m[\"i2\"] < 25 else (\"moderate\" if m[\"i2\"] < 50 else \"substantial\")\nre_lo, re_hi = m[\"re\"] - Z * m[\"re_se\"], m[\"re\"] + Z * m[\"re_se\"]\n\n# sensitivity: S1 SE from the printed CI instead of the printed SE\nse_alt = se.copy(); se_alt[pool.index(\"S1\")] = se1_ci\nm_alt = meta(y, se_alt)\n# baseline: unweighted means of theta\nharm = [k for k in order if items[k][\"harmonisable\"]]\nbase_h = float(np.mean([items[k][\"harmonised_estimate\"] for k in harm]))\nbase_p = float(np.mean(y))\n\nn_input_rows = {\"studies.json\": len(studies), \"README.md\": wc_l(os.path.join(DATA, \"README.md\"))}\nfor x in studies:\n n_input_rows[f\"sources/{x['source']}.txt\"] = wc_l(os.path.join(SRC, x[\"source\"] + \".txt\"))\n\ndata_issues = [\n {\"issue\": \"S1: Section VI-B reports N=47 (AI) + N=46 (no-AI) = 93 developers, while Table III states n = 96.\",\n \"handling\": \"Not resolvable from the extract; reported. sample_as_printed lists both; does not affect theta or SE, which are taken from Table III/Section VI-C.\"},\n {\"issue\": \"S1: Section VI-B gives Mean LogToT=4.6 for the AI group and 4.3 for no-AI while stating AI was faster (96 vs 114 min); the log means appear swapped/inconsistent.\",\n \"handling\": \"These log means are not used.\"},\n {\"issue\": \"S1: Table III is rendered with the four model columns collapsed into one line; SE (0.14) is rounded, and the CI [-0.51, 0.03] implies SE 0.1378.\",\n \"handling\": \"Model 2 taken as the second value per the editorial note; printed SE 0.14 used; CI-based SE reported as sensitivity (sensitivity_s1_se_from_ci).\"},\n {\"issue\": \"S2: The uncertainty of the headline +19% estimate is shown only in Figure 1/Figure 15, which are not reproduced; no SE or p-value printed.\",\n \"handling\": \"Harmonised (theta = ln 1.19) but not poolable; harmonised_se = null.\"},\n {\"issue\": \"S3: Treatment/Control means 44.29/63.10 imply -29.81%, printed improvement is -29.79% (slightly beyond rounding of the printed means); significance shown only by stars with an unusual convention (* = 0.05<p<=0.1).\",\n \"handling\": \"Printed -29.79% used as instructed (difference in theta ~0.0003); not poolable because stars do not identify an SE.\"},\n {\"issue\": \"S4: Table 3 units ambiguous: Malicious/Non-Malicious times per email (1.8-2.8 's') cannot average to Full Corpus times (50.5/56.9 s) under any common unit; Full Corpus ratio 50.5/56.9 implies 11.25% saving vs printed 11.1% (within rounding).\",\n \"handling\": \"Only the Full Corpus column is used; theta depends only on the printed % saving (unit-free). Flagged as a source inconsistency.\"},\n {\"issue\": \"S4: SE is approximate, back-calculated from a two-sided p-value (0.471) under a normal approximation on the log scale, although the paper's test is presumably on the time difference.\",\n \"handling\": \"Marked as approximate SE in transformation; pooled results carry this caveat.\"},\n {\"issue\": \"S5: Table 1 note prints '** p<0.5' (presumably p<0.05); row label rendered as '\\\\times TreatPost'; panel B header lists 11.04 although the text says it was excluded; rendered date line 'August 24, 2026' is an artefact.\",\n \"handling\": \"No effect on the extracted panel A column 3 value; S5 is not harmonisable anyway.\"},\n]\n\nres = {\n \"task_id\": \"T3-synthesis\",\n \"target_definition\": \"theta = ln(T_AI / T_noAI): natural log of the ratio of mean task-completion time with generative-AI assistance to mean completion time without it, for the same tasks, from a randomised comparison (negative = faster with AI).\",\n \"items\": items,\n \"k_extracted\": sum(1 for k in order if items[k][\"estimate_as_printed\"]),\n \"k_harmonised\": sum(1 for k in order if items[k][\"harmonised_estimate\"] is not None),\n \"k_pooled\": len(pool),\n \"pooled_studies\": pool,\n \"fe_estimate\": m[\"fe\"], \"fe_se\": m[\"fe_se\"], \"fe_ci_low\": m[\"fe\"] - Z * m[\"fe_se\"], \"fe_ci_high\": m[\"fe\"] + Z * m[\"fe_se\"],\n \"re_estimate\": m[\"re\"], \"re_se\": m[\"re_se\"], \"re_ci_low\": re_lo, \"re_ci_high\": re_hi,\n \"q_stat\": m[\"q\"], \"q_df\": m[\"df\"], \"q_pvalue\": float(chi2.sf(m[\"q\"], m[\"df\"])),\n \"i2\": m[\"i2\"], \"tau2\": m[\"tau2\"],\n \"heterogeneity_interpretation\": interp,\n \"pooled_significant_5pct\": bool(re_lo > 0 or re_hi < 0),\n \"pooled_pct_change_re\": 100 * (math.exp(m[\"re\"]) - 1),\n \"pooled_pct_change_re_ci\": [100 * (math.exp(re_lo) - 1), 100 * (math.exp(re_hi) - 1)],\n \"weights_fe\": dict(zip(pool, m[\"w_fe\"])), \"weights_re\": dict(zip(pool, m[\"w_re\"])),\n \"baseline_unweighted_mean_theta_harmonised\": base_h,\n \"baseline_unweighted_mean_theta_poolable\": base_p,\n \"sensitivity_s1_se_from_ci\": {\"s1_se\": se1_ci, \"re_estimate\": m_alt[\"re\"], \"re_se\": m_alt[\"re_se\"],\n \"re_ci_low\": m_alt[\"re\"] - Z * m_alt[\"re_se\"], \"re_ci_high\": m_alt[\"re\"] + Z * m_alt[\"re_se\"],\n \"q_stat\": m_alt[\"q\"], \"i2\": m_alt[\"i2\"], \"tau2\": m_alt[\"tau2\"]},\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": data_issues,\n \"n_input_rows\": n_input_rows,\n \"n_analysed\": len(order),\n \"random_seed\": None,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n json.dump(res, fh, indent=1)\n\ncols = [\"study\", \"estimate_as_printed\", \"uncertainty_as_printed\", \"sample_as_printed\", \"definition\", \"locator\",\n \"harmonised_estimate\", \"harmonised_se\", \"harmonisable\", \"poolable\", \"transformation\", \"reason_if_not\"]\nwith open(os.path.join(OUT, \"table1.csv\"), \"w\", newline=\"\") as fh:\n wr = csv.writer(fh); wr.writerow(cols)\n for k in order:\n it = items[k]\n wr.writerow([k] + [(\"\" if it[c] is None else it[c]) for c in cols[1:]])\n\n# ---------------- Forest plot ----------------\nlabels = {\"S1\": \"S1 Paradis et al. 2024 (Google)\", \"S2\": \"S2 Becker et al. 2025 (METR)\",\n \"S3\": \"S3 Bono & Xu 2024 (Security Copilot)\", \"S4\": \"S4 Bono 2025 (phishing agent; approx. SE)\"}\nfig, ax = plt.subplots(figsize=(9, 4.2))\nypos = list(range(len(harm), 0, -1))\nfor yy, k in zip(ypos, harm):\n th = items[k][\"harmonised_estimate\"]; s_ = items[k][\"harmonised_se\"]\n if s_ is not None:\n ax.errorbar(th, yy, xerr=Z * s_, fmt=\"s\", color=\"black\", capsize=3)\n txt = f\"{th:.3f} [{th - Z*s_:.3f}, {th + Z*s_:.3f}]\"\n else:\n ax.plot(th, yy, \"o\", mfc=\"white\", mec=\"gray\")\n txt = f\"{th:.3f} (no SE; not pooled)\"\n ax.text(1.02, yy, txt, transform=ax.get_yaxis_transform(), va=\"center\", fontsize=8)\nd = np.array([[re_lo, 0], [m[\"re\"], 0.2], [re_hi, 0], [m[\"re\"], -0.2]])\nax.fill(d[:, 0], d[:, 1], color=\"tab:blue\")\nax.text(1.02, 0, f\"{m['re']:.3f} [{re_lo:.3f}, {re_hi:.3f}]\", transform=ax.get_yaxis_transform(), va=\"center\", fontsize=8)\nax.axvline(0, color=\"gray\", lw=0.8, ls=\"--\")\nax.set_yticks(ypos + [0]); ax.set_yticklabels([labels[k] for k in harm] + [f\"RE pooled (DL), k={len(pool)}, I2={m['i2']:.0f}%\"])\nax.set_xlabel(\"theta = ln(T_AI / T_noAI) (negative = faster with AI)\")\nax.set_title(\"Harmonised effects of generative-AI access on task time\")\nplt.tight_layout(rect=(0, 0, 0.8, 1))\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=150)\n\nprint(\"== Results ==\")\nfor k in order:\n print(k, items[k][\"harmonised_estimate\"], items[k][\"harmonised_se\"], \"poolable\" if items[k][\"poolable\"] else \"not pooled\")\nprint(f\"FE {m['fe']:.4f} (SE {m['fe_se']:.4f}); RE {m['re']:.4f} [{re_lo:.4f}, {re_hi:.4f}]; Q {m['q']:.4f}; I2 {m['i2']:.2f}; tau2 {m['tau2']:.4f}; pct {res['pooled_pct_change_re']:.2f}%\")","is_error":false}]}}25{"type":"api-request","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","id":"abd34174-1dd5-4f08-aa8c-3ce645fa8d4c","timestamp":"2026-10-30T05:34:11.247Z","version":"2.1.314","querySource":"sdk","shapeHash":"80e183abfbc7335290bfa20318d598a10dbca7f952739a0080b8d8663232c751","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"e93044f4-72a3-4fd1-9e44-1303dea86401","keep":4,"tail":["32a43d72a080e809d91180c2931bdaa19225810d2d103df647e6938cc075c8c8","5ef1d5d94646a1703d35051ba2e60fb1b5971b471c38b69e5057c7df93de5385"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",1],"value":{"type":"ephemeral"}}],"requestDigest":"02c6c05135649d07bdbbd72c60be705b824fd9e2ccfb2978addd65e6a7d9aa67"}26{"type":"last-prompt","lastPrompt":"Review","leafUuid":"00ff97aa-6eb8-4c24-ad9e-2ec409dd317f","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1"}27{"type":"atis-latch","atis":"","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1"}28{"parentUuid":"00ff97aa-6eb8-4c24-ad9e-2ec409dd317f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXspkmcHUgzuvDjwZur9","type":"message","role":"assistant","content":[{"type":"text","text":"Now check the sources and re-run."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":14238,"cache_read_input_tokens":11921,"output_tokens":537,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":14238},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXspkVUz1w5vLoC3iuxy","requestRef":"abd34174-1dd5-4f08-aa8c-3ce645fa8d4c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9d12a78e-07c1-4246-8334-a8b7b4d8c9d3","timestamp":"2026-10-30T05:34:16.820Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008/data/sources","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"9d12a78e-07c1-4246-8334-a8b7b4d8c9d3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXspkmcHUgzuvDjwZur9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011aPDY7cAkwXazsz7CZRpUk","name":"Bash","input":{"command":"cd data/sources; grep -n \"ExpCon\\|Model 2\\|n = 96\\|N=4\\|LogToT\\|editorial\" arxiv-2410.12944v3.txt | head -30; echo ----; grep -n \"19%\\|confidence\\|CI\\|p-value\\|p =\\|p=\" arxiv-2507.09089v2.txt | head -30","description":"Inspect S1 and S2 sources"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":14238,"cache_read_input_tokens":11921,"output_tokens":537,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":14238},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXspkVUz1w5vLoC3iuxy","requestRef":"abd34174-1dd5-4f08-aa8c-3ce645fa8d4c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3d6261c9-bffd-4155-a3b7-8c256e109fc1","timestamp":"2026-10-30T05:34:18.441Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008/data/sources","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}30{"parentUuid":"3d6261c9-bffd-4155-a3b7-8c256e109fc1","isSidechain":false,"promptId":"01f9be6f-d015-4eb3-b8dc-9dd1d87b03b8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011aPDY7cAkwXazsz7CZRpUk","type":"tool_result","content":"7:Extract contents: title, authors and abstract; Section IV-C 'Analytic approach'; Sections VI-B 'RQ1: Impact of AI tools on developer task speed' and VI-C 'RQ2: Impact of covariates on main effect' including Table III (regressions with LogToT as dependent variable).\n33:\\displaystyle LogToT \\displaystyle=\\beta_{1}*ExpCon+\\beta_{2}*AvgProgHrsDay | | |\n39:Given the logged dependent variable, the effect of a variable n on time on task in percentages is represented by (1-exp(\\beta_{n}))*100 . The impact of AI on time on task (our ExpCon variable), expressed in percentages, is thus represented by (1-exp(\\beta_{1}))*100 . We did not use hierarchical linear modeling given the absence of nesting between constructs in our theoretical model.\n48:On average, developers who were exposed to AI features completed the task in 96 min ( N=47,SE=9.3 ), compared to 114 min ( N=46,SE=8.1 ) for developers in the no-AI condition. We conducted a Student’s t-test on log time on task to assess the main effect impact of the experimental condition assignment (AI vs no-AI exposure). Results of this analysis indicated that developers who were in the AI exposure condition (Mean LogToT=4.6 ) were significantly faster at completing the task ( t(83.6)=2.11,p=.038 ), compared to those who were in the no-AI exposure condition (Mean LogToT=4.3 ).\n54:Table III summarizes the regression findings. When controlling for other factors in our theoretical framework, we find that estimates for the impact of the experimental condition on logged time on task range between -0.30 to -0.24 ( \\beta_{1} for ExpCon for Models 1-3 in Table III), suggesting a relatively consistent and negative downward pressure on time on task. Controlling for the developer- and task-level factors described in our theoretical framework (see Section IV-B), we found that this main effect was robust to covariates, making developers between 21% and 26% faster.^2^2\n57:From Model 2, where \\beta_{1}=-0.24 , and therefore the effect of AI =(1-exp(\\beta_{1}))*100=21\\% (with 95% CI = [-0.51, 0.03]). Similarly, 26% from Model 3, where \\beta_{1}=-0.30 .\n58:However, our confidence intervals are large, and as a result the estimate is not statistically significant at the p<0.05 level (estimate of experimental condition on best-fit Model 2, \\beta_{1}=-0.24; 95\\%CI = [-0.51,0.03] , p=0.086 , NS). Our second hypothesis is therefore only partially supported.\n62:Other covariates were not significantly associated with time spent on task. As seen in Table III, the best fit model was Model 2 with developer-level factors only, as evidenced by higher adjusted R^{2} and lower p -value. While the total variance accounted for by Model 2 was low, the model was statistically significant ( p=0.011 ), suggesting an adequate fit to our data.\n66:TABLE III: Summaries of regressions using LogToT as the dependent variable, n = 96. Unstandardized estimates provided, along with standard error of the mean (in parentheses).\n76:-0.25. -0.24. -0.30* -0.14 ExpCon | | | |\n----\n8:Omitted: Sections 1, 2.1-2.2, 2.4, 3.2-3.3, 4-5, Appendices A-C, D.4-D.5, E-G, Tables 1-8, all figures (including Figure 1, which displays the confidence interval of the main estimate graphically), the references.\n34:Surprisingly, we find that allowing AI actually increases completion time by 19%—AI tooling slowed developers down.\n58:Figure 4: Left: Raw average forecasted implementation times. Right: Raw average observed implementation times. The ratio of observed implementation times gives a more extreme slowdown estimate than regression-based estimates (Section D.1) because AI-allowed issues are forecasted (importantly, before treatment assignment) by developers to take slightly longer, which the regression corrects for. Both: Section D.5 describes confidence intervals around ratios of average times.\n62:Developers complete 136 issues with AI-allowed and 110 issues with AI-disallowed. Section G.7 shows the number of issues completed across repositories and developers, respectively. We find that when developers use AI tools, they implement issues in 19% more time on average (Figure 1), and nearly all quantiles of observed implementation time see AI-allowed issues taking longer (Figure 5). That is, developers are slower when using AI is allowed. Colloquially, we refer to this result that issues with AI-allowed take longer than issues with AI-disallowed as slowdown.\n68:Before developers complete each issue, they forecast how long they expect them to take with and without AI assistance. On average, they forecast speedup of 24%. Interestingly, after the experiment they post-hoc estimate that they were sped-up by 20% when using AI is allowed—after they used AI assistance, they estimate similar speedup as before, despite the fact that they are in fact slowed down by 19% (Figure 1).\n72:The raw percentage difference in implementation times between AI-allowed and AI-disallowed issues is 34%, which is larger in absolute value than the 19% slowdown estimated using the regression specified in Section D.1. This is true because AI-allowed issues ended up being slightly more difficult than AI-disallowed issues after randomization, as measured by the forecasted AI-disallowed times (not statistically significant; see Table 4). Our regression accounts for this, while this simple ratio estimator does not. See Figure 13 for results implied by alternative estimators..\n97:By default, we report 95% confidence intervals using HC3 standard errors.