spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:11:53.211Z","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:11:53.217Z","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9"}4{"parentUuid":null,"isSidechain":false,"promptId":"e8fae74d-e98b-49ca-9948-119badf8d264","type":"user","message":{"role":"user","content":"Begin"},"uuid":"b718773f-f0c4-4c7f-a94c-89b7b2605b2e","timestamp":"2026-10-30T05:11:53.221Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","shapeHash":"8c328839765cea6d2c705d451a30ae5d1a69967e2804e4719b5d5d46e0d9f057","timestamp":"2026-10-30T05:11:53.235Z","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/RC0245\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\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":"84a16cfe-c861-45d5-8c4b-958d93441cb9","hash":"d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9","message":{"role":"system","content":[],"output_config":{"effort":"medium"}}}7{"type":"api-request","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","id":"c8ba3eff-f4c1-4471-9e4b-302d21f13867","timestamp":"2026-10-30T05:11:53.235Z","version":"2.1.314","querySource":"sdk","shapeHash":"8c328839765cea6d2c705d451a30ae5d1a69967e2804e4719b5d5d46e0d9f057","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":["252097215a6af3e04abe3f3c5a1cce73f5f388825c554cef2907d381aaf4102e","d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",0,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"093e25a72a16067729c72902e87b6f2edc6e492c19204d6ad5c4a19a7a5da4fa"}8{"parentUuid":"b718773f-f0c4-4c7f-a94c-89b7b2605b2e","isSidechain":false,"attachment":{"type":"session_context","context":{}},"type":"attachment","uuid":"9797b09d-e7a9-4cea-8e7d-431a55d15058","timestamp":"2026-10-30T05:11:53.226Z","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}9{"parentUuid":"9797b09d-e7a9-4cea-8e7d-431a55d15058","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30"},"type":"attachment","uuid":"78f61138-abdd-4401-bc4d-9025a4999096","timestamp":"2026-10-30T05:11:53.226Z","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/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}10{"parentUuid":"78f61138-abdd-4401-bc4d-9025a4999096","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":"7048c420-6f5e-4cdf-b966-7e1b30a6d86a","timestamp":"2026-10-30T05:11:53.227Z","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/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9"}12{"parentUuid":"7048c420-6f5e-4cdf-b966-7e1b30a6d86a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr88W4ZDjTV8EFkPRfu","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":2811,"cache_read_input_tokens":1504,"output_tokens":134,"output_tokens_details":{"thinking_tokens":19},"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":2811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":556,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXr88ESZiaTuhuxUd8gc","requestRef":"c8ba3eff-f4c1-4471-9e4b-302d21f13867","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"af7ff923-30fc-4905-aea6-7caf57e0866c","timestamp":"2026-10-30T05:11:54.871Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"af7ff923-30fc-4905-aea6-7caf57e0866c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr88W4ZDjTV8EFkPRfu","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0195EtLHyQyWxEhSy6ArADPm","name":"Bash","input":{"command":"cat TASK.md; ls -R | head -50; ls out/state 2>/dev/null && cat out/state/log.md","description":"Read task and list workspace"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2811,"cache_read_input_tokens":1504,"output_tokens":134,"output_tokens_details":{"thinking_tokens":19},"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":2811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXr88ESZiaTuhuxUd8gc","requestRef":"c8ba3eff-f4c1-4471-9e4b-302d21f13867","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3758a88c-6b57-4dd6-995b-4ffc47771b1f","timestamp":"2026-10-30T05:11:55.378Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}14{"parentUuid":"3758a88c-6b57-4dd6-995b-4ffc47771b1f","isSidechain":false,"promptId":"e8fae74d-e98b-49ca-9948-119badf8d264","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\n# F5-conf-02 — Finite-sample bias of the OLS estimator of an AR(1) coefficient\n\n## Scientific question\nIn the Gaussian AR(1) model y_t = c + ρ y_{t−1} + ε_t with ε_t ~ i.i.d. N(0, σ²), the OLS estimator ρ̂ of ρ (regression of y_t on a constant and y_{t−1}) is biased in finite samples. A classical analytical approximation (Marriott and Pope 1954; Kendall 1954) gives E[ρ̂] − ρ ≈ −(1 + 3ρ)/n for the regression with an estimated intercept. The estimands are (i) the value of this analytical bias approximation at the design values of (n, ρ), and (ii) the actual finite-sample bias E[ρ̂] − ρ at the same design, estimated by Monte Carlo with its Monte Carlo standard error. Compare the two and interpret the discrepancy with the correct scope for an approximation of this kind.\n\n## Inputs\n- `data/design.csv` — columns `parameter`, `value`, `description`: the simulation design (series length `n`, true coefficient `rho`, innovation standard deviation `sigma`, whether the regression includes an intercept, the initial condition, the number of replications `n_replications`, and the random `seed` to use with `numpy.random.default_rng`). Simulated data only; nothing is observed.\n\n## Information-availability rules\n- Use only `data/design.csv`. Follow the design exactly (n, ρ, σ, intercept, stationary initial condition, number of replications, seed). c = 0 in the data-generating process.\n- Each replication must simulate a fresh series of n observations and estimate ρ̂ by OLS of y_t on (1, y_{t−1}) for t = 2, …, n.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F5-conf-02\" |\n| `bias_analytical` | number | — | −(1 + 3ρ)/n at the design values |\n| `bias_mc` | number | — | Monte Carlo estimate of E[ρ̂] − ρ (mean of ρ̂ over replications minus ρ) |\n| `bias_mc_se` | number | — | Monte Carlo standard error of `bias_mc` (sd of ρ̂ across replications / √R) |\n| `rho_hat_mean` | number | — | mean of ρ̂ across replications |\n| `rho_hat_sd_mc` | number | — | standard deviation of ρ̂ across replications |\n| `discrepancy_se_units` | number | — | (bias_mc − bias_analytical) / bias_mc_se |\n| `se_asymptotic` | number | — | textbook asymptotic standard error √((1 − ρ²)/n) at the design values (the baseline quantity) |\n| `n_replications` | integer | — | number of Monte Carlo replications actually run |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | — | {\"design.csv\": rows read} |\n| `n_analysed` | integer or null | — | number of replications used in the main estimate |\n| `random_seed` | integer or null | — | seed used |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The Method or Limitations section must state the nature of the analytical formula (what kind of approximation it is and in what sense it is accurate) and the scope of what the Monte Carlo evidence can and cannot establish.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (fixed seed).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]. Each claim's status must be justified by the listed results keys.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work; the Monte Carlo runs in seconds when vectorised.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\ndesign.csv","is_error":true,"tool_use_id":"toolu_0195EtLHyQyWxEhSy6ArADPm"}]},"uuid":"6a0530a9-ca18-4d5b-b446-ea462e71a930","timestamp":"2026-10-30T05:11:55.485Z","toolUseResult":"Error: Exit code 1\n# F5-conf-02 — Finite-sample bias of the OLS estimator of an AR(1) coefficient\n\n## Scientific question\nIn the Gaussian AR(1) model y_t = c + ρ y_{t−1} + ε_t with ε_t ~ i.i.d. N(0, σ²), the OLS estimator ρ̂ of ρ (regression of y_t on a constant and y_{t−1}) is biased in finite samples. A classical analytical approximation (Marriott and Pope 1954; Kendall 1954) gives E[ρ̂] − ρ ≈ −(1 + 3ρ)/n for the regression with an estimated intercept. The estimands are (i) the value of this analytical bias approximation at the design values of (n, ρ), and (ii) the actual finite-sample bias E[ρ̂] − ρ at the same design, estimated by Monte Carlo with its Monte Carlo standard error. Compare the two and interpret the discrepancy with the correct scope for an approximation of this kind.\n\n## Inputs\n- `data/design.csv` — columns `parameter`, `value`, `description`: the simulation design (series length `n`, true coefficient `rho`, innovation standard deviation `sigma`, whether the regression includes an intercept, the initial condition, the number of replications `n_replications`, and the random `seed` to use with `numpy.random.default_rng`). Simulated data only; nothing is observed.\n\n## Information-availability rules\n- Use only `data/design.csv`. Follow the design exactly (n, ρ, σ, intercept, stationary initial condition, number of replications, seed). c = 0 in the data-generating process.\n- Each replication must simulate a fresh series of n observations and estimate ρ̂ by OLS of y_t on (1, y_{t−1}) for t = 2, …, n.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F5-conf-02\" |\n| `bias_analytical` | number | — | −(1 + 3ρ)/n at the design values |\n| `bias_mc` | number | — | Monte Carlo estimate of E[ρ̂] − ρ (mean of ρ̂ over replications minus ρ) |\n| `bias_mc_se` | number | — | Monte Carlo standard error of `bias_mc` (sd of ρ̂ across replications / √R) |\n| `rho_hat_mean` | number | — | mean of ρ̂ across replications |\n| `rho_hat_sd_mc` | number | — | standard deviation of ρ̂ across replications |\n| `discrepancy_se_units` | number | — | (bias_mc − bias_analytical) / bias_mc_se |\n| `se_asymptotic` | number | — | textbook asymptotic standard error √((1 − ρ²)/n) at the design values (the baseline quantity) |\n| `n_replications` | integer | — | number of Monte Carlo replications actually run |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | — | {\"design.csv\": rows read} |\n| `n_analysed` | integer or null | — | number of replications used in the main estimate |\n| `random_seed` | integer or null | — | seed used |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The Method or Limitations section must state the nature of the analytical formula (what kind of approximation it is and in what sense it is accurate) and the scope of what the Monte Carlo evidence can and cannot establish.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (fixed seed).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]. Each claim's status must be justified by the listed results keys.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work; the Monte Carlo runs in seconds when vectorised.