spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:10:39.032Z","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:10:39.039Z","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5"}4{"parentUuid":null,"isSidechain":false,"promptId":"9a7dc179-700a-4849-b6af-72de619f4053","type":"user","message":{"role":"user","content":"Begin"},"uuid":"604ceae0-c06c-45af-978b-60ec7251d082","timestamp":"2026-10-30T05:10:39.044Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","shapeHash":"e8a9488e2d454cfbf1ce4842c6347a531fdfc44ec708754ef31a6a0375fb955f","timestamp":"2026-10-30T05:10:39.059Z","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/RC0243\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":"1f0c89ef-6be3-4582-82ba-81df09eacec5","hash":"d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9","message":{"role":"system","content":[],"output_config":{"effort":"medium"}}}7{"type":"api-request","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","id":"70c233a2-0e40-4abe-b764-5c1f9bd3af1d","timestamp":"2026-10-30T05:10:39.059Z","version":"2.1.314","querySource":"sdk","shapeHash":"e8a9488e2d454cfbf1ce4842c6347a531fdfc44ec708754ef31a6a0375fb955f","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":"1b0f1a0cef24b8bcad886efaca7ae90c85dac5330e7025c2b671a9f64dbef444"}8{"parentUuid":"604ceae0-c06c-45af-978b-60ec7251d082","isSidechain":false,"attachment":{"type":"session_context","context":{}},"type":"attachment","uuid":"a07c3a01-82d3-42bd-a710-d93582c58039","timestamp":"2026-10-30T05:10:39.049Z","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}9{"parentUuid":"a07c3a01-82d3-42bd-a710-d93582c58039","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30"},"type":"attachment","uuid":"de43141e-9b24-4735-8f80-29e982ab5820","timestamp":"2026-10-30T05:10:39.049Z","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/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}10{"parentUuid":"de43141e-9b24-4735-8f80-29e982ab5820","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":"54074098-0e3d-49b5-a13c-0ec0c31d6df7","timestamp":"2026-10-30T05:10:39.050Z","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/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5"}12{"parentUuid":"54074098-0e3d-49b5-a13c-0ec0c31d6df7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr2h9RLQcs8g3BZEbrT","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":403,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXr2grp31PiyzzH52DdB","requestRef":"70c233a2-0e40-4abe-b764-5c1f9bd3af1d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"97335edb-0bd2-45a2-81a8-96f642254d2f","timestamp":"2026-10-30T05:10:40.781Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"97335edb-0bd2-45a2-81a8-96f642254d2f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr2h9RLQcs8g3BZEbrT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MgVPhaEJ813YmcKF9xwdio","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_011CgXr2grp31PiyzzH52DdB","requestRef":"70c233a2-0e40-4abe-b764-5c1f9bd3af1d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2183e5e4-f277-4b6d-8dba-9c7b9feb8932","timestamp":"2026-10-30T05:10:41.407Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}14{"parentUuid":"2183e5e4-f277-4b6d-8dba-9c7b9feb8932","isSidechain":false,"promptId":"9a7dc179-700a-4849-b6af-72de619f4053","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_01MgVPhaEJ813YmcKF9xwdio"}]},"uuid":"38125a84-1bbe-4935-8b44-ab73591f4fc5","timestamp":"2026-10-30T05:10:41.465Z","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":"2183e5e4-f277-4b6d-8dba-9c7b9feb8932","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","hash":"877a978db8cce7e4983a1ecb763a6b4bf4d75770735e4f55407bfd1ddc0bfe31","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01MgVPhaEJ813YmcKF9xwdio","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":"1f0c89ef-6be3-4582-82ba-81df09eacec5","id":"55d61d54-a4e4-44f3-a371-a1b51ab83853","timestamp":"2026-10-30T05:10:41.468Z","version":"2.1.314","querySource":"sdk","shapeHash":"e8a9488e2d454cfbf1ce4842c6347a531fdfc44ec708754ef31a6a0375fb955f","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":"70c233a2-0e40-4abe-b764-5c1f9bd3af1d","keep":2,"tail":["877a978db8cce7e4983a1ecb763a6b4bf4d75770735e4f55407bfd1ddc0bfe31","a342c1f08ca8b488ba09ea518e9ff04b36755ae9e49e54dced7e878ca15e7d9d"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"140d8614750ac7b17e1ed18ea8b8be48d4d7cd51a338441683d0eb4367fc1239"}17{"parentUuid":"38125a84-1bbe-4935-8b44-ab73591f4fc5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr2r5CV8Z3GHkWBcoP7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SUGwVHGLBySi6M5c3eKWJ1","name":"Bash","input":{"command":"cat data/design.csv; shasum -a 256 data/design.csv; cat gate_spec.json | head -50","description":"Inspect 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":113,"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_011CgXr2qoKbXRC8WJ6ExERU","requestRef":"55d61d54-a4e4-44f3-a371-a1b51ab83853","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"82fd7aed-ea1f-4b6d-a8f4-e55eeea89331","timestamp":"2026-10-30T05:10:42.996Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}18{"parentUuid":"82fd7aed-ea1f-4b6d-a8f4-e55eeea89331","isSidechain":false,"promptId":"9a7dc179-700a-4849-b6af-72de619f4053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SUGwVHGLBySi6M5c3eKWJ1","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\": {","is_error":false}]},"uuid":"79956861-20bf-4bc0-a5d1-de910898fe13","timestamp":"2026-10-30T05:10:43.024Z","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\": {","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"82fd7aed-ea1f-4b6d-a8f4-e55eeea89331","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}19{"type":"api-request-blob","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","hash":"9a5edf4dfb1a398fcea311bda16cc16e857c4ce155e35c9783acef30382b793f","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01SUGwVHGLBySi6M5c3eKWJ1","name":"Bash","input":{"command":"cat data/design.csv; shasum -a 256 data/design.csv; cat gate_spec.json | head -50","description":"Inspect design and gate spec"}}]}}20{"type":"api-request","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","id":"5dae3ad6-a962-4d3b-a8b2-81c871c58d88","timestamp":"2026-10-30T05:10:43.028Z","version":"2.1.314","querySource":"sdk","shapeHash":"e8a9488e2d454cfbf1ce4842c6347a531fdfc44ec708754ef31a6a0375fb955f","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":"55d61d54-a4e4-44f3-a371-a1b51ab83853","keep":4,"tail":["9a5edf4dfb1a398fcea311bda16cc16e857c4ce155e35c9783acef30382b793f","414a6d59ac2c8e1285d240bab7f91fdd7c51f7972574f6d64cc386f412e7b496"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"13652330c47f3fa8b2d9b3c451b0ebcfc45f39f7be8a86b5a7e3019c385061fb"}21{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"79956861-20bf-4bc0-a5d1-de910898fe13","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5"}22{"type":"atis-latch","atis":"","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5"}23{"parentUuid":"79956861-20bf-4bc0-a5d1-de910898fe13","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr2xjYC6EigW33ktxSK","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":762,"cache_read_input_tokens":6066,"output_tokens":205,"output_tokens_details":{"thinking_tokens":101},"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":762},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":957,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXr2xUfE29hqd6BpYWKK","requestRef":"5dae3ad6-a962-4d3b-a8b2-81c871c58d88","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c7c6a971-2255-46e7-956c-e743beb39f2d","timestamp":"2026-10-30T05:10:45.653Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"c7c6a971-2255-46e7-956c-e743beb39f2d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr2xjYC6EigW33ktxSK","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01T7TQ9JWXiDcrteLU9gBCLu","name":"Bash","input":{"command":"sed -n 50,200p gate_spec.json; cat bundle.json | head -30","description":"Read 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":762,"cache_read_input_tokens":6066,"output_tokens":205,"output_tokens_details":{"thinking_tokens":101},"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":762},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXr2xUfE29hqd6BpYWKK","requestRef":"5dae3ad6-a962-4d3b-a8b2-81c871c58d88","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"89d0e127-8f3c-4d02-85f4-9f1f89af7aca","timestamp":"2026-10-30T05:10:46.249Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"89d0e127-8f3c-4d02-85f4-9f1f89af7aca","isSidechain":false,"promptId":"9a7dc179-700a-4849-b6af-72de619f4053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01T7TQ9JWXiDcrteLU9gBCLu","type":"tool_result","content":"    \"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\",\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}{\n  \"task_id\": \"F5-conf-02\",\n  \"family\": \"F5\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:42:22.079893+00:00\",\n  \"files\": {\n    \"TASK.md\": \"2a389cdf509f84a0f7e0cf2780400eb5b6c1cd45ee0359a2d0708b803641d090\",\n    \"gate_spec.json\": \"1479a11a8587f31338a07b4532d5b9c2da1bba66c2486348acebf18090c30c6b\",\n    \"data/design.csv\": \"2f459c539378cbe54d830c18af466f2fa4230ce48a609aa6464f8334a0d36ecb\"\n  }\n}","is_error":false}]},"uuid":"239ea375-178c-4e0e-8ca1-ad74438f9f64","timestamp":"2026-10-30T05:10:46.267Z","toolUseResult":{"stdout":"    \"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\",\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}{\n  \"task_id\": \"F5-conf-02\",\n  \"family\": \"F5\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:42:22.079893+00:00\",\n  \"files\": {\n    \"TASK.md\": \"2a389cdf509f84a0f7e0cf2780400eb5b6c1cd45ee0359a2d0708b803641d090\",\n    \"gate_spec.json\": \"1479a11a8587f31338a07b4532d5b9c2da1bba66c2486348acebf18090c30c6b\",\n    \"data/design.csv\": \"2f459c539378cbe54d830c18af466f2fa4230ce48a609aa6464f8334a0d36ecb\"\n  }\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"89d0e127-8f3c-4d02-85f4-9f1f89af7aca","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","hash":"9d95f73415e62fa0ebdcea4e5abe5d49213692ecb1b5d73df0194d3166fc7085","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSxgcKEAgTGAI4AUIIdGhpbmtpbmcSDMRthfhFkeAGRKHqWBoMVIzClR5ZjV9OuLK/IjAxMUFV4rrGWUumR0h274CjQ7ogfnU9l9YsMh5k43Q+vd8GHuh+g3P8oe8Vsggw33wq4wbWmNTaiSxSu63uKg6fL7qWP2IO4G2InrJDqIWahyu7MfBl9ltTdEziCnN+o4Aa6yY2PhIJ4AA/gHDn4zlw39pC155bqGV8tv3Ci6B+/zC/kJRX0WB2TnBevSnMVjqm5IAEEfTxIqgw7f27frrIOiAIsJk+bPfwsouNDo4eYK37RRizu9SuabP6tY2Z+Aytuwf+1Ng0Q5qLBWm2f8lEMo3VuHH1BGFKRIthxFR9+O1SkHyPiEvurxOdUOgVr+0XjH3qHrW5xibAqG94EH2G9h4faOeZvZHkpLNg6wGxcs/V0u1Eo9kG3cMZ2aFKys25v0knQqiaGJ0ASNL6SYHTwyRkHqWQRL3cMpbze9TiNw3p828FZ+GIeKaM/BsvLE8f/52+fc2oMZVNIwIPOXXGO2Xuz6GWUS1Iax2lLI8r56u4VQSjuC/EPRkcTf+dldr9AppDgCVHqot5tJ20pnyHZpwCM+kiK3D/E6w0TBuTK+zgk7813QV39c3BshnzBO32W7mIx6OjIcRl9TADC17ld8S32fBZaaGERdqavVEh6JGLmfwQeLnVPFTZJILmuDK8JI1yBGF0BtwoaKqyUpwMXqWaZxJowQQwEX0iV+2jYP/gpf4acXSPM2HCdtYzanuVgTdrVP7DN8BvXWkGozh0NWqf/ZlKxRE+t8eHpTKFkenmnLQ9/Eubj4pcgoMFyN78iuzYeM7Mj6MUMnhLzdEGVnC/VW4apwqYnqyGOmrULrL65+4Pdk3Mcfa63+2Rvm5vP3Wn6YGE42E7v07pJj9gTQVXCKH1rhS6ZHaCRtvgbcqHB4kVi+0CWxeyvIZfwlLXsXuVnxZq408Tz9vPyAZgTBKQ36kWUeFEaWTNSoo0l4PuqMGXRUz1bDawYQ/v1NphxGy6XIoV45pk+URo4FNmFDRVXb3bMD/YxRncFW1zJemt2osbm18Un41zaGSTHEdE/4fD3Wyukw/mb7e+kpxygsnzmUdMC9VP4tLd14DOcI+IQKpA5UW94HTeb1cSjYQFCGIPB6cAjFzVJxsVE7QFDyldwHrXUJUCoP2YsBlNJQY2nUc9DgovEdhkPLjQOnyWPiCDWtTHL6JN5yzFKmODZ9Hk/x/fOL6zjLe3z54B86IiAuPL3efgUdrFEdybsUh3dX0Lj+kYAQ=="},{"type":"tool_use","id":"toolu_01T7TQ9JWXiDcrteLU9gBCLu","name":"Bash","input":{"command":"sed -n 50,200p gate_spec.json; cat bundle.json | head -30","description":"Read rest of gate spec"}}]}}27{"type":"api-request","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","id":"cb19647d-2396-42ff-b866-2d3e3e4de679","timestamp":"2026-10-30T05:10:46.270Z","version":"2.1.314","querySource":"sdk","shapeHash":"e8a9488e2d454cfbf1ce4842c6347a531fdfc44ec708754ef31a6a0375fb955f","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":"5dae3ad6-a962-4d3b-a8b2-81c871c58d88","keep":6,"tail":["9d95f73415e62fa0ebdcea4e5abe5d49213692ecb1b5d73df0194d3166fc7085","4a8f744d1b9968731297bf5d079b7076176190936ccb7075a94b03613c6376e6"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"4ba9e0c668d4732accfcc2423572334ab34ca100ccbf966a64ccdc13b6cdc77c"}28{"parentUuid":"239ea375-178c-4e0e-8ca1-ad74438f9f64","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr3CeTdSieSMrvGeBee","type":"message","role":"assistant","content":[{"type":"text","text":"P0: contract and ledger."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":836,"cache_read_input_tokens":6828,"output_tokens":1081,"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":836},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXr3CMMJmvrKsYnPfbwW","requestRef":"cb19647d-2396-42ff-b866-2d3e3e4de679","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3b8f3018-8664-4e39-a018-d1b25dfdc962","timestamp":"2026-10-30T05:10:47.845Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"3b8f3018-8664-4e39-a018-d1b25dfdc962","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr3CeTdSieSMrvGeBee","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013bZexRU2FdFidwHsRnhhKC","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > 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 large is the finite-sample bias of the OLS rho_hat\n(regression on constant and y_{t-1}, t=2..n), and how well does the Marriott-Pope/Kendall approximation -(1+3rho)/n describe it?\n\n**Estimands.** (i) bias_analytical = -(1+3rho)/n at design (n=30, rho=0.8): a first-order (O(1/n)) asymptotic expansion of E[rho_hat]-rho,\nerror O(n^-2), meaningful under stationarity, |rho|<1, Gaussian errors. (ii) bias_mc = mean(rho_hat)-rho over R replications, with MC SE sd/sqrt(R).\nAlso rho_hat_mean, rho_hat_sd_mc, discrepancy_se_units=(bias_mc-bias_analytical)/bias_mc_se, se_asymptotic=sqrt((1-rho^2)/n).\n\n**Outputs.** out/results.json (keys: task_id, bias_analytical, bias_mc, bias_mc_se, rho_hat_mean, rho_hat_sd_mc, discrepancy_se_units,\nse_asymptotic, n_replications, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed), out/report.md (6 sections),\nout/analysis.py, out/claims.json, plus protocol files (state/, exclusions.csv).\n\n**Information rules.** Only data/design.csv; follow design exactly: n=30, rho=0.8, sigma=1, intercept, stationary y_1, R=20000,\nseed 20261029 via numpy.random.default_rng; fresh series per replication.\n\n**Invalidating conditions.** Missing/inconsistent design parameters; |rho|>=1 (no stationary distribution); deviation from seed/design.\nNote: MC discrepancy vs the approximation is not a \"test\" of the formula's correctness — it's an O(1/n) approximation, and a difference at\nfixed n is expected from higher-order terms; MC only establishes bias at this one design point.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > 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":836,"cache_read_input_tokens":6828,"output_tokens":1081,"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":836},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXr3CMMJmvrKsYnPfbwW","requestRef":"cb19647d-2396-42ff-b866-2d3e3e4de679","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b3174938-9af1-48e4-930e-93a11be348ab","timestamp":"2026-10-30T05:10:56.425Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}30{"parentUuid":"b3174938-9af1-48e4-930e-93a11be348ab","isSidechain":false,"promptId":"9a7dc179-700a-4849-b6af-72de619f4053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013bZexRU2FdFidwHsRnhhKC","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"f218d5fa-41b6-46b5-ba2a-4974f3aa3178","timestamp":"2026-10-30T05:10:56.450Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b3174938-9af1-48e4-930e-93a11be348ab","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243/out/state","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}31{"type":"api-request-blob","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","hash":"bb3532b375d4147bad748372cf6175e4144eb74934da37d39cb50e907fc5c05e","message":{"role":"assistant","content":[{"type":"text","text":"P0: contract and ledger."},{"type":"tool_use","id":"toolu_013bZexRU2FdFidwHsRnhhKC","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > 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 large is the finite-sample bias of the OLS rho_hat\n(regression on constant and y_{t-1}, t=2..n), and how well does the Marriott-Pope/Kendall approximation -(1+3rho)/n describe it?\n\n**Estimands.** (i) bias_analytical = -(1+3rho)/n at design (n=30, rho=0.8): a first-order (O(1/n)) asymptotic expansion of E[rho_hat]-rho,\nerror O(n^-2), meaningful under stationarity, |rho|<1, Gaussian errors. (ii) bias_mc = mean(rho_hat)-rho over R replications, with MC SE sd/sqrt(R).\nAlso rho_hat_mean, rho_hat_sd_mc, discrepancy_se_units=(bias_mc-bias_analytical)/bias_mc_se, se_asymptotic=sqrt((1-rho^2)/n).\n\n**Outputs.** out/results.json (keys: task_id, bias_analytical, bias_mc, bias_mc_se, rho_hat_mean, rho_hat_sd_mc, discrepancy_se_units,\nse_asymptotic, n_replications, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed), out/report.md (6 sections),\nout/analysis.py, out/claims.json, plus protocol files (state/, exclusions.csv).\n\n**Information rules.** Only data/design.csv; follow design exactly: n=30, rho=0.8, sigma=1, intercept, stationary y_1, R=20000,\nseed 20261029 via numpy.random.default_rng; fresh series per replication.\n\n**Invalidating conditions.** Missing/inconsistent design parameters; |rho|>=1 (no stationary distribution); deviation from seed/design.\nNote: MC discrepancy vs the approximation is not a \"test\" of the formula's correctness — it's an O(1/n) approximation, and a difference at\nfixed n is expected from higher-order terms; MC only establishes bias at this one design point.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, log"}}]}}32{"type":"api-request","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","id":"056b65c6-e4e4-49ba-ad77-b0e2d39f6574","timestamp":"2026-10-30T05:10:56.456Z","version":"2.1.314","querySource":"sdk","shapeHash":"e8a9488e2d454cfbf1ce4842c6347a531fdfc44ec708754ef31a6a0375fb955f","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":"cb19647d-2396-42ff-b866-2d3e3e4de679","keep":8,"tail":["bb3532b375d4147bad748372cf6175e4144eb74934da37d39cb50e907fc5c05e","f060db0fa5902d953164494b446287c037a8d9adecaabed3c980cef8319e508c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5edeb7781a6d25f4ecd5654ae1653a3cfff3be8c2fc1a1d5067f79d94cfe2ce5"}33{"parentUuid":"f218d5fa-41b6-46b5-ba2a-4974f3aa3178","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr3xBLZcoVT2B8SRoeq","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":1096,"cache_read_input_tokens":7664,"output_tokens":2204,"output_tokens_details":{"thinking_tokens":272},"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":1096},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2273,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXr3wsyTm25HqVUFvfbf","requestRef":"056b65c6-e4e4-49ba-ad77-b0e2d39f6574","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0fa17207-de9c-4c02-8cb4-da4d4ebe1b71","timestamp":"2026-10-30T05:11:01.942Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243/out/state","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"0fa17207-de9c-4c02-8cb4-da4d4ebe1b71","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr3xBLZcoVT2B8SRoeq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Dnm2YUk9RQZGzu9ymDvu1A","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F5-conf-02: finite-sample bias of OLS AR(1) coefficient (with intercept).\nReads data/design.csv only; writes out/results.json deterministically.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\ndesign = pd.read_csv(os.path.join(ROOT, \"data\", \"design.csv\"))\np = dict(zip(design[\"parameter\"], design[\"value\"]))\n\nn = int(p[\"n\"]); rho = float(p[\"rho\"]); sigma = float(p[\"sigma\"])\nintercept = str(p[\"intercept\"]).strip().lower() == \"yes\"\ninit = str(p[\"initial_condition\"]).strip().lower()\nR = int(p[\"n_replications\"]); seed = int(p[\"seed\"])\ndata_issues = []\nassert intercept and init == \"stationary\" and abs(rho) < 1 and n >= 3 and R > 1\n\n# --- Baseline (closed-form) quantities ---\nbias_analytical = -(1.0 + 3.0 * rho) / n\nse_asymptotic = float(np.sqrt((1.0 - rho ** 2) / n))\n# sensitivity: same formula using effective regression sample size n-1\nbias_analytical_neff = -(1.0 + 3.0 * rho) / (n - 1)\n\n# --- Monte Carlo (vectorised) ---\n# Draw order: first R stationary initial values, then an R x (n-1) innovation matrix.