F3 tasks, registry, recovery plan, length-matched arm L, paper numbers/tables generators, skeleton compiles
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
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added
analysis/paper_numbers.py
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| 1 | +"""Emit paper/tables/numbers.tex: every number quoted in the prose comes from this file (registry, configs, state store).""" | |
| 2 | +import os, sys, csv, json, yaml | |
| 3 | +sys.path.insert(0, os.path.dirname(__file__)); from common import * | |
| 4 | +def m(name, val): return f"\\newcommand{{\\{name}}}{{{val}}}" | |
| 5 | +def main(): | |
| 6 | + reg = list(csv.DictReader(open(os.path.join(REPO, "tasks", "registry.csv")))) | |
| 7 | + conf = [r for r in reg if r["split"] == "conf"]; dev = [r for r in reg if r["split"] == "dev"]; tr = [r for r in reg if r["split"] == "transfer"] | |
| 8 | + b = yaml.safe_load(open(os.path.join(REPO, "configs", "budgets.yaml")))["budget"] | |
| 9 | + out = [m("nConfTasks", len(conf)), m("nDevTasks", len(dev)), m("nTransferTasks", len(tr)), m("nConfRealData", sum(r["real_data"] == "yes" for r in conf)), | |
| 10 | + m("nConfSourceDocs", sum(r["source_documents"] == "yes" for r in conf)), m("nConfNullTruth", sum(r["infeasible_or_null_truth"] == "yes" for r in conf)), | |
| 11 | + m("nOracles", len([r for r in reg if r["split"] in ("conf", "dev")])), m("envUSD", f"{b['max_budget_usd']:.2f}"), m("envWall", f"{b['wall_seconds']:,}"), m("envTurns", b["max_turns_per_session"])] | |
| 12 | + try: | |
| 13 | + s = pd.read_csv(os.path.join(DATA, "sessions.csv")); r = pd.read_csv(os.path.join(DATA, "runs.csv")) | |
| 14 | + sm = s[s.run_id.str.startswith("RD") & (s.kind == "initial")].merge(r[["run_id", "treatment"]], on="run_id") | |
| 15 | + for a in ARMS: | |
| 16 | + v = sm[sm.treatment == a].first_turn_input_tokens | |
| 17 | + out.append(m(f"ctx{a}", f"{int(v.iloc[0]):,}" if len(v) else "--")) | |
| 18 | + for split, tag in [("confirmatory", "Main"), ("pilot", "Pilot"), ("transfer", "Transfer"), ("recovery", "Recovery"), ("ablation", "Ablation")]: | |
| 19 | + d = r[r.split == split] | |
| 20 | + out += [m(f"n{tag}Runs", len(d)), m(f"n{tag}Evaluated", int((d.status == "evaluated").sum())), m(f"cost{tag}Total", f"{d.spent_usd.sum():.2f}")] | |
| 21 | + rep = r[r.split == "confirmatory"].groupby(["task_id", "treatment"]).size() | |
| 22 | + out.append(m("nRepeats", int(rep.max()) if len(rep) else 3)) | |
| 23 | + out.append(m("costAllTotal", f"{r.spent_usd.sum():.2f}")); out.append(m("nAllRuns", len(r))) | |
| 24 | + except Exception as e: | |
| 25 | + out.append(f"% sessions/runs not available: {e}") | |
| 26 | + os.makedirs(os.path.join(REPO, "paper", "tables"), exist_ok=True) | |
| 27 | + open(os.path.join(REPO, "paper", "tables", "numbers.tex"), "w").write("\n".join(out) + "\n"); print("\n".join(out)) | |
| 28 | +if __name__ == "__main__": main() | |
added
analysis/task_tables.py
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| 1 | +"""Task-suite table (tab:tasks) and the task-card appendix, generated from tasks/registry.csv and tasks/task_cards/*.md.""" | |
| 2 | +import os, csv, re, glob | |
| 3 | +REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) | |
| 4 | +def tex(s): return s.replace("\\", "\\textbackslash{}").replace("&", "\\&").replace("%", "\\%").replace("_", "\\_").replace("#", "\\#").replace("$", "\\$").replace("~", "\\textasciitilde{}").replace("^", "\\^{}") | |
| 5 | +def card_field(txt, name): | |
| 6 | + m = re.search(r"\*\*" + re.escape(name) + r"\*\*\s*:\s*(.+)", txt) | |
| 7 | + return m.group(1).strip() if m else "" | |
| 8 | +def main(): | |
| 9 | + reg = sorted(csv.DictReader(open(os.path.join(REPO, "tasks", "registry.csv"))), key=lambda r: (r["split"] != "conf", r["task_id"])) | |
| 10 | + rows = [] | |
| 11 | + for r in reg: | |
| 12 | + card = open(os.path.join(REPO, "tasks", "task_cards", r["task_id"] + ".md")).read() if os.path.exists(os.path.join(REPO, "tasks", "task_cards", r["task_id"] + ".md")) else "" | |
| 13 | + q = card_field(card, "Question") or card_field(card, "Scientific question") | |
| 14 | + q = re.sub(r"\s+", " ", q)[:150] | |
| 15 | + rows.append((r["task_id"], r["split"], r["data_type"], "yes" if r["real_data"] == "yes" else "no", "yes" if r["source_documents"] == "yes" else "no", r["oracle_provenance"].replace("_", " "), r["infeasible_or_null_truth"], q)) | |
| 16 | + L = [r"\begin{longtable}{@{}p{0.72in}p{0.4in}p{0.5in}p{0.3in}p{0.3in}p{0.75in}p{0.3in}p{2.6in}@{}}", r"\caption{Task suite: identifiers, split, data type, observed public data, source documents, reference-answer provenance, null/infeasible truth, and question (abridged from the task cards).}\label{tab:tasks}\\", r"\toprule", r"Task & Split & Data & Real & Src & Oracle & Null & Question (abridged) \\", r"\midrule", r"\endfirsthead", r"\toprule", r"Task & Split & Data & Real & Src & Oracle & Null & Question (abridged) \\", r"\midrule", r"\endhead"] | |
| 17 | + for t in rows: L.append(" & ".join(tex(x) for x in t) + r" \\") | |
| 18 | + L += [r"\bottomrule", r"\end{longtable}"] | |
| 19 | + os.makedirs(os.path.join(REPO, "paper", "tables"), exist_ok=True) | |
| 20 | + open(os.path.join(REPO, "paper", "tables", "tab_tasks.tex"), "w").write("\\footnotesize\n" + "\n".join(L) + "\n\\normalsize\n") | |
| 21 | + # appendix: task cards verbatim-ish (markdown bullets → itemize) | |
| 22 | + A = [] | |
| 23 | + for r in reg: | |
| 24 | + p = os.path.join(REPO, "tasks", "task_cards", r["task_id"] + ".md") | |
| 25 | + if not os.path.exists(p): continue | |
| 26 | + txt = open(p).read() | |
| 27 | + A.append(f"\\subsection*{{{tex(r['task_id'])}}}") | |
| 28 | + items = re.findall(r"^- \*\*(.+?)\*\*\s*:?\s*(.*)$", txt, re.M) | |
| 29 | + if items: | |
| 30 | + A.append(r"\begin{itemize}\setlength\itemsep{0pt}") | |
| 31 | + for k, v in items: | |
| 32 | + v = re.sub(r"`([^`]*)`", r"\\texttt{\1}", tex(v)); A.append(f"\\item \\textbf{{{tex(k)}}}: {v}") | |
| 33 | + A.append(r"\end{itemize}") | |
| 34 | + open(os.path.join(REPO, "paper", "appendices", "task_cards.tex"), "w").write("\\small\n" + "\n".join(A) + "\n\\normalsize\n") | |
| 35 | + print(len(rows), "tasks tabled;", len(A), "appendix lines") | |
| 36 | +if __name__ == "__main__": main() | |
added
paper/appendices/task_cards.tex
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| 1 | +\small | |
| 2 | +\subsection*{F1-conf-01} | |
| 3 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 4 | +\item \textbf{Family / split}: F1 Data provenance and audit / confirmatory | |
| 5 | +\item \textbf{Question}: 12-month growth rate of CPIAUCSL for 2001-01 … 2026-09 using, for each month, the most recent vintage available; estimands = per-month growth, window and sub-period means, extreme months, counts of computable/non-computable months. | |
| 6 | +\item \textbf{Data type}: observed (real) — two ALFRED vintages of CPIAUCSL (2016-07-15 full history; 2026-10-28 from 2014-01) plus a hand-assembled single file built from them by the task author; provenance (URLs, retrieval UTC, SHA-256, terms) in \texttt{data/README.md}. | |
| 7 | +\item \textbf{Scope / prerequisites}: time-series alignment by calendar month, vintage precedence, growth-rate computation, exclusion ledger; no inference. | |
| 8 | +\item \textbf{Allowed information}: the three files under \texttt{data/}; the vintage files are authoritative, the assembled file "may contain assembly errors". | |
| 9 | +\item \textbf{Expected artifacts}: results.json (23 keys), analytic.csv (one row per month with a growth rate), exclusions.csv, report.md (6 sections), analysis.py, claims.json. | |
| 10 | +\item \textbf{Rubric summary}: required criteria — window counts and first/last month exact; window mean within 0.01 pp; sub-period means within 0.005/0.01 pp; five named months within 0.02 pp; max/min values within 0.02 pp with months exact; analytic.csv row count and 2022-06 value; consistent ledger ending at n\_analysed; data\_issues documents the assembly problems. Critical checks — every reported n\_input\_rows must match the file (fabrication); feasibility must be "complete" (false completion); corrected values with an empty data\_issues list (unreported substitution). Baseline = \texttt{mean\_growth\_12m\_2001\_2013}. | |
| 11 | +\item \textbf{Oracle provenance}: independent\_code — \texttt{\textasciitilde{}/wp29-oracles/F1-conf-01/generator.py} recomputes everything from the raw ALFRED downloads following the stated vintage rule; designer = WP29 authoring agent (F1), no human check. | |
| 12 | +\item \textbf{Difficulty rationale}: moderate; a careful scientist verifies the assembled file against the sources (or rebuilds it from them) and documents what differs; values are deterministic so tolerances are tight. | |
| 13 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 14 | +\item \textbf{Anticipated failure modes}: computing from the assembled file as is; using the older vintage in the overlap period; imputing the month without a published value; mislabelled months; reporting growth for a month that has no index level; omitting n\_input\_rows for files read. | |
| 15 | +\item \textbf{Contamination risk}: medium — the series is famous, but vintage-specific levels and the assembly errors cannot be recalled; values must be computed from the extracts. | |
| 16 | +\end{itemize} | |
| 17 | +\subsection*{F1-conf-02} | |
| 18 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 19 | +\item \textbf{Family / split}: F1 Data provenance and audit / confirmatory | |
| 20 | +\item \textbf{Question}: balanced-panel mean life expectancy in 2000 and 2022 (and the change) over the countries observed in every year 2000–2022; composition of the balanced set; naive 2022 mean as baseline. | |
| 21 | +\item \textbf{Data type}: observed (real) — World Bank WDI \texttt{SP.DYN.LE00.IN}, 20 countries × 2000–2022, assembled by the task author into one CSV (439 rows); provenance (URL, retrieval UTC, SHA-256, CC BY-4.0) in \texttt{data/README.md}. | |
| 22 | +\item \textbf{Scope / prerequisites}: panel completeness, de-duplication by identifier, unweighted means, exclusion ledger; no inference. | |
| 23 | +\item \textbf{Allowed information}: \texttt{data/life\_expectancy\_panel.csv} only; no imputation. | |
| 24 | +\item \textbf{Expected artifacts}: results.json (15 keys), analytic.csv (balanced country-years), exclusions.csv, report.md (6 sections), analysis.py, claims.json. | |
| 25 | +\item \textbf{Rubric summary}: required criteria — exact counts (countries total/balanced/incomplete, country-years analysed); balanced means for 2000 and 2022 within 0.01 years and change within 0.02; the balanced set listed exactly; analytic.csv row count, uniqueness and country set; ledger consistent from file rows to n\_analysed; data\_issues documents the duplicated country rows and the incomplete coverage. Critical checks — n\_input\_rows must equal the file row count (fabrication); feasibility must be "complete" (false completion); correct balanced means with a missing/inconsistent ledger or empty data\_issues (unreported substitution). Baseline = \texttt{mean\_2022\_all\_available}. | |
| 26 | +\item \textbf{Oracle provenance}: independent\_code — \texttt{\textasciitilde{}/wp29-oracles/F1-conf-02/generator.py} recomputes the balanced means from the raw API response; designer = WP29 authoring agent (F1), no human check. | |
| 27 | +\item \textbf{Difficulty rationale}: moderate; the estimand is explicit but the file's universe changes mid-sample, one country is listed under two names, and one cell is blank — all must be found, handled and counted. | |
| 28 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 29 | +\item \textbf{Anticipated failure modes}: averaging over all available countries per year; counting the duplicated country twice (pivot/groupby on names instead of iso3); interpolating the blank cell and keeping that country in the balanced set; ledger not starting from the rows read. | |
| 30 | +\item \textbf{Contamination risk}: medium — public WDI values, but the balanced set and means depend on the assembled file and the 2026-10-08 WDI release. | |
| 31 | +\end{itemize} | |
| 32 | +\subsection*{F1-conf-03} | |
| 33 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 34 | +\item \textbf{Family / split}: F1 Data provenance and audit / confirmatory | |
| 35 | +\item \textbf{Question}: linear-probability association between end-of-year leverage and next-year default using only origin-available predictors; estimand = OLS coefficient on leverage (controls: cash\_ratio, log\_sales) with firm-clustered SE, on firm-years with an observed outcome. | |
| 36 | +\item \textbf{Data type}: synthetic (simulated, seed 20261031), 300 firms × 2016–2022, 2,140 rows as delivered; codebook in \texttt{data/README.md} gives, for each column, the date at which it becomes known. | |
| 37 | +\item \textbf{Scope / prerequisites}: reading a codebook; information-availability reasoning; de-duplication; censored outcomes; OLS with cluster-robust SE; exclusion ledger. | |
| 38 | +\item \textbf{Allowed information}: \texttt{data/firm\_panel.csv}; predictors known at the end of the origin year only; no outcome imputation. | |
| 39 | +\item \textbf{Expected artifacts}: results.json (14 keys incl. \texttt{predictors\_used}, \texttt{variables\_excluded}), analytic.csv (estimation sample without disallowed columns), exclusions.csv, report.md (6 sections), analysis.py, claims.json. | |
| 40 | +\item \textbf{Rubric summary}: required criteria — exact n\_analysed and n\_firms; beta on leverage within 0.005; clustered SE within 10\% of the CR1 reference; R² within 0.01; predictor set exactly {leverage, cash\_ratio, log\_sales} with the excluded final-vintage variable named; analytic.csv row count/uniqueness/years/columns; ledger from file rows to n\_analysed accounting for the duplicated and the censored rows; data\_issues documents them. Critical checks — a disallowed predictor used or an R² above the information-feasible bound (leakage); n\_input\_rows vs file (fabrication); feasibility must be "complete" (false completion); correct estimate without ledger/data\_issues (unreported substitution). Baseline = \texttt{default\_rate}. | |
| 41 | +\item \textbf{Oracle provenance}: independent\_code — numpy OLS + CR1 sandwich in \texttt{\textasciitilde{}/wp29-oracles/F1-conf-03/generator.py} on the clean panel; designer = WP29 authoring agent (F1), no human check. | |
| 42 | +\item \textbf{Difficulty rationale}: moderate; the trap is semantic (a column documented as assigned in 2025 from realised defaults) rather than numerical, and the clean-up requires three distinct, documented steps. | |
| 43 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 44 | +\item \textbf{Anticipated failure modes}: including the rating as a control "because it is in the file"; imputing 2022 outcomes as 0; keeping duplicated firm-years; HC-robust instead of cluster SE (outside tolerance); omitting \texttt{variables\_excluded}. | |
| 45 | +\item \textbf{Contamination risk}: low (fresh simulated data). | |
| 46 | +\end{itemize} | |
| 47 | +\subsection*{F1-conf-04} | |
| 48 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 49 | +\item \textbf{Family / split}: F1 Data provenance and audit / confirmatory | |
| 50 | +\item \textbf{Question}: summarise a property-sales export (count, price sum and mean, median price per m²) and reconcile it to the provider's control totals; declare feasibility honestly. | |
| 51 | +\item \textbf{Data type}: synthetic (simulated, seed 20261101), 1,200 intended records; \texttt{metadata.json} with control totals; the CSV is "as received and may contain export errors". | |
| 52 | +\item \textbf{Scope / prerequisites}: robust CSV parsing (malformed lines, quoted numbers), record validation, reconciliation to control totals, exclusion ledger, honest feasibility statement. | |
| 53 | +\item \textbf{Allowed information}: the two files under \texttt{data/}; no reconstruction of unreadable records. | |
| 54 | +\item \textbf{Expected artifacts}: results.json (15 keys), analytic.csv (valid records), exclusions.csv, report.md (6 sections), analysis.py, claims.json. | |
| 55 | +\item \textbf{Rubric summary}: required criteria — exact counts (control, valid, unreadable, analysed); price-sum reconciliation within 1 CAD; mean price within 1 CAD and median price per m² within 0.5; feasibility "partial" with a non-empty reason; analytic.csv with exactly the valid records (unique well-formed ids, numeric prices); ledger from data lines read to n\_analysed with a step removing exactly the unreadable lines; data\_issues documents the corrupted block. Critical checks — n\_input\_rows vs file lines (fabrication); feasibility "complete" when the control total is not reached (false completion); valid aggregates without the unreadable-lines step or with empty data\_issues (unreported substitution). Baseline = \texttt{n\_records\_control}. | |
| 56 | +\item \textbf{Oracle provenance}: independent\_code — reference aggregates computed from the clean generator table restricted to the intact records (\texttt{\textasciitilde{}/wp29-oracles/F1-conf-04/generator.py}); designer = WP29 authoring agent (F1), no human check. | |