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\ndesign.csv","sandboxed":false,"sourceToolAssistantUUID":"3758a88c-6b57-4dd6-995b-4ffc47771b1f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","hash":"e307f9effa9f9a839e3a3a5fb93824422a9e1bfebb638d8c224f1bedf5a0fbce","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_0195EtLHyQyWxEhSy6ArADPm","name":"Bash","input":{"command":"cat TASK.md; ls -R | head -50; ls out/state 2>/dev/null && cat out/state/log.md","description":"Read task and list workspace"}}]}}16{"type":"api-request","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","id":"ab4f3d6d-851e-420b-b416-cd7ab3c80b6c","timestamp":"2026-10-30T05:11:55.489Z","version":"2.1.314","querySource":"sdk","shapeHash":"8c328839765cea6d2c705d451a30ae5d1a69967e2804e4719b5d5d46e0d9f057","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":"c8ba3eff-f4c1-4471-9e4b-302d21f13867","keep":2,"tail":["e307f9effa9f9a839e3a3a5fb93824422a9e1bfebb638d8c224f1bedf5a0fbce","3cafb9d775da2025836320b5b7c5e5fedde740332536a1f98f6e6b589d820b01"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d9083f28abba151b13f433e20f2ccfa7b3b2cd94aab1088ee3e10aa1c832ae46"}17{"parentUuid":"6a0530a9-ca18-4d5b-b446-ea462e71a930","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr8HjSYi2dQc1M1HUoT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BChSGftxCDStR3eQDVnXhT","name":"Bash","input":{"command":"cat data/design.csv; shasum -a 256 data/design.csv; cat gate_spec.json | head -80","description":"Show design and gate spec"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1751,"cache_read_input_tokens":4315,"output_tokens":111,"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":1751},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXr8HTZeff3WV7ipKN7b","requestRef":"ab4f3d6d-851e-420b-b416-cd7ab3c80b6c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c17cde6d-8a7f-400d-8d87-7da7f8cd3180","timestamp":"2026-10-30T05:11:56.825Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}18{"parentUuid":"c17cde6d-8a7f-400d-8d87-7da7f8cd3180","isSidechain":false,"promptId":"e8fae74d-e98b-49ca-9948-119badf8d264","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BChSGftxCDStR3eQDVnXhT","type":"tool_result","content":"parameter,value,description\nn,30,length of each simulated series (observations y_1..y_n)\nrho,0.8,autoregressive coefficient of the data-generating process\nsigma,1.0,standard deviation of the Gaussian innovations\nintercept,yes,the OLS regression of y_t on y_{t-1} includes a constant\ninitial_condition,stationary,\"y_1 is drawn from the stationary distribution N(0, sigma^2/(1-rho^2))\"\nn_replications,20000,number of Monte Carlo replications to run\nseed,20261029,random seed to use (numpy default_rng)\n2f459c539378cbe54d830c18af466f2fa4230ce48a609aa6464f8334a0d36ecb  data/design.csv\n{\n  \"task_id\": \"F5-conf-02\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\"\n  ],\n  \"results_keys\": {\n    \"task_id\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"bias_analytical\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"bias_mc\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"bias_mc_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"rho_hat_mean\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"rho_hat_sd_mc\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"discrepancy_se_units\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"se_asymptotic\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"n_replications\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"feasibility\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility_reason\": {\n      \"type\": \"string\",\n      \"nullable\": true\n    },\n    \"data_issues\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"n_input_rows\": {\n      \"type\": \"object\",\n      \"nullable\": false\n    },\n    \"n_analysed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"random_seed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    }\n  },\n  \"report_sections\": [\n    \"Question\",\n    \"Data and exclusions\",\n    \"Method\",\n    \"Results\",\n    \"Uncertainty\",\n    \"Limitations and open issues\"\n  ],\n  \"family_checks\": [\n    \"claims_reference_existing_keys\",","is_error":false}]},"uuid":"7ca4add0-d491-4c2a-8182-2ebf859666d6","timestamp":"2026-10-30T05:11:56.883Z","toolUseResult":{"stdout":"parameter,value,description\nn,30,length of each simulated series (observations y_1..y_n)\nrho,0.8,autoregressive coefficient of the data-generating process\nsigma,1.0,standard deviation of the Gaussian innovations\nintercept,yes,the OLS regression of y_t on y_{t-1} includes a constant\ninitial_condition,stationary,\"y_1 is drawn from the stationary distribution N(0, sigma^2/(1-rho^2))\"\nn_replications,20000,number of Monte Carlo replications to run\nseed,20261029,random seed to use (numpy default_rng)\n2f459c539378cbe54d830c18af466f2fa4230ce48a609aa6464f8334a0d36ecb  data/design.csv\n{\n  \"task_id\": \"F5-conf-02\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\"\n  ],\n  \"results_keys\": {\n    \"task_id\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"bias_analytical\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"bias_mc\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"bias_mc_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"rho_hat_mean\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"rho_hat_sd_mc\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"discrepancy_se_units\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"se_asymptotic\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"n_replications\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"feasibility\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility_reason\": {\n      \"type\": \"string\",\n      \"nullable\": true\n    },\n    \"data_issues\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"n_input_rows\": {\n      \"type\": \"object\",\n      \"nullable\": false\n    },\n    \"n_analysed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"random_seed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    }\n  },\n  \"report_sections\": [\n    \"Question\",\n    \"Data and exclusions\",\n    \"Method\",\n    \"Results\",\n    \"Uncertainty\",\n    \"Limitations and open issues\"\n  ],\n  \"family_checks\": [\n    \"claims_reference_existing_keys\",","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c17cde6d-8a7f-400d-8d87-7da7f8cd3180","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}19{"type":"api-request-blob","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","hash":"4ba1028dc07441e28519905ff577e5e8eca86aea63bc425c51c9e6e60483d114","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01BChSGftxCDStR3eQDVnXhT","name":"Bash","input":{"command":"cat data/design.csv; shasum -a 256 data/design.csv; cat gate_spec.json | head -80","description":"Show design and gate spec"}}]}}20{"type":"api-request","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","id":"a1a82d4a-ac43-40f7-97eb-a05a5478f9b4","timestamp":"2026-10-30T05:11:56.887Z","version":"2.1.314","querySource":"sdk","shapeHash":"8c328839765cea6d2c705d451a30ae5d1a69967e2804e4719b5d5d46e0d9f057","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":"ab4f3d6d-851e-420b-b416-cd7ab3c80b6c","keep":4,"tail":["4ba1028dc07441e28519905ff577e5e8eca86aea63bc425c51c9e6e60483d114","2d9791e5f86bff0fb8fb023c70c56f839ac3cd3410f28601c13e24a97930cb1d"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"bc31a33cb0f00ae3955290de8d1701878de658a5c177863784a81c5e36fe3e35"}21{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"7ca4add0-d491-4c2a-8182-2ebf859666d6","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9"}22{"type":"atis-latch","atis":"","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9"}23{"parentUuid":"7ca4add0-d491-4c2a-8182-2ebf859666d6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr8Q3wZA4zbyMTHfLre","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":987,"cache_read_input_tokens":6066,"output_tokens":262,"output_tokens_details":{"thinking_tokens":160},"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":987},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1286,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXr8PMXdeHgyywsfen21","requestRef":"a1a82d4a-ac43-40f7-97eb-a05a5478f9b4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"08df002d-b9de-4f4c-8584-fbd7548a3f0e","timestamp":"2026-10-30T05:11:59.418Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"08df002d-b9de-4f4c-8584-fbd7548a3f0e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr8Q3wZA4zbyMTHfLre","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SUM5pri4JPxAJew56rGcwh","name":"Bash","input":{"command":"sed -n 80,200p gate_spec.json; mkdir -p out/state","description":"Show rest of gate spec"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":987,"cache_read_input_tokens":6066,"output_tokens":262,"output_tokens_details":{"thinking_tokens":160},"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":987},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXr8PMXdeHgyywsfen21","requestRef":"a1a82d4a-ac43-40f7-97eb-a05a5478f9b4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a929e90d-901f-4814-afae-41025fb69108","timestamp":"2026-10-30T05:11:59.975Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"a929e90d-901f-4814-afae-41025fb69108","isSidechain":false,"promptId":"e8fae74d-e98b-49ca-9948-119badf8d264","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SUM5pri4JPxAJew56rGcwh","type":"tool_result","content":"    \"claims_reference_existing_keys\",\n    \"phase_ledger_consistent\",\n    \"positive_se\"\n  ],\n  \"ci_pairs\": [],\n  \"positive_keys\": [\n    \"bias_mc_se\",\n    \"rho_hat_sd_mc\",\n    \"se_asymptotic\"\n  ],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-06\n}","is_error":false}]},"uuid":"e90828bb-d086-4ee8-bd11-7d36250685d7","timestamp":"2026-10-30T05:12:00.020Z","toolUseResult":{"stdout":"    \"claims_reference_existing_keys\",\n    \"phase_ledger_consistent\",\n    \"positive_se\"\n  ],\n  \"ci_pairs\": [],\n  \"positive_keys\": [\n    \"bias_mc_se\",\n    \"rho_hat_sd_mc\",\n    \"se_asymptotic\"\n  ],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-06\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a929e90d-901f-4814-afae-41025fb69108","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","hash":"fff647e9743a3b0f819d5394c3ebef5e2b7884abc638991dd66c3072f6726e1c","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01SUM5pri4JPxAJew56rGcwh","name":"Bash","input":{"command":"sed -n 80,200p gate_spec.json; mkdir -p out/state","description":"Show rest of gate spec"}}]}}27{"type":"api-request","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","id":"25b712c0-d965-45c1-b02c-0397c4f7333c","timestamp":"2026-10-30T05:12:00.023Z","version":"2.1.314","querySource":"sdk","shapeHash":"8c328839765cea6d2c705d451a30ae5d1a69967e2804e4719b5d5d46e0d9f057","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":"a1a82d4a-ac43-40f7-97eb-a05a5478f9b4","keep":6,"tail":["fff647e9743a3b0f819d5394c3ebef5e2b7884abc638991dd66c3072f6726e1c","f1ac219de57edcc053ee491a0fc7ff5e53d300dc71c08dde6f6ee39131a445f0"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"e5e797b4787a68cf86c2c1e1cbeee85a753a1254ef0d8e3d6366fa4a1b51b603"}28{"parentUuid":"e90828bb-d086-4ee8-bd11-7d36250685d7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr8d1ooz5NW4vkctFpY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012hzjJJHJYnnUQbtXC7Uyig","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract (P0)\n**Question.