\nrng = np.random.default_rng(seed)\ny = np.empty((R, n))\ny[:, 0] = rng.standard_normal(R) * sigma / np.sqrt(1.0 - rho ** 2)\neps = rng.standard_normal((R, n - 1)) * sigma\nc = 0.0\nfor t in range(1, n):\n    y[:, t] = c + rho * y[:, t - 1] + eps[:, t - 1]\nx = y[:, :-1]; z = y[:, 1:]           # regress y_t (t=2..n) on (1, y_{t-1})\nxd = x - x.mean(axis=1, keepdims=True)\nzd = z - z.mean(axis=1, keepdims=True)\nrho_hat = (xd * zd).sum(axis=1) / (xd ** 2).sum(axis=1)\nfinite = np.isfinite(rho_hat)\nn_nonfinite = int((~finite).sum())\nif n_nonfinite:\n    data_issues.append({\"issue\": f\"{n_nonfinite} non-finite rho_hat\", \"handling\": \"excluded\"})\nrh = rho_hat[finite]\nR_used = int(rh.size)\n\n# cross-check one replication against numpy lstsq\nX0 = np.column_stack([np.ones(n - 1), x[0]])\nb0 = np.linalg.lstsq(X0, z[0], rcond=None)[0][1]\nassert abs(b0 - rho_hat[0]) < 1e-10\n\nrho_hat_mean = float(rh.mean())\nrho_hat_sd = float(rh.std(ddof=1))\nbias_mc = rho_hat_mean - rho\nbias_mc_se = rho_hat_sd / np.sqrt(R_used)\ndisc = (bias_mc - bias_analytical) / bias_mc_se\n\nres = {\n    \"task_id\": \"F5-conf-02\",\n    \"bias_analytical\": bias_analytical,\n    \"bias_mc\": float(bias_mc),\n    \"bias_mc_se\": float(bias_mc_se),\n    \"rho_hat_mean\": rho_hat_mean,\n    \"rho_hat_sd_mc\": rho_hat_sd,\n    \"discrepancy_se_units\": float(disc),\n    \"se_asymptotic\": se_asymptotic,\n    \"n_replications\": R,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": {\"design.csv\": int(len(design))},\n    \"n_analysed\": R_used,\n    \"random_seed\": seed,\n    # supplementary keys\n    \"baseline_bias_analytical\": bias_analytical,\n    \"baseline_se_asymptotic\": se_asymptotic,\n    \"bias_analytical_neff\": bias_analytical_neff,\n    \"discrepancy_se_units_neff\": float((bias_mc - bias_analytical_neff) / bias_mc_se),\n    \"bias_mc_minus_analytical\": float(bias_mc - bias_analytical),\n    \"ratio_bias_mc_to_analytical\": float(bias_mc / bias_analytical),\n    \"rho_hat_median\": float(np.median(rh)),\n    \"share_rho_hat_below_rho\": float((rh < rho).mean()),\n    \"ratio_sd_mc_to_se_asymptotic\": float(rho_hat_sd / se_asymptotic),\n    \"n_regression_obs\": n - 1,\n    \"n_nonfinite_rho_hat\": n_nonfinite,\n}\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py","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":1096,"cache_read_input_tokens":7664,"output_tokens":2204,"output_tokens_details":{"thinking_tokens":272},"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":1096},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXr3wsyTm25HqVUFvfbf","requestRef":"056b65c6-e4e4-49ba-ad77-b0e2d39f6574","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"fbf31e21-d169-4864-833c-e5d8c1d8c744","timestamp":"2026-10-30T05:11:15.311Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243/out/state","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"fbf31e21-d169-4864-833c-e5d8c1d8c744","isSidechain":false,"promptId":"9a7dc179-700a-4849-b6af-72de619f4053","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0243/out/state/out/analysis.py': [Errno 2] No such file or directory","is_error":true,"tool_use_id":"toolu_01Dnm2YUk9RQZGzu9ymDvu1A"}]},"uuid":"771548a8-d6c8-4e53-9cc2-cf7a9153f819","timestamp":"2026-10-30T05:11:15.341Z","toolUseResult":"Error: Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0243/out/state/out/analysis.py': [Errno 2] No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"fbf31e21-d169-4864-833c-e5d8c1d8c744","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243/out/state","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}36{"type":"api-request-blob","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","hash":"71d9251f2be6b7de53e0c56edc2d206453b472301659f5787c00480eaf83a621","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01Dnm2YUk9RQZGzu9ymDvu1A","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F5-conf-02: finite-sample bias of OLS AR(1) coefficient (with intercept).\nReads data/design.csv only; writes out/results.json deterministically.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\ndesign = pd.read_csv(os.path.join(ROOT, \"data\", \"design.csv\"))\np = dict(zip(design[\"parameter\"], design[\"value\"]))\n\nn = int(p[\"n\"]); rho = float(p[\"rho\"]); sigma = float(p[\"sigma\"])\nintercept = str(p[\"intercept\"]).strip().lower() == \"yes\"\ninit = str(p[\"initial_condition\"]).strip().lower()\nR = int(p[\"n_replications\"]); seed = int(p[\"seed\"])\ndata_issues = []\nassert intercept and init == \"stationary\" and abs(rho) < 1 and n >= 3 and R > 1\n\n# --- Baseline (closed-form) quantities ---\nbias_analytical = -(1.0 + 3.0 * rho) / n\nse_asymptotic = float(np.sqrt((1.0 - rho ** 2) / n))\n# sensitivity: same formula using effective regression sample size n-1\nbias_analytical_neff = -(1.0 + 3.0 * rho) / (n - 1)\n\n# --- Monte Carlo (vectorised) ---\n# Draw order: first R stationary initial values, then an R x (n-1) innovation matrix.\nrng = np.random.default_rng(seed)\ny = np.empty((R, n))\ny[:, 0] = rng.standard_normal(R) * sigma / np.sqrt(1.0 - rho ** 2)\neps = rng.standard_normal((R, n - 1)) * sigma\nc = 0.0\nfor t in range(1, n):\n    y[:, t] = c + rho * y[:, t - 1] + eps[:, t - 1]\nx = y[:, :-1]; z = y[:, 1:]           # regress y_t (t=2..n) on (1, y_{t-1})\nxd = x - x.mean(axis=1, keepdims=True)\nzd = z - z.mean(axis=1, keepdims=True)\nrho_hat = (xd * zd).sum(axis=1) / (xd ** 2).sum(axis=1)\nfinite = np.isfinite(rho_hat)\nn_nonfinite = int((~finite).sum())\nif n_nonfinite:\n    data_issues.append({\"issue\": f\"{n_nonfinite} non-finite rho_hat\", \"handling\": \"excluded\"})\nrh = rho_hat[finite]\nR_used = int(rh.size)\n\n# cross-check one replication against numpy lstsq\nX0 = np.column_stack([np.ones(n - 1), x[0]])\nb0 = np.linalg.lstsq(X0, z[0], rcond=None)[0][1]\nassert abs(b0 - rho_hat[0]) < 1e-10\n\nrho_hat_mean = float(rh.mean())\nrho_hat_sd = float(rh.std(ddof=1))\nbias_mc = rho_hat_mean - rho\nbias_mc_se = rho_hat_sd / np.sqrt(R_used)\ndisc = (bias_mc - bias_analytical) / bias_mc_se\n\nres = {\n    \"task_id\": \"F5-conf-02\",\n    \"bias_analytical\": bias_analytical,\n    \"bias_mc\": float(bias_mc),\n    \"bias_mc_se\": float(bias_mc_se),\n    \"rho_hat_mean\": rho_hat_mean,\n    \"rho_hat_sd_mc\": rho_hat_sd,\n    \"discrepancy_se_units\": float(disc),\n    \"se_asymptotic\": se_asymptotic,\n    \"n_replications\": R,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": {\"design.csv\": int(len(design))},\n    \"n_analysed\": R_used,\n    \"random_seed\": seed,\n    # supplementary keys\n    \"baseline_bias_analytical\": bias_analytical,\n    \"baseline_se_asymptotic\": se_asymptotic,\n    \"bias_analytical_neff\": bias_analytical_neff,\n    \"discrepancy_se_units_neff\": float((bias_mc - bias_analytical_neff) / bias_mc_se),\n    \"bias_mc_minus_analytical\": float(bias_mc - bias_analytical),\n    \"ratio_bias_mc_to_analytical\": float(bias_mc / bias_analytical),\n    \"rho_hat_median\": float(np.median(rh)),\n    \"share_rho_hat_below_rho\": float((rh < rho).mean()),\n    \"ratio_sd_mc_to_se_asymptotic\": float(rho_hat_sd / se_asymptotic),\n    \"n_regression_obs\": n - 1,\n    \"n_nonfinite_rho_hat\": n_nonfinite,\n}\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script"}}]}}37{"type":"api-request","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","id":"5d321077-b991-41e6-8d99-2771157dbe7c","timestamp":"2026-10-30T05:11:15.344Z","version":"2.1.314","querySource":"sdk","shapeHash":"e8a9488e2d454cfbf1ce4842c6347a531fdfc44ec708754ef31a6a0375fb955f","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":"056b65c6-e4e4-49ba-ad77-b0e2d39f6574","keep":10,"tail":["71d9251f2be6b7de53e0c56edc2d206453b472301659f5787c00480eaf83a621","5f4f1dabd8312333f7b95654ea6b3106182a5d1b8a7ae77497abe3610305569d"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"68e89a258f9949a1db8fed4ab7657ba1a8739830cd184562237375f5a134075b"}38{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"771548a8-d6c8-4e53-9cc2-cf7a9153f819","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5"}39{"type":"atis-latch","atis":"","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5"}40{"parentUuid":"771548a8-d6c8-4e53-9cc2-cf7a9153f819","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr5LubQYx7NAcT33ZF6","type":"message","role":"assistant","content":[{"type":"text","text":"The shell stayed in out/state; rerunning from root."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2366,"cache_read_input_tokens":8760,"output_tokens":131,"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":2366},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXr5LeTuXNVYzC8g1jx4","requestRef":"5d321077-b991-41e6-8d99-2771157dbe7c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c149b138-babb-4424-8586-8317e82c4dd8","timestamp":"2026-10-30T05:11:16.498Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243/out/state","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"c149b138-babb-4424-8586-8317e82c4dd8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr5LubQYx7NAcT33ZF6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XRnCakJr4HCyqy6sYphFtc","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0243 && pwd && ls out out/state","description":"Return to workspace root"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2366,"cache_read_input_tokens":8760,"output_tokens":131,"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":2366},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXr5LeTuXNVYzC8g1jx4","requestRef":"5d321077-b991-41e6-8d99-2771157dbe7c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"64edc7e7-2d71-4ad8-b754-068b565e6d5c","timestamp":"2026-10-30T05:11:17.103Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243/out/state","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}42{"parentUuid":"64edc7e7-2d71-4ad8-b754-068b565e6d5c","isSidechain":false,"promptId":"9a7dc179-700a-4849-b6af-72de619f4053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XRnCakJr4HCyqy6sYphFtc","type":"tool_result","content":"/Users/simon-pierreboucher/wp29/runs/RC0243\nout:\nstate\n\nout/state:\ncontract.md\nlog.md\nphases.json","is_error":false}]},"uuid":"146fbade-08b9-4738-900d-5f731e939111","timestamp":"2026-10-30T05:11:17.117Z","toolUseResult":{"stdout":"/Users/simon-pierreboucher/wp29/runs/RC0243\nout:\nstate\n\nout/state:\ncontract.md\nlog.md\nphases.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"64edc7e7-2d71-4ad8-b754-068b565e6d5c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}43{"type":"api-request-blob","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","hash":"e90f95a4d6abd73c42172fc9004550d827445b0ad3c2e034a033785a8b09ab2f","message":{"role":"assistant","content":[{"type":"text","text":"The shell stayed in out/state; rerunning from root."