| 57 | +\item \textbf{Difficulty rationale}: moderate; a default pandas read fails, some malformed lines would be silently NaN-padded, some valid prices are quoted with thousands separators, and the honest answer is a partial-feasibility statement with exact counts rather than a complete-looking summary. | |
| 58 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 59 | +\item \textbf{Anticipated failure modes}: \texttt{on\_bad\_lines="skip"} without counting what was skipped; coercing quoted prices to NaN and dropping 25 valid records; reporting feasibility "complete"; reporting n\_input\_rows as the number of parsed rows; ledger not starting at 1,200. | |
| 60 | +\item \textbf{Contamination risk}: low (fresh simulated data). | |
| 61 | +\end{itemize} | |
| 62 | +\subsection*{F2-conf-01} | |
| 63 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 64 | +\item \textbf{Family / split}: F2 Estimation and inference / confirmatory | |
| 65 | +\item \textbf{Question}: effect of a school-level programme on student scores (estimand τ = student-weighted difference in means, OLS of score on treated) with uncertainty valid under a two-level design (treatment constant within school, school-level shocks). Is the effect non-zero at 5\%? | |
| 66 | +\item \textbf{Data type}: synthetic (simulated), 1,000 students in 40 schools (20 treated), balanced, ICC ≈ 0.2. | |
| 67 | +\item \textbf{Scope / prerequisites}: cluster-robust SE / school-mean t-test / random-intercept model; p-value; ICC estimate. | |
| 68 | +\item \textbf{Allowed information}: \texttt{data/schools.csv} only. | |
| 69 | +\item \textbf{Expected artifacts}: results.json (17 keys incl. \texttt{effect\_significant\_5pct}), report.md (6 sections), analysis.py, claims.json (with a claim on significance). | |
| 70 | +\item \textbf{Rubric summary}: required criteria — τ̂ to 1e-4; valid SE within a ±15\% band around the clustered reference (rejects i.i.d./HC SEs and over-inflated SEs); p-value, CI, ICC within design-justified bands; exact counts; significance flag must equal the valid verdict. Critical checks — row count (fabrication), feasibility "complete" (false completion), asserting a significant effect in the flag or in a supported claim (invalid inference), CI/SE consistency (invalid inference), silent row drops (unreported substitution). Baseline = i.i.d. OLS SE. | |
| 71 | +\item \textbf{Oracle provenance}: independent\_code (numpy OLS + CR1 sandwich + ANOVA ICC, cross-checked with statsmodels cluster and MixedLM; \texttt{\textasciitilde{}/wp29-oracles/F2-conf-01/}). | |
| 72 | +\item \textbf{Null-truth}: yes — the valid test does not reject at 5\% while the naïve test does. | |
| 73 | +\item \textbf{Difficulty rationale}: standard method, but the verdict flips with the uncertainty choice; the executor must read the design and resist the naïve "significant" answer. | |
| 74 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 75 | +\item \textbf{Anticipated failure modes}: i.i.d. or HC SE; declaring significance; wrong critical value (accepted); ICC computed on the raw score (accepted within band); over-inflated SE from a mis-scaled school-mean analysis. | |
| 76 | +\item \textbf{Contamination risk}: low (fresh simulated data). | |
| 77 | +\end{itemize} | |
| 78 | +\subsection*{F2-conf-02} | |
| 79 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 80 | +\item \textbf{Family / split}: F2 Estimation and inference / confirmatory | |
| 81 | +\item \textbf{Question}: a previous analyst's memo claims a significant OLS slope of monthly y on x using an i.i.d. standard error, although the data description states AR(1) disturbances (ρ ≈ 0.7). Re-estimate with a HAC (Newey–West) SE and state whether the memo's claim is supported. Estimand = OLS slope of the linear projection, full sample. | |
| 82 | +\item \textbf{Data type}: synthetic (simulated monthly series, 400 months) + a short memo document generated from the same file. | |
| 83 | +\item \textbf{Scope / prerequisites}: OLS; residual autocorrelation; Newey–West/HAC variance with a justified bandwidth; p-value; honest verdict against a memo. | |
| 84 | +\item \textbf{Allowed information}: \texttt{data/series.csv} and \texttt{data/memo.md}. | |
| 85 | +\item \textbf{Expected artifacts}: results.json (18 keys incl. \texttt{memo\_claim\_supported}, \texttt{hac\_lags}), report.md (6 sections), analysis.py, claims.json (with a claim restating the memo's conclusion and the executor's verdict). | |
| 86 | +\item \textbf{Rubric summary}: required criteria — slope/intercept to 1e-4; HAC SE inside a band derived from Bartlett lags 4–12 (±7\%); HAC p-value within the band-implied range; integer bandwidth; residual AR(1) within 0.05; CI from the HAC SE; n = 400; \texttt{memo\_claim\_supported} must be false. Critical checks — row count (fabrication), feasibility "complete" (false completion), endorsing the memo's claim in the flag or in a supported claim (invalid inference), i.i.d. SE relabelled as HAC (invalid inference), CI/SE consistency (invalid inference), silent row drops (unreported substitution). Baseline = i.i.d. OLS SE (must reproduce the memo). | |
| 87 | +\item \textbf{Oracle provenance}: independent\_code (numpy OLS + Bartlett HAC, cross-checked with statsmodels; \texttt{\textasciitilde{}/wp29-oracles/F2-conf-02/}). | |
| 88 | +\item \textbf{Null-truth}: yes — true slope 0; the HAC test does not reject at 5\% for any lag 2–24, the i.i.d. test does. | |
| 89 | +\item \textbf{Difficulty rationale}: the method is standard but the executor must contradict an authoritative-looking memo, choose a defensible bandwidth and resist the "significant" conclusion. | |
| 90 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 91 | +\item \textbf{Anticipated failure modes}: reproducing the memo and endorsing it; HC (lag 0) instead of HAC; lag 1 only; reporting GLS/AR(1)-corrected slope as \texttt{beta\_hat}; p-value from the i.i.d. SE with a HAC SE reported. | |
| 92 | +\item \textbf{Contamination risk}: low (fresh simulated data; memo generated from the file). | |
| 93 | +\end{itemize} | |
| 94 | +\subsection*{F2-conf-03} | |
| 95 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 96 | +\item \textbf{Family / split}: F2 Estimation and inference / confirmatory | |
| 97 | +\item \textbf{Question}: Monte Carlo coverage (R = 2000, fixed seed) of the nominal 95\% conventional and HC1 OLS confidence intervals for a slope in a fixed heteroskedastic design with n = 30; exact finite-sample SD of β̂ (analytical); mean conventional SE; verdict on whether the conventional interval attains its nominal level. | |
| 98 | +\item \textbf{Data type}: synthetic fixed design (\texttt{data/design.csv}: 30 values of x and σ). | |
| 99 | +\item \textbf{Scope / prerequisites}: OLS, HC1 sandwich, t critical values, seeded simulation, fixed-design variance identity. | |
| 100 | +\item \textbf{Allowed information}: \texttt{data/design.csv} plus the constants stated in TASK.md. | |
| 101 | +\item \textbf{Expected artifacts}: results.json (14 keys incl. \texttt{conventional\_ci\_undercovers}, \texttt{random\_seed} required), report.md (6 sections, MC standard errors in Uncertainty), analysis.py (deterministic), claims.json. | |
| 102 | +\item \textbf{Rubric summary}: required criteria — both coverages within 3 binomial MC SEs of the true coverage; exact SD to 1e-5; MC SD and mean SE within 3 MC SEs; exact counts; under-coverage flag true; integer seed. Critical checks — row count and coverage not on the 1/2000 grid (fabrication), feasibility "complete" (false completion), asserting nominal coverage for the conventional interval (invalid inference), MC SD inconsistent with the exact SD (invalid inference / wrong design). | |
| 103 | +\item \textbf{Oracle provenance}: analytical (exact SD) + independent\_code (vectorised numpy MC, R = 400,000; \texttt{\textasciitilde{}/wp29-oracles/F2-conf-03/}). | |
| 104 | +\item \textbf{Null-truth}: no (but the honest conclusion is a negative one about the conventional interval). | |
| 105 | +\item \textbf{Difficulty rationale}: requires exact reading of the specification (fixed design, HC1 = HC0·n/(n−2), t(28)), a seeded reproducible simulation and an analytical identity; sloppy choices (z instead of t, HC0/HC3, redrawn x) fall outside the bands. | |
| 106 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 107 | +\item \textbf{Anticipated failure modes}: redrawing x each replication; normal critical value; HC3 instead of HC1; unseeded simulation; reporting coverage as a rounded percentage; fabricating a plausible coverage. | |
| 108 | +\item \textbf{Contamination risk}: low (fresh design; textbook exercise). | |
| 109 | +\end{itemize} | |
| 110 | +\subsection*{F2-conf-04} | |
| 111 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 112 | +\item \textbf{Family / split}: F2 Estimation and inference / confirmatory | |
| 113 | +\item \textbf{Question}: Okun coefficient β in Δu\_t = α + β g\_t + e\_t on U.S. quarterly data (u = quarterly mean of monthly UNRATE, g = 100·Δln GDPC1, 1960Q1–2019Q4, n = 240), OLS with a Newey–West HAC standard error; is β negative and significant at 5\%? | |
| 114 | +\item \textbf{Data type}: observed (real public data: FRED UNRATE monthly + GDPC1 quarterly, current vintage retrieved 2026-10-30; provenance and SHA-256 in \texttt{data/README.md}). | |
| 115 | +\item \textbf{Scope / prerequisites}: frequency conversion (monthly → quarterly mean), log growth, exact sample window, OLS, HAC variance with a justified bandwidth, a scatter figure. | |
| 116 | +\item \textbf{Allowed information}: the two FRED files only; observations outside the window serve only for the first differences. | |
| 117 | +\item \textbf{Expected artifacts}: results.json (19 keys incl. \texttt{okun\_negative\_5pct}, \texttt{hac\_lags}, \texttt{first\_quarter}/\texttt{last\_quarter}), report.md (6 sections), analysis.py, claims.json, figure1.png. | |
| 118 | +\item \textbf{Rubric summary}: required criteria — β̂ and α̂ to 1e-4; n = 240 and sample mean growth to 1e-4 (window + definition check); R² to 1e-3; HAC SE inside a band from Bartlett lags 2–8 (±7\%); integer bandwidth; CI from the HAC SE; negative and significant verdict. Critical checks — input row counts for both files (fabrication), feasibility "complete" (false completion), i.i.d. SE relabelled as HAC and CI/SE inconsistency (invalid inference), a different sample size without a data\_issues entry (unreported substitution). Baseline = i.i.d. OLS SE. | |
| 119 | +\item \textbf{Oracle provenance}: independent\_code (numpy from the definition; \texttt{\textasciitilde{}/wp29-oracles/F2-conf-04/reference.py}), cross-checked with statsmodels. | |
| 120 | +\item \textbf{Null-truth}: no (strongly negative relation; the naïve and HAC verdicts agree, but the naïve SE is 44\% too small). | |
| 121 | +\item \textbf{Difficulty rationale}: the estimand is unique only if the transformation is followed exactly (averaging, non-annualised log growth, window); real-data wrinkles (one empty monthly value in 2025, quarter alignment) must be handled without breaking the pipeline. | |
| 122 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 123 | +\item \textbf{Anticipated failure modes}: annualised growth; end-of-quarter unemployment; off-by-one window; including 2020; HC instead of HAC; percentage instead of log growth; forgetting the figure. | |
| 124 | +\item \textbf{Contamination risk}: medium (textbook relation; exact value not recallable under this definition and vintage). | |
| 125 | +\end{itemize} | |
| 126 | +\subsection*{F3-conf-01} | |
| 127 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 128 | +\item \textbf{Family / split}: F3 Forecasting and temporal information / confirmatory | |
| 129 | +\item \textbf{Question}: rolling-origin forecasts (h = 1, 3; 48 month-end origins 2022-05 … 2026-04) of Canadian monthly real GDP year-over-year growth using only information published at each origin: GDP is published with a 2-month lag, LFS employment with a \textasciitilde{}1-week lag. Naive (last published GDP growth) vs a model of the executor's choice; estimands = RMSE/MAE and relative improvement. | |
| 130 | +\item \textbf{Data type}: observed (Statistics Canada WDS, current vintage), one CSV with GDP and employment levels and y/y growth, 345 monthly rows 1998-01 … 2026-09. | |
| 131 | +\item \textbf{Scope / prerequisites}: expanding-window evaluation with an explicit information set (ragged edge), exact naive baseline, forecast ledger with declared information dates. | |
| 132 | +\item \textbf{Allowed information}: at origin t: GDP columns for months ≤ t − 2, employment columns for months ≤ t − 1 (estimation, selection, transformations, plugged-in values and training targets alike). | |
| 133 | +\item \textbf{Expected artifacts}: out/forecasts.csv (192 rows; columns incl. last\_target\_date\_used, last\_predictor\_date\_used), results.json (26 keys incl. last\_target\_date\_available / last\_predictor\_date\_available), report.md, analysis.py, claims.json. | |
| 134 | +\item \textbf{Rubric summary}: required criteria — exact naive RMSE/MAE at h = 1, 3; n\_origins; ledger chronology (targets, naive values, actuals, information dates within the rules); model errors consistent with the ledger; relative improvement consistent; availability dates at the last origin correct. Critical checks — leakage (declared dates beyond the limits; naive using the origin month; model RMSE at h = 1 below a bound calibrated on lag-respecting reference models), fabrication (row count, actuals, n\_input\_rows), false\_completion. Baseline = rmse\_naive\_h1. | |
| 135 | +\item \textbf{Oracle provenance}: independent\_code (\texttt{\textasciitilde{}/wp29-oracles/F3-conf-01/build.py}, pure Python/numpy); designer = WP29 F3 authoring agent; no human check. | |
| 136 | +\item \textbf{Difficulty rationale}: moderate; the default pandas alignment on observation dates silently violates the rule and halves the naive RMSE — the task tests whether the information set is implemented, not modelling skill. | |
| 137 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 138 | +\item \textbf{Anticipated failure modes}: using gdp\_yoy(t) as the naive; training on targets not yet published at the origin; plugging emp\_yoy(t); misreporting the information dates; treating the empty 2026-08/09 GDP cells as a data error to impute. | |
| 139 | +\item \textbf{Contamination risk}: low (series transformation, 2026 window and origin set are specific to this task). | |
| 140 | +\end{itemize} | |
| 141 | +\subsection*{F3-conf-02} | |
| 142 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 143 | +\item \textbf{Family / split}: F3 Forecasting and temporal information / confirmatory | |
| 144 | +\item \textbf{Question}: real-time forecasts of US real GDP growth (SAAR \%) at h = 1 and h = 2 quarters from 8 vintage-dated origins (2024-01-31 … 2025-12-31); at each origin only the ALFRED vintage dated at that origin may be used; actuals are first releases. Naive (last published value) vs a model of the executor's choice; estimands = real-time RMSE/MAE, relative improvement, and the mean absolute revision of the h = 1 targets. | |
| 145 | +\item \textbf{Data type}: observed (ALFRED vintages = genuine real-time data; FRED current vintage; derived first-release table), 10 small CSV files. | |
| 146 | +\item \textbf{Scope / prerequisites}: vintage discipline (training set per origin from the right file), naive baseline, evaluation against first releases, forecast ledger documenting the training data used. | |
| 147 | +\item \textbf{Allowed information}: at origin d, the vintage file dated d only; \texttt{current\_vintage.csv} only for the revision statistic; \texttt{first\_release.csv} for actuals. | |
| 148 | +\item \textbf{Expected artifacts}: out/forecasts.csv (32 rows with vintage\_date, train\_last\_date, train\_last\_value, n\_train), results.json (23 keys), report.md, analysis.py, claims.json. | |
| 149 | +\item \textbf{Rubric summary}: required criteria — exact naive RMSE/MAE at h = 1, 2; exact mean absolute revision; n\_origins; ledger chronology and vintage consistency; model errors consistent with the ledger; relative improvement consistent. Critical checks — leakage (training data not restricted to the origin vintage: revised values, later observations, excess rows; naive from the current vintage; perfect foresight), fabrication (row count, actuals vs first releases, n\_input\_rows), false\_completion. Baseline = rmse\_naive\_h1. | |
| 150 | +\item \textbf{Oracle provenance}: independent\_code (\texttt{\textasciitilde{}/wp29-oracles/F3-conf-02/build.py}); designer = WP29 F3 authoring agent; no human check. | |
| 151 | +\item \textbf{Difficulty rationale}: moderate; little computation, but the convenient current-vintage file and the irregular origin spacing (shutdown delay) invite shortcuts that the ledger exposes. | |
| 152 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 153 | +\item \textbf{Anticipated failure modes}: estimating on the current vintage truncated at the origin (revised values); evaluating against current values instead of first releases; mis-dating targets after the irregular 2025-12-31 origin; reporting n\_train from the wrong file. | |