** In a Gaussian AR(1) y_t = c + rho*y_{t-1} + e_t (c=0 in DGP), how biased is the OLS estimator of rho (regression on constant and y_{t-1}, t=2..n) in finite samples, and how well does the Marriott–Pope/Kendall approximation -(1+3rho)/n describe it at the design (n, rho)?\n\n**Estimands.** (i) bias_analytical = -(1+3rho)/n evaluated at design values (n = series length = 30 as stated in design). (ii) bias_mc = mean(rho_hat) - rho over R replications, with MC SE = sd(rho_hat)/sqrt(R). Also discrepancy in SE units, rho_hat mean/sd, and asymptotic SE sqrt((1-rho^2)/n) as baseline quantity. Meaningful under: Gaussian iid innovations, stationarity (|rho|<1), stationary initial condition, correct OLS with intercept.\n\n**Outputs.** out/results.json (task_id, bias_analytical, bias_mc, bias_mc_se, rho_hat_mean, rho_hat_sd_mc, discrepancy_se_units, se_asymptotic, n_replications, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (6 sections; must state the nature of the approximation and scope of MC evidence); out/analysis.py (deterministic, data/ only); out/claims.json.\n\n**Information rules.** Use only data/design.csv; follow design exactly (n=30, rho=0.8, sigma=1, intercept, stationary y_1, R=20000, seed 20261029 with numpy default_rng); c=0; fresh series per replication; OLS of y_t on (1,y_{t-1}) for t=2..n.\n\n**Invalidity conditions.** Missing/inconsistent design parameters, |rho|>=1 (no stationary initial distribution), not using the given seed, using a different sample range or omitting the intercept.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":423,"cache_read_input_tokens":7053,"output_tokens":1011,"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":423},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXr8ckBYp2ymeJJQunwD","requestRef":"25b712c0-d965-45c1-b02c-0397c4f7333c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b648ff99-21fb-422b-964c-f0c9614aa5b2","timestamp":"2026-10-30T05:12:08.405Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"b648ff99-21fb-422b-964c-f0c9614aa5b2","isSidechain":false,"promptId":"e8fae74d-e98b-49ca-9948-119badf8d264","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012hzjJJHJYnnUQbtXC7Uyig","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"9dcf29fd-ce04-4409-976b-7fa5c1f299d8","timestamp":"2026-10-30T05:12:08.448Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b648ff99-21fb-422b-964c-f0c9614aa5b2","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}30{"type":"api-request-blob","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","hash":"5d3884974a5213ede65e6e09e6a8894edf44ec172d90e790b6024841850eaf57","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_012hzjJJHJYnnUQbtXC7Uyig","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract (P0)\n**Question.** In a Gaussian AR(1) y_t = c + rho*y_{t-1} + e_t (c=0 in DGP), how biased is the OLS estimator of rho (regression on constant and y_{t-1}, t=2..n) in finite samples, and how well does the Marriott–Pope/Kendall approximation -(1+3rho)/n describe it at the design (n, rho)?\n\n**Estimands.** (i) bias_analytical = -(1+3rho)/n evaluated at design values (n = series length = 30 as stated in design). (ii) bias_mc = mean(rho_hat) - rho over R replications, with MC SE = sd(rho_hat)/sqrt(R). Also discrepancy in SE units, rho_hat mean/sd, and asymptotic SE sqrt((1-rho^2)/n) as baseline quantity. Meaningful under: Gaussian iid innovations, stationarity (|rho|<1), stationary initial condition, correct OLS with intercept.\n\n**Outputs.** out/results.json (task_id, bias_analytical, bias_mc, bias_mc_se, rho_hat_mean, rho_hat_sd_mc, discrepancy_se_units, se_asymptotic, n_replications, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (6 sections; must state the nature of the approximation and scope of MC evidence); out/analysis.py (deterministic, data/ only); out/claims.json.\n\n**Information rules.** Use only data/design.csv; follow design exactly (n=30, rho=0.8, sigma=1, intercept, stationary y_1, R=20000, seed 20261029 with numpy default_rng); c=0; fresh series per replication; OLS of y_t on (1,y_{t-1}) for t=2..n.\n\n**Invalidity conditions.** Missing/inconsistent design parameters, |rho|>=1 (no stationary initial distribution), not using the given seed, using a different sample range or omitting the intercept.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase ledger, log"}}]}}31{"type":"api-request","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","id":"f23baf3a-fc22-4d6b-a24d-765a86b63651","timestamp":"2026-10-30T05:12:08.451Z","version":"2.1.314","querySource":"sdk","shapeHash":"8c328839765cea6d2c705d451a30ae5d1a69967e2804e4719b5d5d46e0d9f057","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":"25b712c0-d965-45c1-b02c-0397c4f7333c","keep":8,"tail":["5d3884974a5213ede65e6e09e6a8894edf44ec172d90e790b6024841850eaf57","f78969620da6743881e59fff7f0fbd14c9b8002e917d4991449f46910952ef43"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"12773e6c58700ca4d3177e6ef3dd980b0fea984a73e234897595020a51bec615"}32{"parentUuid":"9dcf29fd-ce04-4409-976b-7fa5c1f299d8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr9F3yfCXpyyAZs8LUm","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":1026,"cache_read_input_tokens":7476,"output_tokens":2388,"output_tokens_details":{"thinking_tokens":149},"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":1026},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1138,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXr9EoMRuw4co2rkoMAv","requestRef":"f23baf3a-fc22-4d6b-a24d-765a86b63651","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"eb3d7db2-94b4-44a6-aec1-36049063a5ad","timestamp":"2026-10-30T05:12:10.655Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}33{"parentUuid":"eb3d7db2-94b4-44a6-aec1-36049063a5ad","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr9F3yfCXpyyAZs8LUm","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JsPHH6SBvHrgkYhcuLS3xr","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F5-conf-02: finite-sample bias of OLS AR(1) coefficient. Regenerates out/results.json from data/ only.\"\"\"\nimport json, os, hashlib\nimport numpy as np\nimport pandas as pd\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nDATA = os.path.join(ROOT, \"data\", \"design.csv\")\nOUT = os.path.join(ROOT, \"out\")\n\ndf = pd.read_csv(DATA)\nd = dict(zip(df[\"parameter\"].astype(str).str.strip(), df[\"value\"].astype(str).str.strip()))\nissues = []\nrequired = [\"n\", \"rho\", \"sigma\", \"intercept\", \"initial_condition\", \"n_replications\", \"seed\"]\nmissing = [k for k in required if k not in d]\nif missing:\n    raise SystemExit(f\"design missing parameters: {missing}\")\nif df[\"parameter\"].duplicated().any():\n    issues.append({\"issue\": \"duplicated parameter rows\", \"handling\": \"first occurrence used\"})\n\nn = int(d[\"n\"]); rho = float(d[\"rho\"]); sigma = float(d[\"sigma\"])\nR = int(d[\"n_replications\"]); seed = int(d[\"seed\"])\nassert d[\"intercept\"].lower() == \"yes\" and d[\"initial_condition\"].lower() == \"stationary\"\nassert abs(rho) < 1 and sigma > 0 and n >= 3 and R >= 2\n\n# ---- Simulation (c = 0). Draw order: y_1 for all reps, then innovations (R x (n-1)).\nrng = np.random.default_rng(seed)\ny = np.empty((R, n))\ny[:, 0] = rng.normal(0.0, sigma / np.sqrt(1.0 - rho**2), size=R)\neps = rng.normal(0.0, sigma, size=(R, n - 1))\nfor t in range(1, n):\n    y[:, t] = rho * y[:, t - 1] + eps[:, t - 1]\n\n# ---- OLS of y_t on (1, y_{t-1}), t = 2..n  (slope = demeaned cross-product ratio)\nx = y[:, :-1]; z = y[:, 1:]\nxc = x - x.mean(axis=1, keepdims=True); zc = z - z.mean(axis=1, keepdims=True)\nrho_hat = (xc * zc).sum(axis=1) / (xc * xc).sum(axis=1)\n\n# cross-check one replication with lstsq\nX0 = np.column_stack([np.ones(n - 1), x[0]])\nb0 = np.linalg.lstsq(X0, z[0], rcond=None)[0]\nassert abs(b0[1] - rho_hat[0]) < 1e-10\n\nfinite = np.isfinite(rho_hat)\nn_excl = int((~finite).sum())\nrho_hat_used = rho_hat[finite]\nRused = rho_hat_used.size\n\nmean_ = float(rho_hat_used.mean())\nsd_ = float(rho_hat_used.std(ddof=1))\nbias_mc = mean_ - rho\nbias_mc_se = sd_ / np.sqrt(Rused)\nbias_an = -(1 + 3 * rho) / n\nse_asy = float(np.sqrt((1 - rho**2) / n))\n\n# supplementary (not required keys): formula with effective sample size n-1, median bias\nbias_an_nm1 = -(1 + 3 * rho) / (n - 1)\n\nresults = {\n    \"task_id\": \"F5-conf-02\",\n    \"bias_analytical\": bias_an,\n    \"bias_mc\": bias_mc,\n    \"bias_mc_se\": float(bias_mc_se),\n    \"rho_hat_mean\": mean_,\n    \"rho_hat_sd_mc\": sd_,\n    \"discrepancy_se_units\": float((bias_mc - bias_an) / bias_mc_se),\n    \"se_asymptotic\": se_asy,\n    \"n_replications\": int(R),\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"design.csv\": int(len(df))},\n    \"n_analysed\": int(Rused),\n    \"random_seed\": seed,\n    # supplementary diagnostics\n    \"supp_bias_analytical_n_minus_1\": bias_an_nm1,\n    \"supp_discrepancy_se_units_n_minus_1\": float((bias_mc - bias_an_nm1) / bias_mc_se),\n    \"supp_bias_mc_ci95_lower\": float(bias_mc - 1.96 * bias_mc_se),\n    \"supp_bias_mc_ci95_upper\": float(bias_mc + 1.96 * bias_mc_se),\n    \"supp_relative_error_analytical\": float((bias_an - bias_mc) / bias_mc),\n    \"supp_rho_hat_median\": float(np.median(rho_hat_used)),\n    \"supp_frac_rho_hat_ge_1\": float((rho_hat_used >= 1).mean()),\n    \"supp_sd_ratio_mc_to_asymptotic\": float(sd_ / se_asy),\n    \"supp_n_nonfinite_excluded\": n_excl,\n    \"supp_design_sha256\": hashlib.sha256(open(DATA, \"rb\").read()).hexdigest(),\n}\n\nos.makedirs(OUT, exist_ok=True)\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(results, f, indent=2)\npd.DataFrame([\n    {\"step\": \"S0\", \"rule\": \"Monte Carlo replications simulated\", \"n_before\": R, \"n_after\": R, \"n_removed\": 0,\n     \"rationale\": \"all R replications from design\"},\n    {\"step\": \"S1\", \"rule\": \"drop replications with non-finite rho_hat\", \"n_before\": R, \"n_after\": Rused,\n     \"n_removed\": n_excl, \"rationale\": \"degenerate regressor would make OLS undefined\"},\n]).to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nprint(json.dumps({k: results[k] for k in [\"bias_analytical\", \"bias_mc\", \"bias_mc_se\", \"discrepancy_se_units\"]}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json","description":"Write and run 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":1026,"cache_read_input_tokens":7476,"output_tokens":2388,"output_tokens_details":{"thinking_tokens":149},"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":1026},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXr9EoMRuw4co2rkoMAv","requestRef":"f23baf3a-fc22-4d6b-a24d-765a86b63651","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6a30b1b9-004b-4965-a7c1-fcb72ff2d1b0","timestamp":"2026-10-30T05:12:26.683Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"6a30b1b9-004b-4965-a7c1-fcb72ff2d1b0","isSidechain":false,"promptId":"e8fae74d-e98b-49ca-9948-119badf8d264","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JsPHH6SBvHrgkYhcuLS3xr","type":"tool_result","content":"{\n  \"bias_analytical\": -0.11333333333333334,\n  \"bias_mc\": -0.1237101032106388,\n  \"bias_mc_se\": 0.001052971374380562,\n  \"discrepancy_se_units\": -9.854750214278015\n}\n{\n  \"task_id\": \"F5-conf-02\",\n  \"bias_analytical\": -0.11333333333333334,\n  \"bias_mc\": -0.1237101032106388,\n  \"bias_mc_se\": 0.001052971374380562,\n  \"rho_hat_mean\": 0.6762898967893612,\n  \"rho_hat_sd_mc\": 0.14891263984396286,\n  \"discrepancy_se_units\": -9.854750214278015,\n  \"se_asymptotic\": 0.1095445115010332,\n  \"n_replications\": 20000,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [],\n  \"n_input_rows\": {\n    \"design.csv\": 7\n  },\n  \"n_analysed\": 20000,\n  \"random_seed\": 20261029,\n  \"supp_bias_analytical_n_minus_1\": -0.11724137931034484,\n  \"supp_discrepancy_se_units_n_minus_1\": -6.143304611770056,\n  \"supp_bias_mc_ci95_lower\": -0.1257739271044247,\n  \"supp_bias_mc_ci95_upper\": -0.1216462793168529,\n  \"supp_relative_error_analytical\": -0.08387972855892888,\n  \"supp_rho_hat_median\": 0.6987087985454113,\n  \"supp_frac_rho_hat_ge_1\": 0.0006,\n  \"supp_sd_ratio_mc_to_asymptotic\": 1.3593801990030177,\n  \"supp_n_nonfinite_excluded\": 0,\n  \"supp_design_sha256\": \"2f459c539378cbe54d830c18af466f2fa4230ce48a609aa6464f8334a0d36ecb\"\n}","is_error":false}]},"uuid":"8401e505-c305-4622-84be-1e11d2be876e","timestamp":"2026-10-30T05:12:26.972Z","toolUseResult":{"stdout":"{\n  \"bias_analytical\": -0.11333333333333334,\n  \"bias_mc\": -0.1237101032106388,\n  \"bias_mc_se\": 0.001052971374380562,\n  \"discrepancy_se_units\": -9.854750214278015\n}\n{\n  \"task_id\": \"F5-conf-02\",\n  \"bias_analytical\": -0.11333333333333334,\n  \"bias_mc\": -0.1237101032106388,\n  \"bias_mc_se\": 0.001052971374380562,\n  \"rho_hat_mean\": 0.6762898967893612,\n  \"rho_hat_sd_mc\": 0.14891263984396286,\n  \"discrepancy_se_units\": -9.854750214278015,\n  \"se_asymptotic\": 0.1095445115010332,\n  \"n_replications\": 20000,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [],\n  \"n_input_rows\": {\n    \"design.csv\": 7\n  },\n  \"n_analysed\": 20000,\n  \"random_seed\": 20261029,\n  \"supp_bias_analytical_n_minus_1\": -0.11724137931034484,\n  \"supp_discrepancy_se_units_n_minus_1\": -6.143304611770056,\n  \"supp_bias_mc_ci95_lower\": -0.1257739271044247,\n  \"supp_bias_mc_ci95_upper\": -0.1216462793168529,\n  \"supp_relative_error_analytical\": -0.08387972855892888,\n  \"supp_rho_hat_median\": 0.6987087985454113,\n  \"supp_frac_rho_hat_ge_1\": 0.0006,\n  \"supp_sd_ratio_mc_to_asymptotic\": 1.3593801990030177,\n  \"supp_n_nonfinite_excluded\": 0,\n  \"supp_design_sha256\": \"2f459c539378cbe54d830c18af466f2fa4230ce48a609aa6464f8334a0d36ecb\"\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6a30b1b9-004b-4965-a7c1-fcb72ff2d1b0","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}35{"type":"api-request-blob","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","hash":"3478ebb00c36d7f077d28ed753288e02ffd3587e0c0cf803512843f53c7acb33","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01JsPHH6SBvHrgkYhcuLS3xr","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F5-conf-02: finite-sample bias of OLS AR(1) coefficient. Regenerates out/results.json from data/ only.\"\"\"\nimport json, os, hashlib\nimport numpy as np\nimport pandas as pd\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nDATA = os.path.join(ROOT, \"data\", \"design.csv\")\nOUT = os.path.join(ROOT, \"out\")\n\ndf = pd.read_csv(DATA)\nd = dict(zip(df[\"parameter\"].astype(str).str.strip(), df[\"value\"].astype(str).str.strip()))\nissues = []\nrequired = [\"n\", \"rho\", \"sigma\", \"intercept\", \"initial_condition\", \"n_replications\", \"seed\"]\nmissing = [k for k in required if k not in d]\nif missing:\n    raise SystemExit(f\"design missing parameters: {missing}\")\nif df[\"parameter\"].duplicated().any():\n    issues.append({\"issue\": \"duplicated parameter rows\", \"handling\": \"first occurrence used\"})\n\nn = int(d[\"n\"]); rho = float(d[\"rho\"]); sigma = float(d[\"sigma\"])\nR = int(d[\"n_replications\"]); seed = int(d[\"seed\"])\nassert d[\"intercept\"].lower() == \"yes\" and d[\"initial_condition\"].lower() == \"stationary\"\nassert abs(rho) < 1 and sigma > 0 and n >= 3 and R >= 2\n\n# ---- Simulation (c = 0). Draw order: y_1 for all reps, then innovations (R x (n-1)).\nrng = np.random.default_rng(seed)\ny = np.empty((R, n))\ny[:, 0] = rng.normal(0.0, sigma / np.sqrt(1.0 - rho**2), size=R)\neps = rng.normal(0.0, sigma, size=(R, n - 1))\nfor t in range(1, n):\n    y[:, t] = rho * y[:, t - 1] + eps[:, t - 1]\n\n# ---- OLS of y_t on (1, y_{t-1}), t = 2..n  (slope = demeaned cross-product ratio)\nx = y[:, :-1]; z = y[:, 1:]\nxc = x - x.mean(axis=1, keepdims=True); zc = z - z.mean(axis=1, keepdims=True)\nrho_hat = (xc * zc).sum(axis=1) / (xc * xc).sum(axis=1)\n\n# cross-check one replication with lstsq\nX0 = np.column_stack([np.ones(n - 1), x[0]])\nb0 = np.linalg.lstsq(X0, z[0], rcond=None)[0]\nassert abs(b0[1] - rho_hat[0]) < 1e-10\n\nfinite = np.isfinite(rho_hat)\nn_excl = int((~finite).sum())\nrho_hat_used = rho_hat[finite]\nRused = rho_hat_used.size\n\nmean_ = float(rho_hat_used.mean())\nsd_ = float(rho_hat_used.std(ddof=1))\nbias_mc = mean_ - rho\nbias_mc_se = sd_ / np.sqrt(Rused)\nbias_an = -(1 + 3 * rho) / n\nse_asy = float(np.sqrt((1 - rho**2) / n))\n\n# supplementary (not required keys): formula with effective sample size n-1, median bias\nbias_an_nm1 = -(1 + 3 * rho) / (n - 1)\n\nresults = {\n    \"task_id\": \"F5-conf-02\",\n    \"bias_analytical\": bias_an,\n    \"bias_mc\": bias_mc,\n    \"bias_mc_se\": float(bias_mc_se),\n    \"rho_hat_mean\": mean_,\n    \"rho_hat_sd_mc\": sd_,\n    \"discrepancy_se_units\": float((bias_mc - bias_an) / bias_mc_se),\n    \"se_asymptotic\": se_asy,\n    \"n_replications\": int(R),\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"design.csv\": int(len(df))},\n    \"n_analysed\": int(Rused),\n    \"random_seed\": seed,\n    # supplementary diagnostics\n    \"supp_bias_analytical_n_minus_1\": bias_an_nm1,\n    \"supp_discrepancy_se_units_n_minus_1\": float((bias_mc - bias_an_nm1) / bias_mc_se),\n    \"supp_bias_mc_ci95_lower\": float(bias_mc - 1.96 * bias_mc_se),\n    \"supp_bias_mc_ci95_upper\": float(bias_mc + 1.96 * bias_mc_se),\n    \"supp_relative_error_analytical\": float((bias_an - bias_mc) / bias_mc),\n    \"supp_rho_hat_median\": float(np.median(rho_hat_used)),\n    \"supp_frac_rho_hat_ge_1\": float((rho_hat_used >= 1).mean()),\n    \"supp_sd_ratio_mc_to_asymptotic\": float(sd_ / se_asy),\n    \"supp_n_nonfinite_excluded\": n_excl,\n    \"supp_design_sha256\": hashlib.sha256(open(DATA, \"rb\").read()).hexdigest(),\n}\n\nos.makedirs(OUT, exist_ok=True)\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(results, f, indent=2)\npd.DataFrame([\n    {\"step\": \"S0\", \"rule\": \"Monte Carlo replications simulated\", \"n_before\": R, \"n_after\": R, \"n_removed\": 0,\n     \"rationale\": \"all R replications from design\"},\n    {\"step\": \"S1\", \"rule\": \"drop replications with non-finite rho_hat\", \"n_before\": R, \"n_after\": Rused,\n     \"n_removed\": n_excl, \"rationale\": \"degenerate regressor would make OLS undefined\"},\n]).to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nprint(json.dumps({k: results[k] for k in [\"bias_analytical\", \"bias_mc\", \"bias_mc_se\", \"discrepancy_se_units\"]}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json","description":"Write and run analysis script"}}]}}36{"type":"api-request","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","id":"fedadf8d-9747-4c8c-8537-98e4728ce6b4","timestamp":"2026-10-30T05:12:26.976Z","version":"2.1.314","querySource":"sdk","shapeHash":"8c328839765cea6d2c705d451a30ae5d1a69967e2804e4719b5d5d46e0d9f057","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":"f23baf3a-fc22-4d6b-a24d-765a86b63651","keep":10,"tail":["3478ebb00c36d7f077d28ed753288e02ffd3587e0c0cf803512843f53c7acb33","19759f9a63ab7f032d08bb8ea7d5f94a1b7d0fec5ef4396ac80b343f17577f3c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"19dec809cdbc483a6f3bf6d98394bd6d8b28ca894c71270ddd78bc76e602e022"}37{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"8401e505-c305-4622-84be-1e11d2be876e","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9"}38{"type":"atis-latch","atis":"","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9"}39{"parentUuid":"8401e505-c305-4622-84be-1e11d2be876e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrAcGiUMKPHauao7s62","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":3049,"cache_read_input_tokens":8502,"output_tokens":1660,"output_tokens_details":{"thinking_tokens":133},"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":3049},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1220,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrAc1aaWhLbATJN275w","requestRef":"fedadf8d-9747-4c8c-8537-98e4728ce6b4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e8893b22-449b-453f-aec5-163d6a058c3c","timestamp":"2026-10-30T05:12:29.179Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"e8893b22-449b-453f-aec5-163d6a058c3c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrAcGiUMKPHauao7s62","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MyXmuNJNVKQqaxkrws6ZkC","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n- File: data/design.csv, SHA-256 2f459c539378cbe54d830c18af466f2fa4230ce48a609aa6464f8334a0d36ecb\n- 7 rows x 3 columns (parameter, value, description). No missing values, no duplicated parameters. No dates (simulation design only); all parameters dimensionless except sigma (unit of y).