},{"type":"tool_use","id":"toolu_01XRnCakJr4HCyqy6sYphFtc","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0243 && pwd && ls out out/state","description":"Return to workspace root"}}]}}44{"type":"api-request","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","id":"29b78ecf-5600-4d2a-a0dd-16b91194af4d","timestamp":"2026-10-30T05:11:17.120Z","version":"2.1.314","querySource":"sdk","shapeHash":"e8a9488e2d454cfbf1ce4842c6347a531fdfc44ec708754ef31a6a0375fb955f","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":"5d321077-b991-41e6-8d99-2771157dbe7c","keep":12,"tail":["e90f95a4d6abd73c42172fc9004550d827445b0ad3c2e034a033785a8b09ab2f","db9969053e36a90d6c0103c328a4bf532390a89824d15bfaaa68c7c931f1b087"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"9f5608d046c8a43c5fd91b7c43363b434bf6e7eb60b2cf2c7677ede885bd0d48"}45{"parentUuid":"146fbade-08b9-4738-900d-5f731e939111","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr5UVFw3MddtB4AVxRq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NRuzVbrruXRcn7Vwqzkp2C","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F5-conf-02: finite-sample bias of OLS AR(1) coefficient (with intercept).\nReads data/design.csv only; writes out/results.json deterministically.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\ndesign = pd.read_csv(os.path.join(ROOT, \"data\", \"design.csv\"))\np = dict(zip(design[\"parameter\"], design[\"value\"]))\n\nn = int(p[\"n\"]); rho = float(p[\"rho\"]); sigma = float(p[\"sigma\"])\nintercept = str(p[\"intercept\"]).strip().lower() == \"yes\"\ninit = str(p[\"initial_condition\"]).strip().lower()\nR = int(p[\"n_replications\"]); seed = int(p[\"seed\"])\ndata_issues = []\nassert intercept and init == \"stationary\" and abs(rho) < 1 and n >= 3 and R > 1\n\n# --- Baseline (closed-form) quantities ---\nbias_analytical = -(1.0 + 3.0 * rho) / n\nse_asymptotic = float(np.sqrt((1.0 - rho ** 2) / n))\n# sensitivity: same formula using effective regression sample size n-1\nbias_analytical_neff = -(1.0 + 3.0 * rho) / (n - 1)\n\n# --- Monte Carlo (vectorised) ---\n# Draw order: first R stationary initial values, then an R x (n-1) innovation matrix.\nrng = np.random.default_rng(seed)\ny = np.empty((R, n))\ny[:, 0] = rng.standard_normal(R) * sigma / np.sqrt(1.0 - rho ** 2)\neps = rng.standard_normal((R, n - 1)) * sigma\nc = 0.0\nfor t in range(1, n):\n    y[:, t] = c + rho * y[:, t - 1] + eps[:, t - 1]\nx = y[:, :-1]; z = y[:, 1:]           # regress y_t (t=2..n) on (1, y_{t-1})\nxd = x - x.mean(axis=1, keepdims=True)\nzd = z - z.mean(axis=1, keepdims=True)\nrho_hat = (xd * zd).sum(axis=1) / (xd ** 2).sum(axis=1)\nfinite = np.isfinite(rho_hat)\nn_nonfinite = int((~finite).sum())\nif n_nonfinite:\n    data_issues.append({\"issue\": f\"{n_nonfinite} non-finite rho_hat\", \"handling\": \"excluded\"})\nrh = rho_hat[finite]\nR_used = int(rh.size)\n\n# cross-check one replication against numpy lstsq\nX0 = np.column_stack([np.ones(n - 1), x[0]])\nb0 = np.linalg.lstsq(X0, z[0], rcond=None)[0][1]\nassert abs(b0 - rho_hat[0]) < 1e-10\n\nrho_hat_mean = float(rh.mean())\nrho_hat_sd = float(rh.std(ddof=1))\nbias_mc = rho_hat_mean - rho\nbias_mc_se = rho_hat_sd / np.sqrt(R_used)\ndisc = (bias_mc - bias_analytical) / bias_mc_se\n\nres = {\n    \"task_id\": \"F5-conf-02\",\n    \"bias_analytical\": bias_analytical,\n    \"bias_mc\": float(bias_mc),\n    \"bias_mc_se\": float(bias_mc_se),\n    \"rho_hat_mean\": rho_hat_mean,\n    \"rho_hat_sd_mc\": rho_hat_sd,\n    \"discrepancy_se_units\": float(disc),\n    \"se_asymptotic\": se_asymptotic,\n    \"n_replications\": R,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": {\"design.csv\": int(len(design))},\n    \"n_analysed\": R_used,\n    \"random_seed\": seed,\n    # supplementary keys\n    \"baseline_bias_analytical\": bias_analytical,\n    \"baseline_se_asymptotic\": se_asymptotic,\n    \"bias_analytical_neff\": bias_analytical_neff,\n    \"discrepancy_se_units_neff\": float((bias_mc - bias_analytical_neff) / bias_mc_se),\n    \"bias_mc_minus_analytical\": float(bias_mc - bias_analytical),\n    \"ratio_bias_mc_to_analytical\": float(bias_mc / bias_analytical),\n    \"rho_hat_median\": float(np.median(rh)),\n    \"share_rho_hat_below_rho\": float((rh < rho).mean()),\n    \"ratio_sd_mc_to_se_asymptotic\": float(rho_hat_sd / se_asymptotic),\n    \"n_regression_obs\": n - 1,\n    \"n_nonfinite_rho_hat\": n_nonfinite,\n}\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py","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":185,"cache_read_input_tokens":11126,"output_tokens":1931,"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":185},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXr5UDe6HGb8ZU6h29v4","requestRef":"29b78ecf-5600-4d2a-a0dd-16b91194af4d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a4f1d40e-3df6-4048-868e-beb2e8066fc3","timestamp":"2026-10-30T05:11:30.004Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}46{"parentUuid":"a4f1d40e-3df6-4048-868e-beb2e8066fc3","isSidechain":false,"promptId":"9a7dc179-700a-4849-b6af-72de619f4053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NRuzVbrruXRcn7Vwqzkp2C","type":"tool_result","content":"{\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  \"baseline_bias_analytical\": -0.11333333333333334,\n  \"baseline_se_asymptotic\": 0.1095445115010332,\n  \"bias_analytical_neff\": -0.11724137931034484,\n  \"discrepancy_se_units_neff\": -6.143304611770056,\n  \"bias_mc_minus_analytical\": -0.010376769877305458,\n  \"ratio_bias_mc_to_analytical\": 1.0915597342115186,\n  \"rho_hat_median\": 0.6987087985454112,\n  \"share_rho_hat_below_rho\": 0.7928,\n  \"ratio_sd_mc_to_se_asymptotic\": 1.3593801990030177,\n  \"n_regression_obs\": 29,\n  \"n_nonfinite_rho_hat\": 0\n}","is_error":false}]},"uuid":"f08a4e2c-1765-49f8-a3a3-21987495a1c1","timestamp":"2026-10-30T05:11:30.249Z","toolUseResult":{"stdout":"{\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  \"baseline_bias_analytical\": -0.11333333333333334,\n  \"baseline_se_asymptotic\": 0.1095445115010332,\n  \"bias_analytical_neff\": -0.11724137931034484,\n  \"discrepancy_se_units_neff\": -6.143304611770056,\n  \"bias_mc_minus_analytical\": -0.010376769877305458,\n  \"ratio_bias_mc_to_analytical\": 1.0915597342115186,\n  \"rho_hat_median\": 0.6987087985454112,\n  \"share_rho_hat_below_rho\": 0.7928,\n  \"ratio_sd_mc_to_se_asymptotic\": 1.3593801990030177,\n  \"n_regression_obs\": 29,\n  \"n_nonfinite_rho_hat\": 0\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a4f1d40e-3df6-4048-868e-beb2e8066fc3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}47{"type":"api-request-blob","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","hash":"05d43b6d1cbe2c9ec75302757662eb126538de7438762499d2f923eaa0eaf44b","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01NRuzVbrruXRcn7Vwqzkp2C","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F5-conf-02: finite-sample bias of OLS AR(1) coefficient (with intercept).\nReads data/design.csv only; writes out/results.json deterministically.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\ndesign = pd.read_csv(os.path.join(ROOT, \"data\", \"design.csv\"))\np = dict(zip(design[\"parameter\"], design[\"value\"]))\n\nn = int(p[\"n\"]); rho = float(p[\"rho\"]); sigma = float(p[\"sigma\"])\nintercept = str(p[\"intercept\"]).strip().lower() == \"yes\"\ninit = str(p[\"initial_condition\"]).strip().lower()\nR = int(p[\"n_replications\"]); seed = int(p[\"seed\"])\ndata_issues = []\nassert intercept and init == \"stationary\" and abs(rho) < 1 and n >= 3 and R > 1\n\n# --- Baseline (closed-form) quantities ---\nbias_analytical = -(1.0 + 3.0 * rho) / n\nse_asymptotic = float(np.sqrt((1.0 - rho ** 2) / n))\n# sensitivity: same formula using effective regression sample size n-1\nbias_analytical_neff = -(1.0 + 3.0 * rho) / (n - 1)\n\n# --- Monte Carlo (vectorised) ---\n# Draw order: first R stationary initial values, then an R x (n-1) innovation matrix.\nrng = np.random.default_rng(seed)\ny = np.empty((R, n))\ny[:, 0] = rng.standard_normal(R) * sigma / np.sqrt(1.0 - rho ** 2)\neps = rng.standard_normal((R, n - 1)) * sigma\nc = 0.0\nfor t in range(1, n):\n    y[:, t] = c + rho * y[:, t - 1] + eps[:, t - 1]\nx = y[:, :-1]; z = y[:, 1:]           # regress y_t (t=2..n) on (1, y_{t-1})\nxd = x - x.mean(axis=1, keepdims=True)\nzd = z - z.mean(axis=1, keepdims=True)\nrho_hat = (xd * zd).sum(axis=1) / (xd ** 2).sum(axis=1)\nfinite = np.isfinite(rho_hat)\nn_nonfinite = int((~finite).sum())\nif n_nonfinite:\n    data_issues.append({\"issue\": f\"{n_nonfinite} non-finite rho_hat\", \"handling\": \"excluded\"})\nrh = rho_hat[finite]\nR_used = int(rh.size)\n\n# cross-check one replication against numpy lstsq\nX0 = np.column_stack([np.ones(n - 1), x[0]])\nb0 = np.linalg.lstsq(X0, z[0], rcond=None)[0][1]\nassert abs(b0 - rho_hat[0]) < 1e-10\n\nrho_hat_mean = float(rh.mean())\nrho_hat_sd = float(rh.std(ddof=1))\nbias_mc = rho_hat_mean - rho\nbias_mc_se = rho_hat_sd / np.sqrt(R_used)\ndisc = (bias_mc - bias_analytical) / bias_mc_se\n\nres = {\n    \"task_id\": \"F5-conf-02\",\n    \"bias_analytical\": bias_analytical,\n    \"bias_mc\": float(bias_mc),\n    \"bias_mc_se\": float(bias_mc_se),\n    \"rho_hat_mean\": rho_hat_mean,\n    \"rho_hat_sd_mc\": rho_hat_sd,\n    \"discrepancy_se_units\": float(disc),\n    \"se_asymptotic\": se_asymptotic,\n    \"n_replications\": R,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": {\"design.csv\": int(len(design))},\n    \"n_analysed\": R_used,\n    \"random_seed\": seed,\n    # supplementary keys\n    \"baseline_bias_analytical\": bias_analytical,\n    \"baseline_se_asymptotic\": se_asymptotic,\n    \"bias_analytical_neff\": bias_analytical_neff,\n    \"discrepancy_se_units_neff\": float((bias_mc - bias_analytical_neff) / bias_mc_se),\n    \"bias_mc_minus_analytical\": float(bias_mc - bias_analytical),\n    \"ratio_bias_mc_to_analytical\": float(bias_mc / bias_analytical),\n    \"rho_hat_median\": float(np.median(rh)),\n    \"share_rho_hat_below_rho\": float((rh < rho).mean()),\n    \"ratio_sd_mc_to_se_asymptotic\": float(rho_hat_sd / se_asymptotic),\n    \"n_regression_obs\": n - 1,\n    \"n_nonfinite_rho_hat\": n_nonfinite,\n}\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script"}}]}}48{"type":"api-request","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","id":"79247c8f-50e6-43da-b900-aa43071dfd8b","timestamp":"2026-10-30T05:11:30.252Z","version":"2.1.314","querySource":"sdk","shapeHash":"e8a9488e2d454cfbf1ce4842c6347a531fdfc44ec708754ef31a6a0375fb955f","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":"29b78ecf-5600-4d2a-a0dd-16b91194af4d","keep":14,"tail":["05d43b6d1cbe2c9ec75302757662eb126538de7438762499d2f923eaa0eaf44b","ceaa5277bdd1637f56b42553431d610ee935336e89617286be39287d03e3bb00"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"afac0444d7c886a0dc35702a3e33737cd722b838d158eb614346e5499557b4ee"}49{"parentUuid":"f08a4e2c-1765-49f8-a3a3-21987495a1c1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr6SgPXhAqVSx8XV5v8","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":2463,"cache_read_input_tokens":11311,"output_tokens":1305,"output_tokens_details":{"thinking_tokens":31},"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":2463},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":357,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXr6SQGZ9YeCT8WpsrZC","requestRef":"79247c8f-50e6-43da-b900-aa43071dfd8b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2efed2aa-ee6f-41a6-a73e-bc7d65d7e82b","timestamp":"2026-10-30T05:11:31.956Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}50{"parentUuid":"2efed2aa-ee6f-41a6-a73e-bc7d65d7e82b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr6SgPXhAqVSx8XV5v8","type":"message","role":"assistant","content":[{"type":"text","text":"Now P1/P2/P3 records, then reproducibility check."