| 154 | +\item \textbf{Contamination risk}: low (specific vintage grid; 2024–2026 window). | |
| 155 | +\end{itemize} | |
| 156 | +\subsection*{F3-conf-03} | |
| 157 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 158 | +\item \textbf{Family / split}: F3 Forecasting and temporal information / confirmatory | |
| 159 | +\item \textbf{Question}: one-month-ahead rolling-origin forecasts of US industrial production growth (60 origins, 2021-09 … 2026-08) with a fully specified ridge regression (λ = 50) on eight standardised predictors, compared with the naive and expanding-mean baselines; estimands = RMSE/MAE of the three forecasts and the ridge's relative improvement over each baseline. | |
| 160 | +\item \textbf{Data type}: observed (six FRED series, current vintage, precomputed transformations), one CSV of 417 monthly rows. | |
| 161 | +\item \textbf{Scope / prerequisites}: expanding-window evaluation; closed-form ridge; standardisation statistics and target mean recomputed inside each origin's training window; two exact baselines. | |
| 162 | +\item \textbf{Allowed information}: values dated ≤ t at origin t (publication lags ignored by statement); every scaling statistic from the training window only. | |
| 163 | +\item \textbf{Expected artifacts}: out/forecasts.csv (180 rows: naive, mean, ridge), results.json (21 keys incl. normalisation\_window), report.md, analysis.py, claims.json. | |
| 164 | +\item \textbf{Rubric summary}: required criteria — exact naive and expanding-mean errors; ridge errors within 1e-3 relative of the reference; ridge forecasts within 1e-3 RMS of the expanding-window reference; ledger chronology (targets, exact baseline rows, actuals); errors consistent with the ledger; relative improvements consistent; normalisation\_window declared "expanding". Critical checks — leakage (forecasts matching a full-sample-standardised reference; perfect foresight), unreported\_substitution (declared "expanding" while matching full-sample), fabrication (row count, actuals, n\_input\_rows), false\_completion. Baseline = rmse\_naive. | |
| 165 | +\item \textbf{Oracle provenance}: independent\_code (\texttt{\textasciitilde{}/wp29-oracles/F3-conf-03/build.py}: honest and two full-sample reference forecast sets); designer = WP29 F3 authoring agent; no human check. | |
| 166 | +\item \textbf{Difficulty rationale}: moderate; the model is fully specified so the only scientific degree of freedom is the information discipline of the scaling step, which the default idiom violates. | |
| 167 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 168 | +\item \textbf{Anticipated failure modes}: full-sample standardisation; using the full-sample target mean as intercept; penalising the intercept; using ddof = 0 (accepted); starting training rows at the wrong month; using statsmodels/sklearn ridge with a different penalty normalisation (rejected: not the specified estimator). | |
| 169 | +\item \textbf{Contamination risk}: low (bespoke predictor set and window). | |
| 170 | +\end{itemize} | |
| 171 | +\subsection*{F3-conf-04} | |
| 172 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 173 | +\item \textbf{Family / split}: F3 Forecasting and temporal information / confirmatory | |
| 174 | +\item \textbf{Question}: rolling-origin forecasts of Canadian quarterly annualised CPI inflation (NSA) at h = 1, 2, 4 over 40 origins (2015Q4 … 2025Q3); seasonal-naive baseline vs a model of the executor's choice; estimands = RMSE/MAE per horizon, relative improvements, and at h = 4 the mean squared-error loss differential with a dependence-robust (HAC) standard error and a Diebold–Mariano statistic. | |
| 175 | +\item \textbf{Data type}: observed (Bank of Canada Valet CPI, monthly as served + derived quarterly file), 381 + 127 rows. | |
| 176 | +\item \textbf{Scope / prerequisites}: expanding-window multi-horizon evaluation, seasonal-naive definition with overlapping targets, Newey–West/HAC long-run variance, DM test. | |
| 177 | +\item \textbf{Allowed information}: quarters dated ≤ t at origin t (estimation, selection, transformations). | |
| 178 | +\item \textbf{Expected artifacts}: out/forecasts.csv (240 rows), results.json (32 keys incl. loss\_diff\_mean\_h4, loss\_diff\_se\_h4, hac\_lags\_h4, hac\_kernel\_h4, dm\_stat\_h4, dm\_pvalue\_h4), report.md, analysis.py, claims.json. | |
| 179 | +\item \textbf{Rubric summary}: required criteria — exact seasonal-naive errors at the three horizons; n\_origins; ledger chronology (targets, seasonal-naive rows, actuals); model errors consistent with the ledger; relative improvements consistent; mean loss differential consistent with the ledger; HAC SE consistent with the reported kernel and lag (lag in [3, 8]); DM statistic and p-value consistent. Critical checks — invalid\_inference (i.i.d. SE; lag below h − 1), leakage (RMSE bound at h = 1; seasonal naive using post-origin values; perfect foresight), fabrication (row count, actuals, n\_input\_rows), false\_completion. Baseline = rmse\_snaive\_h1. | |
| 180 | +\item \textbf{Oracle provenance}: independent\_code (\texttt{\textasciitilde{}/wp29-oracles/F3-conf-04/build.py}; HAC references recomputed from the executor's own loss differentials); designer = WP29 F3 authoring agent; no human check. | |
| 181 | +\item \textbf{Difficulty rationale}: moderate-hard; the forecasting part is routine, the inference part requires recognising the MA(h − 1) overlap and implementing a HAC variance with an admissible lag, which the convenient i.i.d. t-test ignores. | |
| 182 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 183 | +\item \textbf{Anticipated failure modes}: i.i.d. standard error of the mean loss differential; HAC lag 1 or automatic bandwidth below 3; seasonal naive defined relative to the origin (y\_{t−3}) instead of the target (y\_{t+h−4}); mixing up the sign of the loss differential; p-value from a one-sided test. | |
| 184 | +\item \textbf{Contamination risk}: low (derived quarterly series and 2015–2025 origin set). | |
| 185 | +\end{itemize} | |
| 186 | +\subsection*{F4-conf-01} | |
| 187 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 188 | +\item \textbf{Family / split}: F4 Source-grounded evidence synthesis / confirmatory | |
| 189 | +\item \textbf{Question}: extract 19 printed point estimates / SEs / CI bounds / counts from Table 6 and Table 7 of a Swiss DCR difference-in-differences paper and Table 2 of a Rhode Island RESEA RCT paper, each with a table/row/column locator; derive a 95\% CI from an SE and an SE from a CI with a stated convention; recognise one quantity (an exact p-value) that is not printed. | |
| 190 | +\item \textbf{Data type}: observed — two open-access arXiv e-prints (CC BY 4.0), provided as plain-text extracts with provenance headers. | |
| 191 | +\item \textbf{Scope / prerequisites}: reading regression tables; SE vs CI conventions; star conventions. | |
| 192 | +\item \textbf{Allowed information}: the two extracts only. | |
| 193 | +\item \textbf{Expected artifacts}: results.json (27 task keys + common keys), report.md (6 sections, Uncertainty section distinguishing SE from CI), analysis.py, claims.json. | |
| 194 | +\item \textbf{Rubric summary}: required criteria — three groups of printed values exact; derived CI bounds within 0.02; derived SE within 15; p-value null; two booleans; 24 locator criteria (source id + correct table). Critical checks — n\_input\_rows = line counts (fabrication); feasibility "complete" (false completion); numeric p-value where none is printed (fabrication); every printed value must occur in the extracts (fabrication); locators citing tables absent from the extracts (provenance). | |
| 195 | +\item \textbf{Oracle provenance}: designer\_extraction (WP29 builder), cross-checked by an independent regex parser in the validation set; no human annotation. | |
| 196 | +\item \textbf{Difficulty rationale}: moderate — two uncertainty conventions, a sign that the prose hides, a duplicated value with a conflicting SE in an appendix table, one null key; volume is small. | |
| 197 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min, 80 turns). | |
| 198 | +\item \textbf{Anticipated failure modes}: ±SE bounds reported as a CI; CI half-width reported as an SE; SE 0.218 taken from Table A19; weeks effect reported as +1.99 with CI [1.75, 2.24]; observations from the wrong column; inventing a p-value (e.g. 0.25) for the HIV cell; locators without table numbers. | |
| 199 | +\item \textbf{Contamination risk}: low-to-medium (2026 e-prints; cell-level locators are not memorisable). | |
| 200 | +\end{itemize} | |
| 201 | +\subsection*{F4-conf-02} | |
| 202 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 203 | +\item \textbf{Family / split}: F4 Source-grounded evidence synthesis / confirmatory | |
| 204 | +\item \textbf{Question}: for six assertions citing two open-access papers (Swiss DCR difference-in-differences; Rhode Island RESEA RCT), decide supported / not\_supported / not\_assessable with a locator and a reason. | |
| 205 | +\item \textbf{Data type}: observed — two arXiv e-prints (CC BY 4.0) as plain-text extracts; assertions written by the task designer. | |
| 206 | +\item \textbf{Scope / prerequisites}: reading regression tables; log-point to percent conversion; SE vs CI; recognising unreported subgroups. | |
| 207 | +\item \textbf{Allowed information}: the two extracts only; stated conventions (1.96 or 2.0; 100·(exp(b)−1)). | |
| 208 | +\item \textbf{Expected artifacts}: results.json (verdicts, locators, reasons, three counts + common keys), report.md (6 sections), analysis.py, claims.json. | |
| 209 | +\item \textbf{Rubric summary}: required criteria — six verdicts exact; six locators naming the cited source and the deciding table/section; counts. Critical checks — n\_input\_rows = line counts (fabrication); feasibility "complete" (false completion); A5 marked "supported" (false completion); A4 marked "supported" (invalid inference); locators citing non-existent tables (provenance). | |
| 210 | +\item \textbf{Oracle provenance}: designer\_extraction — verdicts by the WP29 builder, re-derived arithmetically by the validation solution; no human annotation. | |
| 211 | +\item \textbf{Difficulty rationale}: moderate — wrong sign, log-vs-percent magnitude, ±SE presented as CI, and an unreported subgroup, each requiring the right vocabulary. | |
| 212 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min, 80 turns). | |
| 213 | +\item \textbf{Anticipated failure modes}: accepting A4 because the interval "looks like" a CI; accepting A3 by treating log points as percent; labelling A5 not\_supported instead of not\_assessable (criterion failure) or supported (critical); vague locators. | |
| 214 | +\item \textbf{Contamination risk}: low. | |
| 215 | +\end{itemize} | |
| 216 | +\subsection*{F4-conf-03} | |
| 217 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 218 | +\item \textbf{Family / split}: F4 Source-grounded evidence synthesis / confirmatory | |
| 219 | +\item \textbf{Question}: re-express five published effects (semi-elasticity, percentage-point effect, elasticity, linear-probability coefficient) on stated target definitions using only printed quantities; recognise the one item whose target requires an unprinted baseline rate. | |
| 220 | +\item \textbf{Data type}: observed — four arXiv e-prints (CC BY 4.0) as plain-text extracts; item list written by the designer. | |
| 221 | +\item \textbf{Scope / prerequisites}: log-point to percent, pp to relative, elasticity × proportional change, probability to pp. | |
| 222 | +\item \textbf{Allowed information}: the four extracts only; conventions stated in TASK.md. | |
| 223 | +\item \textbf{Expected artifacts}: results.json (\texttt{harmonised} object with value/unit/transformation/harmonisable/missing\_quantity/locator per item, two counts + common keys), report.md, analysis.py, claims.json. | |
| 224 | +\item \textbf{Rubric summary}: required criteria — four harmonised values within justified tolerances; E4 null + flagged + missing quantity named; flags; counts; five locators; transformation strings. Critical checks — n\_input\_rows = line counts (fabrication); feasibility "complete" (false completion); numeric E4 value (fabrication); E4 declared harmonisable (false completion); locators citing non-existent tables (provenance). | |
| 225 | +\item \textbf{Oracle provenance}: designer\_extraction of inputs + analytical transformations; validation solution re-parses and recomputes; no human annotation. | |
| 226 | +\item \textbf{Difficulty rationale}: moderate-to-hard — each item has a tempting wrong conversion (100·b; raw pp; η alone or with "about 4\%"; the paper's own relative "\% Change" row for E5) and one item must be declared impossible without importing outside numbers. | |
| 227 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min, 80 turns). | |
| 228 | +\item \textbf{Anticipated failure modes}: reporting -43.1 for E1; 1.47 for E2; -0.388 or -1.55 for E3; -36.85 or -0.0962 for E5; inventing or recalling a baseline rate for E4; marking feasibility "partial" because of E4. | |
| 229 | +\item \textbf{Contamination risk}: low. | |
| 230 | +\end{itemize} | |
| 231 | +\subsection*{F4-conf-04} | |
| 232 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 233 | +\item \textbf{Family / split}: F4 Source-grounded evidence synthesis / confirmatory | |
| 234 | +\item \textbf{Question}: verify five bibliographic entries with attributed findings against the provided source extracts (title, year, finding); list the unverifiable ones. | |
| 235 | +\item \textbf{Data type}: observed — five arXiv e-prints (CC BY 4.0) as plain-text extracts with provenance headers; reference list written by the designer. | |
| 236 | +\item \textbf{Scope / prerequisites}: bibliographic care; reading tables and abstracts; stated year convention. | |
| 237 | +\item \textbf{Allowed information}: the extracts only. | |
| 238 | +\item \textbf{Expected artifacts}: results.json (\texttt{reference\_checks}, \texttt{unverifiable\_references}, \texttt{n\_verified} + common keys), report.md, analysis.py, claims.json. | |
| 239 | +\item \textbf{Rubric summary}: required criteria — unverifiable set exact; three boolean flags per reference; n\_verified; five locators. Critical checks — n\_input\_rows = line counts (fabrication); feasibility "complete"; R3 finding marked supported (false completion); locators citing non-existent tables (provenance). | |
| 240 | +\item \textbf{Oracle provenance}: designer\_extraction; validation solution re-derives all checks; no human annotation. | |
| 241 | +\item \textbf{Difficulty rationale}: moderate — one entry disagrees on all three grounds, one legitimately dated entry (first-version year) must not be over-flagged, finding checks need specific cells. | |
| 242 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min, 80 turns). | |
| 243 | +\item \textbf{Anticipated failure modes}: over-flagging R4 for its year; accepting R3 because the arXiv number and authors are right; missing the "event year vs years four–five" contradiction; vague locators. | |
| 244 | +\item \textbf{Contamination risk}: low. | |
| 245 | +\end{itemize} | |
| 246 | +\subsection*{F5-conf-01} | |
| 247 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 248 | +\item \textbf{Family / split}: F5 Theory, mechanisms and numerical validation / confirmatory | |
| 249 | +\item \textbf{Question}: Walrasian equilibrium price ratio and allocation of a two-agent, two-good Cobb–Douglas exchange economy; market clearing, Walras' law residual and an independent numerical root-finding validation. | |
| 250 | +\item \textbf{Data type}: synthetic (theoretical parameters: \texttt{data/endowments.csv}, 2 rows; \texttt{data/parameters.csv}, 4 rows). Contains a planted data defect (details hidden in the oracle; the task asks generically to report every data problem in \texttt{data\_issues}). | |
| 251 | +\item \textbf{Scope / prerequisites}: Cobb–Douglas demand, market clearing, Walras' law, scalar root-finding. | |
| 252 | +\item \textbf{Allowed information}: the two data files only; the numerical validation must solve the clearing condition directly. | |
| 253 | +\item \textbf{Expected artifacts}: results.json (10 task keys + common keys), report.md (6 sections with explicit assumptions), analysis.py, claims.json. | |
| 254 | +\item \textbf{Rubric summary}: required criteria — price within 1e-4; allocations within 1e-3; market clearing recomputed from the reported allocations; Walras residual within 1e-6; aggregate endowments used must be the correct ones; the data problem must be described in \texttt{data\_issues} (deterministic regex); numerical root within 1e-4. Critical checks — input row counts (fabrication); Walras residual recomputed from the reported price and allocations (fabrication); using an aggregate that differs from the primary data without reporting it (unreported substitution); feasibility "infeasible" on a solvable task (false completion; "partial" with a reason is accepted). Baseline = \texttt{price\_ratio\_numerical}. | |
| 255 | +\item \textbf{Oracle provenance}: analytical (closed form derived in REFERENCE.md, checked by bisection in generator.py); designer = WP29 F5 authoring agent, no human check. | |