^19^19\n100:Given that our sampling of developers is non-random and treatment is assigned at the issue level, Abadie et al. [2022] do not necessarily recommend developer-level clustering even if residuals are correlated within-developer. If we are willing to treat developer sampling as random then clustering is appropriate. Figure 15 displays 95% confidence intervals from alternative uncertainty estimation procedures. Standard errors clustered at the developer level and bias-corrected cluster-robust standard errors give similar results; a hierarchical bootstrap resampling developers and then issues within developers yields somewhat wider confidence intervals.\n126:Then under standard regularity assumptions (which our diagnostics suggest hold) our OLS estimator \\hat{\\beta} is normally distributed \\mathcal{N}(\\beta,\\sigma^{2}) for some \\sigma which we can estimate using it’s standard error \\text{SE}[\\hat{\\beta}] . This lets us construct a 95% confidence interval for \\beta in the usual way:\n128:\\text{CI}_{95\\%}=\\left[\\hat{\\beta}-1.96\\cdot\\text{SE}[\\hat{\\beta}],\\hat{\\beta}+1.96\\cdot\\text{SE}[\\hat{\\beta}]\\right] | | | (6)\n130:As \\text{S}=\\exp(\\beta)-1 is a monotonic function of \\beta we can construct a confidence interval for S by simply applying the function to the endpoints of \\beta ’s confidence interval:\n132:\\text{CI}_{95\\%}=\\left[e^{\\hat{\\beta}-1.96\\cdot\\text{SE}[\\hat{\\beta}]}-1,e^{\\hat{\\beta}+1.96\\cdot\\text{SE}[\\hat{\\beta}]}-1\\right] | | | (7)","is_error":false}]},"uuid":"8031d7b4-6f8d-42a0-bd9e-766f21038086","timestamp":"2026-10-30T05:34:18.489Z","toolUseResult":{"stdout":"7:Extract contents: title, authors and abstract; Section IV-C 'Analytic approach'; Sections VI-B 'RQ1: Impact of AI tools on developer task speed' and VI-C 'RQ2: Impact of covariates on main effect' including Table III (regressions with LogToT as dependent variable).\n33:\\displaystyle LogToT \\displaystyle=\\beta_{1}*ExpCon+\\beta_{2}*AvgProgHrsDay | | |\n39:Given the logged dependent variable, the effect of a variable n on time on task in percentages is represented by (1-exp(\\beta_{n}))*100 . The impact of AI on time on task (our ExpCon variable), expressed in percentages, is thus represented by (1-exp(\\beta_{1}))*100 . We did not use hierarchical linear modeling given the absence of nesting between constructs in our theoretical model.\n48:On average, developers who were exposed to AI features completed the task in 96 min ( N=47,SE=9.3 ), compared to 114 min ( N=46,SE=8.1 ) for developers in the no-AI condition. We conducted a Student’s t-test on log time on task to assess the main effect impact of the experimental condition assignment (AI vs no-AI exposure). Results of this analysis indicated that developers who were in the AI exposure condition (Mean LogToT=4.6 ) were significantly faster at completing the task ( t(83.6)=2.11,p=.038 ), compared to those who were in the no-AI exposure condition (Mean LogToT=4.3 ).\n54:Table III summarizes the regression findings. When controlling for other factors in our theoretical framework, we find that estimates for the impact of the experimental condition on logged time on task range between -0.30 to -0.24 ( \\beta_{1} for ExpCon for Models 1-3 in Table III), suggesting a relatively consistent and negative downward pressure on time on task. Controlling for the developer- and task-level factors described in our theoretical framework (see Section IV-B), we found that this main effect was robust to covariates, making developers between 21% and 26% faster.^2^2\n57:From Model 2, where \\beta_{1}=-0.24 , and therefore the effect of AI =(1-exp(\\beta_{1}))*100=21\\% (with 95% CI = [-0.51, 0.03]). Similarly, 26% from Model 3, where \\beta_{1}=-0.30 .\n58:However, our confidence intervals are large, and as a result the estimate is not statistically significant at the p<0.05 level (estimate of experimental condition on best-fit Model 2, \\beta_{1}=-0.24; 95\\%CI = [-0.51,0.03] , p=0.086 , NS). Our second hypothesis is therefore only partially supported.\n62:Other covariates were not significantly associated with time spent on task. As seen in Table III, the best fit model was Model 2 with developer-level factors only, as evidenced by higher adjusted R^{2} and lower p -value. While the total variance accounted for by Model 2 was low, the model was statistically significant ( p=0.011 ), suggesting an adequate fit to our data.\n66:TABLE III: Summaries of regressions using LogToT as the dependent variable, n = 96. Unstandardized estimates provided, along with standard error of the mean (in parentheses).\n76:-0.25. -0.24. -0.30* -0.14 ExpCon | | | |\n----\n8:Omitted: Sections 1, 2.1-2.2, 2.4, 3.2-3.3, 4-5, Appendices A-C, D.4-D.5, E-G, Tables 1-8, all figures (including Figure 1, which displays the confidence interval of the main estimate graphically), the references.\n34:Surprisingly, we find that allowing AI actually increases completion time by 19%—AI tooling slowed developers down.\n58:Figure 4: Left: Raw average forecasted implementation times. Right: Raw average observed implementation times. The ratio of observed implementation times gives a more extreme slowdown estimate than regression-based estimates (Section D.1) because AI-allowed issues are forecasted (importantly, before treatment assignment) by developers to take slightly longer, which the regression corrects for. Both: Section D.5 describes confidence intervals around ratios of average times.\n62:Developers complete 136 issues with AI-allowed and 110 issues with AI-disallowed. Section G.7 shows the number of issues completed across repositories and developers, respectively. We find that when developers use AI tools, they implement issues in 19% more time on average (Figure 1), and nearly all quantiles of observed implementation time see AI-allowed issues taking longer (Figure 5). That is, developers are slower when using AI is allowed. Colloquially, we refer to this result that issues with AI-allowed take longer than issues with AI-disallowed as slowdown.\n68:Before developers complete each issue, they forecast how long they expect them to take with and without AI assistance. On average, they forecast speedup of 24%. Interestingly, after the experiment they post-hoc estimate that they were sped-up by 20% when using AI is allowed—after they used AI assistance, they estimate similar speedup as before, despite the fact that they are in fact slowed down by 19% (Figure 1).\n72:The raw percentage difference in implementation times between AI-allowed and AI-disallowed issues is 34%, which is larger in absolute value than the 19% slowdown estimated using the regression specified in Section D.1. This is true because AI-allowed issues ended up being slightly more difficult than AI-disallowed issues after randomization, as measured by the forecasted AI-disallowed times (not statistically significant; see Table 4). Our regression accounts for this, while this simple ratio estimator does not. See Figure 13 for results implied by alternative estimators..\n97:By default, we report 95% confidence intervals using HC3 standard errors.^19^19\n100:Given that our sampling of developers is non-random and treatment is assigned at the issue level, Abadie et al. [2022] do not necessarily recommend developer-level clustering even if residuals are correlated within-developer. If we are willing to treat developer sampling as random then clustering is appropriate. Figure 15 displays 95% confidence intervals from alternative uncertainty estimation procedures. Standard errors clustered at the developer level and bias-corrected cluster-robust standard errors give similar results; a hierarchical bootstrap resampling developers and then issues within developers yields somewhat wider confidence intervals.\n126:Then under standard regularity assumptions (which our diagnostics suggest hold) our OLS estimator \\hat{\\beta} is normally distributed \\mathcal{N}(\\beta,\\sigma^{2}) for some \\sigma which we can estimate using it’s standard error \\text{SE}[\\hat{\\beta}] . This lets us construct a 95% confidence interval for \\beta in the usual way:\n128:\\text{CI}_{95\\%}=\\left[\\hat{\\beta}-1.96\\cdot\\text{SE}[\\hat{\\beta}],\\hat{\\beta}+1.96\\cdot\\text{SE}[\\hat{\\beta}]\\right] | | | (6)\n130:As \\text{S}=\\exp(\\beta)-1 is a monotonic function of \\beta we can construct a confidence interval for S by simply applying the function to the endpoints of \\beta ’s confidence interval:\n132:\\text{CI}_{95\\%}=\\left[e^{\\hat{\\beta}-1.96\\cdot\\text{SE}[\\hat{\\beta}]}-1,e^{\\hat{\\beta}+1.96\\cdot\\text{SE}[\\hat{\\beta}]}-1\\right] | | | (7)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3d6261c9-bffd-4155-a3b7-8c256e109fc1","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008/data/sources","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}31{"parentUuid":"8031d7b4-6f8d-42a0-bd9e-766f21038086","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXspkmcHUgzuvDjwZur9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01P8eowdnT2cpUEZQbfxfEuB","name":"Bash","input":{"command":"mkdir -p /tmp/rt8 && cp -r data out /tmp/rt8/ 2>/dev/null; cd /tmp/rt8 && ~/wp29/venv/bin/python out/analysis.py | tail -8 && diff <(python3 -m json.tool out/results.json) <(python3 -m json.tool ~/wp29/runs/RT0008/out/results.json) && echo SAME; diff out/table1.csv ~/wp29/runs/RT0008/out/table1.csv && echo SAMECSV; cd ~/wp29/runs/RT0008; shasum -a 256 data/sources/*; cat data/README.md | grep -i sha -A8 | head -20; wc -l data/README.md data/sources/*","description":"Re-run in scratch copy and compare"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":14238,"cache_read_input_tokens":11921,"output_tokens":537,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":14238},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXspkVUz1w5vLoC3iuxy","requestRef":"abd34174-1dd5-4f08-aa8c-3ce645fa8d4c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5efb25a5-f751-4745-bc4d-f81f707e93d6","timestamp":"2026-10-30T05:34:20.785Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008/data/sources","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}32{"parentUuid":"5efb25a5-f751-4745-bc4d-f81f707e93d6","isSidechain":false,"promptId":"01f9be6f-d015-4eb3-b8dc-9dd1d87b03b8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01P8eowdnT2cpUEZQbfxfEuB","type":"tool_result","content":"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/private/tmp/rt8/out/analysis.py': [Errno 2] No such file or directory\nTraceback (most recent call last):\n File \"<frozen runpy>\", line 198, in _run_module_as_main\n File \"<frozen runpy>\", line 88, in _run_code\n File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/json/tool.py\", line 119, in <module>\n main()\n ~~~~^^\n File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/json/tool.py\", line 88, in main\n infile = open(options.infile, encoding='utf-8')\nFileNotFoundError: [Errno 2] No such file or directory: 'out/results.json'\n0a1,165\n> {\n> \"task_id\": \"T3-synthesis\",\n> \"target_definition\": \"theta = ln(T_AI / T_noAI): natural log of the ratio of mean task-completion time with generative-AI assistance to mean completion time without it, for the same tasks, from a randomised comparison (negative = faster with AI).\",\n> \"items\": {\n> \"S1\": {\n> \"estimate_as_printed\": \"-0.24. (beta_1 for ExpCon, Model 2, Table III; Section VI-C: beta_1=-0.24, i.e. 21% faster)\",\n> \"uncertainty_as_printed\": \"SE (0.14); 95% CI = [-0.51, 0.03], p=0.086 (NS)\",\n> \"sample_as_printed\": \"n = 96 (Table III caption); Section VI-B: N=47 AI, N=46 no-AI\",\n> \"definition\": \"OLS coefficient on experimental condition (AI exposure) with natural-log time on task (LogToT) as dependent variable, developer-level covariates (Model 2), one enterprise coding task, randomised\",\n> \"locator\": \"arxiv-2410.12944v3, Section VI-C Table III, row ExpCon, column (2) 'Dev' (second value on collapsed line L76; SE line L78); CI and p in Section VI-C text\",\n> \"harmonisable\": true,\n> \"harmonised_estimate\": -0.24,\n> \"harmonised_se\": 0.14,\n> \"poolable\": true,\n> \"transformation\": \"log-time coefficient: theta = b = -0.24; SE(theta) = SE(b) = 0.14 (printed). Check: SE from CI = (0.03-(-0.51))/(2*1.96) = 0.1378\",\n> \"reason_if_not\": null\n> },\n> \"S2\": {\n> \"estimate_as_printed\": \"+19% (allowing AI increases completion time by 19%; regression-based, Section 3 / Section D.1)\",\n> \"uncertainty_as_printed\": \"none printed (95% CI shown only in Figure 1 / Figure 15, not reproduced in the extract)\",\n> \"sample_as_printed\": \"16 developers, 246 tasks; 136 issues AI-allowed, 110 AI-disallowed\",\n> \"definition\": \"S = E[time AI allowed]/E[time AI disallowed] - 1, estimated as exp(beta)-1 from OLS of log implementation time on AI indicator and log forecast time; issue-level randomisation\",\n> \"locator\": \"arxiv-2507.09089v2, Abstract and Section 3 'Results' (opening paragraph); estimator Sections 2.3, D.1-D.3\",\n> \"harmonisable\": true,\n> \"harmonised_estimate\": 0.17395330712343798,\n> \"harmonised_se\": null,\n> \"poolable\": false,\n> \"transformation\": \"percentage change in time: theta = ln(1 + c) = ln(1 + 0.19) = 0.173953\",\n> \"reason_if_not\": \"No SE, CI or p-value printed in the extract; the CI exists only in a figure that is not reproduced, so SE(theta) cannot be obtained (harmonisable, not poolable).\"\n> },\n> \"S3\": {\n> \"estimate_as_printed\": \"Overall 53.84, Treatment 44.29, Control 63.10 (minutes); Improvement -29.79%***\",\n> \"uncertainty_as_printed\": \"significance stars only: *** (paper's definition 0<p<=0.01); no SE or CI printed\",\n> \"sample_as_printed\": \"181 subjects (randomised half treatment, half control)\",\n> \"definition\": \"difference in mean task duration (minutes) between Copilot and control groups across all tasks, holding accuracy constant (control duration predicted at treatment accuracy, Appendix B), randomised\",\n> \"locator\": \"arxiv-2411.01067v2, Section 4.2 Table 3, 'Aggregate Results' block, row 'All Content', columns Treatment/Control/Improvement\",\n> \"harmonisable\": true,\n> \"harmonised_estimate\": -0.353679434958934,\n> \"harmonised_se\": null,\n> \"poolable\": false,\n> \"transformation\": \"percentage change in time: theta = ln(1 + c) = ln(1 + (-0.2979)) = -0.353679 (cf. ln(44.29/63.10) = -0.353962)\",\n> \"reason_if_not\": \"Only significance stars (p<=0.01) are printed; a p-value threshold does not identify SE(theta) (harmonisable, not poolable).\"\n> },\n> \"S4\": {\n> \"estimate_as_printed\": \"Aware 50.5 s, Control 56.9 s per email; % Time Savings 11.1\",\n> \"uncertainty_as_printed\": \"p-value 0.471\",\n> \"sample_as_printed\": \"Aware N=56, Control N=52 (167 analysts across three arms incl. Blind N=59)\",\n> \"definition\": \"percentage time saving in mean time per email over the full 25-email queue, Aware (agent verdicts/rationale + prioritised queue) vs Control (no AI), randomised arms\",\n> \"locator\": \"arxiv-2511.13860v1, Section 3.3 Table 3 'Comparison of Email Processing Times', column 'Full Corpus', rows Aware/Control time, % Time Savings, p-value\",\n> \"harmonisable\": true,\n> \"harmonised_estimate\": -0.11765804346823246,\n> \"harmonised_se\": 0.1632205919720678,\n> \"poolable\": true,\n> \"transformation\": \"percentage change in time c = -11.1/100 = -0.111; theta = ln(1 + c) = -0.117658; approximate SE from two-sided p: SE = |theta|/z(1-0.471/2) = 0.117658/0.720853 = 0.163221 (approximate SE)\",\n> \"reason_if_not\": null\n> },\n> \"S5\": {\n> \"estimate_as_printed\": \"-0.278 (Treat x Post, Output, column 3, panel A)\",\n> \"uncertainty_as_printed\": \"SE (0.295) (clustered at user level); no significance star\",\n> \"sample_as_printed\": \"N = 18332 (user-day observations, users with affiliation)\",\n> \"definition\": \"difference-in-differences coefficient on aggregate GitHub Output measure (sum of Push, PR, PR comments, commit comments, Create, Issues events) for Italian vs Austrian/French users, pre 27/28.03 vs post 03/04.04.2023 around Italy's ChatGPT ban\",\n> \"locator\": \"arxiv-2304.09339v1, Section 4 Table 1, panel A, row Treat x Post, column (3) Output (Users with Affiliation)\",\n> \"harmonisable\": false,\n> \"harmonised_estimate\": null,\n> \"harmonised_se\": null,\n> \"poolable\": false,\n> \"transformation\": \"none applied (not harmonisable)\",\n> \"reason_if_not\": \"Effect on output counts (GitHub events) from a DiD around withdrawal of access, not a comparison of task-completion times with vs without AI on the same tasks, and not randomised.