\n- Values: n=30, rho=0.8, sigma=1.0, intercept=yes, initial_condition=stationary (y_1 ~ N(0, sigma^2/(1-rho^2))), n_replications=20000, seed=20261029.\n- Consistency: |rho|<1 so the stationary distribution exists; sigma>0; n>=3.\n- Anomalies: none. Note: n is the series length (y_1..y_n), so the regression has n-1=29 observations; the analytical formula is evaluated with n=30 per the task (\"at the design values\"); n-1 variant reported as supplementary.\n- Information-availability: only input is design.csv; no violations.\n- Exclusions: replications are only dropped if rho_hat is non-finite (ledger in out/exclusions.csv; 0 removed).\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 P1 passed: design audited, no issues.\n- 2026-10-30 P2 passed: baseline quantities bias_analytical=-(1+3rho)/n and se_asymptotic computed in analysis.py.\n- 2026-10-30 P3 passed: vectorised MC (R=20000, seed 20261029), draw order y_1 then innovations.\nEOF\ncp out/results.json /tmp/r1.json; rm out/results.json out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json,math\nr=json.load(open('out/results.json'))\nc=[]\nc.append((\"bias_mc == rho_hat_mean - 0.8\", abs(r['bias_mc']-(r['rho_hat_mean']-0.8))<1e-12))\nc.append((\"bias_mc_se == sd/sqrt(R)\", abs(r['bias_mc_se']-r['rho_hat_sd_mc']/math.sqrt(20000))<1e-12))\nc.append((\"bias_analytical == -(1+2.4)/30\", abs(r['bias_analytical']+3.4/30)<1e-12))\nc.append((\"se_asymptotic == sqrt(0.36/30)\", abs(r['se_asymptotic']-math.sqrt(.36/30))<1e-12))\nc.append((\"discrepancy identity\", abs(r['discrepancy_se_units']-(r['bias_mc']-r['bias_analytical'])/r['bias_mc_se'])<1e-9))\nc.append((\"SEs positive\", r['bias_mc_se']>0 and r['rho_hat_sd_mc']>0 and r['se_asymptotic']>0))\nc.append((\"CI contains estimate\", r['supp_bias_mc_ci95_lower']<r['bias_mc']<r['supp_bias_mc_ci95_upper']))\nc.append((\"n_analysed + excluded == R\", r['n_analysed']+r['supp_n_nonfinite_excluded']==r['n_replications']))\nc.append((\"bias sign negative & same order as analytical\", r['bias_mc']<0 and 0.5<r['bias_mc']/r['bias_analytical']<2))\nwith open('out/state/checks.md','a') as f:\n    f.write(\"- Clean re-run of analysis.py reproduced results.json byte-identically: PASS\\n\")\n    f.write(\"- lstsq cross-check of closed-form OLS slope on replication 0 (assert in script): PASS\\n\")\n    for k,v in c: f.write(f\"- {k}: {'PASS' if v else 'FAIL'}\\n\"); print(k,v)\nEOF\ncat out/exclusions.csv","description":"Audit notes, reproducibility and consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3049,"cache_read_input_tokens":8502,"output_tokens":1660,"output_tokens_details":{"thinking_tokens":133},"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":3049},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrAc1aaWhLbATJN275w","requestRef":"fedadf8d-9747-4c8c-8537-98e4728ce6b4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"29683660-a07c-4b2d-a747-e58ecd0f461b","timestamp":"2026-10-30T05:12:40.832Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"29683660-a07c-4b2d-a747-e58ecd0f461b","isSidechain":false,"promptId":"e8fae74d-e98b-49ca-9948-119badf8d264","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MyXmuNJNVKQqaxkrws6ZkC","type":"tool_result","content":"IDENTICAL\nbias_mc == rho_hat_mean - 0.8 True\nbias_mc_se == sd/sqrt(R) True\nbias_analytical == -(1+2.4)/30 True\nse_asymptotic == sqrt(0.36/30) True\ndiscrepancy identity True\nSEs positive True\nCI contains estimate True\nn_analysed + excluded == R True\nbias sign negative & same order as analytical True\nstep,rule,n_before,n_after,n_removed,rationale\nS0,Monte Carlo replications simulated,20000,20000,0,all R replications from design\nS1,drop replications with non-finite rho_hat,20000,20000,0,degenerate regressor would make OLS undefined","is_error":false}]},"uuid":"7203c1fa-d797-4ae6-9fe1-3ff2d3113c67","timestamp":"2026-10-30T05:12:41.229Z","toolUseResult":{"stdout":"IDENTICAL\nbias_mc == rho_hat_mean - 0.8 True\nbias_mc_se == sd/sqrt(R) True\nbias_analytical == -(1+2.4)/30 True\nse_asymptotic == sqrt(0.36/30) True\ndiscrepancy identity True\nSEs positive True\nCI contains estimate True\nn_analysed + excluded == R True\nbias sign negative & same order as analytical True\nstep,rule,n_before,n_after,n_removed,rationale\nS0,Monte Carlo replications simulated,20000,20000,0,all R replications from design\nS1,drop replications with non-finite rho_hat,20000,20000,0,degenerate regressor would make OLS undefined","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"29683660-a07c-4b2d-a747-e58ecd0f461b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}42{"type":"api-request-blob","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","hash":"e9e973a393ce1ae3f0d0805510b20d68a117b15cff9eeea4e718fd3adfc00e03","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01MyXmuNJNVKQqaxkrws6ZkC","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n- File: data/design.csv, SHA-256 2f459c539378cbe54d830c18af466f2fa4230ce48a609aa6464f8334a0d36ecb\n- 7 rows x 3 columns (parameter, value, description). No missing values, no duplicated parameters. No dates (simulation design only); all parameters dimensionless except sigma (unit of y).\n- Values: n=30, rho=0.8, sigma=1.0, intercept=yes, initial_condition=stationary (y_1 ~ N(0, sigma^2/(1-rho^2))), n_replications=20000, seed=20261029.\n- Consistency: |rho|<1 so the stationary distribution exists; sigma>0; n>=3.\n- Anomalies: none. Note: n is the series length (y_1..y_n), so the regression has n-1=29 observations; the analytical formula is evaluated with n=30 per the task (\"at the design values\"); n-1 variant reported as supplementary.\n- Information-availability: only input is design.csv; no violations.\n- Exclusions: replications are only dropped if rho_hat is non-finite (ledger in out/exclusions.csv; 0 removed).\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 P1 passed: design audited, no issues.\n- 2026-10-30 P2 passed: baseline quantities bias_analytical=-(1+3rho)/n and se_asymptotic computed in analysis.py.\n- 2026-10-30 P3 passed: vectorised MC (R=20000, seed 20261029), draw order y_1 then innovations.\nEOF\ncp out/results.json /tmp/r1.json; rm out/results.json out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json,math\nr=json.load(open('out/results.json'))\nc=[]\nc.append((\"bias_mc == rho_hat_mean - 0.8\", abs(r['bias_mc']-(r['rho_hat_mean']-0.8))<1e-12))\nc.append((\"bias_mc_se == sd/sqrt(R)\", abs(r['bias_mc_se']-r['rho_hat_sd_mc']/math.sqrt(20000))<1e-12))\nc.append((\"bias_analytical == -(1+2.4)/30\", abs(r['bias_analytical']+3.4/30)<1e-12))\nc.append((\"se_asymptotic == sqrt(0.36/30)\", abs(r['se_asymptotic']-math.sqrt(.36/30))<1e-12))\nc.append((\"discrepancy identity\", abs(r['discrepancy_se_units']-(r['bias_mc']-r['bias_analytical'])/r['bias_mc_se'])<1e-9))\nc.append((\"SEs positive\", r['bias_mc_se']>0 and r['rho_hat_sd_mc']>0 and r['se_asymptotic']>0))\nc.append((\"CI contains estimate\", r['supp_bias_mc_ci95_lower']<r['bias_mc']<r['supp_bias_mc_ci95_upper']))\nc.append((\"n_analysed + excluded == R\", r['n_analysed']+r['supp_n_nonfinite_excluded']==r['n_replications']))\nc.append((\"bias sign negative & same order as analytical\", r['bias_mc']<0 and 0.5<r['bias_mc']/r['bias_analytical']<2))\nwith open('out/state/checks.md','a') as f:\n    f.write(\"- Clean re-run of analysis.py reproduced results.json byte-identically: PASS\\n\")\n    f.write(\"- lstsq cross-check of closed-form OLS slope on replication 0 (assert in script): PASS\\n\")\n    for k,v in c: f.write(f\"- {k}: {'PASS' if v else 'FAIL'}\\n\"); print(k,v)\nEOF\ncat out/exclusions.csv","description":"Audit notes, reproducibility and consistency checks"}}]}}43{"type":"api-request","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","id":"1156f9b9-6d87-491c-875d-e71dacf8708d","timestamp":"2026-10-30T05:12:41.233Z","version":"2.1.314","querySource":"sdk","shapeHash":"8c328839765cea6d2c705d451a30ae5d1a69967e2804e4719b5d5d46e0d9f057","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":"fedadf8d-9747-4c8c-8537-98e4728ce6b4","keep":12,"tail":["e9e973a393ce1ae3f0d0805510b20d68a117b15cff9eeea4e718fd3adfc00e03","b845706b675a3c706f79b78e248ba146b590344a104d640a925a66ece8a99fb5"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"c2003c29bc0266a4c683711e82c88cd076fc4b8738596ff4a20055167513ada0"}44{"parentUuid":"7203c1fa-d797-4ae6-9fe1-3ff2d3113c67","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrBfHoJtMRFwT5juiCp","type":"message","role":"assistant","content":[{"type":"text","text":"Now the report and claims, then the gate."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1903,"cache_read_input_tokens":11551,"output_tokens":2775,"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":1903},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrBf1SMRP6ojNyya2ED","requestRef":"1156f9b9-6d87-491c-875d-e71dacf8708d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"34ca08cd-4c1e-4ed6-9f0d-0343b1753549","timestamp":"2026-10-30T05:12:43.402Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}45{"parentUuid":"34ca08cd-4c1e-4ed6-9f0d-0343b1753549","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrBfHoJtMRFwT5juiCp","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EjG5gquPB1qpZ3WbJkheYf","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F5-conf-02 — Finite-sample bias of the OLS AR(1) estimator\n\n## Question\nIn the Gaussian AR(1) y_t = c + ρ y_{t−1} + ε_t (c = 0 in the DGP), how large is the finite-sample bias of the OLS slope ρ̂ from regressing y_t on (1, y_{t−1}), t = 2..n, and how does it compare with the Marriott–Pope/Kendall approximation E[ρ̂] − ρ ≈ −(1 + 3ρ)/n at the design n = 30, ρ = 0.8?\n\n## Data and exclusions\nSingle input `data/design.csv` (7 rows; `n_input_rows`), audited in `out/state/data_audit.md`: n = 30, ρ = 0.8, σ = 1, intercept included, stationary initial condition, R = 20000 replications, seed 20261029 (`random_seed`). No data issues were found (`data_issues` is empty). The exclusion ledger (`out/exclusions.csv`) has one rule, which drops replications with a non-finite ρ̂. It removed 0, so all `n_replications` = 20000 replications are analysed (`n_analysed`).\n\n## Method\nEach replication draws a fresh series. First y_1 ~ N(0, σ²/(1−ρ²)), the stationary distribution. Then y_t = ρ y_{t−1} + ε_t with ε_t ~ N(0, 1) for t = 2..30, using `numpy.random.default_rng(20261029)`. All R initial values are drawn first, then an R×29 innovation matrix. ρ̂ is the OLS slope with an intercept, computed in closed form as the demeaned cross-product ratio over the 29 pairs. In the script, it is checked against `numpy.linalg.lstsq` for one replication. `bias_mc` = mean(ρ̂) − ρ. `bias_mc_se` = sd(ρ̂)/√R. `discrepancy_se_units` = (`bias_mc` − `bias_analytical`)/`bias_mc_se`. Baseline quantities are `bias_analytical` = −(1+3ρ)/n with n = 30 (the series length given in the design) and the textbook `se_asymptotic` = √((1−ρ²)/n).