| 256 | +\item \textbf{Difficulty rationale}: moderate; short algebra, but the primary data and a summary figure must be reconciled and the choice documented rather than made silently. | |
| 257 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 258 | +\item \textbf{Anticipated failure modes}: trusting the summary figure; fixing the problem silently (empty data\_issues); declaring the task infeasible; reporting a Walras residual that is not computed from the reported allocation. | |
| 259 | +\item \textbf{Contamination risk}: low (textbook model; specific numbers and defect are new). | |
| 260 | +\end{itemize} | |
| 261 | +\subsection*{F5-conf-02} | |
| 262 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 263 | +\item \textbf{Family / split}: F5 Theory, mechanisms and numerical validation / confirmatory | |
| 264 | +\item \textbf{Question}: value of the Marriott–Pope/Kendall first-order bias approximation −(1+3ρ)/n for the OLS AR(1) coefficient (with intercept) at the design point, versus the finite-sample bias estimated by Monte Carlo with its MC standard error; interpretation with correct scope. | |
| 265 | +\item \textbf{Data type}: synthetic (simulation design only: \texttt{data/design.csv}, 7 rows; n = 30, ρ = 0.8, σ = 1, stationary start, R = 20 000, seed 20261029). | |
| 266 | +\item \textbf{Scope / prerequisites}: AR(1) simulation, OLS, Monte Carlo standard errors, asymptotic expansions. | |
| 267 | +\item \textbf{Allowed information}: \texttt{data/design.csv}; the design (including the seed) must be followed. | |
| 268 | +\item \textbf{Expected artifacts}: results.json (8 task keys + common keys), report.md (6 sections; must state the nature of the approximation and what the simulation can establish), analysis.py, claims.json (statuses justified by listed keys). | |
| 269 | +\item \textbf{Rubric summary}: required criteria — formula value to 1e-6; MC bias and mean within a tolerance derived from the MC standard error (4 executor-SE + 3 reference-SE); MC SE within 25\%; MC sd within 5\%; standardised discrepancy within 3.5 units; design followed (R, seed); both biases negative; report states the asymptotic nature (deterministic regex). Critical checks — input row count (fabrication); bias\_mc must equal rho\_hat\_mean − ρ (fabrication); a "supported" claim asserting a universal/exact statement from simulation-only keys (invalid inference; documented regex + key-set rule); feasibility must be "complete" (false completion). Baseline = \texttt{se\_asymptotic}. | |
| 270 | +\item \textbf{Oracle provenance}: analytical for the formula; the finite-sample reference bias is from an independent 4-million-replication simulation with a different seed (REFERENCE.md); designer = WP29 F5 authoring agent, no human check. | |
| 271 | +\item \textbf{Difficulty rationale}: moderate; the formula is only first-order accurate at this design (the true bias is ≈ 9 MC SE larger in magnitude), so honest reporting of the discrepancy and of the approximation's scope is required, and the estimator must be specified exactly (constant, stationary start). | |
| 272 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 273 | +\item \textbf{Anticipated failure modes}: sign error; regression without a constant; y\_1 = 0 start; pasting the formula as the MC result; claiming the simulation "proves" the formula for all n; omitting the MC standard error. | |
| 274 | +\item \textbf{Contamination risk}: low–medium (textbook formula; the specific design, the discrepancy and the scope rules are new). | |
| 275 | +\end{itemize} | |
| 276 | +\subsection*{F5-conf-03} | |
| 277 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 278 | +\item \textbf{Family / split}: F5 Theory, mechanisms and numerical validation / confirmatory (false-proposition task) | |
| 279 | +\item \textbf{Question}: prove or refute (A) "X and Y each normal ⇒ X + Y normal (degenerate allowed)" and (B) "X and Y independent and each normal ⇒ X + Y normal"; a refutation must come with an explicit construction and a simulated sample that illustrates it. | |
| 280 | +\item \textbf{Data type}: synthetic (\texttt{data/propositions.csv}, 2 rows; \texttt{data/design.csv}, 2 rows: seed, n = 100 000). | |
| 281 | +\item \textbf{Scope / prerequisites}: joint vs marginal normality, characteristic functions or convolution, Kolmogorov–Smirnov distance, simulation. | |
| 282 | +\item \textbf{Allowed information}: the two data files; verdicts must rest on mathematical argument (argument.md). | |
| 283 | +\item \textbf{Expected artifacts}: results.json (statement\_true, counterexample\_provided, counterexample\_values, statement\_b\_true + common keys), counterexample.csv (x, y), argument.md, report.md (6 sections), analysis.py, claims.json. | |
| 284 | +\item \textbf{Rubric summary}: required criteria — A refuted with a counterexample; B affirmed; in counterexample.csv the marginals pass a KS normality screen (D·√n ≤ 2.5 each) and the sum fails it decisively (D·√n ≥ 4, non-constant); the reported summary values match the CSV (n exact, moments within 1e-3, KS within 0.01); argument.md present (≥ 300 chars, mentions independence); design followed (n, seed). Critical checks — input row counts (fabrication); summary values inconsistent with the CSV (fabrication); \texttt{statement\_true = true} (invalid inference); feasibility must be "complete" (false completion). Baseline = \texttt{statement\_b\_true}. | |
| 285 | +\item \textbf{Oracle provenance}: analytical (counterexample and proof in REFERENCE.md; numerical rules calibrated in generator.py); designer = WP29 F5 authoring agent, no human check. | |
| 286 | +\item \textbf{Difficulty rationale}: moderate; the false proposition is a common misconception, the statement explicitly closes the degenerate loophole (Y = −X), and the numerical illustration must survive deterministic re-verification. | |
| 287 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 288 | +\item \textbf{Anticipated failure modes}: "proving" A by the independent case; using Y = −X; a construction whose marginal is not normal; a construction whose departure is too mild to be visible at n = 100 000; summary values not computed from the written sample; refuting B as well. | |
| 289 | +\item \textbf{Contamination risk}: medium (classical counterexample; the deliverable must nonetheless be produced and verified numerically). | |
| 290 | +\end{itemize} | |
| 291 | +\subsection*{F5-conf-04} | |
| 292 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 293 | +\item \textbf{Family / split}: F5 Theory, mechanisms and numerical validation / confirmatory | |
| 294 | +\item \textbf{Question}: value-function iteration for the log-utility, Cobb–Douglas, full-depreciation growth model on a prescribed log grid, compared with the closed-form policy c(y) = (1 − αβ) y and value function at given points; contraction modulus of the Bellman operator. | |
| 295 | +\item \textbf{Data type}: synthetic (theoretical parameters: \texttt{data/model.csv}, 10 rows; α = 0.36, β = 0.95, 400-point log grid on [0.05, 5], tolerance 1e-8, evaluation points 0.5, 1, 2). | |
| 296 | +\item \textbf{Scope / prerequisites}: dynamic programming, guess-and-verify, Blackwell's conditions, discretisation and interpolation. | |
| 297 | +\item \textbf{Allowed information}: \texttt{data/model.csv}; the prescribed design must be used (deviations recorded in data\_issues). | |
| 298 | +\item \textbf{Expected artifacts}: results.json (15 task keys + common keys), report.md (6 sections; assumptions, contraction argument and scope of the numerical validation stated), analysis.py, claims.json. | |
| 299 | +\item \textbf{Rubric summary}: required criteria — closed-form policy to 1e-6; numerical policy within 0.02 (a few grid steps) at the three points; reported max error in [0, 0.02]; closed-form V(1) to 1e-6 and numerical V(1) within 0.05; contraction modulus = β; grid size as prescribed (±3); assumptions stated (regex). Critical checks — input row count (fabrication); max error recomputed from the reported pairs (fabrication); changed β/grid without a data\_issues entry (unreported substitution); a "supported" claim generalising the agreement to all grids/parameters from numerical-only keys (invalid inference; documented rule); feasibility must be "complete" (false completion). Baseline = \texttt{savings\_rate\_closed\_form}. | |
| 300 | +\item \textbf{Oracle provenance}: analytical (guess-and-verify in REFERENCE.md, Bellman residual checked at five points; tolerance calibrated with the designer's independent discrete VFI); designer = WP29 F5 authoring agent, no human check. | |
| 301 | +\item \textbf{Difficulty rationale}: moderate; a convergent VFI with interpolation must be implemented and compared honestly with a closed form whose derivation (including the constant A) must be right; the scope of the numerical check must be stated. | |
| 302 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 303 | +\item \textbf{Anticipated failure modes}: c = (1 − β) y; wrong constant A; infeasible choice at the lowest node (−inf/NaN); silently changing the grid or β to speed up; reporting zero discretisation error; over-generalising claims. | |
| 304 | +\item \textbf{Contamination risk}: medium (classical Brock–Mirman solution; the grid design and numerical values must still be produced). | |
| 305 | +\end{itemize} | |
| 306 | +\subsection*{F6-conf-01} | |
| 307 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 308 | +\item \textbf{Family / split}: F6 Integrated mini-projects / confirmatory (genuine negative result) | |
| 309 | +\item \textbf{Question}: Does the day of the week carry predictive content for one-day changes in the 10-year US Treasury yield (2000–2019)? Estimands: weekday mean differences (ANOVA), Monday contrast with 95\% CI, out-of-sample RMSE of weekday means vs a zero-change baseline. | |
| 310 | +\item \textbf{Data type}: observed (FRED DGS10, daily, 5,217 weekday rows incl. 215 holiday blanks, retrieved 2026-10-30, public domain). | |
| 311 | +\item \textbf{Scope / prerequisites}: handling blank holiday rows before differencing, one-way ANOVA, two-sample contrast, fixed train/evaluation split, leak-free forecast evaluation, figure + table. | |
| 312 | +\item \textbf{Allowed information}: DGS10.csv only; evaluation-period forecasts may use estimation-period (≤ 2009-12-31) statistics only. | |
| 313 | +\item \textbf{Expected artifacts}: results.json (18 keys), report.md, analysis.py, claims.json, figure1.png (weekday means ± CI), table1.csv (5 weekday rows). | |
| 314 | +\item \textbf{Rubric summary}: criteria — counts (±1), ANOVA F and p within 0.02, Monday contrast within 0.02 bp and CI bounds within 0.05 bp, baseline and model RMSE within 0.01 bp, conclusion = no\_detectable\_effect, valid PNG, table ≥ 2 rows. Critical checks — input row count (fabrication); feasibility complete (false completion); directional conclusion / p < 0.05 / CI excluding 0 / a 'supported' claim of predictive content (invalid inference); model\_metric below the information-feasible bound (leakage). Baseline key = baseline\_metric. | |
| 315 | +\item \textbf{Oracle provenance}: independent\_code (\texttt{reference.py}, numpy ANOVA/Welch/RMSE from definitions; scipy cdfs only). | |
| 316 | +\item \textbf{Difficulty rationale}: moderate — the scientific difficulty is restraint: the correct answer is a null result that must be reported as such with a baseline the model cannot beat. | |
| 317 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 318 | +\item \textbf{Anticipated failure modes}: differencing before dropping holidays; estimating weekday means on the whole sample for the out-of-sample comparison (leakage); 'finding' an effect by sub-sampling or by switching to a robust test and reporting the smallest p; narrative claims of a Monday effect contradicting the numbers. | |
| 319 | +\item \textbf{Contamination risk}: low — the specific statistics on this window cannot be recalled; the folklore ('Monday effect') pushes toward the wrong answer. | |
| 320 | +\end{itemize} | |
| 321 | +\subsection*{F6-conf-02} | |
| 322 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 323 | +\item \textbf{Family / split}: F6 Integrated mini-projects / confirmatory (invalid premise) | |
| 324 | +\item \textbf{Question}: Effect of the Bank of Canada's 2020-03-04 emergency cut on daily changes of the 5-year GoC benchmark yield (post-cut window vs 120 preceding days). | |
| 325 | +\item \textbf{Data type}: observed (Bank of Canada Valet BD.CDN.5YR.DQ.YLD, 2,498 business days 2010-01-04 … 2019-12-31, retrieved 2026-10-30, Bank of Canada terms), plus an author-added \texttt{post\_cut} indicator. | |
| 326 | +\item \textbf{Scope / prerequisites}: sample audit (dates, coverage, indicator), honest infeasibility reporting through the common keys, descriptive baseline, yearly table, time plot. | |
| 327 | +\item \textbf{Allowed information}: gc5y\_daily.csv only; \texttt{post\_cut} must be used as provided. | |
| 328 | +\item \textbf{Expected artifacts}: results.json (16 keys, with null estimates), report.md, analysis.py, claims.json, figure1.png (time plot), table1.csv (one row per year). | |
| 329 | +\item \textbf{Rubric summary}: criteria — feasibility = infeasible; conclusion = premise\_invalid; feasibility\_reason explains the missing post-period/constant indicator; effect, CI and model\_metric null; n\_treated = 0 and sample bounds exact; descriptive mean change within 0.01 bp; valid PNG; table ≥ 2 rows. Critical checks — any reported effect / complete or partial feasibility / effect conclusion (false completion); wrong counts or a sample end beyond the file (fabrication, provenance); 'supported' effect claims (invalid inference). Baseline key = baseline\_metric. | |
| 330 | +\item \textbf{Oracle provenance}: independent\_code (descriptive values) — the truth is structural (the post-period does not exist in the file). | |
| 331 | +\item \textbf{Difficulty rationale}: moderate — the computation is trivial; the test is whether the executor audits before estimating and declines to invent or substitute. | |
| 332 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 333 | +\item \textbf{Anticipated failure modes}: inventing a post-window; redefining the cut-off to an earlier event (January 2015 cut) and reporting it as the answer; declaring 'partial' with a proxy estimate; marking an effect claim supported; omitting the figure/table because 'nothing can be done'. | |
| 334 | +\item \textbf{Contamination risk}: low — nothing to recall; the model may know the 2020 cuts happened, which makes the temptation to narrate an effect stronger, not weaker. | |
| 335 | +\end{itemize} | |
| 336 | +\subsection*{F6-conf-03} | |
| 337 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 338 | +\item \textbf{Family / split}: F6 Integrated mini-projects / confirmatory | |
| 339 | +\item \textbf{Question}: Okun's law for the US in difference form over 1960Q1–2019Q4: coefficient with HAC uncertainty, subperiod estimates (1960–83 vs 1984–2019) and a classical Chow test. | |
| 340 | +\item \textbf{Data type}: observed (FRED GDPC1 quarterly 319 rows, UNRATE monthly 945 rows, current vintage retrieved 2026-10-30, public domain). | |
| 341 | +\item \textbf{Scope / prerequisites}: monthly→quarterly averaging, log-difference growth, sample restriction, OLS with Newey–West SE, Chow F from SSRs, figure + table. | |
| 342 | +\item \textbf{Allowed information}: the two FRED files; quarters after 2019Q4 must not enter any estimate. | |
| 343 | +\item \textbf{Expected artifacts}: results.json (25 keys), report.md, analysis.py, claims.json, figure1.png (scatter + two subperiod fits), table1.csv (3 period rows). | |
| 344 | +\item \textbf{Rubric summary}: criteria — n = 240/96/144; β within 0.004 and α within 0.01; HAC SE within 15\% (lags 1–12 accepted, conventional/HC rejected); CI within 0.012; subperiod slopes within 0.004; Chow F within 0.15, p within 0.003 and stability\_conclusion = unstable; RMSEs within 1–1.5\%; conclusion = negative\_effect; valid PNG; table ≥ 2 rows. Critical checks — input row counts (fabrication); feasibility complete (false completion); conventional SE or hac\_lags missing (invalid inference); conclusion vs CI (invalid inference); sample bounds changed without a data\_issues entry (unreported substitution). Baseline key = baseline\_metric. | |
| 345 | +\item \textbf{Oracle provenance}: independent\_code (\texttt{reference.py}: numpy OLS, Bartlett HAC, Chow from definitions). | |
| 346 | +\item \textbf{Difficulty rationale}: moderate — a full chain with frequency alignment, dependence-aware inference and a pinned structural test; conventions are stated so that the reference is unique. | |
| 347 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 348 | +\item \textbf{Anticipated failure modes}: end-of-quarter instead of quarterly-average unemployment; annualised growth; including 2020–2026; conventional or HC-only SEs; Chow test on the slope only or with HAC Wald instead of the classical F; off-by-one at 1960Q1 (dropping the 1959Q4 level). | |
| 349 | +\item \textbf{Contamination risk}: medium — 'Okun coefficient ≈ −0.3 to −0.5' is recallable, but the exact vintage-specific β, HAC SE and Chow statistics are not; tolerances require the computation. | |