\"\n> }\n> },\n> \"k_extracted\": 5,\n> \"k_harmonised\": 4,\n> \"k_pooled\": 2,\n> \"pooled_studies\": [\n> \"S1\",\n> \"S4\"\n> ],\n> \"fe_estimate\": -0.18814332871148873,\n> \"fe_se\": 0.10626483095451474,\n> \"fe_ci_low\": -0.39642239738233764,\n> \"fe_ci_high\": 0.02013573995936016,\n> \"re_estimate\": -0.18814332871148873,\n> \"re_se\": 0.10626483095451474,\n> \"re_ci_low\": -0.39642239738233764,\n> \"re_ci_high\": 0.02013573995936016,\n> \"q_stat\": 0.3236860522786328,\n> \"q_df\": 1,\n> \"q_pvalue\": 0.5694008376973132,\n> \"i2\": 0.0,\n> \"tau2\": 0.0,\n> \"heterogeneity_interpretation\": \"low\",\n> \"pooled_significant_5pct\": false,\n> \"pooled_pct_change_re\": -17.15040485348688,\n> \"pooled_pct_change_re_ci\": [\n> -32.72775202989503,\n> 2.0339831514392115\n> ],\n> \"weights_fe\": {\n> \"S1\": 0.5761333825403874,\n> \"S4\": 0.4238666174596126\n> },\n> \"weights_re\": {\n> \"S1\": 0.5761333825403874,\n> \"S4\": 0.4238666174596126\n> },\n> \"baseline_unweighted_mean_theta_harmonised\": -0.13434604282593213,\n> \"baseline_unweighted_mean_theta_poolable\": -0.17882902173411622,\n> \"sensitivity_s1_se_from_ci\": {\n> \"s1_se\": 0.13775510204081634,\n> \"re_estimate\": -0.1891067723193802,\n> \"re_se\": 0.10527305720065441,\n> \"re_ci_low\": -0.39544196443266283,\n> \"re_ci_high\": 0.01722841979390244,\n> \"q_stat\": 0.3281104262022853,\n> \"i2\": 0.0,\n> \"tau2\": 0.0\n> },\n> \"feasibility\": \"complete\",\n> \"feasibility_reason\": null,\n> \"data_issues\": [\n> {\n> \"issue\": \"S1: Section VI-B reports N=47 (AI) + N=46 (no-AI) = 93 developers, while Table III states n = 96.\",\n> \"handling\": \"Not resolvable from the extract; reported. sample_as_printed lists both; does not affect theta or SE, which are taken from Table III/Section VI-C.\"\n> },\n> {\n> \"issue\": \"S1: Section VI-B gives Mean LogToT=4.6 for the AI group and 4.3 for no-AI while stating AI was faster (96 vs 114 min); the log means appear swapped/inconsistent.\",\n> \"handling\": \"These log means are not used.\"\n> },\n> {\n> \"issue\": \"S1: Table III is rendered with the four model columns collapsed into one line; SE (0.14) is rounded, and the CI [-0.51, 0.03] implies SE 0.1378.\",\n> \"handling\": \"Model 2 taken as the second value per the editorial note; printed SE 0.14 used; CI-based SE reported as sensitivity (sensitivity_s1_se_from_ci).\"\n> },\n> {\n> \"issue\": \"S2: The uncertainty of the headline +19% estimate is shown only in Figure 1/Figure 15, which are not reproduced; no SE or p-value printed.\",\n> \"handling\": \"Harmonised (theta = ln 1.19) but not poolable; harmonised_se = null.\"\n> },\n> {\n> \"issue\": \"S3: Treatment/Control means 44.29/63.10 imply -29.81%, printed improvement is -29.79% (slightly beyond rounding of the printed means); significance shown only by stars with an unusual convention (* = 0.05<p<=0.1).\",\n> \"handling\": \"Printed -29.79% used as instructed (difference in theta ~0.0003); not poolable because stars do not identify an SE.\"\n> },\n> {\n> \"issue\": \"S4: Table 3 units ambiguous: Malicious/Non-Malicious times per email (1.8-2.8 's') cannot average to Full Corpus times (50.5/56.9 s) under any common unit; Full Corpus ratio 50.5/56.9 implies 11.25% saving vs printed 11.1% (within rounding).\",\n> \"handling\": \"Only the Full Corpus column is used; theta depends only on the printed % saving (unit-free). Flagged as a source inconsistency.\"\n> },\n> {\n> \"issue\": \"S4: SE is approximate, back-calculated from a two-sided p-value (0.471) under a normal approximation on the log scale, although the paper's test is presumably on the time difference.\",\n> \"handling\": \"Marked as approximate SE in transformation; pooled results carry this caveat.\"\n> },\n> {\n> \"issue\": \"S5: Table 1 note prints '** p<0.5' (presumably p<0.05); row label rendered as '\\\\times TreatPost'; panel B header lists 11.04 although the text says it was excluded; rendered date line 'August 24, 2026' is an artefact.\",\n> \"handling\": \"No effect on the extracted panel A column 3 value; S5 is not harmonisable anyway.\"\n> }\n> ],\n> \"n_input_rows\": {\n> \"studies.json\": 5,\n> \"README.md\": 17,\n> \"sources/arxiv-2410.12944v3.txt\": 112,\n> \"sources/arxiv-2507.09089v2.txt\": 132,\n> \"sources/arxiv-2411.01067v2.txt\": 109,\n> \"sources/arxiv-2511.13860v1.txt\": 162,\n> \"sources/arxiv-2304.09339v1.txt\": 108\n> },\n> \"n_analysed\": 5,\n> \"random_seed\": null\n> }\ndiff: out/table1.csv: No such file or directory\n1db48b1c1908b00cab3cc1bfe0ec8ef28bc7d85387aea2477ca709c40a0ec06e data/sources/arxiv-2304.09339v1.txt\nd5270b4435cfbdd516b7268d29c03670509eac7c93604a4466bdb192cbfb803b data/sources/arxiv-2410.12944v3.txt\n46a1ee4e200abb52e3d5148ca0e7ee5c1b8cded80363c0cdceb66c249913053a data/sources/arxiv-2411.01067v2.txt\nb6af8a2bc41012ce64c75e7764fa3a999546df95986adb8860f3442ecd0aed2d data/sources/arxiv-2507.09089v2.txt\n8fb2520f4e9343e47eb39d551c9da49c0d56a7c58ce455fa9073642e1621dfa5 data/sources/arxiv-2511.13860v1.txt\nAll files under `data/sources/` are plain-text extracts of publicly accessible arXiv e-prints, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections, editorial notes). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables the line following a coefficient row holds the standard errors in parentheses as printed. Figures are not reproduced: a value that appears only in a figure is not available in the extract. No numbers were altered.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2410.12944v3.txt` | Paradis, E., Grey, K., Madison, Q., Nam, D., Macvean, A., Meimand, V., Zhang, N., Ferrari-Church, B., & Chandra, S. (2024). How much does AI impact development speed? An enterprise-based randomized controlled trial. arXiv:2410.12944v3 [cs.SE]. https://arxiv.org/abs/2410.12944v3 (full text: https://arxiv.org/html/2410.12944v3) | 2026-10-30T04:19:39Z | CC BY 4.0 | d5270b4435cfbdd516b7268d29c03670509eac7c93604a4466bdb192cbfb803b |\n| `sources/arxiv-2507.09089v2.txt` | Becker, J., Rush, N., Barnes, B., & Rein, D. (2025). Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity. arXiv:2507.09089v2 [cs.AI]. https://arxiv.org/abs/2507.09089v2 (full text: https://arxiv.org/html/2507.09089v2) | 2026-10-30T04:19:45Z | CC BY 4.0 | b6af8a2bc41012ce64c75e7764fa3a999546df95986adb8860f3442ecd0aed2d |\n| `sources/arxiv-2411.01067v2.txt` | Bono, J., & Xu, A. (2024). Randomized Controlled Trials for Security Copilot for IT Administrators. arXiv:2411.01067v2 [econ.GN]. https://arxiv.org/abs/2411.01067v2 (full text: https://arxiv.org/html/2411.01067v2) | 2026-10-30T04:20:32Z | CC BY 4.0 | 46a1ee4e200abb52e3d5148ca0e7ee5c1b8cded80363c0cdceb66c249913053a |\n| `sources/arxiv-2511.13860v1.txt` | Bono, J. (2025). Randomized Controlled Trials for Phishing Triage Agent. arXiv:2511.13860v1 [econ.GN]. https://arxiv.org/abs/2511.13860v1 (full text: https://arxiv.org/html/2511.13860v1) | 2026-10-30T04:20:39Z | CC BY 4.0 | 8fb2520f4e9343e47eb39d551c9da49c0d56a7c58ce455fa9073642e1621dfa5 |\n| `sources/arxiv-2304.09339v1.txt` | Kreitmeir, D. H., & Raschky, P. A. (2023). The Unintended Consequences of Censoring Digital Technology – Evidence from Italy's ChatGPT Ban. arXiv:2304.09339v1 [econ.GN]. https://arxiv.org/abs/2304.09339v1 (full text: https://arxiv.org/html/2304.09339v1) | 2026-10-30T04:19:51Z | CC BY 4.0 | 1db48b1c1908b00cab3cc1bfe0ec8ef28bc7d85387aea2477ca709c40a0ec06e |\n\nTask-specific input file (prepared by the task designer, not observed data):\n 17 data/README.md\n 108 data/sources/arxiv-2304.09339v1.txt\n 112 data/sources/arxiv-2410.12944v3.txt\n 109 data/sources/arxiv-2411.01067v2.txt\n 132 data/sources/arxiv-2507.09089v2.txt\n 162 data/sources/arxiv-2511.13860v1.txt\n 640 total","is_error":false}]},"uuid":"9585e9e5-5506-421a-8133-4f4561f9db29","timestamp":"2026-10-30T05:34:20.900Z","toolUseResult":{"stdout":"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/private/tmp/rt8/out/analysis.py': [Errno 2] No such file or directory\nTraceback (most recent call last):\n File \"<frozen runpy>\", line 198, in _run_module_as_main\n File \"<frozen runpy>\", line 88, in _run_code\n File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/json/tool.py\", line 119, in <module>\n main()\n ~~~~^^\n File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/json/tool.py\", line 88, in main\n infile = open(options.infile, encoding='utf-8')\nFileNotFoundError: [Errno 2] No such file or directory: 'out/results.json'\n0a1,165\n> {\n> \"task_id\": \"T3-synthesis\",\n> \"target_definition\": \"theta = ln(T_AI / T_noAI): natural log of the ratio of mean task-completion time with generative-AI assistance to mean completion time without it, for the same tasks, from a randomised comparison (negative = faster with AI).\",\n> \"items\": {\n> \"S1\": {\n> \"estimate_as_printed\": \"-0.24. (beta_1 for ExpCon, Model 2, Table III; Section VI-C: beta_1=-0.24, i.e. 21% faster)\",\n> \"uncertainty_as_printed\": \"SE (0.14); 95% CI = [-0.51, 0.03], p=0.086 (NS)\",\n> \"sample_as_printed\": \"n = 96 (Table III caption); Section VI-B: N=47 AI, N=46 no-AI\",\n> \"definition\": \"OLS coefficient on experimental condition (AI exposure) with natural-log time on task (LogToT) as dependent variable, developer-level covariates (Model 2), one enterprise coding task, randomised\",\n> \"locator\": \"arxiv-2410.12944v3, Section VI-C Table III, row ExpCon, column (2) 'Dev' (second value on collapsed line L76; SE line L78); CI and p in Section VI-C text\",\n> \"harmonisable\": true,\n> \"harmonised_estimate\": -0.24,\n> \"harmonised_se\": 0.14,\n> \"poolable\": true,\n> \"transformation\": \"log-time coefficient: theta = b = -0.24; SE(theta) = SE(b) = 0.14 (printed). Check: SE from CI = (0.03-(-0.51))/(2*1.96) = 0.1378\",\n> \"reason_if_not\": null\n> },\n> \"S2\": {\n> \"estimate_as_printed\": \"+19% (allowing AI increases completion time by 19%; regression-based, Section 3 / Section D.1)\",\n> \"uncertainty_as_printed\": \"none printed (95% CI shown only in Figure 1 / Figure 15, not reproduced in the extract)\",\n> \"sample_as_printed\": \"16 developers, 246 tasks; 136 issues AI-allowed, 110 AI-disallowed\",\n> \"definition\": \"S = E[time AI allowed]/E[time AI disallowed] - 1, estimated as exp(beta)-1 from OLS of log implementation time on AI indicator and log forecast time; issue-level randomisation\",\n> \"locator\": \"arxiv-2507.09089v2, Abstract and Section 3 'Results' (opening paragraph); estimator Sections 2.3, D.1-D.3\",\n> \"harmonisable\": true,\n> \"harmonised_estimate\": 0.17395330712343798,\n> \"harmonised_se\": null,\n> \"poolable\": false,\n> \"transformation\": \"percentage change in time: theta = ln(1 + c) = ln(1 + 0.19) = 0.173953\",\n> \"reason_if_not\": \"No SE, CI or p-value printed in the extract; the CI exists only in a figure that is not reproduced, so SE(theta) cannot be obtained (harmonisable, not poolable).\"\n> },\n> \"S3\": {\n> \"estimate_as_printed\": \"Overall 53.84, Treatment 44.29, Control 63.10 (minutes); Improvement -29.79%***\",\n> \"uncertainty_as_printed\": \"significance stars only: *** (paper's definition 0<p<=0.01); no SE or CI printed\",\n> \"sample_as_printed\": \"181 subjects (randomised half treatment, half control)\",\n> \"definition\": \"difference in mean task duration (minutes) between Copilot and control groups across all tasks, holding accuracy constant (control duration predicted at treatment accuracy, Appendix B), randomised\",\n> \"locator\": \"arxiv-2411.01067v2, Section 4.2 Table 3, 'Aggregate Results' block, row 'All Content', columns Treatment/Control/Improvement\",\n> \"harmonisable\": true,\n> \"harmonised_estimate\": -0.353679434958934,\n> \"harmonised_se\": null,\n> \"poolable\": false,\n> \"transformation\": \"percentage change in time: theta = ln(1 + c) = ln(1 + (-0.2979)) = -0.353679 (cf. ln(44.29/63.10) = -0.353962)\",\n> \"reason_if_not\": \"Only significance stars (p<=0.01) are printed; a p-value threshold does not identify SE(theta) (harmonisable, not poolable).\"\n> },\n> \"S4\": {\n> \"estimate_as_printed\": \"Aware 50.5 s, Control 56.9 s per email; % Time Savings 11.1\",\n> \"uncertainty_as_printed\": \"p-value 0.471\",\n> \"sample_as_printed\": \"Aware N=56, Control N=52 (167 analysts across three arms incl. Blind N=59)\",\n> \"definition\": \"percentage time saving in mean time per email over the full 25-email queue, Aware (agent verdicts/rationale + prioritised queue) vs Control (no AI), randomised arms\",\n> \"locator\": \"arxiv-2511.13860v1, Section 3.3 Table 3 'Comparison of Email Processing Times', column 'Full Corpus', rows Aware/Control time, % Time Savings, p-value\",\n> \"harmonisable\": true,\n> \"harmonised_estimate\": -0.11765804346823246,\n> \"harmonised_se\": 0.1632205919720678,\n> \"poolable\": true,\n> \"transformation\": \"percentage change in time c = -11.1/100 = -0.111; theta = ln(1 + c) = -0.117658; approximate SE from two-sided p: SE = |theta|/z(1-0.471/2) = 0.117658/0.720853 = 0.163221 (approximate SE)\",\n> \"reason_if_not\": null\n> },\n> \"S5\": {\n> \"estimate_as_printed\": \"-0.278 (Treat x Post, Output, column 3, panel A)\",\n> \"uncertainty_as_printed\": \"SE (0.295) (clustered at user level); no significance star\",\n> \"sample_as_printed\": \"N = 18332 (user-day observations, users with affiliation)\",\n> \"definition\": \"difference-in-differences coefficient on aggregate GitHub Output measure (sum of Push, PR, PR comments, commit comments, Create, Issues events) for Italian vs Austrian/French users, pre 27/28.03 vs post 03/04.04.2023 around Italy's ChatGPT ban\",\n> \"locator\": \"arxiv-2304.09339v1, Section 4 Table 1, panel A, row Treat x Post, column (3) Output (Users with Affiliation)\",\n> \"harmonisable\": false,\n> \"harmonised_estimate\": null,\n> \"harmonised_se\": null,\n> \"poolable\": false,\n> \"transformation\": \"none applied (not harmonisable)\",\n> \"reason_if_not\": \"Effect on output counts (GitHub events) from a DiD around withdrawal of access, not a comparison of task-completion times with vs without AI on the same tasks, and not randomised.\"\n> }\n> },\n> \"k_extracted\": 5,\n> \"k_harmonised\": 4,\n> \"k_pooled\": 2,\n> \"pooled_studies\": [\n> \"S1\",\n> \"S4\"\n> ],\n> \"fe_estimate\": -0.18814332871148873,\n> \"fe_se\": 0.10626483095451474,\n> \"fe_ci_low\": -0.39642239738233764,\n> \"fe_ci_high\": 0.02013573995936016,\n> \"re_estimate\": -0.18814332871148873,\n> \"re_se\": 0.10626483095451474,\n> \"re_ci_low\": -0.39642239738233764,\n> \"re_ci_high\": 0.02013573995936016,\n> \"q_stat\": 0.3236860522786328,\n> \"q_df\": 1,\n> \"q_pvalue\": 0.5694008376973132,\n> \"i2\": 0.0,\n> \"tau2\": 0.0,\n> \"heterogeneity_interpretation\": \"low\",\n> \"pooled_significant_5pct\": false,\n> \"pooled_pct_change_re\": -17.15040485348688,\n> \"pooled_pct_change_re_ci\": [\n> -32.72775202989503,\n> 2.0339831514392115\n> ],\n> \"weights_fe\": {\n> \"S1\": 0.5761333825403874,\n> \"S4\": 0.4238666174596126\n> },\n> \"weights_re\": {\n> \"S1\": 0.5761333825403874,\n> \"S4\": 0.4238666174596126\n> },\n> \"baseline_unweighted_mean_theta_harmonised\": -0.13434604282593213,\n> \"baseline_unweighted_mean_theta_poolable\": -0.17882902173411622,\n> \"sensitivity_s1_se_from_ci\": {\n> \"s1_se\": 0.13775510204081634,\n> \"re_estimate\": -0.1891067723193802,\n> \"re_se\": 0.10527305720065441,\n> \"re_ci_low\": -0.39544196443266283,\n> \"re_ci_high\": 0.01722841979390244,\n> \"q_stat\": 0.3281104262022853,\n> \"i2\": 0.0,\n> \"tau2\": 0.0\n> },\n> \"feasibility\": \"complete\",\n> \"feasibility_reason\": null,\n> \"data_issues\": [\n> {\n> \"issue\": \"S1: Section VI-B reports N=47 (AI) + N=46 (no-AI) = 93 developers, while Table III states n = 96.\",\n> \"handling\": \"Not resolvable from the extract; reported. sample_as_printed lists both; does not affect theta or SE, which are taken from Table III/Section VI-C.