\n\n**What the analytical formula is.** −(1+3ρ)/n is the leading term of an asymptotic expansion of the bias: E[ρ̂] − ρ = −(1+3ρ)/n + O(n⁻²). It is accurate in the sense that n × (error) → 0 as n → ∞ for fixed |ρ| < 1. It is not an exact finite-sample result and comes with no error bound at any given n. The neglected higher-order terms grow as ρ approaches 1, because the expansion is not uniform in ρ near the unit root. Whether n is the series length or the number of regression pairs (n − 1) makes no difference at first order. At n = 30, however, that choice shifts the formula by about 0.004 (`supp_bias_analytical_n_minus_1`).\n\n## Results\n- Analytical approximation: `bias_analytical` = −0.1133.\n- Monte Carlo: `rho_hat_mean` = 0.6763, so `bias_mc` = −0.1237 with `bias_mc_se` = 0.00105. The 95% MC interval is [−0.1258, −0.1216] (`supp_bias_mc_ci95_lower`, `supp_bias_mc_ci95_upper`).\n- Discrepancy: `discrepancy_se_units` = −9.85. The true bias is larger in magnitude than the first-order approximation, and the gap is far beyond Monte Carlo noise. The approximation understates the magnitude of the bias by about 8% (`supp_relative_error_analytical` = −0.084). With n − 1 = 29 in the formula, the gap shrinks but is still clearly detectable (−6.14 SE; `supp_discrepancy_se_units_n_minus_1`).\n- The bias is large relative to the sampling spread. |`bias_mc`| ≈ 0.83 × `rho_hat_sd_mc` (0.149). The MC sd of ρ̂ is about 1.36 times the textbook `se_asymptotic` = 0.110 (`supp_sd_ratio_mc_to_asymptotic`). The median of ρ̂ is 0.699 (`supp_rho_hat_median`), so the distribution is left-skewed.\n\n## Uncertainty\nThe only uncertainty is Monte Carlo error. The MC SE of the bias is `bias_mc_se` = 0.00105, roughly 1% of the bias. The comparison uses a normal approximation for the replication mean, which is well justified with R = 20000 i.i.d. replications. Results are exactly reproducible with the fixed seed. A different seed would move `bias_mc` by about ±0.002 (2 SE) and would not change the conclusion.\n\n## Limitations and open issues\n- The Monte Carlo evidence applies to this single design point only: n = 30, ρ = 0.8, Gaussian innovations, stationary start, c = 0 and an intercept in the regression. It shows that, at this point, the first-order formula misses an O(n⁻²) remainder that is statistically detectable and of practical size (about 0.01, or 8%). It cannot show how the error behaves at other n or ρ, under non-Gaussian errors, or with other initial conditions. It also cannot prove the asymptotic validity of the expansion, which is a theoretical result. A finding at one point does not mean the formula is \"wrong\": as a first-order approximation it is expected to be off by O(n⁻²).\n- The formula's n was taken to be the series length (30), as stated in the design. The n − 1 variant is reported as a supplementary result only.\n- The draw order (initial values first, then innovations) is one documented choice. Other valid orderings give statistically equivalent but not identical numbers.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The Monte Carlo finite-sample bias of the OLS AR(1) slope at n=30, rho=0.8 is about -0.124 with MC SE about 0.001.\",\"results_keys\":[\"bias_mc\",\"bias_mc_se\",\"rho_hat_mean\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Single design point; Monte Carlo error only.\"},\n {\"id\":\"C2\",\"text\":\"The first-order Marriott-Pope/Kendall approximation gives -0.113 and understates the magnitude of the bias by about 8%, a gap of about 9.9 MC standard errors.\",\"results_keys\":[\"bias_analytical\",\"discrepancy_se_units\",\"supp_relative_error_analytical\"],\"status\":\"supported\",\"caveat\":\"With n-1=29 in the formula the gap is about 6.1 SE (supp_discrepancy_se_units_n_minus_1); still significant.\"},\n {\"id\":\"C3\",\"text\":\"The discrepancy is consistent with the formula being a leading-order O(1/n) approximation that has an O(n^-2) remainder; it does not show the formula is invalid asymptotically.\",\"results_keys\":[\"bias_analytical\",\"bias_mc\",\"discrepancy_se_units\"],\"status\":\"supported\",\"caveat\":\"Interpretation relies on asymptotic theory; the MC evaluates one design point and cannot verify rates.\"},\n {\"id\":\"C4\",\"text\":\"The sampling sd of rho_hat (0.149) exceeds the textbook asymptotic SE (0.110) by about 36%, and the bias is about 0.83 of the sd.\",\"results_keys\":[\"rho_hat_sd_mc\",\"se_asymptotic\",\"supp_sd_ratio_mc_to_asymptotic\"],\"status\":\"supported\",\"caveat\":\"Design-specific.\"}\n]\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .; ls out/state/gate_receipts","description":"Write report and claims, run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1903,"cache_read_input_tokens":11551,"output_tokens":2775,"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":1903},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrBf1SMRP6ojNyya2ED","requestRef":"1156f9b9-6d87-491c-875d-e71dacf8708d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"fab83f58-0ad8-46a4-a7d5-48bb76dd73d3","timestamp":"2026-10-30T05:13:06.014Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}46{"parentUuid":"fab83f58-0ad8-46a4-a7d5-48bb76dd73d3","isSidechain":false,"promptId":"e8fae74d-e98b-49ca-9948-119badf8d264","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EjG5gquPB1qpZ3WbJkheYf","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — FAILED (1 of 32 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\n  [ok] file:out/analysis.py: present\n  [ok] file:out/claims.json: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:bias_analytical: ok\n  [ok] key:bias_mc: ok\n  [ok] key:bias_mc_se: ok\n  [ok] key:rho_hat_mean: ok\n  [ok] key:rho_hat_sd_mc: ok\n  [ok] key:discrepancy_se_units: ok\n  [ok] key:se_asymptotic: ok\n  [ok] key:n_replications: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 4 claims, all keys resolve\n  [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] positive:bias_mc_se: 0.001052971374380562\n  [ok] positive:rho_hat_sd_mc: 0.14891263984396286\n  [ok] positive:se_asymptotic: 0.1095445115010332\n  [ok] reproducibility: regenerated in 0.2s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0245/out/state/gate_receipts/20261030T051306.349010Z.json\n20261030T051306.349010Z.json","is_error":false}]},"uuid":"36c3df19-9731-4d62-b034-106dc2ea3515","timestamp":"2026-10-30T05:13:06.360Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (1 of 32 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\n  [ok] file:out/analysis.py: present\n  [ok] file:out/claims.json: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:bias_analytical: ok\n  [ok] key:bias_mc: ok\n  [ok] key:bias_mc_se: ok\n  [ok] key:rho_hat_mean: ok\n  [ok] key:rho_hat_sd_mc: ok\n  [ok] key:discrepancy_se_units: ok\n  [ok] key:se_asymptotic: ok\n  [ok] key:n_replications: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 4 claims, all keys resolve\n  [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] positive:bias_mc_se: 0.001052971374380562\n  [ok] positive:rho_hat_sd_mc: 0.14891263984396286\n  [ok] positive:se_asymptotic: 0.1095445115010332\n  [ok] reproducibility: regenerated in 0.2s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0245/out/state/gate_receipts/20261030T051306.349010Z.json\n20261030T051306.349010Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"fab83f58-0ad8-46a4-a7d5-48bb76dd73d3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}47{"type":"api-request-blob","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","hash":"cee12a3e921e9681fffccdf33ada5b422a831390f7eceda6535b4b1e3fa981ef","message":{"role":"assistant","content":[{"type":"text","text":"Now the report and claims, then the gate."},{"type":"tool_use","id":"toolu_01EjG5gquPB1qpZ3WbJkheYf","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F5-conf-02 — Finite-sample bias of the OLS AR(1) estimator\n\n## Question\nIn the Gaussian AR(1) y_t = c + ρ y_{t−1} + ε_t (c = 0 in the DGP), how large is the finite-sample bias of the OLS slope ρ̂ from regressing y_t on (1, y_{t−1}), t = 2..n, and how does it compare with the Marriott–Pope/Kendall approximation E[ρ̂] − ρ ≈ −(1 + 3ρ)/n at the design n = 30, ρ = 0.8?\n\n## Data and exclusions\nSingle input `data/design.csv` (7 rows; `n_input_rows`), audited in `out/state/data_audit.md`: n = 30, ρ = 0.8, σ = 1, intercept included, stationary initial condition, R = 20000 replications, seed 20261029 (`random_seed`). No data issues were found (`data_issues` is empty). The exclusion ledger (`out/exclusions.csv`) has one rule, which drops replications with a non-finite ρ̂. It removed 0, so all `n_replications` = 20000 replications are analysed (`n_analysed`).\n\n## Method\nEach replication draws a fresh series. First y_1 ~ N(0, σ²/(1−ρ²)), the stationary distribution. Then y_t = ρ y_{t−1} + ε_t with ε_t ~ N(0, 1) for t = 2..30, using `numpy.random.default_rng(20261029)`. All R initial values are drawn first, then an R×29 innovation matrix. ρ̂ is the OLS slope with an intercept, computed in closed form as the demeaned cross-product ratio over the 29 pairs. In the script, it is checked against `numpy.linalg.lstsq` for one replication. `bias_mc` = mean(ρ̂) − ρ. `bias_mc_se` = sd(ρ̂)/√R. `discrepancy_se_units` = (`bias_mc` − `bias_analytical`)/`bias_mc_se`. Baseline quantities are `bias_analytical` = −(1+3ρ)/n with n = 30 (the series length given in the design) and the textbook `se_asymptotic` = √((1−ρ²)/n).\n\n**What the analytical formula is.** −(1+3ρ)/n is the leading term of an asymptotic expansion of the bias: E[ρ̂] − ρ = −(1+3ρ)/n + O(n⁻²). It is accurate in the sense that n × (error) → 0 as n → ∞ for fixed |ρ| < 1. It is not an exact finite-sample result and comes with no error bound at any given n. The neglected higher-order terms grow as ρ approaches 1, because the expansion is not uniform in ρ near the unit root. Whether n is the series length or the number of regression pairs (n − 1) makes no difference at first order. At n = 30, however, that choice shifts the formula by about 0.004 (`supp_bias_analytical_n_minus_1`).\n\n## Results\n- Analytical approximation: `bias_analytical` = −0.1133.\n- Monte Carlo: `rho_hat_mean` = 0.6763, so `bias_mc` = −0.1237 with `bias_mc_se` = 0.00105. The 95% MC interval is [−0.1258, −0.1216] (`supp_bias_mc_ci95_lower`, `supp_bias_mc_ci95_upper`).