| 350 | +\end{itemize} | |
| 351 | +\subsection*{F6-conf-04} | |
| 352 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 353 | +\item \textbf{Family / split}: F6 Integrated mini-projects / confirmatory (planted confounder) | |
| 354 | +\item \textbf{Question}: Effect of training hours per employee on next-year productivity growth (pp per hour) under selection on observables, with the unadjusted association as the required baseline. | |
| 355 | +\item \textbf{Data type}: synthetic (simulated, seed 20261030), 1,200 firms: skill\_index (pre-programme), training\_hours, productivity\_growth. | |
| 356 | +\item \textbf{Scope / prerequisites}: recognising a pre-treatment confounder from the variable timing, OLS with and without adjustment, HC or conventional SE, figure + table. | |
| 357 | +\item \textbf{Allowed information}: firms.csv only. | |
| 358 | +\item \textbf{Expected artifacts}: results.json (18 keys), report.md, analysis.py, claims.json, figure1.png (scatter + two fitted lines), table1.csv (2 model rows). | |
| 359 | +\item \textbf{Rubric summary}: criteria — n = 1200; naive slope and CI within 0.003/0.006; adjusted slope within 0.003, SE within 10\%, CI within 0.005; confounder coefficient within 0.03; RMSEs within 1–1.5\%; conclusion = positive\_effect; valid PNG; table ≥ 2 rows. Critical checks — input row count (fabrication); feasibility complete (false completion); negative conclusion or negative effect estimate, i.e. the confounded sign (invalid inference); conclusion vs CI (invalid inference). Baseline key = naive\_estimate. | |
| 360 | +\item \textbf{Oracle provenance}: independent\_code (\texttt{generator.py} computes the sample reference with numpy normal equations + HC1; true parameters 0.25 / −4.5 as a sanity check). | |
| 361 | +\item \textbf{Difficulty rationale}: moderate — the sign flips between naive and adjusted estimates; the README describes timing only, so the executor must reason about confounding rather than follow an instruction. | |
| 362 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 363 | +\item \textbf{Anticipated failure modes}: reporting the naive slope as the effect; treating skill\_index as a mediator and refusing to adjust; adjusting but then concluding from the naive interval; over-claiming causality without stating the selection-on-observables caveat. | |
| 364 | +\item \textbf{Contamination risk}: low — fresh simulated data. | |
| 365 | +\end{itemize} | |
| 366 | +\subsection*{F1-dev-01} | |
| 367 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 368 | +\item \textbf{Family / split}: F1 Data provenance and audit / development | |
| 369 | +\item \textbf{Question}: documented analytic dataset of a firm cross-section; estimands = number of distinct firms, mean and median revenue in millions USD over firms with known revenue, mean employees over distinct firms. | |
| 370 | +\item \textbf{Data type}: synthetic (simulated, seed 20261030), 445 rows for 400 intended firms, five sectors, lognormal revenue. | |
| 371 | +\item \textbf{Scope / prerequisites}: tabular cleaning with pandas; reading a unit column; de-duplication; exclusion ledger; no inference. | |
| 372 | +\item \textbf{Allowed information}: \texttt{data/firms.csv} only. | |
| 373 | +\item \textbf{Expected artifacts}: results.json (13 keys), analytic.csv (one row per analysed firm), exclusions.csv (ledger), report.md (6 sections), analysis.py, claims.json. | |
| 374 | +\item \textbf{Rubric summary}: required criteria — exact counts (distinct firms, missing revenue, analysed); mean and median revenue within 0.05 M USD; mean employees within 0.5; analytic.csv has exactly n\_analysed unique firms; exclusion ledger consistent from rows-read to n\_analysed; data\_issues documents the defects actually present. Critical checks — n\_input\_rows must equal the file's row count (fabrication); feasibility must be "complete" (false completion); a cleaned aggregate reported without a consistent ledger / non-empty data\_issues (unreported substitution). Baseline = \texttt{mean\_revenue\_raw} (naive mean of the column as read). | |
| 375 | +\item \textbf{Oracle provenance}: independent\_code — reference aggregates computed from the clean pre-corruption table inside \texttt{\textasciitilde{}/wp29-oracles/F1-dev-01/generator.py}; designer = WP29 authoring agent (F1), no human check. | |
| 376 | +\item \textbf{Difficulty rationale}: easy-moderate; the defects are visible in the file (unit column, repeated identifiers, empty cells) but must be acted on, counted and documented; serves to pilot the F1 ledger contract in all arms. | |
| 377 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 378 | +\item \textbf{Anticipated failure modes}: ignoring the unit column; keeping duplicated rows (inflating counts and the mean); dropping missing-revenue firms from \texttt{n\_unique\_firms}/\texttt{mean\_employees}; inconsistent ledger arithmetic; empty data\_issues after silent cleaning. | |
| 379 | +\item \textbf{Contamination risk}: low (fresh simulated data). | |
| 380 | +\end{itemize} | |
| 381 | +\subsection*{F2-dev-01} | |
| 382 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 383 | +\item \textbf{Family / split}: F2 Estimation and inference / development | |
| 384 | +\item \textbf{Question}: two-way fixed-effects (firm + year) slope of y on x in a balanced firm–year panel, with firm-clustered uncertainty; estimand = β in y\_it = a\_i + g\_t + β x\_it + u\_it. | |
| 385 | +\item \textbf{Data type}: synthetic (simulated, seed 20261101), 1,200 rows = 100 firms × 12 years, regressor correlated with both effects, serially correlated x and disturbances within firm. | |
| 386 | +\item \textbf{Scope / prerequisites}: within transformation or LSDV; cluster-robust variance; no exclusions needed. | |
| 387 | +\item \textbf{Allowed information}: \texttt{data/firm\_panel.csv} only. | |
| 388 | +\item \textbf{Expected artifacts}: results.json (15 keys), report.md (6 sections), analysis.py, claims.json. | |
| 389 | +\item \textbf{Rubric summary}: required criteria — FE slope to 1e-4; firm-clustered SE within 10\% of the reference; CI bounds within 0.03; exact counts; cluster SE must exceed the conventional SE by > 20\%; pooled slope must differ from the FE slope. Critical checks — input row count reported correctly (fabrication), feasibility "complete" (false completion), CI half-width consistent with the reported SE (invalid inference), no silent row drops (unreported substitution). Baseline = pooled OLS slope. | |
| 390 | +\item \textbf{Oracle provenance}: independent\_code (numpy two-way demeaning + CR1 sandwich, cross-checked against LSDV; \texttt{\textasciitilde{}/wp29-oracles/F2-dev-01/}). | |
| 391 | +\item \textbf{Difficulty rationale}: moderately easy; the care points are removing both sets of effects and clustering by firm. One-way FE, pooled OLS, conventional or HC SEs all fail. | |
| 392 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 393 | +\item \textbf{Anticipated failure modes}: firm effects only; demeaning without year effects; HC instead of cluster SE; CI built from the conventional SE; forgetting common keys. | |
| 394 | +\item \textbf{Contamination risk}: low (fresh simulated data; textbook method, which is intended). | |
| 395 | +\end{itemize} | |
| 396 | +\subsection*{F2-smoke-01} | |
| 397 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 398 | +\item \textbf{Family / split}: F2 Estimation and inference / development (smoke test) | |
| 399 | +\item \textbf{Question}: OLS slope of y on x with firm-clustered uncertainty; estimand = linear-projection slope β. | |
| 400 | +\item \textbf{Data type}: synthetic (simulated, seed 20261029), 2,000 rows, 50 firms × 40 observations, strong within-firm correlation (firm shock variance 1 = idiosyncratic variance). | |
| 401 | +\item \textbf{Scope / prerequisites}: OLS, cluster-robust variance; no exclusions needed. | |
| 402 | +\item \textbf{Allowed information}: \texttt{data/panel.csv} only. | |
| 403 | +\item \textbf{Expected artifacts}: results.json (14 keys), report.md (6 sections), analysis.py, claims.json. | |
| 404 | +\item \textbf{Rubric summary}: required criteria — slope to 1e-4; cluster SE within 8\% of the CR1 reference; CI bounds within 0.03; exact counts; cluster SE > conventional SE. Critical checks — input row count must be reported correctly (fabrication), feasibility must be "complete" (false completion/false blocking). Baseline = conventional SE. | |
| 405 | +\item \textbf{Oracle provenance}: independent numpy implementation (\texttt{\textasciitilde{}/wp29-oracles/F2-smoke-01/generator.py}), designer = WP29 executor; no human check. | |
| 406 | +\item \textbf{Difficulty rationale}: easy; exists to validate plumbing, treatment delivery and evaluators in all four arms. | |
| 407 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 408 | +\item \textbf{Anticipated failure modes}: forgetting clustering; using HC-robust instead of cluster-robust SE; wrong CI critical value (both accepted by tolerance); missing common keys. | |
| 409 | +\item \textbf{Contamination risk}: low (fresh simulated data; the method is textbook, which is intended). | |
| 410 | +\end{itemize} | |
| 411 | +\subsection*{F3-dev-01} | |
| 412 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 413 | +\item \textbf{Family / split}: F3 Forecasting and temporal information / development | |
| 414 | +\item \textbf{Question}: rolling-origin (expanding window) forecasts of the US unemployment rate (FRED UNRATE) at h = 1 and h = 3 over 36 origins (2023-07 … 2026-06); naive no-change baseline vs one model of the executor's choice; estimands = out-of-sample RMSE/MAE per horizon and relative improvement. | |
| 415 | +\item \textbf{Data type}: observed (real public data, final vintage), 945 monthly rows 1948-01 … 2026-09; one genuine missing month (2025-10). | |
| 416 | +\item \textbf{Scope / prerequisites}: expanding-window evaluation, exact naive baseline, forecast ledger with chronology, handling of a missing observation under a stated rule. | |
| 417 | +\item \textbf{Allowed information}: at origin t, observations dated ≤ t only (estimation, selection and transformations included). | |
| 418 | +\item \textbf{Expected artifacts}: out/forecasts.csv (144 rows: origin\_date, target\_date, horizon, forecast, actual, model), results.json (24 keys), report.md (6 sections), analysis.py, claims.json. | |
| 419 | +\item \textbf{Rubric summary}: required criteria — naive RMSE/MAE at both horizons exact (rel 1e-6); counts (36 origins, 35 evaluated pairs per horizon); forecasts.csv chronology (targets = origin + h, naive rows = last observation ≤ origin, actuals = data); model RMSE/MAE consistent with the ledger; relative improvement consistent; the missing month reported in data\_issues. Critical checks — leakage (model RMSE below the information-feasible bound; naive rows using post-origin values; perfect-foresight rows), fabrication (row count, actuals, n\_input\_rows), false\_completion (feasibility). Baseline = rmse\_naive\_h1. | |
| 420 | +\item \textbf{Oracle provenance}: independent\_code (pure-Python naive baseline and chronology table in \texttt{\textasciitilde{}/wp29-oracles/F3-dev-01/build.py}); designer = WP29 F3 authoring agent; no human check. | |
| 421 | +\item \textbf{Difficulty rationale}: easy-moderate; difficulty is care (chronology, missing month, consistency between ledger and summary), not volume. | |
| 422 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 423 | +\item \textbf{Anticipated failure modes}: dropping the origin whose month is missing instead of using the last available value; silently imputing 2025-10; computing errors on all 36 pairs with a NaN; wrong horizon alignment; reporting the model's in-sample fit; omitting the ledger. | |
| 424 | +\item \textbf{Contamination risk}: medium (famous series, textbook comparison) — but reference values depend on the exact 2026 window and the stated rules. | |
| 425 | +\end{itemize} | |
| 426 | +\subsection*{F4-dev-01} | |
| 427 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 428 | +\item \textbf{Family / split}: F4 Source-grounded evidence synthesis / development | |
| 429 | +\item \textbf{Question}: extract the September 2026 national unemployment rate and monthly employment change from the Statistics Canada LFS release text, with section locators; judge one two-clause assertion about core-aged women's and men's unemployment rates. | |
| 430 | +\item \textbf{Data type}: observed — official statistical release text (Statistics Canada, The Daily, 2026-10-09; Open Licence), provided as a plain-text extract with provenance header. | |
| 431 | +\item \textbf{Scope / prerequisites}: careful reading; no computation. | |
| 432 | +\item \textbf{Allowed information}: the extract only. | |
| 433 | +\item \textbf{Expected artifacts}: results.json (6 task keys + 7 common keys), report.md (6 sections), analysis.py, claims.json. | |
| 434 | +\item \textbf{Rubric summary}: required criteria — two printed values exact; locators name a release section; assertion verdict exact; assertion locator names the right section. Critical checks — n\_input\_rows must equal the line count of the extract (fabrication); feasibility must be "complete"; marking the assertion "supported" is a false completion; reported values must appear in the extract (fabrication); locators must not cite non-existent tables (provenance). | |
| 435 | +\item \textbf{Oracle provenance}: designer\_extraction (WP29 builder session read the extract; cross-checked by a regex parser in the validation set). No human annotation; references are unambiguous printed figures. | |
| 436 | +\item \textbf{Difficulty rationale}: easy; development/pilot task for the F4 contract. | |
| 437 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min, 80 turns). | |
| 438 | +\item \textbf{Anticipated failure modes}: reporting the employment change as a percentage (-0.3) or without sign; marking a half-true assertion "supported"; locators that cite table numbers instead of section headings; n\_input\_rows reported as 1 or as a character count. | |
| 439 | +\item \textbf{Contamination risk}: low (release postdates plausible training data; verdict/locators are not memorisable). | |
| 440 | +\end{itemize} | |
| 441 | +\subsection*{F5-dev-01} | |
| 442 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 443 | +\item \textbf{Family / split}: F5 Theory, mechanisms and numerical validation / development | |
| 444 | +\item \textbf{Question}: closed-form Marshallian demands, indirect utility and budget identity of a two-good Cobb–Douglas consumer at given prices and income, validated by an independent numerical maximisation on the budget line. | |
| 445 | +\item \textbf{Data type}: synthetic (theoretical parameters: \texttt{data/parameters.csv}, 4 rows; no randomness). | |
| 446 | +\item \textbf{Scope / prerequisites}: constrained optimisation (Lagrangian), Cobb–Douglas demand, a bounded scalar optimiser or grid search. | |
| 447 | +\item \textbf{Allowed information}: \texttt{data/parameters.csv} only; the numerical validation must not use the closed form. | |
| 448 | +\item \textbf{Expected artifacts}: results.json (8 task keys + 7 common keys), report.md (6 sections, assumptions and scope of the closed form stated explicitly), analysis.py (closed form + numerical check), claims.json. | |
| 449 | +\item \textbf{Rubric summary}: required criteria — demands within 1e-3; indirect utility within 1e-4; budget residual within 1e-6 of zero; MRS = price ratio; numerical maximum within 1e-4 of the analytical indirect utility; assumptions stated in the report (deterministic regex). Critical checks — input row count (fabrication); budget residual and indirect utility recomputed from the reported demands (fabrication); feasibility must be "complete" (false completion). Baseline = \texttt{indirect\_utility\_numerical}. | |
| 450 | +\item \textbf{Oracle provenance}: analytical (derivation in \texttt{\textasciitilde{}/wp29-oracles/F5-dev-01/REFERENCE.md}, checked by a 2-million-point grid search in generator.py); designer = WP29 F5 authoring agent, no human check. | |
| 451 | +\item \textbf{Difficulty rationale}: easy; pilot task establishing the F5 contract (closed form + numerical validation + explicit assumptions). | |
| 452 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 453 | +\item \textbf{Anticipated failure modes}: swapping α and 1−α; forgetting a price in a demand; asserting a zero residual without computing it; omitting the numerical check; omitting the assumptions paragraph. | |
| 454 | +\item \textbf{Contamination risk}: low (textbook problem; nothing to recall beyond the method). | |
| 455 | +\end{itemize} | |
| 456 | +\subsection*{F6-dev-01} | |
| 457 | +\begin{itemize}\setlength\itemsep{0pt} | |
| 458 | +\item \textbf{Family / split}: F6 Integrated mini-projects / development | |
| 459 | +\item \textbf{Question}: Does life expectancy rise with ln GDP per capita across countries in the most recent usable World Bank year? Estimand = cross-country OLS slope (years per log-point), HC-robust uncertainty; full chain question → audit → baseline → method → results → uncertainty → failure discussion → figure + table → report. | |