\"\n> },\n> {\n> \"issue\": \"S1: Section VI-B gives Mean LogToT=4.6 for the AI group and 4.3 for no-AI while stating AI was faster (96 vs 114 min); the log means appear swapped/inconsistent.\",\n> \"handling\": \"These log means are not used.\"\n> },\n> {\n> \"issue\": \"S1: Table III is rendered with the four model columns collapsed into one line; SE (0.14) is rounded, and the CI [-0.51, 0.03] implies SE 0.1378.\",\n> \"handling\": \"Model 2 taken as the second value per the editorial note; printed SE 0.14 used; CI-based SE reported as sensitivity (sensitivity_s1_se_from_ci).\"\n> },\n> {\n> \"issue\": \"S2: The uncertainty of the headline +19% estimate is shown only in Figure 1/Figure 15, which are not reproduced; no SE or p-value printed.\",\n> \"handling\": \"Harmonised (theta = ln 1.19) but not poolable; harmonised_se = null.\"\n> },\n> {\n> \"issue\": \"S3: Treatment/Control means 44.29/63.10 imply -29.81%, printed improvement is -29.79% (slightly beyond rounding of the printed means); significance shown only by stars with an unusual convention (* = 0.05<p<=0.1).\",\n> \"handling\": \"Printed -29.79% used as instructed (difference in theta ~0.0003); not poolable because stars do not identify an SE.\"\n> },\n> {\n> \"issue\": \"S4: Table 3 units ambiguous: Malicious/Non-Malicious times per email (1.8-2.8 's') cannot average to Full Corpus times (50.5/56.9 s) under any common unit; Full Corpus ratio 50.5/56.9 implies 11.25% saving vs printed 11.1% (within rounding).\",\n> \"handling\": \"Only the Full Corpus column is used; theta depends only on the printed % saving (unit-free). Flagged as a source inconsistency.\"\n> },\n> {\n> \"issue\": \"S4: SE is approximate, back-calculated from a two-sided p-value (0.471) under a normal approximation on the log scale, although the paper's test is presumably on the time difference.\",\n> \"handling\": \"Marked as approximate SE in transformation; pooled results carry this caveat.\"\n> },\n> {\n> \"issue\": \"S5: Table 1 note prints '** p<0.5' (presumably p<0.05); row label rendered as '\\\\times TreatPost'; panel B header lists 11.04 although the text says it was excluded; rendered date line 'August 24, 2026' is an artefact.\",\n> \"handling\": \"No effect on the extracted panel A column 3 value; S5 is not harmonisable anyway.\"\n> }\n> ],\n> \"n_input_rows\": {\n> \"studies.json\": 5,\n> \"README.md\": 17,\n> \"sources/arxiv-2410.12944v3.txt\": 112,\n> \"sources/arxiv-2507.09089v2.txt\": 132,\n> \"sources/arxiv-2411.01067v2.txt\": 109,\n> \"sources/arxiv-2511.13860v1.txt\": 162,\n> \"sources/arxiv-2304.09339v1.txt\": 108\n> },\n> \"n_analysed\": 5,\n> \"random_seed\": null\n> }\ndiff: out/table1.csv: No such file or directory\n1db48b1c1908b00cab3cc1bfe0ec8ef28bc7d85387aea2477ca709c40a0ec06e data/sources/arxiv-2304.09339v1.txt\nd5270b4435cfbdd516b7268d29c03670509eac7c93604a4466bdb192cbfb803b data/sources/arxiv-2410.12944v3.txt\n46a1ee4e200abb52e3d5148ca0e7ee5c1b8cded80363c0cdceb66c249913053a data/sources/arxiv-2411.01067v2.txt\nb6af8a2bc41012ce64c75e7764fa3a999546df95986adb8860f3442ecd0aed2d data/sources/arxiv-2507.09089v2.txt\n8fb2520f4e9343e47eb39d551c9da49c0d56a7c58ce455fa9073642e1621dfa5 data/sources/arxiv-2511.13860v1.txt\nAll files under `data/sources/` are plain-text extracts of publicly accessible arXiv e-prints, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections, editorial notes). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables the line following a coefficient row holds the standard errors in parentheses as printed. Figures are not reproduced: a value that appears only in a figure is not available in the extract. No numbers were altered.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2410.12944v3.txt` | Paradis, E., Grey, K., Madison, Q., Nam, D., Macvean, A., Meimand, V., Zhang, N., Ferrari-Church, B., & Chandra, S. (2024). How much does AI impact development speed? An enterprise-based randomized controlled trial. arXiv:2410.12944v3 [cs.SE]. https://arxiv.org/abs/2410.12944v3 (full text: https://arxiv.org/html/2410.12944v3) | 2026-10-30T04:19:39Z | CC BY 4.0 | d5270b4435cfbdd516b7268d29c03670509eac7c93604a4466bdb192cbfb803b |\n| `sources/arxiv-2507.09089v2.txt` | Becker, J., Rush, N., Barnes, B., & Rein, D. (2025). Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity. arXiv:2507.09089v2 [cs.AI]. https://arxiv.org/abs/2507.09089v2 (full text: https://arxiv.org/html/2507.09089v2) | 2026-10-30T04:19:45Z | CC BY 4.0 | b6af8a2bc41012ce64c75e7764fa3a999546df95986adb8860f3442ecd0aed2d |\n| `sources/arxiv-2411.01067v2.txt` | Bono, J., & Xu, A. (2024). Randomized Controlled Trials for Security Copilot for IT Administrators. arXiv:2411.01067v2 [econ.GN]. https://arxiv.org/abs/2411.01067v2 (full text: https://arxiv.org/html/2411.01067v2) | 2026-10-30T04:20:32Z | CC BY 4.0 | 46a1ee4e200abb52e3d5148ca0e7ee5c1b8cded80363c0cdceb66c249913053a |\n| `sources/arxiv-2511.13860v1.txt` | Bono, J. (2025). Randomized Controlled Trials for Phishing Triage Agent. arXiv:2511.13860v1 [econ.GN]. https://arxiv.org/abs/2511.13860v1 (full text: https://arxiv.org/html/2511.13860v1) | 2026-10-30T04:20:39Z | CC BY 4.0 | 8fb2520f4e9343e47eb39d551c9da49c0d56a7c58ce455fa9073642e1621dfa5 |\n| `sources/arxiv-2304.09339v1.txt` | Kreitmeir, D. H., & Raschky, P. A. (2023). The Unintended Consequences of Censoring Digital Technology – Evidence from Italy's ChatGPT Ban. arXiv:2304.09339v1 [econ.GN]. https://arxiv.org/abs/2304.09339v1 (full text: https://arxiv.org/html/2304.09339v1) | 2026-10-30T04:19:51Z | CC BY 4.0 | 1db48b1c1908b00cab3cc1bfe0ec8ef28bc7d85387aea2477ca709c40a0ec06e |\n\nTask-specific input file (prepared by the task designer, not observed data):\n 17 data/README.md\n 108 data/sources/arxiv-2304.09339v1.txt\n 112 data/sources/arxiv-2410.12944v3.txt\n 109 data/sources/arxiv-2411.01067v2.txt\n 132 data/sources/arxiv-2507.09089v2.txt\n 162 data/sources/arxiv-2511.13860v1.txt\n 640 total","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5efb25a5-f751-4745-bc4d-f81f707e93d6","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}33{"type":"api-request-blob","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","hash":"8f3fad256c2ae0b2ae73be14afbf46740866b13b5e26cf3c20b46b7a0034bdeb","message":{"role":"assistant","content":[{"type":"text","text":"Now check the sources and re-run."},{"type":"tool_use","id":"toolu_011aPDY7cAkwXazsz7CZRpUk","name":"Bash","input":{"command":"cd data/sources; grep -n \"ExpCon\\|Model 2\\|n = 96\\|N=4\\|LogToT\\|editorial\" arxiv-2410.12944v3.txt | head -30; echo ----; grep -n \"19%\\|confidence\\|CI\\|p-value\\|p =\\|p=\" arxiv-2507.09089v2.txt | head -30","description":"Inspect S1 and S2 sources"}},{"type":"tool_use","id":"toolu_01P8eowdnT2cpUEZQbfxfEuB","name":"Bash","input":{"command":"mkdir -p /tmp/rt8 && cp -r data out /tmp/rt8/ 2>/dev/null; cd /tmp/rt8 && ~/wp29/venv/bin/python out/analysis.py | tail -8 && diff <(python3 -m json.tool out/results.json) <(python3 -m json.tool ~/wp29/runs/RT0008/out/results.json) && echo SAME; diff out/table1.csv ~/wp29/runs/RT0008/out/table1.csv && echo SAMECSV; cd ~/wp29/runs/RT0008; shasum -a 256 data/sources/*; cat data/README.md | grep -i sha -A8 | head -20; wc -l data/README.md data/sources/*","description":"Re-run in scratch copy and compare"}}]}}34{"type":"api-request-blob","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","hash":"d60116dbfcd521628bf2215fd0b3d583eee3b3743fdd797186d707a861a04dab","message":{"role":"user","content":[{"tool_use_id":"toolu_011aPDY7cAkwXazsz7CZRpUk","type":"tool_result","content":"7:Extract contents: title, authors and abstract; Section IV-C 'Analytic approach'; Sections VI-B 'RQ1: Impact of AI tools on developer task speed' and VI-C 'RQ2: Impact of covariates on main effect' including Table III (regressions with LogToT as dependent variable).\n33:\\displaystyle LogToT \\displaystyle=\\beta_{1}*ExpCon+\\beta_{2}*AvgProgHrsDay | | |\n39:Given the logged dependent variable, the effect of a variable n on time on task in percentages is represented by (1-exp(\\beta_{n}))*100 . The impact of AI on time on task (our ExpCon variable), expressed in percentages, is thus represented by (1-exp(\\beta_{1}))*100 . We did not use hierarchical linear modeling given the absence of nesting between constructs in our theoretical model.\n48:On average, developers who were exposed to AI features completed the task in 96 min ( N=47,SE=9.3 ), compared to 114 min ( N=46,SE=8.1 ) for developers in the no-AI condition. We conducted a Student’s t-test on log time on task to assess the main effect impact of the experimental condition assignment (AI vs no-AI exposure). Results of this analysis indicated that developers who were in the AI exposure condition (Mean LogToT=4.6 ) were significantly faster at completing the task ( t(83.6)=2.11,p=.038 ), compared to those who were in the no-AI exposure condition (Mean LogToT=4.3 ).\n54:Table III summarizes the regression findings. When controlling for other factors in our theoretical framework, we find that estimates for the impact of the experimental condition on logged time on task range between -0.30 to -0.24 ( \\beta_{1} for ExpCon for Models 1-3 in Table III), suggesting a relatively consistent and negative downward pressure on time on task. Controlling for the developer- and task-level factors described in our theoretical framework (see Section IV-B), we found that this main effect was robust to covariates, making developers between 21% and 26% faster.^2^2\n57:From Model 2, where \\beta_{1}=-0.24 , and therefore the effect of AI =(1-exp(\\beta_{1}))*100=21\\% (with 95% CI = [-0.51, 0.03]). Similarly, 26% from Model 3, where \\beta_{1}=-0.30 .\n58:However, our confidence intervals are large, and as a result the estimate is not statistically significant at the p<0.05 level (estimate of experimental condition on best-fit Model 2, \\beta_{1}=-0.24; 95\\%CI = [-0.51,0.03] , p=0.086 , NS). Our second hypothesis is therefore only partially supported.\n62:Other covariates were not significantly associated with time spent on task. As seen in Table III, the best fit model was Model 2 with developer-level factors only, as evidenced by higher adjusted R^{2} and lower p -value. While the total variance accounted for by Model 2 was low, the model was statistically significant ( p=0.011 ), suggesting an adequate fit to our data.\n66:TABLE III: Summaries of regressions using LogToT as the dependent variable, n = 96. Unstandardized estimates provided, along with standard error of the mean (in parentheses).\n76:-0.25. -0.24. -0.30* -0.14 ExpCon | | | |\n----\n8:Omitted: Sections 1, 2.1-2.2, 2.4, 3.2-3.3, 4-5, Appendices A-C, D.4-D.5, E-G, Tables 1-8, all figures (including Figure 1, which displays the confidence interval of the main estimate graphically), the references.\n34:Surprisingly, we find that allowing AI actually increases completion time by 19%—AI tooling slowed developers down.\n58:Figure 4: Left: Raw average forecasted implementation times. Right: Raw average observed implementation times. The ratio of observed implementation times gives a more extreme slowdown estimate than regression-based estimates (Section D.1) because AI-allowed issues are forecasted (importantly, before treatment assignment) by developers to take slightly longer, which the regression corrects for. Both: Section D.5 describes confidence intervals around ratios of average times.\n62:Developers complete 136 issues with AI-allowed and 110 issues with AI-disallowed. Section G.7 shows the number of issues completed across repositories and developers, respectively. We find that when developers use AI tools, they implement issues in 19% more time on average (Figure 1), and nearly all quantiles of observed implementation time see AI-allowed issues taking longer (Figure 5). That is, developers are slower when using AI is allowed. Colloquially, we refer to this result that issues with AI-allowed take longer than issues with AI-disallowed as slowdown.\n68:Before developers complete each issue, they forecast how long they expect them to take with and without AI assistance. On average, they forecast speedup of 24%. Interestingly, after the experiment they post-hoc estimate that they were sped-up by 20% when using AI is allowed—after they used AI assistance, they estimate similar speedup as before, despite the fact that they are in fact slowed down by 19% (Figure 1).\n72:The raw percentage difference in implementation times between AI-allowed and AI-disallowed issues is 34%, which is larger in absolute value than the 19% slowdown estimated using the regression specified in Section D.1. This is true because AI-allowed issues ended up being slightly more difficult than AI-disallowed issues after randomization, as measured by the forecasted AI-disallowed times (not statistically significant; see Table 4). Our regression accounts for this, while this simple ratio estimator does not. See Figure 13 for results implied by alternative estimators..\n97:By default, we report 95% confidence intervals using HC3 standard errors.^19^19\n100:Given that our sampling of developers is non-random and treatment is assigned at the issue level, Abadie et al. [2022] do not necessarily recommend developer-level clustering even if residuals are correlated within-developer. If we are willing to treat developer sampling as random then clustering is appropriate. Figure 15 displays 95% confidence intervals from alternative uncertainty estimation procedures. Standard errors clustered at the developer level and bias-corrected cluster-robust standard errors give similar results; a hierarchical bootstrap resampling developers and then issues within developers yields somewhat wider confidence intervals.\n126:Then under standard regularity assumptions (which our diagnostics suggest hold) our OLS estimator \\hat{\\beta} is normally distributed \\mathcal{N}(\\beta,\\sigma^{2}) for some \\sigma which we can estimate using it’s standard error \\text{SE}[\\hat{\\beta}] . This lets us construct a 95% confidence interval for \\beta in the usual way:\n128:\\text{CI}_{95\\%}=\\left[\\hat{\\beta}-1.96\\cdot\\text{SE}[\\hat{\\beta}],\\hat{\\beta}+1.96\\cdot\\text{SE}[\\hat{\\beta}]\\right] | | | (6)\n130:As \\text{S}=\\exp(\\beta)-1 is a monotonic function of \\beta we can construct a confidence interval for S by simply applying the function to the endpoints of \\beta ’s confidence interval:\n132:\\text{CI}_{95\\%}=\\left[e^{\\hat{\\beta}-1.96\\cdot\\text{SE}[\\hat{\\beta}]}-1,e^{\\hat{\\beta}+1.96\\cdot\\text{SE}[\\hat{\\beta}]}-1\\right] | | | (7)","is_error":false},{"tool_use_id":"toolu_01P8eowdnT2cpUEZQbfxfEuB","type":"tool_result","content":"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/private/tmp/rt8/out/analysis.py': [Errno 2] No such file or directory\nTraceback (most recent call last):\n File \"<frozen runpy>\", line 198, in _run_module_as_main\n File \"<frozen runpy>\", line 88, in _run_code\n File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/json/tool.py\", line 119, in <module>\n main()\n ~~~~^^\n File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/json/tool.py\", line 88, in main\n infile = open(options.infile, encoding='utf-8')\nFileNotFoundError: [Errno 2] No such file or directory: 'out/results.json'\n0a1,165\n> {\n> \"task_id\": \"T3-synthesis\",\n> \"target_definition\": \"theta = ln(T_AI / T_noAI): natural log of the ratio of mean task-completion time with generative-AI assistance to mean completion time without it, for the same tasks, from a randomised comparison (negative = faster with AI).