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2463,"cache_read_input_tokens":11311,"output_tokens":1305,"output_tokens_details":{"thinking_tokens":31},"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":2463},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXr6SQGZ9YeCT8WpsrZC","requestRef":"79247c8f-50e6-43da-b900-aa43071dfd8b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1bcc05d9-4a3f-47e9-baef-49fa00c79f7d","timestamp":"2026-10-30T05:11:31.956Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}51{"parentUuid":"1bcc05d9-4a3f-47e9-baef-49fa00c79f7d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr6SgPXhAqVSx8XV5v8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EZrMrBqw5FVBnyqf2pu3vB","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp /tmp/r1.json out/results.json && echo IDENTICAL\ncat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | sha256 | rows | cols |\n|---|---|---|---|\n| data/design.csv | 2f459c539378cbe54d830c18af466f2fa4230ce48a609aa6464f8334a0d36ecb (matches bundle.json) | 7 | 3 (parameter, value, description) |\n\nParameters: 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.\nNo missing values, no duplicated parameters, no dates (simulation design only). Units: dimensionless.\nAnomalies: none. |rho|<1 so the stationary distribution exists. Note (not an error): the regression uses n-1=29 observations (t=2..n);\nthe formula is evaluated at n=30 as the task specifies, with n-1 reported as sensitivity.\nInformation-availability: only input is design.csv; no violations.\nEOF\ncat > out/exclusions.csv <<'EOF'\nstep,rule,n_before,n_after,n_removed,rationale\n1,design.csv rows read (no filtering),7,7,0,all design parameters used\n2,drop replications with non-finite rho_hat,20000,20000,0,guard against degenerate regressors; none occurred\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Reproducibility: re-ran out/analysis.py; results.json byte-identical (cmp) — PASS.\n2. OLS formula cross-check: demeaned slope equals np.linalg.lstsq on (1, y_{t-1}) for replication 1 (|diff|<1e-10, asserted in script) — PASS.\n3. bias_mc = rho_hat_mean - rho: 0.67629 - 0.8 = -0.12371 — PASS.\n4. bias_mc_se = rho_hat_sd_mc/sqrt(20000) = 0.148913/141.42 = 0.001053 — PASS; positive SEs (bias_mc_se, rho_hat_sd_mc, se_asymptotic) — PASS.\n5. bias_analytical = -(1+2.4)/30 = -0.11333; se_asymptotic = sqrt(0.36/30) = 0.10954 — PASS.\n6. discrepancy_se_units = (-0.12371+0.11333)/0.001053 = -9.85 — PASS.\n7. Counts: n_input_rows 7; n_replications = n_analysed = 20000; exclusions remove 0 — PASS.\n8. Magnitude vs baseline: MC bias same sign and ~1.09x the analytical value; MC sd (0.149) > asymptotic SE (0.110), plausible for rho near 1 at n=30 — PASS.\n9. Design followed: seed 20261029, default_rng, stationary y_1, c=0, sigma=1 — PASS.\nEOF\necho \"- 2026-10-30 P1 passed (audit, exclusions). P2 baseline: bias_analytical, se_asymptotic computed in closed form. P3: vectorised MC run, results written.\" >> out/state/log.md","description":"Repro check and write audit/check files"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2463,"cache_read_input_tokens":11311,"output_tokens":1305,"output_tokens_details":{"thinking_tokens":31},"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":2463},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXr6SQGZ9YeCT8WpsrZC","requestRef":"79247c8f-50e6-43da-b900-aa43071dfd8b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"855c4c08-2a9d-424f-ba26-47527855fe8e","timestamp":"2026-10-30T05:11:42.220Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"855c4c08-2a9d-424f-ba26-47527855fe8e","isSidechain":false,"promptId":"9a7dc179-700a-4849-b6af-72de619f4053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EZrMrBqw5FVBnyqf2pu3vB","type":"tool_result","content":"IDENTICAL","is_error":false}]},"uuid":"9a9e9f02-27e0-4ba0-a166-3c49e7ba8c6d","timestamp":"2026-10-30T05:11:42.524Z","toolUseResult":{"stdout":"IDENTICAL","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"855c4c08-2a9d-424f-ba26-47527855fe8e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}53{"type":"api-request-blob","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","hash":"f2ee4aab8c0a1ff856638aec016a9e19387bc3c89517be09b13bcc6d91fea5ef","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Now P1/P2/P3 records, then reproducibility check."},{"type":"tool_use","id":"toolu_01EZrMrBqw5FVBnyqf2pu3vB","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp /tmp/r1.json out/results.json && echo IDENTICAL\ncat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | sha256 | rows | cols |\n|---|---|---|---|\n| data/design.csv | 2f459c539378cbe54d830c18af466f2fa4230ce48a609aa6464f8334a0d36ecb (matches bundle.json) | 7 | 3 (parameter, value, description) |\n\nParameters: 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.\nNo missing values, no duplicated parameters, no dates (simulation design only). Units: dimensionless.\nAnomalies: none. |rho|<1 so the stationary distribution exists. Note (not an error): the regression uses n-1=29 observations (t=2..n);\nthe formula is evaluated at n=30 as the task specifies, with n-1 reported as sensitivity.\nInformation-availability: only input is design.csv; no violations.\nEOF\ncat > out/exclusions.csv <<'EOF'\nstep,rule,n_before,n_after,n_removed,rationale\n1,design.csv rows read (no filtering),7,7,0,all design parameters used\n2,drop replications with non-finite rho_hat,20000,20000,0,guard against degenerate regressors; none occurred\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Reproducibility: re-ran out/analysis.py; results.json byte-identical (cmp) — PASS.\n2. OLS formula cross-check: demeaned slope equals np.linalg.lstsq on (1, y_{t-1}) for replication 1 (|diff|<1e-10, asserted in script) — PASS.\n3. bias_mc = rho_hat_mean - rho: 0.67629 - 0.8 = -0.12371 — PASS.\n4. bias_mc_se = rho_hat_sd_mc/sqrt(20000) = 0.148913/141.42 = 0.001053 — PASS; positive SEs (bias_mc_se, rho_hat_sd_mc, se_asymptotic) — PASS.\n5. bias_analytical = -(1+2.4)/30 = -0.11333; se_asymptotic = sqrt(0.36/30) = 0.10954 — PASS.\n6. discrepancy_se_units = (-0.12371+0.11333)/0.001053 = -9.85 — PASS.\n7. Counts: n_input_rows 7; n_replications = n_analysed = 20000; exclusions remove 0 — PASS.\n8. Magnitude vs baseline: MC bias same sign and ~1.09x the analytical value; MC sd (0.149) > asymptotic SE (0.110), plausible for rho near 1 at n=30 — PASS.\n9. Design followed: seed 20261029, default_rng, stationary y_1, c=0, sigma=1 — PASS.\nEOF\necho \"- 2026-10-30 P1 passed (audit, exclusions). P2 baseline: bias_analytical, se_asymptotic computed in closed form. P3: vectorised MC run, results written.\" >> out/state/log.md","description":"Repro check and write audit/check files"}}]}}54{"type":"api-request","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","id":"e61e91f5-3fbb-4d07-a5d7-f74732189479","timestamp":"2026-10-30T05:11:42.528Z","version":"2.1.314","querySource":"sdk","shapeHash":"e8a9488e2d454cfbf1ce4842c6347a531fdfc44ec708754ef31a6a0375fb955f","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":"79247c8f-50e6-43da-b900-aa43071dfd8b","keep":16,"tail":["f2ee4aab8c0a1ff856638aec016a9e19387bc3c89517be09b13bcc6d91fea5ef","fd5e46b60dda823445689282cfb8cc4d0c61c66f69240aa7fd94bd61255a71a2"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"063b84efb96a736c0d4c2f219b9123adddbd81c0d6950b42c33afce54d399f2c"}55{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"9a9e9f02-27e0-4ba0-a166-3c49e7ba8c6d","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5"}56{"type":"atis-latch","atis":"","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5"}57{"parentUuid":"9a9e9f02-27e0-4ba0-a166-3c49e7ba8c6d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr7M9HpCcUuvwhZfiEK","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019GvuBMT1dLXpMdMYYzSrLD","name":"Bash","input":{"command":"cat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"sha256 matches bundle.json\",\"7 rows x 3 cols, no missing/duplicates\",\"exclusions.csv written (0 removed)\",\"no availability-rule violations\"],\"notes\":\"\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"bias_analytical=-0.11333 and se_asymptotic=0.10954 computed in closed form (baseline_* keys)\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py runs, seed 20261029 recorded\",\"OLS cross-check vs lstsq asserted\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"rerun byte-identical results.json\",\"checks.md items 1-9 PASS\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Update ledger and run gate (P4)"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1317,"cache_read_input_tokens":13774,"output_tokens":469,"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":1317},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXr7Lsv1hvHfwDSznGK1","requestRef":"e61e91f5-3fbb-4d07-a5d7-f74732189479","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"98d309dc-b441-4d17-b17d-3a672e4e51ca","timestamp":"2026-10-30T05:11:46.795Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}58{"parentUuid":"98d309dc-b441-4d17-b17d-3a672e4e51ca","isSidechain":false,"promptId":"9a7dc179-700a-4849-b6af-72de619f4053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019GvuBMT1dLXpMdMYYzSrLD","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 