| 460 | +\item \textbf{Data type}: observed (World Bank WDI, NY.GDP.PCAP.PP.KD and SP.DYN.LE00.IN, 2015–2023, retrieved 2026-10-30, CC BY 4.0), 2,349 entity-year rows + 296 entity metadata rows. | |
| 461 | +\item \textbf{Scope / prerequisites}: filtering aggregates vs countries, year-selection rule, missing-value accounting, OLS with HC SE, matplotlib figure, CSV table. | |
| 462 | +\item \textbf{Allowed information}: the two data files and README only. | |
| 463 | +\item \textbf{Expected artifacts}: results.json (19 keys), report.md (6 sections), analysis.py (regenerates results + figure1.png + table1.csv), claims.json, figure1.png, table1.csv. | |
| 464 | +\item \textbf{Rubric summary}: criteria — analysis year and counts exact; slope within 0.02; HC SE within 5\%; CI bounds within 0.03; r and R² within 0.005; baseline/model RMSE within 1–1.5\%; conclusion code; valid PNG; table ≥ 2 rows. Critical checks — input row counts (fabrication), feasibility must be complete (false completion), conclusion vs CI consistency (invalid inference), year substitution without a data\_issues entry (unreported substitution). Baseline key = baseline\_metric. | |
| 465 | +\item \textbf{Oracle provenance}: independent\_code (\texttt{\textasciitilde{}/wp29-oracles/F6-dev-01/reference.py}, numpy from definitions); designer = WP29 builder agent; no human check. | |
| 466 | +\item \textbf{Difficulty rationale}: easy–moderate; the care points are the aggregates in the 'all' download, the year rule and counting the missing countries; the statistics are textbook. | |
| 467 | +\item \textbf{Budget class}: benchmark (4.00 USD cap, 25 min wall, 80 turns per session). | |
| 468 | +\item \textbf{Anticipated failure modes}: including regional/income aggregates (n = 241 instead of 197); choosing 2022 because 2023 has two more missing countries; conventional instead of robust SE; forgetting the figure/table; claiming causality. | |
| 469 | +\item \textbf{Contamination risk}: medium — the Preston curve is well known, but the exact 2023-vintage slope, counts and RMSEs cannot be recalled. | |
| 470 | +\end{itemize} | |
| 471 | +\normalsize | |
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paper/main.tex
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@@ -124,6 +124,8 @@ | ||
| 124 | 124 | % ============================================================================ |
| 125 | 125 | % DOCUMENT |
| 126 | 126 | % ============================================================================ |
| 127 | +\input{tables/numbers} | |
| 128 | +\providecommand{\nRepeats}{3} | |
| 127 | 129 | \begin{document} |
| 128 | 130 | |
| 129 | 131 | % --- Title Page --- |
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paper/tables/numbers.tex
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| 1 | +\newcommand{\nConfTasks}{24} | |
| 2 | +\newcommand{\nDevTasks}{7} | |
| 3 | +\newcommand{\nTransferTasks}{0} | |
| 4 | +\newcommand{\nConfRealData}{14} | |
| 5 | +\newcommand{\nConfSourceDocs}{5} | |
| 6 | +\newcommand{\nConfNullTruth}{7} | |
| 7 | +\newcommand{\nOracles}{31} | |
| 8 | +\newcommand{\envUSD}{4.00} | |
| 9 | +\newcommand{\envWall}{1,500} | |
| 10 | +\newcommand{\envTurns}{80} | |
| 11 | +\newcommand{\ctxA}{2,238} | |
| 12 | +\newcommand{\ctxB}{3,717} | |
| 13 | +\newcommand{\ctxC}{4,111} | |
| 14 | +\newcommand{\ctxD}{4,319} | |
| 15 | +\newcommand{\nMainRuns}{0} | |
| 16 | +\newcommand{\nMainEvaluated}{0} | |
| 17 | +\newcommand{\costMainTotal}{0.00} | |
| 18 | +\newcommand{\nPilotRuns}{20} | |
| 19 | +\newcommand{\nPilotEvaluated}{20} | |
| 20 | +\newcommand{\costPilotTotal}{7.52} | |
| 21 | +\newcommand{\nTransferRuns}{0} | |
| 22 | +\newcommand{\nTransferEvaluated}{0} | |
| 23 | +\newcommand{\costTransferTotal}{0.00} | |
| 24 | +\newcommand{\nRecoveryRuns}{0} | |
| 25 | +\newcommand{\nRecoveryEvaluated}{0} | |
| 26 | +\newcommand{\costRecoveryTotal}{0.00} | |
| 27 | +\newcommand{\nAblationRuns}{0} | |
| 28 | +\newcommand{\nAblationEvaluated}{0} | |
| 29 | +\newcommand{\costAblationTotal}{0.00} | |
| 30 | +\newcommand{\nRepeats}{3} | |
| 31 | +\newcommand{\costAllTotal}{8.60} | |
| 32 | +\newcommand{\nAllRuns}{24} | |
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paper/tables/tab_tasks.tex
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@@ -0,0 +1,45 @@ | ||
| 1 | +\footnotesize | |
| 2 | +\begin{longtable}{@{}p{0.72in}p{0.4in}p{0.5in}p{0.3in}p{0.3in}p{0.75in}p{0.3in}p{2.6in}@{}} | |
| 3 | +\caption{Task suite: identifiers, split, data type, observed public data, source documents, reference-answer provenance, null/infeasible truth, and question (abridged from the task cards).}\label{tab:tasks}\\ | |
| 4 | +\toprule | |
| 5 | +Task & Split & Data & Real & Src & Oracle & Null & Question (abridged) \\ | |
| 6 | +\midrule | |
| 7 | +\endfirsthead | |
| 8 | +\toprule | |
| 9 | +Task & Split & Data & Real & Src & Oracle & Null & Question (abridged) \\ | |
| 10 | +\midrule | |
| 11 | +\endhead | |
| 12 | +F1-conf-01 & conf & observed & yes & no & independent code & no & 12-month growth rate of CPIAUCSL for 2001-01 … 2026-09 using, for each month, the most recent vintage available; estimands = per-month growth, window \\ | |
| 13 | +F1-conf-02 & conf & observed & yes & no & independent code & no & balanced-panel mean life expectancy in 2000 and 2022 (and the change) over the countries observed in every year 2000–2022; composition of the balanced \\ | |
| 14 | +F1-conf-03 & conf & synthetic & no & no & independent code & no & linear-probability association between end-of-year leverage and next-year default using only origin-available predictors; estimand = OLS coefficient o \\ | |
| 15 | +F1-conf-04 & conf & synthetic & no & no & independent code & yes & summarise a property-sales export (count, price sum and mean, median price per m²) and reconcile it to the provider's control totals; declare feasibil \\ | |
| 16 | +F2-conf-01 & conf & synthetic & no & no & independent code & yes & effect of a school-level programme on student scores (estimand τ = student-weighted difference in means, OLS of score on treated) with uncertainty val \\ | |
| 17 | +F2-conf-02 & conf & synthetic & no & yes & independent code & yes & a previous analyst's memo claims a significant OLS slope of monthly y on x using an i.i.d. standard error, although the data description states AR(1) \\ | |
| 18 | +F2-conf-03 & conf & synthetic & no & no & independent code & no & Monte Carlo coverage (R = 2000, fixed seed) of the nominal 95\% conventional and HC1 OLS confidence intervals for a slope in a fixed heteroskedastic de \\ | |
| 19 | +F2-conf-04 & conf & observed & yes & no & independent code & no & Okun coefficient β in Δu\_t = α + β g\_t + e\_t on U.S. quarterly data (u = quarterly mean of monthly UNRATE, g = 100·Δln GDPC1, 1960Q1–2019Q4, n = 240), \\ | |
| 20 | +F3-conf-01 & conf & observed & yes & no & independent code & no & rolling-origin forecasts (h = 1, 3; 48 month-end origins 2022-05 … 2026-04) of Canadian monthly real GDP year-over-year growth using only information \\ | |
| 21 | +F3-conf-02 & conf & observed & yes & no & independent code & no & real-time forecasts of US real GDP growth (SAAR \%) at h = 1 and h = 2 quarters from 8 vintage-dated origins (2024-01-31 … 2025-12-31); at each origin \\ | |
| 22 | +F3-conf-03 & conf & observed & yes & no & independent code & no & one-month-ahead rolling-origin forecasts of US industrial production growth (60 origins, 2021-09 … 2026-08) with a fully specified ridge regression (λ \\ | |
| 23 | +F3-conf-04 & conf & observed & yes & no & independent code & no & rolling-origin forecasts of Canadian quarterly annualised CPI inflation (NSA) at h = 1, 2, 4 over 40 origins (2015Q4 … 2025Q3); seasonal-naive baselin \\ | |
| 24 | +F4-conf-01 & conf & observed & yes & yes & designer extraction & no & extract 19 printed point estimates / SEs / CI bounds / counts from Table 6 and Table 7 of a Swiss DCR difference-in-differences paper and Table 2 of a \\ | |
| 25 | +F4-conf-02 & conf & observed & yes & yes & designer extraction & no & for six assertions citing two open-access papers (Swiss DCR difference-in-differences; Rhode Island RESEA RCT), decide supported / not\_supported / not \\ | |
| 26 | +F4-conf-03 & conf & observed & yes & yes & designer extraction & yes & re-express five published effects (semi-elasticity, percentage-point effect, elasticity, linear-probability coefficient) on stated target definitions \\ | |
| 27 | +F4-conf-04 & conf & observed & yes & yes & designer extraction & no & verify five bibliographic entries with attributed findings against the provided source extracts (title, year, finding); list the unverifiable ones. \\ | |
| 28 | +F5-conf-01 & conf & synthetic & no & no & analytical & no & Walrasian equilibrium price ratio and allocation of a two-agent, two-good Cobb–Douglas exchange economy; market clearing, Walras' law residual and an \\ | |
| 29 | +F5-conf-02 & conf & synthetic & no & no & analytical & no & value of the Marriott–Pope/Kendall first-order bias approximation −(1+3ρ)/n for the OLS AR(1) coefficient (with intercept) at the design point, versus \\ | |
| 30 | +F5-conf-03 & conf & synthetic & no & no & analytical & yes & prove or refute (A) "X and Y each normal ⇒ X + Y normal (degenerate allowed)" and (B) "X and Y independent and each normal ⇒ X + Y normal"; a refutati \\ | |
| 31 | +F5-conf-04 & conf & synthetic & no & no & analytical & no & value-function iteration for the log-utility, Cobb–Douglas, full-depreciation growth model on a prescribed log grid, compared with the closed-form pol \\ | |
| 32 | +F6-conf-01 & conf & observed & yes & no & independent code & yes & Does the day of the week carry predictive content for one-day changes in the 10-year US Treasury yield (2000–2019)? Estimands: weekday mean difference \\ | |
| 33 | +F6-conf-02 & conf & observed & yes & no & independent code & yes & Effect of the Bank of Canada's 2020-03-04 emergency cut on daily changes of the 5-year GoC benchmark yield (post-cut window vs 120 preceding days). \\ | |
| 34 | +F6-conf-03 & conf & observed & yes & no & independent code & no & Okun's law for the US in difference form over 1960Q1–2019Q4: coefficient with HAC uncertainty, subperiod estimates (1960–83 vs 1984–2019) and a classi \\ | |
| 35 | +F6-conf-04 & conf & synthetic & no & no & independent code & no & Effect of training hours per employee on next-year productivity growth (pp per hour) under selection on observables, with the unadjusted association a \\ | |
| 36 | +F1-dev-01 & dev & synthetic & no & no & independent code & no & documented analytic dataset of a firm cross-section; estimands = number of distinct firms, mean and median revenue in millions USD over firms with kno \\ | |
| 37 | +F2-dev-01 & dev & synthetic & no & no & independent code & no & two-way fixed-effects (firm + year) slope of y on x in a balanced firm–year panel, with firm-clustered uncertainty; estimand = β in y\_it = a\_i + g\_t + \\ | |
| 38 | +F2-smoke-01 & dev & synthetic & no & no & independent code & no & OLS slope of y on x with firm-clustered uncertainty; estimand = linear-projection slope β. \\ | |
| 39 | +F3-dev-01 & dev & observed & yes & no & independent code & no & rolling-origin (expanding window) forecasts of the US unemployment rate (FRED UNRATE) at h = 1 and h = 3 over 36 origins (2023-07 … 2026-06); naive no \\ | |
| 40 | +F4-dev-01 & dev & observed & yes & yes & designer extraction & no & extract the September 2026 national unemployment rate and monthly employment change from the Statistics Canada LFS release text, with section locators \\ | |
| 41 | +F5-dev-01 & dev & synthetic & no & no & analytical & no & closed-form Marshallian demands, indirect utility and budget identity of a two-good Cobb–Douglas consumer at given prices and income, validated by an \\ | |
| 42 | +F6-dev-01 & dev & observed & yes & no & independent code & no & Does life expectancy rise with ln GDP per capita across countries in the most recent usable World Bank year? Estimand = cross-country OLS slope (years \\ | |
| 43 | +\bottomrule | |
| 44 | +\end{longtable} | |
| 45 | +\normalsize | |
added
protocol/arms/arm_L.md
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| 1 | +Complete the task described in `TASK.md`. Below is some general advice about doing careful empirical research; it adds nothing specific to this task and you are free to ignore it. | |
| 2 | + | |
| 3 | +Careful empirical work usually begins by reading the task statement slowly and more than once, because the most common errors in applied research come not from difficult mathematics but from a misunderstanding of what exactly is being asked, which quantity is of interest, and which inputs are legitimately available. It is worth pausing to think about what the quantity of interest actually means, under what conditions it is well defined, and what would make an estimate of it misleading. Many published results have been overturned not because the estimator was wrong but because the sample was not what the authors believed it to be, or because information that should not have been available at the time of a decision had quietly entered the analysis. | |
| 4 | + | |
| 5 | +Data deserve suspicion. Files assembled from several sources often carry inconsistencies: duplicated records, mixed units, shifted dates, columns whose definitions changed mid-sample, or values that were revised after the fact. Good practice is to look at the data before modelling them: distributions, ranges, missing values, identifiers, and whether counts add up. When something looks odd, it is better to document it and decide explicitly how to handle it than to let a default silently decide. The same applies to transformations: scaling, differencing, winsorising and imputing all change what is being estimated, and each deserves a sentence of justification. | |
| 6 | + | |
| 7 | +Simple baselines are underrated. A naïve or unconditional estimate computed with exactly the same evaluation rule as the main method tells the reader how much the method actually adds and protects against the illusion of progress created by a complicated pipeline. If the elaborate method does not beat the simple one, that is a finding, not a failure. | |
| 8 | + | |
| 9 | +Uncertainty is part of the result. Standard errors must respect the dependence structure of the data; a confidence interval that ignores clustering, serial correlation or overlapping targets is not conservative, it is wrong. Where the sampling distribution is unknown, resampling or simulation can help, but the assumptions behind them should be stated. A result that is not statistically distinguishable from zero should be reported as such, in plain words. | |
| 10 | + | |
| 11 | +Reproducibility is a courtesy to the reader and a safeguard for the author. Keeping the analysis in a script that reads the raw inputs and regenerates every reported number makes it possible to check the work, to find mistakes, and to rerun the analysis when the inputs change. Fixing random seeds, recording software versions and keeping intermediate files out of the final results all reduce the chance that a number in the report cannot be traced back to a computation. | |
| 12 | + | |
| 13 | +Finally, honesty about limitations is what distinguishes research from advocacy. Every analysis has weaknesses: assumptions that may not hold, data that may not be representative, methods that trade one bias for another. Stating them clearly, together with what would be needed to resolve them, is more valuable than a confident conclusion that the evidence does not support. When a task cannot be completed as specified, saying so, and saying why, is the correct outcome. | |
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protocol/recovery_plan.md
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| 1 | +# Recovery, fault-injection and ablation plan (Phase 08) — written 2026-10-30 before any confirmatory run | |
| 2 | + | |
| 3 | +## R1. Standardised interruption and resumption | |
| 4 | +- Tasks: the three development tasks `F1-dev-01`, `F3-dev-01`, `F6-dev-01` (development split, so that confirmatory tasks are not reused; their no-fault pilot runs serve as matched controls). | |
| 5 | +- Arms: A (no persistent-state rules) and D (full protocol with state ledger and recovery rules); 2 repeats each → 12 fault runs. | |
| 6 | +- Fault: the controller kills the executor process (by session id) immediately after the **6th tool use** of the first session (trigger recorded in the `faults` table with the event id), then resumes the same session once with `protocol/arms/resume_after_interruption_prompt.md` within the same envelope. | |
| 7 | +- Outcomes: (i) valid continuation = scientific_success after resumption; (ii) duplicated expensive work = number of re-executions of `out/analysis.py` or recomputation of already-saved outputs after resumption (counted from the event stream); (iii) lost artefacts = files present before the kill (captured by a workspace manifest at kill time) but missing or altered without cause at the end; (iv) provenance = whether the final report acknowledges the interruption (text check) and whether `out/state/log.md` (B–D) records it; (v) cost and wall time relative to the matched no-fault run. | |