\",\n> \"items\": {\n> \"S1\": {\n> \"estimate_as_printed\": \"-0.24. (beta_1 for ExpCon, Model 2, Table III; Section VI-C: beta_1=-0.24, i.e. 21% faster)\",\n> \"uncertainty_as_printed\": \"SE (0.14); 95% CI = [-0.51, 0.03], p=0.086 (NS)\",\n> \"sample_as_printed\": \"n = 96 (Table III caption); Section VI-B: N=47 AI, N=46 no-AI\",\n> \"definition\": \"OLS coefficient on experimental condition (AI exposure) with natural-log time on task (LogToT) as dependent variable, developer-level covariates (Model 2), one enterprise coding task, randomised\",\n> \"locator\": \"arxiv-2410.12944v3, Section VI-C Table III, row ExpCon, column (2) 'Dev' (second value on collapsed line L76; SE line L78); CI and p in Section VI-C text\",\n> \"harmonisable\": true,\n> \"harmonised_estimate\": -0.24,\n> \"harmonised_se\": 0.14,\n> \"poolable\": true,\n> \"transformation\": \"log-time coefficient: theta = b = -0.24; SE(theta) = SE(b) = 0.14 (printed). Check: SE from CI = (0.03-(-0.51))/(2*1.96) = 0.1378\",\n> \"reason_if_not\": null\n> },\n> \"S2\": {\n> \"estimate_as_printed\": \"+19% (allowing AI increases completion time by 19%; regression-based, Section 3 / Section D.1)\",\n> \"uncertainty_as_printed\": \"none printed (95% CI shown only in Figure 1 / Figure 15, not reproduced in the extract)\",\n> \"sample_as_printed\": \"16 developers, 246 tasks; 136 issues AI-allowed, 110 AI-disallowed\",\n> \"definition\": \"S = E[time AI allowed]/E[time AI disallowed] - 1, estimated as exp(beta)-1 from OLS of log implementation time on AI indicator and log forecast time; issue-level randomisation\",\n> \"locator\": \"arxiv-2507.09089v2, Abstract and Section 3 'Results' (opening paragraph); estimator Sections 2.3, D.1-D.3\",\n> \"harmonisable\": true,\n> \"harmonised_estimate\": 0.17395330712343798,\n> \"harmonised_se\": null,\n> \"poolable\": false,\n> \"transformation\": \"percentage change in time: theta = ln(1 + c) = ln(1 + 0.19) = 0.173953\",\n> \"reason_if_not\": \"No SE, CI or p-value printed in the extract; the CI exists only in a figure that is not reproduced, so SE(theta) cannot be obtained (harmonisable, not poolable).\"\n> },\n> \"S3\": {\n> \"estimate_as_printed\": \"Overall 53.84, Treatment 44.29, Control 63.10 (minutes); Improvement -29.79%***\",\n> \"uncertainty_as_printed\": \"significance stars only: *** (paper's definition 0<p<=0.01); no SE or CI printed\",\n> \"sample_as_printed\": \"181 subjects (randomised half treatment, half control)\",\n> \"definition\": \"difference in mean task duration (minutes) between Copilot and control groups across all tasks, holding accuracy constant (control duration predicted at treatment accuracy, Appendix B), randomised\",\n> \"locator\": \"arxiv-2411.01067v2, Section 4.2 Table 3, 'Aggregate Results' block, row 'All Content', columns Treatment/Control/Improvement\",\n> \"harmonisable\": true,\n> \"harmonised_estimate\": -0.353679434958934,\n> \"harmonised_se\": null,\n> \"poolable\": false,\n> \"transformation\": \"percentage change in time: theta = ln(1 + c) = ln(1 + (-0.2979)) = -0.353679 (cf. ln(44.29/63.10) = -0.353962)\",\n> \"reason_if_not\": \"Only significance stars (p<=0.01) are printed; a p-value threshold does not identify SE(theta) (harmonisable, not poolable).\"\n> },\n> \"S4\": {\n> \"estimate_as_printed\": \"Aware 50.5 s, Control 56.9 s per email; % Time Savings 11.1\",\n> \"uncertainty_as_printed\": \"p-value 0.471\",\n> \"sample_as_printed\": \"Aware N=56, Control N=52 (167 analysts across three arms incl. Blind N=59)\",\n> \"definition\": \"percentage time saving in mean time per email over the full 25-email queue, Aware (agent verdicts/rationale + prioritised queue) vs Control (no AI), randomised arms\",\n> \"locator\": \"arxiv-2511.13860v1, Section 3.3 Table 3 'Comparison of Email Processing Times', column 'Full Corpus', rows Aware/Control time, % Time Savings, p-value\",\n> \"harmonisable\": true,\n> \"harmonised_estimate\": -0.11765804346823246,\n> \"harmonised_se\": 0.1632205919720678,\n> \"poolable\": true,\n> \"transformation\": \"percentage change in time c = -11.1/100 = -0.111; theta = ln(1 + c) = -0.117658; approximate SE from two-sided p: SE = |theta|/z(1-0.471/2) = 0.117658/0.720853 = 0.163221 (approximate SE)\",\n> \"reason_if_not\": null\n> },\n> \"S5\": {\n> \"estimate_as_printed\": \"-0.278 (Treat x Post, Output, column 3, panel A)\",\n> \"uncertainty_as_printed\": \"SE (0.295) (clustered at user level); no significance star\",\n> \"sample_as_printed\": \"N = 18332 (user-day observations, users with affiliation)\",\n> \"definition\": \"difference-in-differences coefficient on aggregate GitHub Output measure (sum of Push, PR, PR comments, commit comments, Create, Issues events) for Italian vs Austrian/French users, pre 27/28.03 vs post 03/04.04.2023 around Italy's ChatGPT ban\",\n> \"locator\": \"arxiv-2304.09339v1, Section 4 Table 1, panel A, row Treat x Post, column (3) Output (Users with Affiliation)\",\n> \"harmonisable\": false,\n> \"harmonised_estimate\": null,\n> \"harmonised_se\": null,\n> \"poolable\": false,\n> \"transformation\": \"none applied (not harmonisable)\",\n> \"reason_if_not\": \"Effect on output counts (GitHub events) from a DiD around withdrawal of access, not a comparison of task-completion times with vs without AI on the same tasks, and not randomised.\"\n> }\n> },\n> \"k_extracted\": 5,\n> \"k_harmonised\": 4,\n> \"k_pooled\": 2,\n> \"pooled_studies\": [\n> \"S1\",\n> \"S4\"\n> ],\n> \"fe_estimate\": -0.18814332871148873,\n> \"fe_se\": 0.10626483095451474,\n> \"fe_ci_low\": -0.39642239738233764,\n> \"fe_ci_high\": 0.02013573995936016,\n> \"re_estimate\": -0.18814332871148873,\n> \"re_se\": 0.10626483095451474,\n> \"re_ci_low\": -0.39642239738233764,\n> \"re_ci_high\": 0.02013573995936016,\n> \"q_stat\": 0.3236860522786328,\n> \"q_df\": 1,\n> \"q_pvalue\": 0.5694008376973132,\n> \"i2\": 0.0,\n> \"tau2\": 0.0,\n> \"heterogeneity_interpretation\": \"low\",\n> \"pooled_significant_5pct\": false,\n> \"pooled_pct_change_re\": -17.15040485348688,\n> \"pooled_pct_change_re_ci\": [\n> -32.72775202989503,\n> 2.0339831514392115\n> ],\n> \"weights_fe\": {\n> \"S1\": 0.5761333825403874,\n> \"S4\": 0.4238666174596126\n> },\n> \"weights_re\": {\n> \"S1\": 0.5761333825403874,\n> \"S4\": 0.4238666174596126\n> },\n> \"baseline_unweighted_mean_theta_harmonised\": -0.13434604282593213,\n> \"baseline_unweighted_mean_theta_poolable\": -0.17882902173411622,\n> \"sensitivity_s1_se_from_ci\": {\n> \"s1_se\": 0.13775510204081634,\n> \"re_estimate\": -0.1891067723193802,\n> \"re_se\": 0.10527305720065441,\n> \"re_ci_low\": -0.39544196443266283,\n> \"re_ci_high\": 0.01722841979390244,\n> \"q_stat\": 0.3281104262022853,\n> \"i2\": 0.0,\n> \"tau2\": 0.0\n> },\n> \"feasibility\": \"complete\",\n> \"feasibility_reason\": null,\n> \"data_issues\": [\n> {\n> \"issue\": \"S1: Section VI-B reports N=47 (AI) + N=46 (no-AI) = 93 developers, while Table III states n = 96.\",\n> \"handling\": \"Not resolvable from the extract; reported. sample_as_printed lists both; does not affect theta or SE, which are taken from Table III/Section VI-C.\"\n> },\n> {\n> \"issue\": \"S1: Section VI-B gives Mean LogToT=4.6 for the AI group and 4.3 for no-AI while stating AI was faster (96 vs 114 min); the log means appear swapped/inconsistent.\",\n> \"handling\": \"These log means are not used.\"\n> },\n> {\n> \"issue\": \"S1: Table III is rendered with the four model columns collapsed into one line; SE (0.14) is rounded, and the CI [-0.51, 0.03] implies SE 0.1378.\",\n> \"handling\": \"Model 2 taken as the second value per the editorial note; printed SE 0.14 used; CI-based SE reported as sensitivity (sensitivity_s1_se_from_ci).\"\n> },\n> {\n> \"issue\": \"S2: The uncertainty of the headline +19% estimate is shown only in Figure 1/Figure 15, which are not reproduced; no SE or p-value printed.\",\n> \"handling\": \"Harmonised (theta = ln 1.19) but not poolable; harmonised_se = null.\"\n> },\n> {\n> \"issue\": \"S3: Treatment/Control means 44.29/63.10 imply -29.81%, printed improvement is -29.79% (slightly beyond rounding of the printed means); significance shown only by stars with an unusual convention (* = 0.05<p<=0.1).\",\n> \"handling\": \"Printed -29.79% used as instructed (difference in theta ~0.0003); not poolable because stars do not identify an SE.\"\n> },\n> {\n> \"issue\": \"S4: Table 3 units ambiguous: Malicious/Non-Malicious times per email (1.8-2.8 's') cannot average to Full Corpus times (50.5/56.9 s) under any common unit; Full Corpus ratio 50.5/56.9 implies 11.25% saving vs printed 11.1% (within rounding).\",\n> \"handling\": \"Only the Full Corpus column is used; theta depends only on the printed % saving (unit-free). Flagged as a source inconsistency.\"\n> },\n> {\n> \"issue\": \"S4: SE is approximate, back-calculated from a two-sided p-value (0.471) under a normal approximation on the log scale, although the paper's test is presumably on the time difference.\",\n> \"handling\": \"Marked as approximate SE in transformation; pooled results carry this caveat.\"\n> },\n> {\n> \"issue\": \"S5: Table 1 note prints '** p<0.5' (presumably p<0.05); row label rendered as '\\\\times TreatPost'; panel B header lists 11.04 although the text says it was excluded; rendered date line 'August 24, 2026' is an artefact.\",\n> \"handling\": \"No effect on the extracted panel A column 3 value; S5 is not harmonisable anyway.\"\n> }\n> ],\n> \"n_input_rows\": {\n> \"studies.json\": 5,\n> \"README.md\": 17,\n> \"sources/arxiv-2410.12944v3.txt\": 112,\n> \"sources/arxiv-2507.09089v2.txt\": 132,\n> \"sources/arxiv-2411.01067v2.txt\": 109,\n> \"sources/arxiv-2511.13860v1.txt\": 162,\n> \"sources/arxiv-2304.09339v1.txt\": 108\n> },\n> \"n_analysed\": 5,\n> \"random_seed\": null\n> }\ndiff: out/table1.csv: No such file or directory\n1db48b1c1908b00cab3cc1bfe0ec8ef28bc7d85387aea2477ca709c40a0ec06e data/sources/arxiv-2304.09339v1.txt\nd5270b4435cfbdd516b7268d29c03670509eac7c93604a4466bdb192cbfb803b data/sources/arxiv-2410.12944v3.txt\n46a1ee4e200abb52e3d5148ca0e7ee5c1b8cded80363c0cdceb66c249913053a data/sources/arxiv-2411.01067v2.txt\nb6af8a2bc41012ce64c75e7764fa3a999546df95986adb8860f3442ecd0aed2d data/sources/arxiv-2507.09089v2.txt\n8fb2520f4e9343e47eb39d551c9da49c0d56a7c58ce455fa9073642e1621dfa5 data/sources/arxiv-2511.13860v1.txt\nAll files under `data/sources/` are plain-text extracts of publicly accessible arXiv e-prints, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections, editorial notes). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables the line following a coefficient row holds the standard errors in parentheses as printed. Figures are not reproduced: a value that appears only in a figure is not available in the extract. No numbers were altered.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2410.12944v3.txt` | Paradis, E., Grey, K., Madison, Q., Nam, D., Macvean, A., Meimand, V., Zhang, N., Ferrari-Church, B., & Chandra, S. (2024). How much does AI impact development speed? An enterprise-based randomized controlled trial. arXiv:2410.12944v3 [cs.SE]. https://arxiv.org/abs/2410.12944v3 (full text: https://arxiv.org/html/2410.12944v3) | 2026-10-30T04:19:39Z | CC BY 4.0 | d5270b4435cfbdd516b7268d29c03670509eac7c93604a4466bdb192cbfb803b |\n| `sources/arxiv-2507.09089v2.txt` | Becker, J., Rush, N., Barnes, B., & Rein, D. (2025). Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity. arXiv:2507.09089v2 [cs.AI]. https://arxiv.org/abs/2507.09089v2 (full text: https://arxiv.org/html/2507.09089v2) | 2026-10-30T04:19:45Z | CC BY 4.0 | b6af8a2bc41012ce64c75e7764fa3a999546df95986adb8860f3442ecd0aed2d |\n| `sources/arxiv-2411.01067v2.txt` | Bono, J., & Xu, A. (2024). Randomized Controlled Trials for Security Copilot for IT Administrators. arXiv:2411.01067v2 [econ.GN]. https://arxiv.org/abs/2411.01067v2 (full text: https://arxiv.org/html/2411.01067v2) | 2026-10-30T04:20:32Z | CC BY 4.0 | 46a1ee4e200abb52e3d5148ca0e7ee5c1b8cded80363c0cdceb66c249913053a |\n| `sources/arxiv-2511.13860v1.txt` | Bono, J. (2025). Randomized Controlled Trials for Phishing Triage Agent. arXiv:2511.13860v1 [econ.GN]. https://arxiv.org/abs/2511.13860v1 (full text: https://arxiv.org/html/2511.13860v1) | 2026-10-30T04:20:39Z | CC BY 4.0 | 8fb2520f4e9343e47eb39d551c9da49c0d56a7c58ce455fa9073642e1621dfa5 |\n| `sources/arxiv-2304.09339v1.txt` | Kreitmeir, D. H., & Raschky, P. A. (2023). The Unintended Consequences of Censoring Digital Technology – Evidence from Italy's ChatGPT Ban. arXiv:2304.09339v1 [econ.GN]. https://arxiv.org/abs/2304.09339v1 (full text: https://arxiv.org/html/2304.09339v1) | 2026-10-30T04:19:51Z | CC BY 4.0 | 1db48b1c1908b00cab3cc1bfe0ec8ef28bc7d85387aea2477ca709c40a0ec06e |\n\nTask-specific input file (prepared by the task designer, not observed data):\n 17 data/README.md\n 108 data/sources/arxiv-2304.09339v1.txt\n 112 data/sources/arxiv-2410.12944v3.txt\n 109 data/sources/arxiv-2411.01067v2.txt\n 132 data/sources/arxiv-2507.09089v2.txt\n 162 data/sources/arxiv-2511.13860v1.txt\n 640 total","is_error":false}]}}35{"type":"api-request","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","id":"5082671d-3e22-4e64-9c1a-319631b0d025","timestamp":"2026-10-30T05:34:20.903Z","version":"2.1.314","querySource":"sdk","shapeHash":"80e183abfbc7335290bfa20318d598a10dbca7f952739a0080b8d8663232c751","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"abd34174-1dd5-4f08-aa8c-3ce645fa8d4c","keep":6,"tail":["8f3fad256c2ae0b2ae73be14afbf46740866b13b5e26cf3c20b46b7a0034bdeb","d60116dbfcd521628bf2215fd0b3d583eee3b3743fdd797186d707a861a04dab"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",1],"value":{"type":"ephemeral"}}],"requestDigest":"426bb1c1e378019b5add664c7193f2855ab998f0f4cb83717a460d72c63b3d33"}36{"type":"last-prompt","lastPrompt":"Review","leafUuid":"9585e9e5-5506-421a-8133-4f4561f9db29","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1"}37{"type":"atis-latch","atis":"","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1"}38{"parentUuid":"9585e9e5-5506-421a-8133-4f4561f9db29","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsqU4JBohgRGLhbbXE6","type":"message","role":"assistant","content":[{"type":"text","text":"The copy failed (sandbox?); retry."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":9946,"cache_read_input_tokens":26159,"output_tokens":264,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":9946},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsqTmgiMzCsMtDknBoE","requestRef":"5082671d-3e22-4e64-9c1a-319631b0d025","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c5a89627-805b-4163-a49d-c4f149dc6646","timestamp":"2026-10-30T05:34:22.477Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}39{"parentUuid":"c5a89627-805b-4163-a49d-c4f149dc6646","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsqU4JBohgRGLhbbXE6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GxGw72GhH8hWPgwL7H5yti","name":"Bash","input":{"command":"D=$(mktemp -d); cp -R data \"$D/\"; mkdir \"$D/out\"; cp out/analysis.py \"$D/out/\"; cd \"$D\" && ~/wp29/venv/bin/python out/analysis.py | tail -8; cmp out/results.json ~/wp29/runs/RT0008/out/results.json && echo SAMEJSON; cmp out/table1.csv ~/wp29/runs/RT0008/out/table1.csv && echo SAMECSV","description":"Re-run analysis in temp dir and compare outputs"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":9946,"cache_read_input_tokens":26159,"output_tokens":264,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":9946},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsqTmgiMzCsMtDknBoE","requestRef":"5082671d-3e22-4e64-9c1a-319631b0d025","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a416bb57-d4ba-4b6e-93f0-2b2e677a45d8","timestamp":"2026-10-30T05:34:24.307Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"a416bb57-d4ba-4b6e-93f0-2b2e677a45d8","isSidechain":false,"promptId":"01f9be6f-d015-4eb3-b8dc-9dd1d87b03b8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GxGw72GhH8hWPgwL7H5yti","type":"tool_result","content":"[arxiv-2304.09339v1 L88] N | 18332 | 18332 | 18332 | 18332 | 14496 | 14496 | 14496 | 14496\n== Results ==\nS1 -0.24 0.14 poolable\nS2 0.17395330712343798 None not pooled\nS3 -0.353679434958934 None not pooled\nS4 -0.11765804346823246 0.1632205919720678 poolable\nS5 None None not pooled\nFE -0.1881 (SE 0.1063); RE -0.1881 [-0.3964, 0.0201]; Q 0.3237; I2 0.00; tau2 0.0000; pct -17.15%\nSAMEJSON\nSAMECSV\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RT0008","is_error":false}]},"uuid":"ae38b3c4-8e99-4f7e-875f-8000b76297c3","timestamp":"2026-10-30T05:34:24.863Z","toolUseResult":{"stdout":"[arxiv-2304.09339v1 L88] N | 18332 | 18332 | 18332 | 18332 | 14496 | 14496 | 14496 | 14496\n== Results ==\nS1 -0.24 0.14 poolable\nS2 0.17395330712343798 None not pooled\nS3 -0.353679434958934 None not pooled\nS4 -0.11765804346823246 0.1632205919720678 poolable\nS5 None None not pooled\nFE -0.1881 (SE 0.1063); RE -0.1881 [-0.3964, 0.0201]; Q 0.3237; I2 0.00; tau2 0.0000; pct -17.15%\nSAMEJSON\nSAMECSV","stderr":"\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RT0008","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a416bb57-d4ba-4b6e-93f0-2b2e677a45d8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}41{"type":"api-request-blob","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","hash":"afdaa90ed90bf56de3337203e1b4d68a1983952f18fa2f3ce9e0a693340509f1","message":{"role":"assistant","content":[{"type":"text","text":"The copy failed (sandbox?); retry."