32 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\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  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['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 0.3s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0243/out/state/gate_receipts/20261030T051147.139508Z.json\n20261030T051147.139508Z.json","is_error":false}]},"uuid":"e7301b0d-a797-4024-905e-a447604ccf34","timestamp":"2026-10-30T05:11:47.151Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 32 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\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  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['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 0.3s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0243/out/state/gate_receipts/20261030T051147.139508Z.json\n20261030T051147.139508Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"98d309dc-b441-4d17-b17d-3a672e4e51ca","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}59{"type":"api-request-blob","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","hash":"224be37cc4de8b3c082fce3754c7cb09b7b67ec7bad4f6cefc60fadb23d606e7","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_019GvuBMT1dLXpMdMYYzSrLD","name":"Bash","input":{"command":"cat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"sha256 matches bundle.json\",\"7 rows x 3 cols, no missing/duplicates\",\"exclusions.csv written (0 removed)\",\"no availability-rule violations\"],\"notes\":\"\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"bias_analytical=-0.11333 and se_asymptotic=0.10954 computed in closed form (baseline_* keys)\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py runs, seed 20261029 recorded\",\"OLS cross-check vs lstsq asserted\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"rerun byte-identical results.json\",\"checks.md items 1-9 PASS\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Update ledger and run gate (P4)"}}]}}60{"type":"api-request","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","id":"1d18d7c2-721f-4736-9430-52a1fb20c31f","timestamp":"2026-10-30T05:11:47.156Z","version":"2.1.314","querySource":"sdk","shapeHash":"e8a9488e2d454cfbf1ce4842c6347a531fdfc44ec708754ef31a6a0375fb955f","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":"e61e91f5-3fbb-4d07-a5d7-f74732189479","keep":18,"tail":["224be37cc4de8b3c082fce3754c7cb09b7b67ec7bad4f6cefc60fadb23d606e7","a085d31e570b02e75c4bb4f75a6879cbb46f95baf05bae182b79b899c2d1d4a1"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"030b94373f555991bccc2fe4c13f7b43a83f2b0afec751dad70f89533c2b8080"}61{"parentUuid":"e7301b0d-a797-4024-905e-a447604ccf34","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr7gwvBozfz7kXvbvdv","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":1203,"cache_read_input_tokens":15091,"output_tokens":3810,"output_tokens_details":{"thinking_tokens":53},"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":1203},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":960,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXr7ggHwr7f5gq3ghD8q","requestRef":"1d18d7c2-721f-4736-9430-52a1fb20c31f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f5e06dda-20ed-4645-84ac-853a2fbabbb6","timestamp":"2026-10-30T05:11:49.591Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}62{"parentUuid":"f5e06dda-20ed-4645-84ac-853a2fbabbb6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr7gwvBozfz7kXvbvdv","type":"message","role":"assistant","content":[{"type":"text","text":"The P4 contract checks pass: keys, positive SEs, and reproducibility. The remaining failures are the P5 deliverables, which don't exist yet. Next I'm writing the report and the claims ledger."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1203,"cache_read_input_tokens":15091,"output_tokens":3810,"output_tokens_details":{"thinking_tokens":53},"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":1203},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXr7ggHwr7f5gq3ghD8q","requestRef":"1d18d7c2-721f-4736-9430-52a1fb20c31f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8b0a69e0-b3ca-4933-a9bd-d3d38a8d120a","timestamp":"2026-10-30T05:11:49.591Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}63{"parentUuid":"8b0a69e0-b3ca-4933-a9bd-d3d38a8d120a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXr7gwvBozfz7kXvbvdv","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Xi71UG9niEhddmjWLmwuBZ","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F5-conf-02 — Finite-sample bias of OLS for an AR(1) coefficient\n\n## Question\nThe model is a Gaussian AR(1), y_t = c + ρ y_{t−1} + ε_t, with c = 0 and σ = 1. The design is n = 30 and ρ = 0.8, with a stationary initial value. How large is the finite-sample bias of the OLS estimator ρ̂ from the regression of y_t on (1, y_{t−1})? And how well does the Marriott–Pope/Kendall approximation E[ρ̂] − ρ ≈ −(1+3ρ)/n describe that bias at this design?\n\n## Data and exclusions\nThe only input is `data/design.csv`: 7 rows (`n_input_rows`), SHA-256 matching `bundle.json`. It specifies n = 30, ρ = 0.8, σ = 1, an intercept, a stationary y_1, R = 20000 replications and seed 20261029. No data problems were found (`data_issues` is empty). The only filtering rule drops replications with a non-finite ρ̂. It removed none, so `n_analysed` = `n_replications` = 20000 (`out/exclusions.csv`). Nothing is observed; all series are simulated.\n\n## Method\n- **Baseline (closed form).** The baseline is `bias_analytical` = −(1+3·0.8)/30 = −0.1133, evaluated at n = 30 as specified. Alongside it is the textbook asymptotic SE, `se_asymptotic` = √((1−ρ²)/n) = 0.1095.\n- **Monte Carlo.** I used `numpy.random.default_rng(20261029)` (`random_seed`). The script first draws R stationary initial values y_1 ~ N(0, σ²/(1−ρ²)), then an R×(n−1) matrix of N(0, σ²) innovations. The series are built recursively with c = 0, and each replication is a fresh series of 30 observations.\n- **Per-replication estimate.** ρ̂ is the OLS slope from regressing y_t on (1, y_{t−1}) for t = 2..30, which is 29 regression observations. It is computed in vectorised demeaned form. For one replication it was checked against `numpy.linalg.lstsq`.\n- **Reported quantities.** `bias_mc` = mean(ρ̂) − ρ; `bias_mc_se` = sd(ρ̂)/√R; `discrepancy_se_units` = (`bias_mc` − `bias_analytical`)/`bias_mc_se`.\n- **What kind of formula this is.** −(1+3ρ)/n is the leading term of an asymptotic expansion in 1/n of E[ρ̂] − ρ. It is derived for a stationary Gaussian AR(1) with an estimated mean, and the error it leaves out is of order O(n⁻²). It is accurate in the sense that n·(E[ρ̂] − ρ) → −(1+3ρ) as n → ∞. It is **not** an exact finite-sample result. At fixed n, the left-out higher-order terms can be noticeable, especially when ρ is close to 1, where the expansion converges more slowly.\n\n## Results\n| quantity | key | value |\n|---|---|---|\n| Analytical approximation | `bias_analytical` | −0.11333 |\n| Monte Carlo bias | `bias_mc` | −0.12371 |\n| MC standard error | `bias_mc_se` | 0.00105 |\n| Mean of ρ̂ | `rho_hat_mean` | 0.67629 |\n| SD of ρ̂ across replications | `rho_hat_sd_mc` | 0.14891 |\n| Discrepancy in MC-SE units | `discrepancy_se_units` | −9.85 |\n| Asymptotic SE | `se_asymptotic` | 0.10954 |\n\n- **Size of the bias.** OLS clearly underestimates ρ: the mean estimate is 0.676 against a true value of 0.8 (`rho_hat_mean`). The median is 0.699 (`rho_hat_median`), and 79.3% of the estimates fall below ρ (`share_rho_hat_below_rho`).\n- **How well the formula does.** The formula gets the sign and roughly the size of the bias right. The simulated bias is 1.09 times the approximation (`ratio_bias_mc_to_analytical`), a difference of −0.0104 (`bias_mc_minus_analytical`). So the formula understates the bias by about 9%.\n- **The gap is not simulation noise.** The difference is about 9.9 Monte Carlo SEs (`discrepancy_se_units`).\n- **Using 29 instead of 30 in the formula.** With n−1 = 29, the number of regression observations, the approximation becomes −0.1172 (`bias_analytical_neff`). The gap narrows but stays at 6.1 MC SEs (`discrepancy_se_units_neff`).\n- **The bias is about as large as the sampling error.** The bias is comparable in size to the textbook asymptotic SE (0.110, `se_asymptotic`). The simulated sampling SD is 1.36 times larger than that SE (`ratio_sd_mc_to_se_asymptotic`).\n\n## Uncertainty\n- **Monte Carlo error.** The only randomness is simulation noise. The MC SE of the bias is 0.00105 (`bias_mc_se`), so an approximate 95% MC interval for the bias is −0.1237 ± 0.0021. That interval excludes the analytical value −0.1133.\n- **Reproducibility.** The results are fully reproducible for the given seed. A different seed would move `bias_mc` by roughly ±0.001 (one MC SE).\n- **No uncertainty in the formula value.** `bias_analytical` and `se_asymptotic` are deterministic functions of the design.\n\n## Limitations and open issues\n- **What the discrepancy does and does not mean.** The statistically clear gap (`discrepancy_se_units` ≈ −9.9) does **not** show that the Marriott–Pope/Kendall formula is \"wrong\". The formula is a first-order O(1/n) approximation, and a difference of order 1/n² at n = 30 with ρ = 0.8 is what one expects. The Monte Carlo only shows that, at this one design point, the approximation captures about 92% of the true bias (1/1.09) and leaves a small but real remainder.\n- **What the Monte Carlo cannot tell us.** It cannot establish how fast the approximation error shrinks with n. It cannot say how the formula performs at other values of (n, ρ), with non-Gaussian errors, without an intercept, or with a non-stationary start. Only one (n, ρ) point was simulated.\n- **Which n to plug in.** The task specifies n = 30, the series length. The regression uses 29 observations, and the result with that choice is reported as a sensitivity check (`bias_analytical_neff`).\n- **Sampling-distribution caveat.** ρ̂ is skewed to the left; the median lies above the mean (`rho_hat_median`). The mean bias therefore does not fully describe the sampling distribution.\n- **Scope.** No higher-order bias formulas or bias corrections were evaluated; the task does not require them.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At n=30, rho=0.8 the Monte Carlo bias of OLS rho_hat is about -0.124 (MC SE 0.001), i.e. rho_hat underestimates rho on average (mean 0.676).