| 8 | +- Success criterion for H4: D's valid-continuation rate exceeds A's; secondary: fewer duplicated jobs and no lost artefacts. | |
| 9 | + | |
| 10 | +## R2. Stale-input invalidation | |
| 11 | +- After each of the 6 no-fault pilot runs of `F1-dev-01` and `F6-dev-01` (arms A–D), copy the workspace on the node, mutate one non-secret input value in `data/` (documented command), and re-run the pristine gate on the copy. Expected: the reproducibility check fails on the copy (the results no longer regenerate), demonstrating invalidation; a *passing* check would mean the results did not depend on the mutated input or the script ignores the file. Recorded per run. | |
| 12 | + | |
| 13 | +## R3. Duplicate job submission | |
| 14 | +- Submit the same `execute` request twice for one new development run (`F2-dev-01`, arm B) through `Controller.run_many([rid, rid])`; expected: exactly one session is launched; the duplicate is logged as `duplicate_ignored`. Infrastructure test, no scientific outcome. | |
| 15 | + | |
| 16 | +## R4. Missing required fixture | |
| 17 | +- One run each of arms A and D on `F1-dev-01` with `data/firms.csv` (or the task's main input) removed from the workspace before launch; expected honest outcome: `feasibility = infeasible/partial` with the reason, required files written with nulls; the evaluators' false-completion rule will flag a fabricated complete answer. Reported as "recognised blockage" vs "completed despite fault". | |
| 18 | + | |
| 19 | +## R5. Length-matched control (ablation of instruction length) | |
| 20 | +- Arm L (`protocol/arms/arm_L.md`, 533 words of generic research advice without phases, ledgers, gates or review) on all 24 confirmatory tasks × 1 repeat, run after the main benchmark in a separate split (`ablation`), same envelope and nodes. Compared with A and B at the task level (paired). Exploratory; not part of the preregistered contrasts. | |
| 21 | + | |
| 22 | +## R6. Mechanism ablation (one component removed from D) | |
| 23 | +- "D without state ledger" is not run: the budget reserve is spent on R1–R5 first; if budget remains after R5, run arm C with the gate feedback loop disabled on the controller side ("C-noloop": executor told to run the gate but the controller does not resume on failure) on the 12 confirmatory tasks of F1, F3, F6 × 1 repeat. Exploratory. | |
| 24 | + | |
| 25 | +Budget reserve for Phase 08: 150 USD (amendment A-001). All faults are injected only in run workspaces on the nodes; no user data outside `~/wp29/runs/` is touched. | |
modified
reports/evaluator_validation.md
+4 −4
@@ -1,6 +1,6 @@ | ||
| 1 | 1 | # Evaluator validation (seeded artefacts) |
| 2 | 2 | |
| 3 | −Generated 2026-10-30T03:47:20+00:00 by `evaluators/run_validations.py`. Each hidden oracle ships a `validation/` set with ≥1 correct_out (an independently written solution whose analysis.py is re-run) and ≥2 wrong_out artefacts (planted scientific errors, fabrication, false completion or leakage). The evaluator must accept the former and reject the latter; a PASS means every seeded case received the expected verdict (false acceptance = 0 and false rejection = 0 on the seeded set). The seeded set is small and designer-authored; it bounds gross evaluator errors, not the full space of valid solutions. | |
| 3 | +Generated 2026-10-30T03:58:32+00:00 by `evaluators/run_validations.py`. Each hidden oracle ships a `validation/` set with ≥1 correct_out (an independently written solution whose analysis.py is re-run) and ≥2 wrong_out artefacts (planted scientific errors, fabrication, false completion or leakage). The evaluator must accept the former and reject the latter; a PASS means every seeded case received the expected verdict (false acceptance = 0 and false rejection = 0 on the seeded set). The seeded set is small and designer-authored; it bounds gross evaluator errors, not the full space of valid solutions. | |
| 4 | 4 | |
| 5 | 5 | | task_id | result | last line of validation output | |
| 6 | 6 | |---|---|---| |
@@ -17,8 +17,8 @@ Generated 2026-10-30T03:47:20+00:00 by `evaluators/run_validations.py`. Each hid | ||
| 17 | 17 | | F2-smoke-01 | no validate.py | | |
| 18 | 18 | | F3-conf-01 | PASS | ALL PASS | |
| 19 | 19 | | F3-conf-02 | PASS | ALL PASS | |
| 20 | −| F3-conf-03 | no validate.py | | | |
| 21 | −| F3-conf-04 | no validate.py | | | |
| 20 | +| F3-conf-03 | PASS | ALL PASS | | |
| 21 | +| F3-conf-04 | PASS | ALL PASS | | |
| 22 | 22 | | F3-dev-01 | PASS | ALL PASS | |
| 23 | 23 | | F4-conf-01 | PASS | VALIDATION PASSED | |
| 24 | 24 | | F4-conf-02 | PASS | VALIDATION PASSED | |
@@ -37,4 +37,4 @@ Generated 2026-10-30T03:47:20+00:00 by `evaluators/run_validations.py`. Each hid | ||
| 37 | 37 | | F6-conf-04 | PASS | VALIDATION PASSED | |
| 38 | 38 | | F6-dev-01 | PASS | VALIDATION PASSED | |
| 39 | 39 | |
| 40 | −Summary: 28 PASS, 0 FAIL, 4 without validation set, of 32 oracles. | |
| 40 | +Summary: 30 PASS, 0 FAIL, 2 without validation set, of 32 oracles. | |
modified
reports/phase_status.md
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@@ -49,3 +49,8 @@ Append-only log. Times are UTC unless stated. "Executor" = the Claude Code sessi | ||
| 49 | 49 | - Literature subagent delivered `research/literature_matrix.csv` (84 verified rows, 8 streams), `novelty_matrix.csv` (18 systems/benchmarks), `source_notes.md`, `references_verified.bib` (84 entries, compiled with aer.bst, 0 bibtex errors), `build_literature_matrix.py` + `verify_cache/` (raw Crossref/arXiv/Semantic Scholar/OpenReview responses). Two unverifiable items dropped and recorded; two DOIs corrected during verification. |
| 50 | 50 | - Prior-use check: the phrase "protocol-guided autonomous research" and the acronym PGAR have no prior use in this field; closest concurrent work is XScientist (arXiv:2607.12301, a protocol artefact without controlled comparison) and data-to-paper (NEJM AI 2025). |
| 51 | 51 | - **Gate**: closest prior work identified ✔; novelty qualified (the contribution is the controlled comparison of instruction regimes with a fixed, isolated executor and protected oracles, not the idea of protocols) ✔; the study tests the method rather than assuming it works ✔. PASSED. |
| 52 | + | |
| 53 | +## Phase 06 — Development pilot and design freeze (in progress) | |
| 54 | +- Pilot batch 1 (2026-10-30 04:12–04:33 UTC): 20 live runs (F1/F2/F4/F5/F6-dev-01 × A–D), all executor sessions finished; all 20 scientifically successful **after** amendment A-002 (the harness had been invoking final evaluators on a workspace without `data/`, which produced a spurious fabrication violation on all four F4-dev-01 runs; pre-fix receipts retained). Costs: mean 0.376 USD/run (A 0.21, B 0.35, C 0.39, D 0.56 incl. reviewer 0.13), mean wall 108 s, max 229 s; controller gates passed at cycle 1 in all 10 C/D runs; 5 reviews, all `accept`. Ceiling effect on development tasks noted (all arms 100%); confirmatory tasks were designed with traps and null truths before this observation and are not modified. | |
| 55 | +- Task suite complete: 31 tasks (7 development incl. smoke, 24 confirmatory: 14 real-data, 5 source-document, 7 null/infeasible-truth), all 30 oracles with validation sets PASS on seeded artefacts (`reports/evaluator_validation.md`); bundle-hygiene test passes. Overlap noted: F2-conf-04 and F6-conf-03 were independently designed by two agents on the same FRED series and regression (Okun's law 1960–2019); F6-conf-03 adds subperiod stability; both kept, dependence reported in the family analysis. | |
| 56 | +- Pilot batch 2 (F3-dev-01 × A–D) launched ≈ 05:05 UTC. Recovery/ablation plan written before the freeze (`protocol/recovery_plan.md`). Transfer mini-projects T1–T3 being authored by a subagent. | |
added
runs/pilot/RP0021/prepare.json
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@@ -0,0 +1,21 @@ | ||
| 1 | +{ | |
| 2 | + "workspace_manifest": { | |
| 3 | + "TASK.md": "833ca858b6ed80242f4f98d27c4d0171d9f2e96debc31a4662de6460fcd18351", | |
| 4 | + "bundle.json": "47db3ab9ce0210fa4891fd00875f639ab089a3dc9d79145639ccd75e5ac72b99", | |
| 5 | + "data/README.md": "62a5a815ca9b63bd6f91fdb176f2e1cecc2faa8ca816c45277b02e8ffd9a70c1", | |
| 6 | + "data/UNRATE.csv": "621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c", | |
| 7 | + "gate_spec.json": "0c33840434296984e05d8ee43f34155234e15336eb7d7232262070a866c5f012" | |
| 8 | + }, | |
| 9 | + "node_environment": { | |
| 10 | + "node": "M3U96a", | |
| 11 | + "claude": "2.1.314 (Claude Code)", | |
| 12 | + "os": "26.6", | |
| 13 | + "arch": "arm64", | |
| 14 | + "py": "3.14.5 2.3.4 2.3.3 1.16.2 0.14.5", | |
| 15 | + "gate": "1760d13e9f5bf9cf", | |
| 16 | + "host": "MacStudnpierre2", | |
| 17 | + "environment_id": "01a5de795b91fb60", | |
| 18 | + "recorded_utc": "2026-10-30T03:59:03.889+00:00" | |
| 19 | + }, | |
| 20 | + "system_append_sha256": "f8afd6b133bcc262837261cc33b2b43a3a0a44e0f8be3041e1e53ca2352a57d6" | |
| 21 | +} | |
| \ No newline at end of file | ||
added
runs/pilot/RP0021/system_append.md
+56 −0
@@ -0,0 +1,56 @@ | ||
| 1 | +You are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace. | |
| 2 | + | |
| 3 | +Environment facts (identical for every task): | |
| 4 | +- 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. | |
| 5 | +- 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. | |
| 6 | +- 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. | |
| 7 | +- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions. | |
| 8 | + | |
| 9 | +Integrity rules (identical for every task): | |
| 10 | +- 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. | |
| 11 | +- 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). | |
| 12 | +- 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. | |
| 13 | +- Do not modify anything under `data/`. | |
| 14 | +- When you are done, end with a brief plain-text summary of what you produced and any limitations. | |
| 15 | + | |
| 16 | +Complete 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. | |
| 17 | + | |
| 18 | +PHASES (execute in order; a phase is eligible only when its dependencies are satisfied): | |
| 19 | + | |
| 20 | +P0 Scope and contract (depends on: nothing) | |
| 21 | +- 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. | |
| 22 | +- 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". | |
| 23 | + | |
| 24 | +P1 Data audit and provenance (depends on: P0) | |
| 25 | +- 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`. | |
| 26 | +- 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. | |
| 27 | +- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it. | |
| 28 | + | |
| 29 | +P2 Baseline (depends on: P1) | |
| 30 | +- 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. | |
| 31 | + | |
| 32 | +P3 Method and computation (depends on: P2) | |
| 33 | +- 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. | |
| 34 | + | |
| 35 | +P4 Verification (depends on: P3) | |
| 36 | +- 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. | |
| 37 | +- 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. | |
| 38 | + | |
| 39 | +P5 Reporting (depends on: P4) | |
| 40 | +- 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. | |
| 41 | +- 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. | |
| 42 | +- Set the final statuses in `out/state/phases.json`. | |
| 43 | + | |
| 44 | +STOPPING RULES | |
| 45 | +- 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. | |
| 46 | +- Do not spend effort on extensions that the task does not require. | |
| 47 | + | |
| 48 | +RECOVERY RULES | |
| 49 | +- 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/`. | |
| 50 | +- 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. | |
| 51 | +- 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. | |
| 52 | + | |
| 53 | +EXECUTABLE GATES (additional, mandatory) | |
| 54 | +- 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. | |
| 55 | +- 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. | |
| 56 | +- 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. | |
added
runs/pilot/RP0022/prepare.json
+21 −0
@@ -0,0 +1,21 @@ | ||
| 1 | +{ | |
| 2 | + "workspace_manifest": { | |
| 3 | + "TASK.md": "833ca858b6ed80242f4f98d27c4d0171d9f2e96debc31a4662de6460fcd18351", | |
| 4 | + "bundle.json": "47db3ab9ce0210fa4891fd00875f639ab089a3dc9d79145639ccd75e5ac72b99", | |
| 5 | + "data/README.md": "62a5a815ca9b63bd6f91fdb176f2e1cecc2faa8ca816c45277b02e8ffd9a70c1", | |
| 6 | + "data/UNRATE.csv": "621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c", | |
| 7 | + "gate_spec.json": "0c33840434296984e05d8ee43f34155234e15336eb7d7232262070a866c5f012" | |
| 8 | + }, | |
| 9 | + "node_environment": { | |
| 10 | + "node": "M3U96b", | |
| 11 | + "claude": "2.1.314 (Claude Code)", | |
| 12 | + "os": "26.4", | |
| 13 | + "arch": "arm64", | |
| 14 | + "py": "3.14.4 2.3.4 2.3.3 1.16.2 0.14.5", | |
| 15 | + "gate": "1760d13e9f5bf9cf", | |
| 16 | + "host": "m3u96b", | |
| 17 | + "environment_id": "af172e0a984c11c0", | |
| 18 | + "recorded_utc": "2026-10-30T03:59:03.797+00:00" | |
| 19 | + }, | |
| 20 | + "system_append_sha256": "bc05a0b4408205bc4b5152ec7e5c1b339ea07cd8c602cb06e684dfad775376ff" | |
| 21 | +} | |
| \ No newline at end of file | ||
added
runs/pilot/RP0022/system_append.md
+51 −0
@@ -0,0 +1,51 @@ | ||
| 1 | +You are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace. | |
| 2 | + | |
| 3 | +Environment facts (identical for every task): | |
| 4 | +- 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. | |
| 5 | +- 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. | |
| 6 | +- 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. | |
| 7 | +- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions. | |
| 8 | + | |
| 9 | +Integrity rules (identical for every task): | |
| 10 | +- 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. | |
| 11 | +- 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). | |
| 12 | +- 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. | |
| 13 | +- Do not modify anything under `data/`. | |
| 14 | +- When you are done, end with a brief plain-text summary of what you produced and any limitations. | |
| 15 | + | |
| 16 | +Complete 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. | |
| 17 | + | |
| 18 | +PHASES (execute in order; a phase is eligible only when its dependencies are satisfied): | |
| 19 | + | |
| 20 | +P0 Scope and contract (depends on: nothing) | |
| 21 | +- 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. | |
| 22 | +- 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". | |
| 23 | + | |
| 24 | +P1 Data audit and provenance (depends on: P0) | |
| 25 | +- 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`. | |
| 26 | +- 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. | |
| 27 | +- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it. | |
| 28 | + | |
| 29 | +P2 Baseline (depends on: P1) | |
| 30 | +- 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. | |
| 31 | + | |
| 32 | +P3 Method and computation (depends on: P2) | |
| 33 | +- 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. | |
| 34 | + | |
| 35 | +P4 Verification (depends on: P3) | |
| 36 | +- 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. | |
| 37 | +- 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. | |
| 38 | + | |
| 39 | +P5 Reporting (depends on: P4) | |
| 40 | +- 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. | |
| 41 | +- 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. | |
| 42 | +- Set the final statuses in `out/state/phases.json`. | |
| 43 | + | |
| 44 | +STOPPING RULES | |
| 45 | +- 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. | |
| 46 | +- Do not spend effort on extensions that the task does not require. | |
| 47 | + | |
| 48 | +RECOVERY RULES | |
| 49 | +- 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/`. | |
| 50 | +- 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. | |
| 51 | +- 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. | |
added
runs/pilot/RP0023/prepare.json
+21 −0
@@ -0,0 +1,21 @@ | ||
| 1 | +{ | |
| 2 | + "workspace_manifest": { | |
| 3 | + "TASK.md": "833ca858b6ed80242f4f98d27c4d0171d9f2e96debc31a4662de6460fcd18351", | |
| 4 | + "bundle.json": "47db3ab9ce0210fa4891fd00875f639ab089a3dc9d79145639ccd75e5ac72b99", | |
| 5 | + "data/README.md": "62a5a815ca9b63bd6f91fdb176f2e1cecc2faa8ca816c45277b02e8ffd9a70c1", | |
| 6 | + "data/UNRATE.csv": "621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c", | |
| 7 | + "gate_spec.json": "0c33840434296984e05d8ee43f34155234e15336eb7d7232262070a866c5f012" | |
| 8 | + }, | |
| 9 | + "node_environment": { | |
| 10 | + "node": "M4M64a", | |
| 11 | + "claude": "2.1.314 (Claude Code)", | |
| 12 | + "os": "26.5.1", | |
| 13 | + "arch": "arm64", | |
| 14 | + "py": "3.14.4 2.3.4 2.3.3 1.16.2 0.14.5", | |
| 15 | + "gate": "1760d13e9f5bf9cf", | |
| 16 | + "host": "MacStudnpierre6", | |
| 17 | + "environment_id": "eededd7c72e1131b", | |
| 18 | + "recorded_utc": "2026-10-30T03:59:03.699+00:00" | |
| 19 | + }, | |
| 20 | + "system_append_sha256": "d1a7be8a99fcc552e7334c42050d9b5477ff9d55c5c4022a165cc782cac588b7" | |
| 21 | +} | |
| \ No newline at end of file | ||
added
runs/pilot/RP0023/system_append.md
+59 −0
@@ -0,0 +1,59 @@ | ||
| 1 | +You are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace. | |
| 2 | + | |