},{"type":"tool_use","id":"toolu_01GxGw72GhH8hWPgwL7H5yti","name":"Bash","input":{"command":"D=$(mktemp -d); cp -R data \"$D/\"; mkdir \"$D/out\"; cp out/analysis.py \"$D/out/\"; cd \"$D\" && ~/wp29/venv/bin/python out/analysis.py | tail -8; cmp out/results.json ~/wp29/runs/RT0008/out/results.json && echo SAMEJSON; cmp out/table1.csv ~/wp29/runs/RT0008/out/table1.csv && echo SAMECSV","description":"Re-run analysis in temp dir and compare outputs"}}]}}42{"type":"api-request","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","id":"4d713f64-48f3-45ad-9dc3-9cb28c540a52","timestamp":"2026-10-30T05:34:24.866Z","version":"2.1.314","querySource":"sdk","shapeHash":"80e183abfbc7335290bfa20318d598a10dbca7f952739a0080b8d8663232c751","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"5082671d-3e22-4e64-9c1a-319631b0d025","keep":8,"tail":["afdaa90ed90bf56de3337203e1b4d68a1983952f18fa2f3ce9e0a693340509f1","d3a901796752b82eca49b3efea96279491c21135a4feb05e2bcf38c8a2dc09dc"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"eded829493fb99c095f1a08658de09c39d0d8881f5b77e66a81bc349821e8aa2"}43{"parentUuid":"ae38b3c4-8e99-4f7e-875f-8000b76297c3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsqm12J8ZzttYyk7UVT","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":542,"cache_read_input_tokens":36105,"output_tokens":305,"output_tokens_details":{"thinking_tokens":23},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":542},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":373,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsqkhAfkKEkNYFvNyey","requestRef":"4d713f64-48f3-45ad-9dc3-9cb28c540a52","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e76d59a8-0eb6-4e2b-b835-51c324c54dde","timestamp":"2026-10-30T05:34:26.874Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}44{"parentUuid":"e76d59a8-0eb6-4e2b-b835-51c324c54dde","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsqm12J8ZzttYyk7UVT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Uw6RcdJJTAKoXLWh6DRb4D","name":"Bash","input":{"command":"cd data/sources; sed -n 70,82p arxiv-2410.12944v3.txt; echo ----; grep -n \"All Content\\|181\\|\\*\\*\\*\\|p-value\\|bootstrap\\|standard error\" arxiv-2411.01067v2.txt | head -20; echo ----; grep -n \"Time per Email\\|Time Savings\\|p-value\\|N{=}\\|Full Corpus\\|seconds\\|minutes\" arxiv-2511.13860v1.txt | head -30; echo ---; sed -n 75,95p arxiv-2304.09339v1.txt","description":"Inspect S1 table, S3, S4, S5 sources"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":542,"cache_read_input_tokens":36105,"output_tokens":305,"output_tokens_details":{"thinking_tokens":23},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":542},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsqkhAfkKEkNYFvNyey","requestRef":"4d713f64-48f3-45ad-9dc3-9cb28c540a52","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f6b17f8e-1239-4d63-bfd4-b1c286900562","timestamp":"2026-10-30T05:34:28.685Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}45{"parentUuid":"f6b17f8e-1239-4d63-bfd4-b1c286900562","isSidechain":false,"promptId":"01f9be6f-d015-4eb3-b8dc-9dd1d87b03b8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Uw6RcdJJTAKoXLWh6DRb4D","type":"tool_result","content":"(1) (2) (3) (4) | | | |\n\n5.36*** 5.41*** 4.55*** 4.66*** Constant | | | |\n\n(0.42) (0.37) (0.19) (0.11) | | | |\n\n-0.25. -0.24. -0.30* -0.14 ExpCon | | | |\n\n(0.14) (0.14) (0.14) (0.17) | | | |\n\n-0.38* -0.38* -0.16 AvgProgHrsDay | | | |\n\n(0.15) (0.15) (0.22) | | | |\n----\n9:Rendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n10:Editorial note: the paper marks significance with asterisks only (defined in the methodology as * 0.05<p<=0.1, ** 0.01<p<=0.05, *** 0<p<=0.01); no standard errors or confidence intervals are printed for the time-savings estimates.\n38:We had 181 subjects complete the task satisfactorily.^3^3\n45:The results answer RQ1 in the affirmative: Copilot does offer speed and accuracy improvements in common IT scenarios. Across all tasks, we measure a 34.53%*** increase in accuracy and a 29.79%*** reduction in task completion time. In the subsections on accuracy and speed below, we present detailed results answering RQ1-RQ4.\n49:We measure statistically significant time savings in all scenarios. Table 3 summarizes these results by task type and scenario. The “Aggregate Results, All Content (Holding accuracy constant)” numbers represent our headline speed improvements, a 29.79%*** time savings for Copilot users. We also note that Copilot generally involves greater latency than typical page-loading in the admin centers, which is common among GAI tools. This necessarily slowed the Copilot users. Product improvements should reduce this latency and further increase the time savings for users with Copilot.\n55:All Content | 53.84 | 44.29 | 63.10 | -29.79%***\n57:SATA | 17.81 | 15.25 | 20.14 | -24.28%***\n59:MC | 10.91 | 12.02 | 9.60 | 25.13%***\n63:Free Response | 30.98 | 17.03 | 43.82 | -61.14%***\n67:All Content | 24.04 | 16.63 | 30.46 | -45.41%***\n69:SATA | 6.41 | 5.04 | 7.63 | -33.98%***\n71:Free Response Content | 17.37 | 11.59 | 22.40 | -48.23%***\n75:All Content | 20.00 | 17.45 | 22.23 | -21.64%***\n77:MC | 10.91 | 12.02 | 9.60 | 25.13%***\n81:Free Response Content | 12.17 | 5.43 | 19.27 | -71.80%***\n85:SATA | 11.24 | 10.21 | 12.11 | -15.69%***\n89:We measure time savings for Copilot across all three scenarios. The time savings included 45.41%*** for sign-in troubleshooting, 21.64%*** for device policy management, and 15.69%*** for device troubleshooting.\n93:We find that Copilot’s time savings track its accuracy improvements across task types. That is, the biggest gains again come from free response (61.14%*** time savings), followed by SATA (24.28%***). Lastly, the MC questions show a decrease in speed for Copilot users (25.13%*** time increase). For these MC questions we also estimate a negative relationship between accuracy and time spent for the control group. This is likely because control subjects who knew the answer responded quickly, and those who did not know it gained little accuracy by spending more time by, for example, searching the internet. This can explain the data we see, where even though Copilot provided an advantage in responding accurately, controlling for accuracy in calculating its time savings does not make up for the disadvantage of its latency.\n99:As we did with accuracy, we examine time savings across different experience levels and domains. Our findings once again reveal statistically significant point estimates of time savings for all groups, as shown in figure 3. However, we do not observe statistically significant differences in estimated effect sizes between any of the groups. The left panel illustrates Copilot’s impact on task completion times for the sign-in troubleshooting task, with a 50.1%*** and 45.2%*** time savings for Copilot users with 0-3 and 3+ years of experience, respectively. These point estimates are consistent with intuition and our accuracy findings.\n101:The right panel of figure 3 shows time savings of 12.6%*** and 30.2%*** for 0-3 and 3+ years of experience, respectively, for device management tasks. This suggests that experienced network management subjects benefit more from Copilot in terms of time savings than their less experienced counterparts, which runs counter to intuition and expectation. This is driven by the fact that less experienced control subjects are driving the negative relationship between task completion time and accuracy for the device policy management MC question, mentioned above. This shrinks the possible time savings for the less experienced Copilot subjects.\n----\n31:Control ( N{=}52 ): Random queue order, no references to AI.\n35:Aware ( N{=}56 ): Queue ordered by the agent’s verdicts; participants see the agent’s decision, a brief rationale, and an accuracy calibration.\n39:Blind ( N{=}59 ): Identical ordering as Aware but without any AI references; neither verdicts nor rationales are revealed.\n88:Metric | Malicious | Non-Malicious | Full Corpus\n90:Aware Time per Email (s) | 2.8 | 1.8 | 50.5\n92:Control Time per Email (s) | 1.9 | 2.4 | 56.9\n94:% Time Savings | -52.9 | 23.6 | 11.1\n96:p-value | 0.006 | 0.126 | 0.471\n116:\\bar{T}_{c,m} is the mean time (in seconds) for control group to process malicious emails\n120:\\bar{T}_{c,b} is the mean time (in seconds) for control group to process benign emails\n---\n\n| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)\n\n| A: Pre 27/28.03 - Post 03/04.04 | | | | | | |\n\n\\times TreatPost | 0.115 | -0.012** | -0.278 | -0.227 | 0.005 | -0.003 | 0.188 | 0.192\n\n| (0.109) | (0.005) | (0.295) | (0.207) | (0.120) | (0.005) | (0.221) | (0.189)\n\nPost | 0.052 | 0.002 | 0.453*** | 0.323*** | -0.013 | 0.002 | 0.040 | 0.051\n\n| (0.053) | (0.002) | (0.123) | (0.094) | (0.060) | (0.002) | (0.119) | (0.101)\n\nN | 18332 | 18332 | 18332 | 18332 | 14496 | 14496 | 14496 | 14496\n\n| B: Pre 27/28/29.03 - Post 05/06/11.04 | | | | | | |\n\n\\times TreatPost | 0.048 | -0.003 | 0.135 | 0.073 | 0.003 | 0.000 | 0.067 | 0.058\n\n| (0.090) | (0.004) | (0.223) | (0.163) | (0.101) | (0.004) | (0.175) | (0.142)","is_error":false}]},"uuid":"06d0e4e5-1aaa-4727-8bf5-127ced541c34","timestamp":"2026-10-30T05:34:28.744Z","toolUseResult":{"stdout":"(1) (2) (3) (4) | | | |\n\n5.36*** 5.41*** 4.55*** 4.66*** Constant | | | |\n\n(0.42) (0.37) (0.19) (0.11) | | | |\n\n-0.25. -0.24. -0.30* -0.14 ExpCon | | | |\n\n(0.14) (0.14) (0.14) (0.17) | | | |\n\n-0.38* -0.38* -0.16 AvgProgHrsDay | | | |\n\n(0.15) (0.15) (0.22) | | | |\n----\n9:Rendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. Figures are not reproduced: a value that appears only in a figure is not in this extract. No numbers were altered.\n10:Editorial note: the paper marks significance with asterisks only (defined in the methodology as * 0.05<p<=0.1, ** 0.01<p<=0.05, *** 0<p<=0.01); no standard errors or confidence intervals are printed for the time-savings estimates.\n38:We had 181 subjects complete the task satisfactorily.^3^3\n45:The results answer RQ1 in the affirmative: Copilot does offer speed and accuracy improvements in common IT scenarios. Across all tasks, we measure a 34.53%*** increase in accuracy and a 29.79%*** reduction in task completion time. In the subsections on accuracy and speed below, we present detailed results answering RQ1-RQ4.\n49:We measure statistically significant time savings in all scenarios. Table 3 summarizes these results by task type and scenario. The “Aggregate Results, All Content (Holding accuracy constant)” numbers represent our headline speed improvements, a 29.79%*** time savings for Copilot users. We also note that Copilot generally involves greater latency than typical page-loading in the admin centers, which is common among GAI tools. This necessarily slowed the Copilot users. Product improvements should reduce this latency and further increase the time savings for users with Copilot.\n55:All Content | 53.84 | 44.29 | 63.10 | -29.79%***\n57:SATA | 17.81 | 15.25 | 20.14 | -24.28%***\n59:MC | 10.91 | 12.02 | 9.60 | 25.13%***\n63:Free Response | 30.98 | 17.03 | 43.82 | -61.14%***\n67:All Content | 24.04 | 16.63 | 30.46 | -45.41%***\n69:SATA | 6.41 | 5.04 | 7.63 | -33.98%***\n71:Free Response Content | 17.37 | 11.59 | 22.40 | -48.23%***\n75:All Content | 20.00 | 17.45 | 22.23 | -21.64%***\n77:MC | 10.91 | 12.02 | 9.60 | 25.13%***\n81:Free Response Content | 12.17 | 5.43 | 19.27 | -71.80%***\n85:SATA | 11.24 | 10.21 | 12.11 | -15.69%***\n89:We measure time savings for Copilot across all three scenarios. The time savings included 45.41%*** for sign-in troubleshooting, 21.64%*** for device policy management, and 15.69%*** for device troubleshooting.\n93:We find that Copilot’s time savings track its accuracy improvements across task types. That is, the biggest gains again come from free response (61.14%*** time savings), followed by SATA (24.28%***). Lastly, the MC questions show a decrease in speed for Copilot users (25.13%*** time increase). For these MC questions we also estimate a negative relationship between accuracy and time spent for the control group. This is likely because control subjects who knew the answer responded quickly, and those who did not know it gained little accuracy by spending more time by, for example, searching the internet. This can explain the data we see, where even though Copilot provided an advantage in responding accurately, controlling for accuracy in calculating its time savings does not make up for the disadvantage of its latency.\n99:As we did with accuracy, we examine time savings across different experience levels and domains. Our findings once again reveal statistically significant point estimates of time savings for all groups, as shown in figure 3. However, we do not observe statistically significant differences in estimated effect sizes between any of the groups. The left panel illustrates Copilot’s impact on task completion times for the sign-in troubleshooting task, with a 50.1%*** and 45.2%*** time savings for Copilot users with 0-3 and 3+ years of experience, respectively. These point estimates are consistent with intuition and our accuracy findings.\n101:The right panel of figure 3 shows time savings of 12.6%*** and 30.2%*** for 0-3 and 3+ years of experience, respectively, for device management tasks. This suggests that experienced network management subjects benefit more from Copilot in terms of time savings than their less experienced counterparts, which runs counter to intuition and expectation. This is driven by the fact that less experienced control subjects are driving the negative relationship between task completion time and accuracy for the device policy management MC question, mentioned above. This shrinks the possible time savings for the less experienced Copilot subjects.\n----\n31:Control ( N{=}52 ): Random queue order, no references to AI.\n35:Aware ( N{=}56 ): Queue ordered by the agent’s verdicts; participants see the agent’s decision, a brief rationale, and an accuracy calibration.\n39:Blind ( N{=}59 ): Identical ordering as Aware but without any AI references; neither verdicts nor rationales are revealed.