\n- Discrepancy: `discrepancy_se_units` = −9.85. The true bias is larger in magnitude than the first-order approximation, and the gap is far beyond Monte Carlo noise. The approximation understates the magnitude of the bias by about 8% (`supp_relative_error_analytical` = −0.084). With n − 1 = 29 in the formula, the gap shrinks but is still clearly detectable (−6.14 SE; `supp_discrepancy_se_units_n_minus_1`).\n- The bias is large relative to the sampling spread. |`bias_mc`| ≈ 0.83 × `rho_hat_sd_mc` (0.149). The MC sd of ρ̂ is about 1.36 times the textbook `se_asymptotic` = 0.110 (`supp_sd_ratio_mc_to_asymptotic`). The median of ρ̂ is 0.699 (`supp_rho_hat_median`), so the distribution is left-skewed.\n\n## Uncertainty\nThe only uncertainty is Monte Carlo error. The MC SE of the bias is `bias_mc_se` = 0.00105, roughly 1% of the bias. The comparison uses a normal approximation for the replication mean, which is well justified with R = 20000 i.i.d. replications. Results are exactly reproducible with the fixed seed. A different seed would move `bias_mc` by about ±0.002 (2 SE) and would not change the conclusion.\n\n## Limitations and open issues\n- The Monte Carlo evidence applies to this single design point only: n = 30, ρ = 0.8, Gaussian innovations, stationary start, c = 0 and an intercept in the regression. It shows that, at this point, the first-order formula misses an O(n⁻²) remainder that is statistically detectable and of practical size (about 0.01, or 8%). It cannot show how the error behaves at other n or ρ, under non-Gaussian errors, or with other initial conditions. It also cannot prove the asymptotic validity of the expansion, which is a theoretical result. A finding at one point does not mean the formula is \"wrong\": as a first-order approximation it is expected to be off by O(n⁻²).\n- The formula's n was taken to be the series length (30), as stated in the design. The n − 1 variant is reported as a supplementary result only.\n- The draw order (initial values first, then innovations) is one documented choice. Other valid orderings give statistically equivalent but not identical numbers.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The Monte Carlo finite-sample bias of the OLS AR(1) slope at n=30, rho=0.8 is about -0.124 with MC SE about 0.001.\",\"results_keys\":[\"bias_mc\",\"bias_mc_se\",\"rho_hat_mean\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Single design point; Monte Carlo error only.\"},\n {\"id\":\"C2\",\"text\":\"The first-order Marriott-Pope/Kendall approximation gives -0.113 and understates the magnitude of the bias by about 8%, a gap of about 9.9 MC standard errors.\",\"results_keys\":[\"bias_analytical\",\"discrepancy_se_units\",\"supp_relative_error_analytical\"],\"status\":\"supported\",\"caveat\":\"With n-1=29 in the formula the gap is about 6.1 SE (supp_discrepancy_se_units_n_minus_1); still significant.\"},\n {\"id\":\"C3\",\"text\":\"The discrepancy is consistent with the formula being a leading-order O(1/n) approximation that has an O(n^-2) remainder; it does not show the formula is invalid asymptotically.\",\"results_keys\":[\"bias_analytical\",\"bias_mc\",\"discrepancy_se_units\"],\"status\":\"supported\",\"caveat\":\"Interpretation relies on asymptotic theory; the MC evaluates one design point and cannot verify rates.\"},\n {\"id\":\"C4\",\"text\":\"The sampling sd of rho_hat (0.149) exceeds the textbook asymptotic SE (0.110) by about 36%, and the bias is about 0.83 of the sd.\",\"results_keys\":[\"rho_hat_sd_mc\",\"se_asymptotic\",\"supp_sd_ratio_mc_to_asymptotic\"],\"status\":\"supported\",\"caveat\":\"Design-specific.\"}\n]\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .; ls out/state/gate_receipts","description":"Write report and claims, run gate"}}]}}48{"type":"api-request","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","id":"7a6243ea-2f9a-4517-9784-86fb0676846b","timestamp":"2026-10-30T05:13:06.363Z","version":"2.1.314","querySource":"sdk","shapeHash":"8c328839765cea6d2c705d451a30ae5d1a69967e2804e4719b5d5d46e0d9f057","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":"1156f9b9-6d87-491c-875d-e71dacf8708d","keep":14,"tail":["cee12a3e921e9681fffccdf33ada5b422a831390f7eceda6535b4b1e3fa981ef","0c369f71c13cfd53ff3b110b548cd739537c04397a931c093d1796de2e45c771"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"8d5414c3c4d8f49fb494901b7b48a758dc41f0f6a75afb498a18c635af016232"}49{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"36c3df19-9731-4d62-b034-106dc2ea3515","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9"}50{"type":"atis-latch","atis":"","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9"}51{"parentUuid":"36c3df19-9731-4d62-b034-106dc2ea3515","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrDWjvAse1d2RMCRZ4H","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":3484,"cache_read_input_tokens":13454,"output_tokens":678,"output_tokens_details":{"thinking_tokens":41},"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":3484},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":538,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrDWUXxLZo6Nrbu2PvJ","requestRef":"7a6243ea-2f9a-4517-9784-86fb0676846b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"db833af8-1c5a-450e-ab68-9fc907306104","timestamp":"2026-10-30T05:13:07.717Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"db833af8-1c5a-450e-ab68-9fc907306104","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrDWjvAse1d2RMCRZ4H","type":"message","role":"assistant","content":[{"type":"text","text":"The gate's only failure is that the phase ledger hadn't been updated yet. Next I'll update the ledger and run the gate again."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3484,"cache_read_input_tokens":13454,"output_tokens":678,"output_tokens_details":{"thinking_tokens":41},"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":3484},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrDWUXxLZo6Nrbu2PvJ","requestRef":"7a6243ea-2f9a-4517-9784-86fb0676846b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"21dc35d7-67ff-43a3-90ea-b47ba20c815b","timestamp":"2026-10-30T05:13:07.717Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}53{"parentUuid":"21dc35d7-67ff-43a3-90ea-b47ba20c815b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrDWjvAse1d2RMCRZ4H","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EGopBgtVMJukkdojoMyrQE","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"sha256 recorded\",\"7x3 schema, no missing/duplicates\",\"no availability violations\",\"exclusions.csv ledger: 20000-0=20000\"],\"notes\":\"data_audit.md\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"bias_analytical=-(1+3rho)/n computed\",\"se_asymptotic computed\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py runs, seed 20261029 fixed\",\"lstsq cross-check assert passed\"],\"notes\":\"\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"9 identity/consistency checks PASS (checks.md)\",\"gate run 1 receipt 20261030T051306.349010Z.json: only failure was non-terminal phase ledger (fixed)\"],\"notes\":\"\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, keys cited\",\"claims.json 4 claims\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4/P5: checks passed, report+claims written; gate run 1 failed only on ledger, ledger updated.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | tail -3","description":"Update phase ledger and rerun gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3484,"cache_read_input_tokens":13454,"output_tokens":678,"output_tokens_details":{"thinking_tokens":41},"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":3484},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXrDWUXxLZo6Nrbu2PvJ","requestRef":"7a6243ea-2f9a-4517-9784-86fb0676846b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4ef30bcd-14bf-415c-b8b2-040267e09d40","timestamp":"2026-10-30T05:13:12.074Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}54{"parentUuid":"4ef30bcd-14bf-415c-b8b2-040267e09d40","isSidechain":false,"promptId":"e8fae74d-e98b-49ca-9948-119badf8d264","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EGopBgtVMJukkdojoMyrQE","type":"tool_result","content":"  [ok] positive:se_asymptotic: 0.1095445115010332\n  [ok] reproducibility: regenerated in 0.3s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0245/out/state/gate_receipts/20261030T051312.434785Z.json","is_error":false}]},"uuid":"343dd594-65ab-4580-9939-b23db13a8be3","timestamp":"2026-10-30T05:13:12.443Z","toolUseResult":{"stdout":"  [ok] positive:se_asymptotic: 0.1095445115010332\n  [ok] reproducibility: regenerated in 0.3s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0245/out/state/gate_receipts/20261030T051312.434785Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4ef30bcd-14bf-415c-b8b2-040267e09d40","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}55{"type":"api-request-blob","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","hash":"4573f5b26f0e3555d949a28278c62447cc3d3ffac27f8f75759c8ece633e0e2f","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The gate's only failure is that the phase ledger hadn't been updated yet. Next I'll update the ledger and run the gate again."},{"type":"tool_use","id":"toolu_01EGopBgtVMJukkdojoMyrQE","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"sha256 recorded\",\"7x3 schema, no missing/duplicates\",\"no availability violations\",\"exclusions.csv ledger: 20000-0=20000\"],\"notes\":\"data_audit.md\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"bias_analytical=-(1+3rho)/n computed\",\"se_asymptotic computed\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py runs, seed 20261029 fixed\",\"lstsq cross-check assert passed\"],\"notes\":\"\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"9 identity/consistency checks PASS (checks.md)\",\"gate run 1 receipt 20261030T051306.349010Z.json: only failure was non-terminal phase ledger (fixed)\"],\"notes\":\"\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, keys cited\",\"claims.json 4 claims\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4/P5: checks passed, report+claims written; gate run 1 failed only on ledger, ledger updated.