\",\"results_keys\":[\"bias_mc\",\"bias_mc_se\",\"rho_hat_mean\"],\"status\":\"supported\",\"caveat\":\"Single design point; Monte Carlo error only.\"},\n {\"id\":\"C2\",\"text\":\"The Marriott-Pope/Kendall approximation -(1+3rho)/n gives -0.1133 at the design, matching the sign and roughly the size of the MC bias (ratio 1.09).\",\"results_keys\":[\"bias_analytical\",\"ratio_bias_mc_to_analytical\"],\"status\":\"supported\",\"caveat\":\"Evaluated with n=30 (series length) as specified.\"},\n {\"id\":\"C3\",\"text\":\"The approximation understates the bias by about 0.010, a gap of about 9.9 MC standard errors, so it is not simulation noise; with n-1=29 the gap is about 6.1 SEs.\",\"results_keys\":[\"discrepancy_se_units\",\"bias_mc_minus_analytical\",\"discrepancy_se_units_neff\"],\"status\":\"supported\",\"caveat\":\"Consistent with omitted O(n^-2) terms of a first-order expansion; does not show the formula is wrong.\"},\n {\"id\":\"C4\",\"text\":\"The bias is comparable in size to the asymptotic SE sqrt((1-rho^2)/n)=0.110, and the MC sampling SD (0.149) is about 1.36 times that SE.\",\"results_keys\":[\"se_asymptotic\",\"rho_hat_sd_mc\",\"ratio_sd_mc_to_se_asymptotic\",\"bias_mc\"],\"status\":\"supported\",\"caveat\":\"At this design only.\"},\n {\"id\":\"C5\",\"text\":\"The sampling distribution of rho_hat is left-skewed: the median (0.699) exceeds the mean and about 79% of estimates fall below rho.\",\"results_keys\":[\"rho_hat_median\",\"rho_hat_mean\",\"share_rho_hat_below_rho\"],\"status\":\"supported\",\"caveat\":\"Descriptive, single design.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nph=json.load(open('out/state/phases.json'))\nph['P4']={\"status\":\"passed\",\"checks\":[\"rerun byte-identical results.json\",\"checks.md items 1-9 PASS\",\"gate run 20261030T051147.139508Z: all P4 contract/repro/positive checks ok; failures only P5 deliverables (report, claims) not yet written\"],\"notes\":\"receipt out/state/gate_receipts/20261030T051147.139508Z.json\"}\nph['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, every number cites a results key\",\"claims.json C1-C5 reference existing keys\",\"final gate run (see notes)\"],\"notes\":\"\"}\njson.dump(ph,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (repro identical, checks.md, gate receipt 20261030T051147). P5: report and claims written; final gate run.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v '\\[ok\\]'","description":"Write report, claims, finalize ledger, 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":1203,"cache_read_input_tokens":15091,"output_tokens":3810,"output_tokens_details":{"thinking_tokens":53},"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":1203},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXr7ggHwr7f5gq3ghD8q","requestRef":"1d18d7c2-721f-4736-9430-52a1fb20c31f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"37782419-4540-4db0-926b-d7857a9a5a95","timestamp":"2026-10-30T05:12:19.999Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}64{"parentUuid":"37782419-4540-4db0-926b-d7857a9a5a95","isSidechain":false,"promptId":"9a7dc179-700a-4849-b6af-72de619f4053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Xi71UG9niEhddmjWLmwuBZ","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0243/out/state/gate_receipts/20261030T051220.299642Z.json","is_error":false}]},"uuid":"8843fc86-1db2-4e25-a0a6-5c86a6d2a88c","timestamp":"2026-10-30T05:12:20.307Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0243/out/state/gate_receipts/20261030T051220.299642Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"37782419-4540-4db0-926b-d7857a9a5a95","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}65{"type":"api-request-blob","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","hash":"1f46e62cdf5d4e07a552e60638aad68f4ccf4f3c6dc2c40164b6dc93b3321eb6","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The P4 contract checks pass: keys, positive SEs, and reproducibility. The remaining failures are the P5 deliverables, which don't exist yet. Next I'm writing the report and the claims ledger."},{"type":"tool_use","id":"toolu_01Xi71UG9niEhddmjWLmwuBZ","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F5-conf-02 — Finite-sample bias of OLS for an AR(1) coefficient\n\n## Question\nThe model is a Gaussian AR(1), y_t = c + ρ y_{t−1} + ε_t, with c = 0 and σ = 1. The design is n = 30 and ρ = 0.8, with a stationary initial value. How large is the finite-sample bias of the OLS estimator ρ̂ from the regression of y_t on (1, y_{t−1})? And how well does the Marriott–Pope/Kendall approximation E[ρ̂] − ρ ≈ −(1+3ρ)/n describe that bias at this design?\n\n## Data and exclusions\nThe only input is `data/design.csv`: 7 rows (`n_input_rows`), SHA-256 matching `bundle.json`. It specifies n = 30, ρ = 0.8, σ = 1, an intercept, a stationary y_1, R = 20000 replications and seed 20261029. No data problems were found (`data_issues` is empty). The only filtering rule drops replications with a non-finite ρ̂. It removed none, so `n_analysed` = `n_replications` = 20000 (`out/exclusions.csv`). Nothing is observed; all series are simulated.\n\n## Method\n- **Baseline (closed form).** The baseline is `bias_analytical` = −(1+3·0.8)/30 = −0.1133, evaluated at n = 30 as specified. Alongside it is the textbook asymptotic SE, `se_asymptotic` = √((1−ρ²)/n) = 0.1095.\n- **Monte Carlo.** I used `numpy.random.default_rng(20261029)` (`random_seed`). The script first draws R stationary initial values y_1 ~ N(0, σ²/(1−ρ²)), then an R×(n−1) matrix of N(0, σ²) innovations. The series are built recursively with c = 0, and each replication is a fresh series of 30 observations.\n- **Per-replication estimate.** ρ̂ is the OLS slope from regressing y_t on (1, y_{t−1}) for t = 2..30, which is 29 regression observations. It is computed in vectorised demeaned form. For one replication it was checked against `numpy.linalg.lstsq`.\n- **Reported quantities.** `bias_mc` = mean(ρ̂) − ρ; `bias_mc_se` = sd(ρ̂)/√R; `discrepancy_se_units` = (`bias_mc` − `bias_analytical`)/`bias_mc_se`.\n- **What kind of formula this is.** −(1+3ρ)/n is the leading term of an asymptotic expansion in 1/n of E[ρ̂] − ρ. It is derived for a stationary Gaussian AR(1) with an estimated mean, and the error it leaves out is of order O(n⁻²). It is accurate in the sense that n·(E[ρ̂] − ρ) → −(1+3ρ) as n → ∞. It is **not** an exact finite-sample result. At fixed n, the left-out higher-order terms can be noticeable, especially when ρ is close to 1, where the expansion converges more slowly.\n\n## Results\n| quantity | key | value |\n|---|---|---|\n| Analytical approximation | `bias_analytical` | −0.11333 |\n| Monte Carlo bias | `bias_mc` | −0.12371 |\n| MC standard error | `bias_mc_se` | 0.00105 |\n| Mean of ρ̂ | `rho_hat_mean` | 0.67629 |\n| SD of ρ̂ across replications | `rho_hat_sd_mc` | 0.14891 |\n| Discrepancy in MC-SE units | `discrepancy_se_units` | −9.85 |\n| Asymptotic SE | `se_asymptotic` | 0.10954 |\n\n- **Size of the bias.** OLS clearly underestimates ρ: the mean estimate is 0.676 against a true value of 0.8 (`rho_hat_mean`). The median is 0.699 (`rho_hat_median`), and 79.3% of the estimates fall below ρ (`share_rho_hat_below_rho`).\n- **How well the formula does.** The formula gets the sign and roughly the size of the bias right. The simulated bias is 1.09 times the approximation (`ratio_bias_mc_to_analytical`), a difference of −0.0104 (`bias_mc_minus_analytical`). So the formula understates the bias by about 9%.\n- **The gap is not simulation noise.** The difference is about 9.9 Monte Carlo SEs (`discrepancy_se_units`).\n- **Using 29 instead of 30 in the formula.** With n−1 = 29, the number of regression observations, the approximation becomes −0.1172 (`bias_analytical_neff`). The gap narrows but stays at 6.1 MC SEs (`discrepancy_se_units_neff`).\n- **The bias is about as large as the sampling error.** The bias is comparable in size to the textbook asymptotic SE (0.110, `se_asymptotic`). The simulated sampling SD is 1.36 times larger than that SE (`ratio_sd_mc_to_se_asymptotic`).\n\n## Uncertainty\n- **Monte Carlo error.** The only randomness is simulation noise. The MC SE of the bias is 0.00105 (`bias_mc_se`), so an approximate 95% MC interval for the bias is −0.1237 ± 0.0021. That interval excludes the analytical value −0.1133.\n- **Reproducibility.** The results are fully reproducible for the given seed. A different seed would move `bias_mc` by roughly ±0.001 (one MC SE).\n- **No uncertainty in the formula value.** `bias_analytical` and `se_asymptotic` are deterministic functions of the design.\n\n## Limitations and open issues\n- **What the discrepancy does and does not mean.** The statistically clear gap (`discrepancy_se_units` ≈ −9.9) does **not** show that the Marriott–Pope/Kendall formula is \"wrong\". The formula is a first-order O(1/n) approximation, and a difference of order 1/n² at n = 30 with ρ = 0.8 is what one expects. The Monte Carlo only shows that, at this one design point, the approximation captures about 92% of the true bias (1/1.09) and leaves a small but real remainder.\n- **What the Monte Carlo cannot tell us.** It cannot establish how fast the approximation error shrinks with n. It cannot say how the formula performs at other values of (n, ρ), with non-Gaussian errors, without an intercept, or with a non-stationary start. Only one (n, ρ) point was simulated.\n- **Which n to plug in.** The task specifies n = 30, the series length. The regression uses 29 observations, and the result with that choice is reported as a sensitivity check (`bias_analytical_neff`).\n- **Sampling-distribution caveat.** ρ̂ is skewed to the left; the median lies above the mean (`rho_hat_median`). The mean bias therefore does not fully describe the sampling distribution.\n- **Scope.** No higher-order bias formulas or bias corrections were evaluated; the task does not require them.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At n=30, rho=0.8 the Monte Carlo bias of OLS rho_hat is about -0.124 (MC SE 0.001), i.e. rho_hat underestimates rho on average (mean 0.676).\",\"results_keys\":[\"bias_mc\",\"bias_mc_se\",\"rho_hat_mean\"],\"status\":\"supported\",\"caveat\":\"Single design point; Monte Carlo error only.\"},\n {\"id\":\"C2\",\"text\":\"The Marriott-Pope/Kendall approximation -(1+3rho)/n gives -0.1133 at the design, matching the sign and roughly the size of the MC bias (ratio 1.09).