\n88:Metric | Malicious | Non-Malicious | Full Corpus\n90:Aware Time per Email (s) | 2.8 | 1.8 | 50.5\n92:Control Time per Email (s) | 1.9 | 2.4 | 56.9\n94:% Time Savings | -52.9 | 23.6 | 11.1\n96:p-value | 0.006 | 0.126 | 0.471\n116:\\bar{T}_{c,m} is the mean time (in seconds) for control group to process malicious emails\n120:\\bar{T}_{c,b} is the mean time (in seconds) for control group to process benign emails\n---\n\n| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)\n\n| A: Pre 27/28.03 - Post 03/04.04 | | | | | | |\n\n\\times TreatPost | 0.115 | -0.012** | -0.278 | -0.227 | 0.005 | -0.003 | 0.188 | 0.192\n\n| (0.109) | (0.005) | (0.295) | (0.207) | (0.120) | (0.005) | (0.221) | (0.189)\n\nPost | 0.052 | 0.002 | 0.453*** | 0.323*** | -0.013 | 0.002 | 0.040 | 0.051\n\n| (0.053) | (0.002) | (0.123) | (0.094) | (0.060) | (0.002) | (0.119) | (0.101)\n\nN | 18332 | 18332 | 18332 | 18332 | 14496 | 14496 | 14496 | 14496\n\n| B: Pre 27/28/29.03 - Post 05/06/11.04 | | | | | | |\n\n\\times TreatPost | 0.048 | -0.003 | 0.135 | 0.073 | 0.003 | 0.000 | 0.067 | 0.058\n\n| (0.090) | (0.004) | (0.223) | (0.163) | (0.101) | (0.004) | (0.175) | (0.142)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f6b17f8e-1239-4d63-bfd4-b1c286900562","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008/data/sources","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}46{"type":"api-request-blob","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","hash":"8cab948de3c88511f41eb721e9b32658d6256ec6c34a5c121a90ba651e94934e","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01Uw6RcdJJTAKoXLWh6DRb4D","name":"Bash","input":{"command":"cd data/sources; sed -n 70,82p arxiv-2410.12944v3.txt; echo ----; grep -n \"All Content\\|181\\|\\*\\*\\*\\|p-value\\|bootstrap\\|standard error\" arxiv-2411.01067v2.txt | head -20; echo ----; grep -n \"Time per Email\\|Time Savings\\|p-value\\|N{=}\\|Full Corpus\\|seconds\\|minutes\" arxiv-2511.13860v1.txt | head -30; echo ---; sed -n 75,95p arxiv-2304.09339v1.txt","description":"Inspect S1 table, S3, S4, S5 sources"}}]}}47{"type":"api-request","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","id":"71d43c30-fac0-49f0-a49f-26e0a518bb28","timestamp":"2026-10-30T05:34:28.747Z","version":"2.1.314","querySource":"sdk","shapeHash":"80e183abfbc7335290bfa20318d598a10dbca7f952739a0080b8d8663232c751","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"4d713f64-48f3-45ad-9dc3-9cb28c540a52","keep":10,"tail":["8cab948de3c88511f41eb721e9b32658d6256ec6c34a5c121a90ba651e94934e","670ce7aa25bdb927d308cd665b97d54e17f4653825a37af58675ccdc06f89ded"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"fa105532d2cefa0e313e7e175dbf9140b40dad2cffea7988252e920edd4c2319"}48{"parentUuid":"06d0e4e5-1aaa-4727-8bf5-127ced541c34","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsr3ZgJk21EvqoS4SdM","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2915,"cache_read_input_tokens":36647,"output_tokens":384,"output_tokens_details":{"thinking_tokens":275},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2915},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2599,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsr3JKAteyvYZZ65aPP","requestRef":"71d43c30-fac0-49f0-a49f-26e0a518bb28","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ef71e55a-ba23-463a-9d91-1fbb7e07186b","timestamp":"2026-10-30T05:34:32.769Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008/data/sources","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}49{"parentUuid":"ef71e55a-ba23-463a-9d91-1fbb7e07186b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsr3ZgJk21EvqoS4SdM","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01N3BYGUVpn2df1EpvQKwnwS","name":"Bash","input":{"command":"sed -n 80,162p data/sources/arxiv-2511.13860v1.txt","description":"Read S4 table 3 and appendix"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2915,"cache_read_input_tokens":36647,"output_tokens":384,"output_tokens_details":{"thinking_tokens":275},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2915},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsr3JKAteyvYZZ65aPP","requestRef":"71d43c30-fac0-49f0-a49f-26e0a518bb28","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a482135f-645e-4445-a4af-c3a584edf59e","timestamp":"2026-10-30T05:34:33.447Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008/data/sources","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}50{"parentUuid":"a482135f-645e-4445-a4af-c3a584edf59e","isSidechain":false,"promptId":"01f9be6f-d015-4eb3-b8dc-9dd1d87b03b8","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nsed: data/sources/arxiv-2511.13860v1.txt: No such file or directory","is_error":true,"tool_use_id":"toolu_01N3BYGUVpn2df1EpvQKwnwS"}]},"uuid":"50c2b18b-9d97-47f3-9451-0c369edd159e","timestamp":"2026-10-30T05:34:33.484Z","toolUseResult":"Error: Exit code 1\nsed: data/sources/arxiv-2511.13860v1.txt: No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"a482135f-645e-4445-a4af-c3a584edf59e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008/data/sources","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}51{"type":"api-request-blob","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","hash":"096d176c88c2a193d37e2d04251130b41c393f8899aa1d42cea6fe08b607aaf0","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01N3BYGUVpn2df1EpvQKwnwS","name":"Bash","input":{"command":"sed -n 80,162p data/sources/arxiv-2511.13860v1.txt","description":"Read S4 table 3 and appendix"}}]}}52{"type":"api-request","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","id":"29258c76-d296-4798-a378-bd91d62155ab","timestamp":"2026-10-30T05:34:33.489Z","version":"2.1.314","querySource":"sdk","shapeHash":"80e183abfbc7335290bfa20318d598a10dbca7f952739a0080b8d8663232c751","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"71d43c30-fac0-49f0-a49f-26e0a518bb28","keep":12,"tail":["096d176c88c2a193d37e2d04251130b41c393f8899aa1d42cea6fe08b607aaf0","56b4f7d0d9193a143682e571ee3dd2c94de1109dd5e3f737f27e065cfcf911a1"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"b79fe0654e3128d90482d3d99c2fafc02c1b24692ebed95fc126e067431f3836"}53{"type":"last-prompt","lastPrompt":"Review","leafUuid":"50c2b18b-9d97-47f3-9451-0c369edd159e","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1"}54{"type":"atis-latch","atis":"","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1"}55{"parentUuid":"50c2b18b-9d97-47f3-9451-0c369edd159e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsrPmcaubtoFrXk1RbE","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NvKMW5QEZbAz6oKcBYVHuh","name":"Bash","input":{"command":"cd ~/wp29/runs/RT0008; sed -n 80,162p data/sources/arxiv-2511.13860v1.txt","description":"Read S4 table 3 and appendix"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":432,"cache_read_input_tokens":39562,"output_tokens":125,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":432},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsrPahAetGHwdbQ4NWp","requestRef":"29258c76-d296-4798-a378-bd91d62155ab","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b2344604-d78e-4ad8-98da-0263d9687f8f","timestamp":"2026-10-30T05:34:35.232Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008/data/sources","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}56{"parentUuid":"b2344604-d78e-4ad8-98da-0263d9687f8f","isSidechain":false,"promptId":"01f9be6f-d015-4eb3-b8dc-9dd1d87b03b8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NvKMW5QEZbAz6oKcBYVHuh","type":"tool_result","content":"Pessimistic counterfactual | 3.07 | 83 | 17\n\n### 3.3 Behavior: Effect of Agent on Analyst Behavior\n\nNext, we look at subject behavior across the entire queue, not just the agent-prioritized portion, i.e., this is how analysts would perform if we removed the resolve-benign protocol. First, we find that the Aware group spends 53% more time per malicious email. They spend marginally less on non-malicious email, and the overall time is roughly the same. This indicates they are utilizing the agent to help optimize their time and attention.\n\nTable 3: Comparison of Email Processing Times\n\nMetric | Malicious | Non-Malicious | Full Corpus\n\nAware Time per Email (s) | 2.8 | 1.8 | 50.5\n\nControl Time per Email (s) | 1.9 | 2.4 | 56.9\n\n% Time Savings | -52.9 | 23.6 | 11.1\n\np-value | 0.006 | 0.126 | 0.471\n\nWe also find that the Aware group is no more likely to confirm the agent’s TPs or miss the agent’s SFPs, which are precisely the samples the Aware group would see under the resolve-benign protocol. Taken together with the evidence that Aware subjects spend more time on malicious emails, this means they are not rubber-stamping the agent’s malicious verdicts. Rather, they use the agent’s insights to guide their own investigation.\n\nHowever, we do find they are more likely to confirm agent TNs by 21 percentage points and more likely to miss agent SFNs by 29 percentage points. This lends further support to the use of the resolve-benign protocol. In addition to the productivity gains documented above, the protection benefit of having analysts spend time on emails the agent has already classified as non-malicious is minimal because analysts are likely to agree with the agent anyway.\n\n## Appendix B TP per Analyst Minute\n\nLet p denote the malicious email rate in the corpus. We define the following parameters and variables:\n\n### B.1 Control Group Performance\n\nFor the control group, the expected time to process an email is:\n\n\\mathbb{E}[T_{c}]=p\\cdot\\bar{T}_{c,m}+(1-p)\\cdot\\bar{T}_{c,b} | |\n\nwhere:\n\n•\n\n\\bar{T}_{c,m} is the mean time (in seconds) for control group to process malicious emails\n\n•\n\n\\bar{T}_{c,b} is the mean time (in seconds) for control group to process benign emails\n\nThe true positive rate for the control group is defined as:\n\n\\text{TPR}_{c}=\\frac{\\text{TP}_{c}}{n_{m}} | |\n\nwhere \\text{TP}_{c} is the number of true positives identified by control subjects and n_{m} is the total number of malicious emails in the sample.\n\nThe productivity metric for the control group (true positives per minute) is:\n\n\\rho_{c}=\\frac{p\\cdot\\text{TPR}_{c}}{\\mathbb{E}[T_{c}]} | |\n\n### B.2 Aware Group Performance (Priority Queue)\n\nFor the Aware group processing a priority queue filtered by an agent with precision \\pi and recall \\sigma , the queue composition changes. The priority queue contains only items flagged by the agent as malicious. The expected time to process an item in the priority queue is:\n\n\\mathbb{E}[T_{a}]=\\pi\\cdot\\bar{T}_{a,\\text{TP}}+(1-\\pi)\\cdot\\bar{T}_{a,\\text{FP}} | |\n\nwhere:\n\n•\n\n\\bar{T}_{a,\\text{TP}} is the mean time for Aware group to process agent-identified true positives\n\n•\n\n\\bar{T}_{a,\\text{FP}} is the mean time for Aware group to process agent-identified false positives\n\nThe accuracy of the Aware group on agent-identified true positives is:\n\n\\alpha_{a,\\text{TP}}=\\mathbb{E}[\\mathbbm{1}_{\\text{correct}}|\\text{Agent}=\\text{TP}] | |\n\nThe productivity metric for the Aware group (true positives per minute) is:\n\n\\rho_{a}=\\frac{\\pi\\cdot\\alpha_{a,\\text{TP}}}{\\mathbb{E}[T_{a}]} | |\n\n### B.3 Productivity Factor\n\nThe productivity factor, representing the multiplicative improvement of the Aware group over the control group, is:\n\n\\phi^{(p)}=\\frac{\\rho_{a}}{\\rho_{c}} | |\n\nA value of \\phi^{(p)}>1 indicates improved productivity, with \\phi^{(p)}=2 representing a doubling of productivity (twice as many true positives identified per unit time).","is_error":false}]},"uuid":"9ad8a260-5e34-48e9-a97d-11b3551ab84c","timestamp":"2026-10-30T05:34:35.277Z","toolUseResult":{"stdout":"Pessimistic counterfactual | 3.07 | 83 | 17\n\n### 3.3 Behavior: Effect of Agent on Analyst Behavior\n\nNext, we look at subject behavior across the entire queue, not just the agent-prioritized portion, i.e., this is how analysts would perform if we removed the resolve-benign protocol. First, we find that the Aware group spends 53% more time per malicious email. They spend marginally less on non-malicious email, and the overall time is roughly the same. This indicates they are utilizing the agent to help optimize their time and attention.\n\nTable 3: Comparison of Email Processing Times\n\nMetric | Malicious | Non-Malicious | Full Corpus\n\nAware Time per Email (s) | 2.8 | 1.8 | 50.5\n\nControl Time per Email (s) | 1.9 | 2.4 | 56.9\n\n% Time Savings | -52.9 | 23.6 | 11.1\n\np-value | 0.006 | 0.126 | 0.471\n\nWe also find that the Aware group is no more likely to confirm the agent’s TPs or miss the agent’s SFPs, which are precisely the samples the Aware group would see under the resolve-benign protocol. Taken together with the evidence that Aware subjects spend more time on malicious emails, this means they are not rubber-stamping the agent’s malicious verdicts. Rather, they use the agent’s insights to guide their own investigation.\n\nHowever, we do find they are more likely to confirm agent TNs by 21 percentage points and more likely to miss agent SFNs by 29 percentage points. This lends further support to the use of the resolve-benign protocol. In addition to the productivity gains documented above, the protection benefit of having analysts spend time on emails the agent has already classified as non-malicious is minimal because analysts are likely to agree with the agent anyway.\n\n## Appendix B TP per Analyst Minute\n\nLet p denote the malicious email rate in the corpus. We define the following parameters and variables:\n\n### B.1 Control Group Performance\n\nFor the control group, the expected time to process an email is:\n\n\\mathbb{E}[T_{c}]=p\\cdot\\bar{T}_{c,m}+(1-p)\\cdot\\bar{T}_{c,b} | |\n\nwhere:\n\n•\n\n\\bar{T}_{c,m} is the mean time (in seconds) for control group to process malicious emails\n\n•\n\n\\bar{T}_{c,b} is the mean time (in seconds) for control group to process benign emails\n\nThe true positive rate for the control group is defined as:\n\n\\text{TPR}_{c}=\\frac{\\text{TP}_{c}}{n_{m}} | |\n\nwhere \\text{TP}_{c} is the number of true positives identified by control subjects and n_{m} is the total number of malicious emails in the sample.\n\nThe productivity metric for the control group (true positives per minute) is:\n\n\\rho_{c}=\\frac{p\\cdot\\text{TPR}_{c}}{\\mathbb{E}[T_{c}]} | |\n\n### B.2 Aware Group Performance (Priority Queue)\n\nFor the Aware group processing a priority queue filtered by an agent with precision \\pi and recall \\sigma , the queue composition changes. The priority queue contains only items flagged by the agent as malicious. The expected time to process an item in the priority queue is:\n\n\\mathbb{E}[T_{a}]=\\pi\\cdot\\bar{T}_{a,\\text{TP}}+(1-\\pi)\\cdot\\bar{T}_{a,\\text{FP}} | |\n\nwhere:\n\n•\n\n\\bar{T}_{a,\\text{TP}} is the mean time for Aware group to process agent-identified true positives\n\n•\n\n\\bar{T}_{a,\\text{FP}} is the mean time for Aware group to process agent-identified false positives\n\nThe accuracy of the Aware group on agent-identified true positives is:\n\n\\alpha_{a,\\text{TP}}=\\mathbb{E}[\\mathbbm{1}_{\\text{correct}}|\\text{Agent}=\\text{TP}] | |\n\nThe productivity metric for the Aware group (true positives per minute) is:\n\n\\rho_{a}=\\frac{\\pi\\cdot\\alpha_{a,\\text{TP}}}{\\mathbb{E}[T_{a}]} | |\n\n### B.3 Productivity Factor\n\nThe productivity factor, representing the multiplicative improvement of the Aware group over the control group, is:\n\n\\phi^{(p)}=\\frac{\\rho_{a}}{\\rho_{c}} | |\n\nA value of \\phi^{(p)}>1 indicates improved productivity, with \\phi^{(p)}=2 representing a doubling of productivity (twice as many true positives identified per unit time).","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b2344604-d78e-4ad8-98da-0263d9687f8f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}57{"type":"api-request-blob","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","hash":"c14b2846acb04847f15c1938217155b542981bc4e37e83260fe672f59fb6f75a","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01NvKMW5QEZbAz6oKcBYVHuh","name":"Bash","input":{"command":"cd ~/wp29/runs/RT0008; sed -n 80,162p data/sources/arxiv-2511.13860v1.txt","description":"Read S4 table 3 and