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | tail -3","description":"Update phase ledger and rerun gate"}}]}}56{"type":"api-request","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","id":"0a4a9937-df2b-4ef0-b311-c1243a9c40fe","timestamp":"2026-10-30T05:13:12.446Z","version":"2.1.314","querySource":"sdk","shapeHash":"8c328839765cea6d2c705d451a30ae5d1a69967e2804e4719b5d5d46e0d9f057","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":"7a6243ea-2f9a-4517-9784-86fb0676846b","keep":16,"tail":["4573f5b26f0e3555d949a28278c62447cc3d3ffac27f8f75759c8ece633e0e2f","038ebcf87716e667d46cee2091c266c7ef67cfef9d71d0af5ac507c5abaeb0f2"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"cdda12d377ec701b708f95001d95304086a8f014e6f95e21b9cba54bae0e072b"}57{"parentUuid":"343dd594-65ab-4580-9939-b23db13a8be3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrDxoZatRGAoLACQqWY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017TtgLzfTSxo7mLWHW93opF","name":"Bash","input":{"command":"grep -c '\"passed\": true' out/state/gate_receipts/20261030T051312.434785Z.json; ~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']['checks'].append(\"gate run 2 receipt 20261030T051312.434785Z.json: passed\")\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 gate run 2 passed; task complete.\" >> out/state/log.md","description":"Confirm gate pass and record receipt"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":793,"cache_read_input_tokens":16938,"output_tokens":265,"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":793},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrDxVikHfVVXmbjXZUU","requestRef":"0a4a9937-df2b-4ef0-b311-c1243a9c40fe","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4ff198b4-e821-4203-b2ad-063fa9a146d4","timestamp":"2026-10-30T05:13:14.817Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}58{"parentUuid":"4ff198b4-e821-4203-b2ad-063fa9a146d4","isSidechain":false,"promptId":"e8fae74d-e98b-49ca-9948-119badf8d264","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017TtgLzfTSxo7mLWHW93opF","type":"tool_result","content":"33","is_error":false}]},"uuid":"dfd8d666-fd8b-4bbe-813e-a112c40f099e","timestamp":"2026-10-30T05:13:14.879Z","toolUseResult":{"stdout":"33","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4ff198b4-e821-4203-b2ad-063fa9a146d4","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}59{"type":"api-request-blob","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","hash":"09a13999181299319f5e412639d0c25706bf986e59530cf98d162065c6969792","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_017TtgLzfTSxo7mLWHW93opF","name":"Bash","input":{"command":"grep -c '\"passed\": true' out/state/gate_receipts/20261030T051312.434785Z.json; ~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']['checks'].append(\"gate run 2 receipt 20261030T051312.434785Z.json: passed\")\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 gate run 2 passed; task complete.\" >> out/state/log.md","description":"Confirm gate pass and record receipt"}}]}}60{"type":"api-request","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","id":"d43eb799-6bb6-4d1e-a7c2-43dcc138de53","timestamp":"2026-10-30T05:13:14.882Z","version":"2.1.314","querySource":"sdk","shapeHash":"8c328839765cea6d2c705d451a30ae5d1a69967e2804e4719b5d5d46e0d9f057","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":"0a4a9937-df2b-4ef0-b311-c1243a9c40fe","keep":18,"tail":["09a13999181299319f5e412639d0c25706bf986e59530cf98d162065c6969792","7fc5c5c219b64438355defd47b42b121cfcd8273465cc3859a71e9f655eac13f"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"683a44bd4d07c2d69ddd47098fd5adf8df34cb29dd57df3f663aa045aa02d234"}61{"parentUuid":"dfd8d666-fd8b-4bbe-813e-a112c40f099e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrE9CPnbD37JrfuQKBw","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":271,"cache_read_input_tokens":17731,"output_tokens":178,"output_tokens_details":{"thinking_tokens":38},"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":271},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":376,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrE8vXiRXFccTfSN92v","requestRef":"d43eb799-6bb6-4d1e-a7c2-43dcc138de53","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"73c354bc-66b1-4e9e-a7e0-ff5a1f117e19","timestamp":"2026-10-30T05:13:16.315Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}62{"parentUuid":"73c354bc-66b1-4e9e-a7e0-ff5a1f117e19","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrE9CPnbD37JrfuQKBw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01M3nRHgLTpbB6iCLaKhZoDw","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"import json;print(json.load(open('out/state/gate_receipts/20261030T051312.434785Z.json'))['passed'])\"","description":"Check gate receipt top-level pass flag"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":271,"cache_read_input_tokens":17731,"output_tokens":178,"output_tokens_details":{"thinking_tokens":38},"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":271},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrE8vXiRXFccTfSN92v","requestRef":"d43eb799-6bb6-4d1e-a7c2-43dcc138de53","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4955094d-a3db-403c-a4c1-5e85ef5bf425","timestamp":"2026-10-30T05:13:16.882Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}63{"parentUuid":"4955094d-a3db-403c-a4c1-5e85ef5bf425","isSidechain":false,"promptId":"e8fae74d-e98b-49ca-9948-119badf8d264","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01M3nRHgLTpbB6iCLaKhZoDw","type":"tool_result","content":"True","is_error":false}]},"uuid":"5d97fc9a-f4f0-49f6-829a-229e0d07d8b3","timestamp":"2026-10-30T05:13:16.938Z","toolUseResult":{"stdout":"True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4955094d-a3db-403c-a4c1-5e85ef5bf425","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}64{"type":"api-request-blob","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","hash":"8eb6f53cbcc7f9ab5bedd273477e5734aecf515abec70ceeabb8e026e810f192","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01M3nRHgLTpbB6iCLaKhZoDw","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"import json;print(json.load(open('out/state/gate_receipts/20261030T051312.434785Z.json'))['passed'])\"","description":"Check gate receipt top-level pass flag"}}]}}65{"type":"api-request","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","id":"690b6772-db02-4502-8289-ff03229b0a9a","timestamp":"2026-10-30T05:13:16.941Z","version":"2.1.314","querySource":"sdk","shapeHash":"8c328839765cea6d2c705d451a30ae5d1a69967e2804e4719b5d5d46e0d9f057","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":"d43eb799-6bb6-4d1e-a7c2-43dcc138de53","keep":20,"tail":["8eb6f53cbcc7f9ab5bedd273477e5734aecf515abec70ceeabb8e026e810f192","49410ccaff2cb1ab7061cd26af803187e535e2875474d15350d192296c2a8dac"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"ffc38639bde6534ab4c1ba5dffbe2e407c9aee068a7eda6f0591eb407e5ba5c5"}66{"parentUuid":"5d97fc9a-f4f0-49f6-829a-229e0d07d8b3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrEHzEZ9mQTEBKirnqF","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":184,"cache_read_input_tokens":18002,"output_tokens":813,"output_tokens_details":{"thinking_tokens":30},"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":184},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6888,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrEHirgxLGoEGc9BXYJ","requestRef":"690b6772-db02-4502-8289-ff03229b0a9a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b546524f-8196-45d3-8a17-164ca0f9b3da","timestamp":"2026-10-30T05:13:25.368Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}67{"parentUuid":"b546524f-8196-45d3-8a17-164ca0f9b3da","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrEHzEZ9mQTEBKirnqF","type":"message","role":"assistant","content":[{"type":"text","text":"I ran the simulation and the actual bias is about 8% larger than the classical formula predicts. That gap is too big to be simulation noise. The output gate passes, all phases P0–P5 are marked passed, and re-running the script reproduces `results.json` exactly.\n\n**Results** (n = 30, ρ = 0.8, 20,000 runs, seed 20261029):\n\n| Quantity | Value |\n|---|---|\n| Formula bias, −(1+3ρ)/n | −0.1133 |\n| Simulated bias | −0.1237 |\n| Standard error of the simulated bias | 0.00105 |\n| Mean estimate of ρ | 0.676 (true value 0.8) |\n| Spread of the estimates (sd) | 0.149 |\n| Textbook asymptotic standard error | 0.110 |\n\n- **How far apart they are:** the simulated bias sits about 9.9 standard errors below the formula's value. In other words, the formula understates the size of the bias by about 8%.\n- **Why that's not a flaw in the formula:** it is only the first term of a large-sample expansion, so it is expected to miss smaller terms that shrink like 1/n². At n = 30 and ρ = 0.8, those missing terms are about 0.01 and clearly detectable.\n- **Which n goes in the formula:** I used n = 30, the series length given in the design. The regression actually has 29 data pairs; with 29 in the formula the gap shrinks to about 6.1 standard errors but is still clear. That figure is reported as an extra result.\n- **Spread of the estimates:** their actual spread is about 36% wider than the textbook asymptotic standard error.\n\nLimitations, all stated in the report:\n- **One design point:** the evidence covers only these settings (Gaussian errors, stationary start). It says nothing about other n or ρ, and can't confirm the formula's large-sample behaviour.\n- **Draw order:** the script draws all starting values first, then the shocks. That is one reasonable choice; another order would give statistically equivalent but not identical numbers.\n- **Final ledger edit:** after the passing gate run I added one line to `phases.json` recording that run's receipt. Nothing else changed.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `claims.json`\n- `exclusions.csv`\n- `state/`"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":184,"cache_read_input_tokens":18002,"output_tokens":813,"output_tokens_details":{"thinking_tokens":30},"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":184},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrEHirgxLGoEGc9BXYJ","requestRef":"690b6772-db02-4502-8289-ff03229b0a9a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"367dff04-0002-4a16-924f-9f649ebc2628","timestamp":"2026-10-30T05:13:25.682Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0245","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","version":"2.1.314","gitBranch":"HEAD"}68{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"367dff04-0002-4a16-924f-9f649ebc2628","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9"}69{"type":"cost-state","sessionId":"84a16cfe-c861-45d5-8c4b-958d93441cb9","totalCostUSD":0.31152440000000003,"totalAPIDuration":90769,"totalAPIDurationWithoutRetries":90762,"totalToolDuration":1691,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":92614,"startTime":1793337113074,"modelUsage":{"claude-opus-5-5":{"inputTokens":24,"outputTokens":10275,"thinkingTokens":570,"cacheReadInputTokens":112592,"cacheCreationInputTokens":16682,"cacheCreationEphemeral5mInputTokens":16682,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.31152440000000003}},"hasUnknownModelCost":false}70