\",\"results_keys\":[\"bias_analytical\",\"ratio_bias_mc_to_analytical\"],\"status\":\"supported\",\"caveat\":\"Evaluated with n=30 (series length) as specified.\"},\n {\"id\":\"C3\",\"text\":\"The approximation understates the bias by about 0.010, a gap of about 9.9 MC standard errors, so it is not simulation noise; with n-1=29 the gap is about 6.1 SEs.\",\"results_keys\":[\"discrepancy_se_units\",\"bias_mc_minus_analytical\",\"discrepancy_se_units_neff\"],\"status\":\"supported\",\"caveat\":\"Consistent with omitted O(n^-2) terms of a first-order expansion; does not show the formula is wrong.\"},\n {\"id\":\"C4\",\"text\":\"The bias is comparable in size to the asymptotic SE sqrt((1-rho^2)/n)=0.110, and the MC sampling SD (0.149) is about 1.36 times that SE.\",\"results_keys\":[\"se_asymptotic\",\"rho_hat_sd_mc\",\"ratio_sd_mc_to_se_asymptotic\",\"bias_mc\"],\"status\":\"supported\",\"caveat\":\"At this design only.\"},\n {\"id\":\"C5\",\"text\":\"The sampling distribution of rho_hat is left-skewed: the median (0.699) exceeds the mean and about 79% of estimates fall below rho.\",\"results_keys\":[\"rho_hat_median\",\"rho_hat_mean\",\"share_rho_hat_below_rho\"],\"status\":\"supported\",\"caveat\":\"Descriptive, single design.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nph=json.load(open('out/state/phases.json'))\nph['P4']={\"status\":\"passed\",\"checks\":[\"rerun byte-identical results.json\",\"checks.md items 1-9 PASS\",\"gate run 20261030T051147.139508Z: all P4 contract/repro/positive checks ok; failures only P5 deliverables (report, claims) not yet written\"],\"notes\":\"receipt out/state/gate_receipts/20261030T051147.139508Z.json\"}\nph['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, every number cites a results key\",\"claims.json C1-C5 reference existing keys\",\"final gate run (see notes)\"],\"notes\":\"\"}\njson.dump(ph,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (repro identical, checks.md, gate receipt 20261030T051147). P5: report and claims written; final gate run.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v '\\[ok\\]'","description":"Write report, claims, finalize ledger, run gate"}}]}}66{"type":"api-request","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","id":"82c20af5-282e-42bf-9d13-cedf94f11415","timestamp":"2026-10-30T05:12:20.310Z","version":"2.1.314","querySource":"sdk","shapeHash":"e8a9488e2d454cfbf1ce4842c6347a531fdfc44ec708754ef31a6a0375fb955f","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":"1d18d7c2-721f-4736-9430-52a1fb20c31f","keep":20,"tail":["1f46e62cdf5d4e07a552e60638aad68f4ccf4f3c6dc2c40164b6dc93b3321eb6","ca75a54787c31d7f1f15df91534984a7497f3763ea9e2f9e96bb4df799844187"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"adfdb739158508a98849a8478959bc8cdfc304e3973e7182570a3a7412cfca1c"}67{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"8843fc86-1db2-4e25-a0a6-5c86a6d2a88c","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5"}68{"type":"atis-latch","atis":"","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5"}69{"parentUuid":"8843fc86-1db2-4e25-a0a6-5c86a6d2a88c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrA8gmkmyZ7Hyk3CzFA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LPB4MuTGQBtkWwucdhLo3M","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\nph=json.load(open('out/state/phases.json'))\nph['P5']['notes']=\"final gate receipt out/state/gate_receipts/20261030T051220.299642Z.json: PASSED 0/32 failed\"\njson.dump(ph,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P5 passed; final gate PASSED (receipt 20261030T051220.299642Z). Task complete.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1","description":"Record final receipt and confirm gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3905,"cache_read_input_tokens":16294,"output_tokens":303,"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":3905},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrA8Qu7zqzemFMfmgPP","requestRef":"82c20af5-282e-42bf-9d13-cedf94f11415","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0a025841-0be3-4315-a88c-9b2adb60f010","timestamp":"2026-10-30T05:12:24.060Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}70{"parentUuid":"0a025841-0be3-4315-a88c-9b2adb60f010","isSidechain":false,"promptId":"9a7dc179-700a-4849-b6af-72de619f4053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LPB4MuTGQBtkWwucdhLo3M","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)","is_error":false}]},"uuid":"d19e8e43-6384-42c9-8daa-22f6aa380cf2","timestamp":"2026-10-30T05:12:24.402Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0a025841-0be3-4315-a88c-9b2adb60f010","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}71{"type":"api-request-blob","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","hash":"6ac07bba143f89e6a76ab4d541795cd67ef1ef4d7739753a0a31baec6ef62c3a","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01LPB4MuTGQBtkWwucdhLo3M","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\nph=json.load(open('out/state/phases.json'))\nph['P5']['notes']=\"final gate receipt out/state/gate_receipts/20261030T051220.299642Z.json: PASSED 0/32 failed\"\njson.dump(ph,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P5 passed; final gate PASSED (receipt 20261030T051220.299642Z). Task complete.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1","description":"Record final receipt and confirm gate"}}]}}72{"type":"api-request","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","id":"8bf240ec-60d6-480f-8be0-5a756cf77807","timestamp":"2026-10-30T05:12:24.405Z","version":"2.1.314","querySource":"sdk","shapeHash":"e8a9488e2d454cfbf1ce4842c6347a531fdfc44ec708754ef31a6a0375fb955f","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":"82c20af5-282e-42bf-9d13-cedf94f11415","keep":22,"tail":["6ac07bba143f89e6a76ab4d541795cd67ef1ef4d7739753a0a31baec6ef62c3a","01bc280fe0c24d423a4ad36e593531a5598c51a90c7313e4f6dd217ec9255f27"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"04f32fa8d65b511ca9835d5801b044e7da31c85433fed63abb0e9fb599ba100e"}73{"parentUuid":"d19e8e43-6384-42c9-8daa-22f6aa380cf2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrASCjLvEkPjAvbc7kT","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":343,"cache_read_input_tokens":20199,"output_tokens":897,"output_tokens_details":{"thinking_tokens":16},"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":343},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6490,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrARv7UJ2zpYFzWfeUM","requestRef":"8bf240ec-60d6-480f-8be0-5a756cf77807","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e8e7447f-c172-4a08-84a3-055987e44e05","timestamp":"2026-10-30T05:12:32.187Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}74{"parentUuid":"e8e7447f-c172-4a08-84a3-055987e44e05","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrASCjLvEkPjAvbc7kT","type":"message","role":"assistant","content":[{"type":"text","text":"At n = 30 and ρ = 0.8, the OLS estimate of ρ is biased downward by about −0.124, which is about 9% more than the Marriott–Pope/Kendall formula predicts (−0.113). All required files are written, and the final gate run passed with 0 of 32 checks failed. Re-running the script reproduced `out/results.json` exactly.\n\n| Quantity | Value |\n|---|---|\n| Formula value, −(1+3ρ)/n | −0.11333 |\n| Simulated bias (20,000 runs, seed 20261029) | −0.12371 |\n| Simulation standard error of that bias | 0.00105 |\n| Average ρ̂ (true ρ is 0.8) | 0.6763 |\n| Spread of ρ̂ across runs (SD) | 0.1489 |\n| Gap between simulation and formula | −9.85 standard errors |\n| Textbook asymptotic SE, √((1−ρ²)/n) | 0.1095 |\n\n- **The formula is roughly right but measurably short.** It gets the sign and roughly the size of the bias, but the real bias is 1.09 times larger. The gap is about 10 simulation standard errors, so it is not noise.\n- **Why the gap is expected.** The formula is only the leading term of a large-sample approximation, accurate up to terms of order 1/n². A small leftover at n = 30 with ρ close to 1 is what that kind of approximation predicts, so the result doesn't mean the formula is wrong.\n- **Which n to use.** The task says to use n = 30, the series length, but the regression actually uses 29 observations. Plugging in 29 gives −0.1172, which narrows the gap to 6.1 standard errors without closing it. I report this as a sensitivity check.\n- **Bias versus sampling error.** The bias is about as large as the textbook standard error, and the actual spread of ρ̂ is 1.36 times that standard error.\n\n**Limitations:**\n- Only one combination of n and ρ was simulated. The result says nothing about other sample sizes, other values of ρ, non-Gaussian errors, or how fast the formula's error shrinks as n grows.\n- The estimates are skewed to the left: the median (0.699) is above the mean and 79% fall below the true ρ. So the average bias doesn't fully describe how ρ̂ behaves.\n- Nothing was excluded and the design file had no problems.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `claims.json`\n- `exclusions.csv`\n- `state/` (contract, data audit, checks, phase ledger, log, gate receipts)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":343,"cache_read_input_tokens":20199,"output_tokens":897,"output_tokens_details":{"thinking_tokens":16},"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":343},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrARv7UJ2zpYFzWfeUM","requestRef":"8bf240ec-60d6-480f-8be0-5a756cf77807","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ee07400f-687e-4250-bf05-8c77418612a5","timestamp":"2026-10-30T05:12:33.010Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0243","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","version":"2.1.314","gitBranch":"HEAD"}75{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"ee07400f-687e-4250-bf05-8c77418612a5","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5"}76{"type":"cost-state","sessionId":"1f0c89ef-6be3-4582-82ba-81df09eacec5","totalCostUSD":0.3715404,"totalAPIDuration":112286,"totalAPIDurationWithoutRetries":112277,"totalToolDuration":1652,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":114136,"startTime":1793337038882,"modelUsage":{"claude-opus-5-5":{"inputTokens":26,"outputTokens":12583,"thinkingTokens":492,"cacheReadInputTokens":122932,"cacheCreationInputTokens":19038,"cacheCreationEphemeral5mInputTokens":19038,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.3715404}},"hasUnknownModelCost":false}77