appendix"}}]}}58{"type":"api-request","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","id":"aee470b0-1236-4166-b2ab-d2dc0fd40f28","timestamp":"2026-10-30T05:34:35.282Z","version":"2.1.314","querySource":"sdk","shapeHash":"80e183abfbc7335290bfa20318d598a10dbca7f952739a0080b8d8663232c751","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"29258c76-d296-4798-a378-bd91d62155ab","keep":14,"tail":["c14b2846acb04847f15c1938217155b542981bc4e37e83260fe672f59fb6f75a","87558e4613bbb1f82058787fadff247a3c52f2b00d64965e82626de13a84cc96"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"e60d10764ac84e0c65e428675ea3a87f001b51e3104166b98eac6563c5224795"}59{"type":"tools-used","tools":["Bash","Read"],"sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1"}60{"parentUuid":"9ad8a260-5e34-48e9-a97d-11b3551ab84c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsrXXCBuGjePcp1thwj","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1677,"cache_read_input_tokens":39994,"output_tokens":852,"output_tokens_details":{"thinking_tokens":771},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1677},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":7184,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsrXFZrcrRXADouzh8V","requestRef":"aee470b0-1236-4166-b2ab-d2dc0fd40f28","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"fac1fe8d-75a7-4946-a8be-4f0df15482b9","timestamp":"2026-10-30T05:34:43.826Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}61{"parentUuid":"fac1fe8d-75a7-4946-a8be-4f0df15482b9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsrXXCBuGjePcp1thwj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014SgAx6LZaiDusptvht65Ae","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/wp29/runs/RT0008/out/figure1.png"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1677,"cache_read_input_tokens":39994,"output_tokens":852,"output_tokens_details":{"thinking_tokens":771},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1677},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsrXFZrcrRXADouzh8V","requestRef":"aee470b0-1236-4166-b2ab-d2dc0fd40f28","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"37ea25fe-49e4-4a24-9625-5062d537fe73","timestamp":"2026-10-30T05:34:44.242Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}62{"parentUuid":"37ea25fe-49e4-4a24-9625-5062d537fe73","isSidechain":false,"promptId":"01f9be6f-d015-4eb3-b8dc-9dd1d87b03b8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014SgAx6LZaiDusptvht65Ae","type":"tool_result","content":[{"type":"image","source":{"type":"base64","data":"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","media_type":"image/png"}}]}]},"uuid":"c1d1ed22-2985-45f6-a6ef-cfee18953347","timestamp":"2026-10-30T05:34:44.275Z","toolUseResult":{"type":"image","file":{"base64":"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","type":"image/png","originalSize":65260,"dimensions":{"originalWidth":1350,"originalHeight":630,"displayWidth":1350,"displayHeight":630}}},"sourceToolAssistantUUID":"37ea25fe-49e4-4a24-9625-5062d537fe73","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}63{"type":"api-request-blob","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","hash":"1d12d78e04a218e73f893d2f18c046ab4e38d187e45950453ceb4ca50aae4ec3","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_014SgAx6LZaiDusptvht65Ae","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/wp29/runs/RT0008/out/figure1.png"}}]}}64{"type":"api-request","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","id":"931fe63f-40cd-4036-8b27-41f29c3a96f5","timestamp":"2026-10-30T05:34:44.282Z","version":"2.1.314","querySource":"sdk","shapeHash":"80e183abfbc7335290bfa20318d598a10dbca7f952739a0080b8d8663232c751","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"aee470b0-1236-4166-b2ab-d2dc0fd40f28","keep":16,"tail":["1d12d78e04a218e73f893d2f18c046ab4e38d187e45950453ceb4ca50aae4ec3","9238713ad6c964f5c38a018d10e7f142a3c388340983f6ceedb5ed600614e649"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"169a8553c56d50e99c06e917cadc3f7912bdfd9e236ac37c910a7d01cc63388c"}65{"type":"last-prompt","lastPrompt":"Review","leafUuid":"c1d1ed22-2985-45f6-a6ef-cfee18953347","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1"}66{"type":"atis-latch","atis":"","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1"}67{"parentUuid":"c1d1ed22-2985-45f6-a6ef-cfee18953347","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXssByjMN9L35fyKoaJ5","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1998,"cache_read_input_tokens":41671,"output_tokens":1751,"output_tokens_details":{"thinking_tokens":571},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1998},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":5257,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXssBiqkCmBkAM7YwWYe","requestRef":"931fe63f-40cd-4036-8b27-41f29c3a96f5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6831c663-f779-47b9-a1ed-f9eb7759e263","timestamp":"2026-10-30T05:35:03.713Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}68{"parentUuid":"6831c663-f779-47b9-a1ed-f9eb7759e263","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXssByjMN9L35fyKoaJ5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016XT87FUaJPDLGTpQVF5tpD","name":"Bash","input":{"command":"cd ~/wp29/runs/RT0008; cat > out/review/review.json <<'EOF'\n{\n \"verdict\": \"accept\",\n \"issues\": [\n {\"id\": \"R1\", \"severity\": \"minor\", \"location\": \"out/report.md (Data and exclusions); results.json data_issues S4\",\n \"issue\": \"The report says none of the input problems changes a harmonised value by more than about 0.0003. For S4, the printed Full Corpus means give ln(50.5/56.9) = -0.1193, while the printed 11.1% gives -0.1177, a difference of about 0.0017. Separately, the S4 Malicious and Non-Malicious % savings do not match their own printed times (2.8 vs 1.9 implies -47.4%, not -52.9%; 1.8 vs 2.4 implies 25.0%, not 23.6%). This mismatch is not listed. Neither column is used, so the pooled results are unaffected.\",\n \"suggested_fix\": \"Change the bound to about 0.002, or name S4 explicitly. Add the within-table % mismatch to the S4 data_issue.\"},\n {\"id\": \"R2\", \"severity\": \"minor\", \"location\": \"out/report.md (Limitations), items.S1.sample_as_printed\",\n \"issue\": \"The Limitations section calls S1 'one 96-developer coding task'. It does not repeat that the text reports 47 + 46 = 93; that mismatch appears only in data_issues.\",\n \"suggested_fix\": \"Write 'n = 96 per Table III (93 per Section VI-B)' or similar.\"},\n {\"id\": \"R3\", \"severity\": \"minor\", \"location\": \"items.S2.estimate_as_printed\",\n \"issue\": \"The source prints '19%' together with the word 'increases'. The '+' sign was added by the analyst, and the task asks for printed numbers to be given exactly as printed.\",\n \"suggested_fix\": \"Use '19% (increase in completion time)' and keep the sign only in the harmonised fields.\"}\n ],\n \"summary\": \"The deliverables are complete and reproducible. Re-running analysis.py in a scratch copy regenerated byte-identical results.json and table1.csv. All required keys are present. Every printed value matches the extracts: S1 Model 2 ExpCon -0.24 (0.14), CI [-0.51, 0.03], p = 0.086; S2 19%; S3 -29.79%***; S4 11.1% with p = 0.471; S5 -0.278 (0.295), N = 18332. The harmonisation choices follow the TASK conventions: S5 is not harmonisable (DiD on output counts); S2 (CI only in a figure) and S3 (stars only) are harmonisable but not poolable; S4 has an SE recovered from its p-value and is marked approximate. The code reads only data/ and uses no outside values. The FE/DL pooling, Q, I², τ², the percentage conversion and the significance flag are computed correctly. The report's conclusions are appropriately hedged: k = 2, a non-significant pooled effect, I² that tells us little, an approximate S4 SE, and excluded studies that point in opposite directions. Only minor wording and data-issue completeness points remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok","description":"Write review JSON and validate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1998,"cache_read_input_tokens":41671,"output_tokens":1751,"output_tokens_details":{"thinking_tokens":571},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1998},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXssBiqkCmBkAM7YwWYe","requestRef":"931fe63f-40cd-4036-8b27-41f29c3a96f5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d7c9c737-b235-4854-8f58-203d4d474b90","timestamp":"2026-10-30T05:35:13.505Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}69{"parentUuid":"d7c9c737-b235-4854-8f58-203d4d474b90","isSidechain":false,"promptId":"01f9be6f-d015-4eb3-b8dc-9dd1d87b03b8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016XT87FUaJPDLGTpQVF5tpD","type":"tool_result","content":"ok","is_error":false}]},"uuid":"b9ab2e69-1aa1-4f94-9de5-7145280b7dc8","timestamp":"2026-10-30T05:35:13.587Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"d7c9c737-b235-4854-8f58-203d4d474b90","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}70{"type":"api-request-blob","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","hash":"81978bf7790130215427e25f3fb7a72b971b1074b1a636d0d593e60309cef1bf","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_016XT87FUaJPDLGTpQVF5tpD","name":"Bash","input":{"command":"cd ~/wp29/runs/RT0008; cat > out/review/review.json <<'EOF'\n{\n \"verdict\": \"accept\",\n \"issues\": [\n {\"id\": \"R1\", \"severity\": \"minor\", \"location\": \"out/report.md (Data and exclusions); results.json data_issues S4\",\n \"issue\": \"The report says none of the input problems changes a harmonised value by more than about 0.0003. For S4, the printed Full Corpus means give ln(50.5/56.9) = -0.1193, while the printed 11.1% gives -0.1177, a difference of about 0.0017. Separately, the S4 Malicious and Non-Malicious % savings do not match their own printed times (2.8 vs 1.9 implies -47.4%, not -52.9%; 1.8 vs 2.4 implies 25.0%, not 23.6%). This mismatch is not listed. Neither column is used, so the pooled results are unaffected.\",\n \"suggested_fix\": \"Change the bound to about 0.002, or name S4 explicitly. Add the within-table % mismatch to the S4 data_issue.\"},\n {\"id\": \"R2\", \"severity\": \"minor\", \"location\": \"out/report.md (Limitations), items.S1.sample_as_printed\",\n \"issue\": \"The Limitations section calls S1 'one 96-developer coding task'. It does not repeat that the text reports 47 + 46 = 93; that mismatch appears only in data_issues.\",\n \"suggested_fix\": \"Write 'n = 96 per Table III (93 per Section VI-B)' or similar.\"},\n {\"id\": \"R3\", \"severity\": \"minor\", \"location\": \"items.S2.estimate_as_printed\",\n \"issue\": \"The source prints '19%' together with the word 'increases'. The '+' sign was added by the analyst, and the task asks for printed numbers to be given exactly as printed.\",\n \"suggested_fix\": \"Use '19% (increase in completion time)' and keep the sign only in the harmonised fields.\"}\n ],\n \"summary\": \"The deliverables are complete and reproducible. Re-running analysis.py in a scratch copy regenerated byte-identical results.json and table1.csv. All required keys are present. Every printed value matches the extracts: S1 Model 2 ExpCon -0.24 (0.14), CI [-0.51, 0.03], p = 0.086; S2 19%; S3 -29.79%***; S4 11.1% with p = 0.471; S5 -0.278 (0.295), N = 18332. The harmonisation choices follow the TASK conventions: S5 is not harmonisable (DiD on output counts); S2 (CI only in a figure) and S3 (stars only) are harmonisable but not poolable; S4 has an SE recovered from its p-value and is marked approximate. The code reads only data/ and uses no outside values. The FE/DL pooling, Q, I², τ², the percentage conversion and the significance flag are computed correctly. The report's conclusions are appropriately hedged: k = 2, a non-significant pooled effect, I² that tells us little, an approximate S4 SE, and excluded studies that point in opposite directions. Only minor wording and data-issue completeness points remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok","description":"Write review JSON and validate"}}]}}71{"type":"api-request","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","id":"04a10a13-9b1f-46d5-86d8-6b6f6515172c","timestamp":"2026-10-30T05:35:13.590Z","version":"2.1.314","querySource":"sdk","shapeHash":"80e183abfbc7335290bfa20318d598a10dbca7f952739a0080b8d8663232c751","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"931fe63f-40cd-4036-8b27-41f29c3a96f5","keep":18,"tail":["81978bf7790130215427e25f3fb7a72b971b1074b1a636d0d593e60309cef1bf","c80590c16d83645e15b0bf45449007dbf250a3aa675c5eacabbc5bf5491f30d6"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"b03356a516fcb2427b839eb5231708156c1c78fcde77d95c2a68bb76df2733c7"}72{"parentUuid":"b9ab2e69-1aa1-4f94-9de5-7145280b7dc8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsuMFdXRFLwu1t83z4p","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1758,"cache_read_input_tokens":43669,"output_tokens":812,"output_tokens_details":{"thinking_tokens":45},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1758},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":7038,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsuM3yjdDCf1pbzXTaW","requestRef":"04a10a13-9b1f-46d5-86d8-6b6f6515172c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a5f835d9-9a18-437e-a722-4792d334e9ff","timestamp":"2026-10-30T05:35:22.229Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}73{"parentUuid":"a5f835d9-9a18-437e-a722-4792d334e9ff","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsuMFdXRFLwu1t83z4p","type":"message","role":"assistant","content":[{"type":"text","text":"I recommend **accept**, with three minor issues; the review is in `out/review/review.json`.\n\nI re-ran `out/analysis.py` on a scratch copy of `data/`. It reproduced `results.json` and `table1.csv` byte for byte.\n\n- **Extraction:** every printed value matches the extracts:\n - S1: −0.24 (0.14), CI [−0.51, 0.03], p = 0.086\n - S2: 19%\n - S3: −29.79%***\n - S4: 11.1%, p = 0.471\n - S5: −0.278 (0.295), N = 18332\n- **Information rules:** the code reads only `data/` and brings in no figure-only or remembered values.\n- **Harmonisation:** the choices follow `TASK.md`:\n - S5 cannot be harmonised: it measures effects on GitHub output counts, not completion times.\n - S2 and S3 are harmonised but not pooled. S2's interval appears only in a figure the extract leaves out, and S3 reports significance stars only.\n - S4's standard error is back-calculated from its p-value and labelled approximate, as the rules allow.\n- **Pooling:** fixed-effect, random-effects, Q, I², τ², the percentage change and the significance flag are all computed correctly. The pooled estimate is θ = −0.188 (about 17% less time), 95% interval [−0.396, 0.020], so not significant at 5%.\n- **Report and claims:** the conclusions are suitably hedged. They note that only two studies are pooled, that I² = 0% means little with two studies, that S4's standard error is approximate, and that the two studies left out of the pool point in opposite directions.\n- **Minor issues:**\n - The report says no input problem changes a harmonised value by more than about 0.0003. For S4 the printed times imply a value about 0.0017 away from the printed percentage. Also, S4's two other columns (malicious and non-malicious emails) have percentages that don't match their own times, and this isn't flagged. Neither column is used, so the results are unaffected.\n - The limitations section describes S1 as a \"96-developer\" study without repeating that the paper's text gives 47 + 46 = 93.\n - S2's \"+19%\" adds a plus sign that isn't in the source, which prints \"19%\" with the word \"increases\"."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1758,"cache_read_input_tokens":43669,"output_tokens":812,"output_tokens_details":{"thinking_tokens":45},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1758},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsuM3yjdDCf1pbzXTaW","requestRef":"04a10a13-9b1f-46d5-86d8-6b6f6515172c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"49b6bd51-338b-48ab-b13e-0589a8f81294","timestamp":"2026-10-30T05:35:22.407Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0008","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","version":"2.1.314","gitBranch":"HEAD"}74{"type":"last-prompt","lastPrompt":"Review","leafUuid":"49b6bd51-338b-48ab-b13e-0589a8f81294","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1"}75{"type":"cost-state","sessionId":"435e011c-d55f-403f-a2c5-a264c852b4e1","totalCostUSD":0.38255340000000004,"totalAPIDuration":75164,"totalAPIDurationWithoutRetries":75162,"totalToolDuration":924,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":76172,"startTime":1793338446262,"modelUsage":{"claude-opus-5-5":{"inputTokens":22,"outputTokens":5348,"thinkingTokens":1702,"cacheReadInputTokens":279452,"cacheCreationInputTokens":43923,"cacheCreationEphemeral5mInputTokens":43923,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.38255340000000004}},"hasUnknownModelCost":false}76