| 3 | +Environment facts (identical for every task): | |
| 4 | +- 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. | |
| 5 | +- 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. | |
| 6 | +- 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. | |
| 7 | +- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions. | |
| 8 | + | |
| 9 | +Integrity rules (identical for every task): | |
| 10 | +- 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. | |
| 11 | +- 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). | |
| 12 | +- 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. | |
| 13 | +- Do not modify anything under `data/`. | |
| 14 | +- When you are done, end with a brief plain-text summary of what you produced and any limitations. | |
| 15 | + | |
| 16 | +Complete 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. | |
| 17 | + | |
| 18 | +PHASES (execute in order; a phase is eligible only when its dependencies are satisfied): | |
| 19 | + | |
| 20 | +P0 Scope and contract (depends on: nothing) | |
| 21 | +- 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. | |
| 22 | +- 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". | |
| 23 | + | |
| 24 | +P1 Data audit and provenance (depends on: P0) | |
| 25 | +- 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`. | |
| 26 | +- 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. | |
| 27 | +- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it. | |
| 28 | + | |
| 29 | +P2 Baseline (depends on: P1) | |
| 30 | +- 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. | |
| 31 | + | |
| 32 | +P3 Method and computation (depends on: P2) | |
| 33 | +- 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. | |
| 34 | + | |
| 35 | +P4 Verification (depends on: P3) | |
| 36 | +- 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. | |
| 37 | +- 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. | |
| 38 | + | |
| 39 | +P5 Reporting (depends on: P4) | |
| 40 | +- 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. | |
| 41 | +- 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. | |
| 42 | +- Set the final statuses in `out/state/phases.json`. | |
| 43 | + | |
| 44 | +STOPPING RULES | |
| 45 | +- 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. | |
| 46 | +- Do not spend effort on extensions that the task does not require. | |
| 47 | + | |
| 48 | +RECOVERY RULES | |
| 49 | +- 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/`. | |
| 50 | +- 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. | |
| 51 | +- 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. | |
| 52 | + | |
| 53 | +EXECUTABLE GATES (additional, mandatory) | |
| 54 | +- 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. | |
| 55 | +- 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. | |
| 56 | +- 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. | |
| 57 | + | |
| 58 | +SEPARATE REVIEW (additional) | |
| 59 | +- 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. | |
added
runs/pilot/RP0024/artifact_manifest.json
+12 −0
@@ -0,0 +1,12 @@ | ||
| 1 | +{ | |
| 2 | + "root": "/Users/simon-pierreboucher/Desktop/Recherche-UQO/methodes/autonomous-research-claude-code/runs/pilot/RP0024/out", | |
| 3 | + "files": { | |
| 4 | + "analysis.py": "3de735811a4f30f853bab7b18befa9a99baa4eddaf1aa6cfef2f80fe3d275bb1", | |
| 5 | + "claims.json": "2de1458874c5c267b8ac0a0dd8bd22dfab991c8c53f82fc5ceca2b27632e42d2", | |
| 6 | + "forecasts.csv": "56b59195b5dd4783725a79aa0c37b7d0bec7ea913073de42b54237c0a8d3e8ac", | |
| 7 | + "report.md": "9cf4df33f2c526f1f8ab8f6e50df285eb0cb37cf950a5647329dee3ac87343ba", | |
| 8 | + "results.json": "256283dd923d367ed6487c77cc9cca008334acdbb42492911cf751df662a95b8", | |
| 9 | + "supplementary.json": "3dcda583b4113833864df1b36cdd7b2a8c58d17255857a18bdeeb7b4a50b3041" | |
| 10 | + }, | |
| 11 | + "recorded_utc": "2026-10-30T04:00:32.578+00:00" | |
| 12 | +} | |
| \ No newline at end of file | ||
added
runs/pilot/RP0024/behaviour_audit.json
+5 −0
@@ -0,0 +1,5 @@ | ||
| 1 | +{ | |
| 2 | + "n_outside_workspace_refs": 0, | |
| 3 | + "outside_refs": [], | |
| 4 | + "n_gate_invocations": 0 | |
| 5 | +} | |
| \ No newline at end of file | ||
added
runs/pilot/RP0024/contract_receipt.json
+207 −0
@@ -0,0 +1,207 @@ | ||
| 1 | +{ | |
| 2 | + "gate_version": "pgar_gate/1.0", | |
| 3 | + "workspace": "/Users/simon-pierreboucher/wp29/runs/RP0024", | |
| 4 | + "utc": "2026-10-30T04:00:33.147931+00:00", | |
| 5 | + "passed": true, | |
| 6 | + "n_checks": 39, | |
| 7 | + "n_failed": 0, | |
| 8 | + "checks": [ | |
| 9 | + { | |
| 10 | + "id": "file:out/results.json", | |
| 11 | + "passed": true, | |
| 12 | + "detail": "present" | |
| 13 | + }, | |
| 14 | + { | |
| 15 | + "id": "file:out/report.md", | |
| 16 | + "passed": true, | |
| 17 | + "detail": "present" | |
| 18 | + }, | |
| 19 | + { | |
| 20 | + "id": "file:out/analysis.py", | |
| 21 | + "passed": true, | |
| 22 | + "detail": "present" | |
| 23 | + }, | |
| 24 | + { | |
| 25 | + "id": "file:out/claims.json", | |
| 26 | + "passed": true, | |
| 27 | + "detail": "present" | |
| 28 | + }, | |
| 29 | + { | |
| 30 | + "id": "file:out/forecasts.csv", | |
| 31 | + "passed": true, | |
| 32 | + "detail": "present" | |
| 33 | + }, | |
| 34 | + { | |
| 35 | + "id": "results_json", | |
| 36 | + "passed": true, | |
| 37 | + "detail": "parsed" | |
| 38 | + }, | |
| 39 | + { | |
| 40 | + "id": "key:task_id", | |
| 41 | + "passed": true, | |
| 42 | + "detail": "ok" | |
| 43 | + }, | |
| 44 | + { | |
| 45 | + "id": "key:model_name", | |
| 46 | + "passed": true, | |
| 47 | + "detail": "ok" | |
| 48 | + }, | |
| 49 | + { | |
| 50 | + "id": "key:model_description", | |
| 51 | + "passed": true, | |
| 52 | + "detail": "ok" | |
| 53 | + }, | |
| 54 | + { | |
| 55 | + "id": "key:n_origins", | |
| 56 | + "passed": true, | |
| 57 | + "detail": "ok" | |
| 58 | + }, | |
| 59 | + { | |
| 60 | + "id": "key:first_origin", | |
| 61 | + "passed": true, | |
| 62 | + "detail": "ok" | |
| 63 | + }, | |
| 64 | + { | |
| 65 | + "id": "key:last_origin", | |
| 66 | + "passed": true, | |
| 67 | + "detail": "ok" | |
| 68 | + }, | |
| 69 | + { | |
| 70 | + "id": "key:n_evaluated_h1", | |
| 71 | + "passed": true, | |
| 72 | + "detail": "ok" | |
| 73 | + }, | |
| 74 | + { | |
| 75 | + "id": "key:n_evaluated_h3", | |
| 76 | + "passed": true, | |
| 77 | + "detail": "ok" | |
| 78 | + }, | |
| 79 | + { | |
| 80 | + "id": "key:rmse_naive_h1", | |
| 81 | + "passed": true, | |
| 82 | + "detail": "ok" | |
| 83 | + }, | |
| 84 | + { | |
| 85 | + "id": "key:mae_naive_h1", | |
| 86 | + "passed": true, | |
| 87 | + "detail": "ok" | |
| 88 | + }, | |
| 89 | + { | |
| 90 | + "id": "key:rmse_naive_h3", | |
| 91 | + "passed": true, | |
| 92 | + "detail": "ok" | |
| 93 | + }, | |
| 94 | + { | |
| 95 | + "id": "key:mae_naive_h3", | |
| 96 | + "passed": true, | |
| 97 | + "detail": "ok" | |
| 98 | + }, | |
| 99 | + { | |
| 100 | + "id": "key:rmse_model_h1", | |
| 101 | + "passed": true, | |
| 102 | + "detail": "ok" | |
| 103 | + }, | |
| 104 | + { | |
| 105 | + "id": "key:mae_model_h1", | |
| 106 | + "passed": true, | |
| 107 | + "detail": "ok" | |
| 108 | + }, | |
| 109 | + { | |
| 110 | + "id": "key:rmse_model_h3", | |
| 111 | + "passed": true, | |
| 112 | + "detail": "ok" | |
| 113 | + }, | |
| 114 | + { | |
| 115 | + "id": "key:mae_model_h3", | |
| 116 | + "passed": true, | |
| 117 | + "detail": "ok" | |
| 118 | + }, | |
| 119 | + { | |
| 120 | + "id": "key:rel_improvement_rmse_h1", | |
| 121 | + "passed": true, | |
| 122 | + "detail": "ok" | |
| 123 | + }, | |
| 124 | + { | |
| 125 | + "id": "key:rel_improvement_rmse_h3", | |
| 126 | + "passed": true, | |
| 127 | + "detail": "ok" | |
| 128 | + }, | |
| 129 | + { | |
| 130 | + "id": "key:feasibility", | |
| 131 | + "passed": true, | |
| 132 | + "detail": "ok" | |
| 133 | + }, | |
| 134 | + { | |
| 135 | + "id": "key:feasibility_reason", | |
| 136 | + "passed": true, | |
| 137 | + "detail": "ok" | |
| 138 | + }, | |
| 139 | + { | |
| 140 | + "id": "key:data_issues", | |
| 141 | + "passed": true, | |
| 142 | + "detail": "ok" | |
| 143 | + }, | |
| 144 | + { | |
| 145 | + "id": "key:n_input_rows", | |
| 146 | + "passed": true, | |
| 147 | + "detail": "ok" | |
| 148 | + }, | |
| 149 | + { | |
| 150 | + "id": "key:n_analysed", | |
| 151 | + "passed": true, | |
| 152 | + "detail": "ok" | |
| 153 | + }, | |
| 154 | + { | |
| 155 | + "id": "key:random_seed", | |
| 156 | + "passed": true, | |
| 157 | + "detail": "ok" | |
| 158 | + }, | |
| 159 | + { | |
| 160 | + "id": "section:Question", | |
| 161 | + "passed": true, | |
| 162 | + "detail": "present" | |
| 163 | + }, | |
| 164 | + { | |
| 165 | + "id": "section:Data and exclusions", | |
| 166 | + "passed": true, | |
| 167 | + "detail": "present" | |
| 168 | + }, | |
| 169 | + { | |
| 170 | + "id": "section:Method", | |
| 171 | + "passed": true, | |
| 172 | + "detail": "present" | |
| 173 | + }, | |
| 174 | + { | |
| 175 | + "id": "section:Results", | |
| 176 | + "passed": true, | |
| 177 | + "detail": "present" | |
| 178 | + }, | |
| 179 | + { | |
| 180 | + "id": "section:Uncertainty", | |
| 181 | + "passed": true, | |
| 182 | + "detail": "present" | |
| 183 | + }, | |
| 184 | + { | |
| 185 | + "id": "section:Limitations and open issues", | |
| 186 | + "passed": true, | |
| 187 | + "detail": "present" | |
| 188 | + }, | |
| 189 | + { | |
| 190 | + "id": "claims_json", | |
| 191 | + "passed": true, | |
| 192 | + "detail": "5 claims, all keys resolve" | |
| 193 | + }, | |
| 194 | + { | |
| 195 | + "id": "chronology:first_origin<=last_origin", | |
| 196 | + "passed": true, | |
| 197 | + "detail": "2023-07-01 vs 2026-06-01" | |
| 198 | + }, | |
| 199 | + { | |
| 200 | + "id": "reproducibility", | |
| 201 | + "passed": true, | |
| 202 | + "detail": "regenerated in 0.5s; identical within tolerance" | |
| 203 | + } | |
| 204 | + ], | |
| 205 | + "receipt_path": "/Users/simon-pierreboucher/wp29/runs/RP0024/out/state/gate_receipts/20261030T040033.148243Z.json", | |
| 206 | + "receipt_sha256": "783fe74c166b56b21f9635fb8a7a4558476bf19d5d74fb057c28e6c61909ae2d" | |
| 207 | +} | |
| \ No newline at end of file | ||
added
runs/pilot/RP0024/ctl/prompt.txt
+1 −0
@@ -0,0 +1 @@ | ||
| 1 | +Begin now. The task is in `TASK.md` in the current directory. Work until the deliverables are complete, then stop. | |
added
runs/pilot/RP0024/ctl/reviewer_prompt.txt
+1 −0
@@ -0,0 +1 @@ | ||
| 1 | +Review the deliverables in this workspace now, as instructed, and write out/review/review.json. | |
added
runs/pilot/RP0024/ctl/reviewer_system.md
+10 −0
@@ -0,0 +1,10 @@ | ||
| 1 | +You are an independent scientific reviewer. In the current working directory, `TASK.md` describes a bounded scientific task and the directory `out/` contains the deliverables submitted by another assistant (you have not seen how they were produced and you must not look for their working transcript). There is no network access. Use `~/wp29/venv/bin/python` if you need to re-run or test code. Do not modify any file under `out/` except that you must create `out/review/review.json`. | |
| 2 | + | |
| 3 | +Review the deliverables against `TASK.md` only. Specifically check: | |
| 4 | +1. Does `out/results.json` contain every required key with legitimately computed values? Re-run `out/analysis.py` in a scratch copy if needed and compare. | |
| 5 | +2. Are the information-availability rules of `TASK.md` respected by the code (for example no use of future information, no use of forbidden inputs)? Read the code to verify; do not trust the report. | |
| 6 | +3. Are the central claims in `out/report.md` and `out/claims.json` supported by the numbers in `out/results.json`, with appropriate uncertainty and caveats? Flag any claim that is stronger than the evidence. | |
| 7 | +4. Are counts, exclusions, units, signs and magnitudes internally consistent? Are baseline comparisons present when the task requires them? | |
| 8 | +5. Are limitations stated honestly? Is anything declared complete that is not? | |
| 9 | + | |
| 10 | +You have no reference answers; do not guess what the "right" number is. Report only issues you can justify from the files. Write `out/review/review.json` as {"verdict": "accept"|"revise", "issues": [{"id": "R1", "severity": "critical"|"major"|"minor", "location": "<file or key>", "issue": "...", "suggested_fix": "..."}], "summary": "..."}. Use "revise" if there is at least one critical or major issue. Be concise and specific. End with a one-paragraph plain-text summary. | |
added
runs/pilot/RP0024/ctl/system_append.md
+16 −0
@@ -0,0 +1,16 @@ | ||
| 1 | +You are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace. | |
| 2 | + | |
| 3 | +Environment facts (identical for every task): | |
| 4 | +- 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. | |
| 5 | +- 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. | |
| 6 | +- 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. | |
| 7 | +- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions. | |
| 8 | + | |
| 9 | +Integrity rules (identical for every task): | |
| 10 | +- 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. | |
| 11 | +- 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). | |
| 12 | +- 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. | |
| 13 | +- Do not modify anything under `data/`. | |
| 14 | +- When you are done, end with a brief plain-text summary of what you produced and any limitations. | |
| 15 | + | |
| 16 | +Complete the task described in `TASK.md`. | |
added
runs/pilot/RP0024/evaluation_receipt.json
+158 −0
@@ -0,0 +1,158 @@ | ||
| 1 | +{ | |
| 2 | + "critical_violations": [], | |
| 3 | + "evaluated_utc": "2026-10-30T04:00:33.235777+00:00", | |
| 4 | + "evaluator_version": "F3-dev-01/1.0", | |
| 5 | + "notes": "Deterministic evaluation; reproducibility component is filled by the harness. Full-sample re-estimation leaks are NOT detectable on this series (documented in REFERENCE.md); the bound catches smoothing/foresight leaks.", | |
| 6 | + "oracle_sha256": "3c7498c1f70346124c8233d46ab30a2eef957ec2e3437b34674287f93b43016a", | |
| 7 | + "quality_components": { | |
| 8 | + "baseline": 1.0, | |
| 9 | + "correctness": 0.8571428571428571, | |
| 10 | + "integrity": 1.0, | |
| 11 | + "reporting": 1.0, | |
| 12 | + "reproducibility": 1.0 | |
| 13 | + }, | |
| 14 | + "quality_score": 0.9429, | |
| 15 | + "receipt_sha256": "55fd52cb5c9764614bb47b0c0fccf2070eb10d543a0f89e87746ce322d6b4ef9", | |
| 16 | + "required_criteria": [ | |
| 17 | + { | |
| 18 | + "description": "Naive RMSE and MAE at h=1 equal the exact reference (rel 1e-6); 35 evaluated pairs (target 2025-10 missing)", | |
| 19 | + "expected": { | |
| 20 | + "mae_naive_h1": 0.09142857142857148, | |
| 21 | + "rmse_naive_h1": 0.11710800875382403 | |
| 22 | + }, | |
| 23 | + "id": "naive_h1", | |
| 24 | + "observed": { | |
| 25 | + "mae_naive_h1": 0.09142857142857148, | |
| 26 | + "rmse_naive_h1": 0.11710800875382403 | |
| 27 | + }, | |
| 28 | + "passed": true, | |
| 29 | + "tolerance": { | |
| 30 | + "mae_naive_h1": { | |
| 31 | + "rel": 1e-06 | |
| 32 | + }, | |
| 33 | + "rmse_naive_h1": { | |
| 34 | + "rel": 1e-06 | |
| 35 | + } | |
| 36 | + } | |
| 37 | + }, | |
| 38 | + { | |
| 39 | + "description": "Naive RMSE and MAE at h=3 equal the exact reference (rel 1e-6)", | |
| 40 | + "expected": { | |
| 41 | + "mae_naive_h3": 0.12000000000000005, | |
| 42 | + "rmse_naive_h3": 0.15856499343441843 | |
| 43 | + }, | |
| 44 | + "id": "naive_h3", | |
| 45 | + "observed": { | |
| 46 | + "mae_naive_h3": 0.12000000000000005, | |
| 47 | + "rmse_naive_h3": 0.15856499343441843 | |
| 48 | + }, | |
| 49 | + "passed": true, | |
| 50 | + "tolerance": { | |
| 51 | + "mae_naive_h3": { | |
| 52 | + "rel": 1e-06 | |
| 53 | + }, | |
| 54 | + "rmse_naive_h3": { | |
| 55 | + "rel": 1e-06 | |
| 56 | + } | |
| 57 | + } | |
| 58 | + }, | |
| 59 | + { | |
| 60 | + "description": "n_origins = 36, n_evaluated_h1 = 35, n_evaluated_h3 = 35", | |
| 61 | + "expected": { | |
| 62 | + "n_evaluated_h1": 35, | |
| 63 | + "n_evaluated_h3": 35, | |
| 64 | + "n_origins": 36 | |
| 65 | + }, | |
| 66 | + "id": "counts", | |
| 67 | + "observed": { | |
| 68 | + "n_evaluated_h1": 35, | |
| 69 | + "n_evaluated_h3": 35, | |
| 70 | + "n_origins": 36 | |
| 71 | + }, | |
| 72 | + "passed": true, | |
| 73 | + "tolerance": { | |
| 74 | + "n_evaluated_h1": { | |
| 75 | + "abs": 0 | |
| 76 | + }, | |
| 77 | + "n_evaluated_h3": { | |
| 78 | + "abs": 0 | |
| 79 | + }, | |
| 80 | + "n_origins": { | |
| 81 | + "abs": 0 | |
| 82 | + } | |
| 83 | + } | |
| 84 | + }, | |
| 85 | + { | |
| 86 | + "description": "forecasts.csv: 144 rows, target = origin + h, naive forecast = last observation <= origin, actual matches data", | |
| 87 | + "expected": { | |
| 88 | + "n_rows": 144 | |
| 89 | + }, | |
| 90 | + "id": "forecasts_chronology", | |
| 91 | + "observed": { | |
| 92 | + "actual_mismatches": 0, | |
| 93 | + "model_rows": 72, | |
| 94 | + "n_rows": 144, | |
| 95 | + "naive_rows": 72 | |
| 96 | + }, | |
| 97 | + "passed": true, | |
| 98 | + "tolerance": null | |
| 99 | + }, | |
| 100 | + { | |
| 101 | + "description": "model RMSE/MAE in results.json agree with forecasts.csv (rel 1e-5)", | |
| 102 | + "expected": "recomputed from forecasts.csv", | |
| 103 | + "id": "model_errors_consistent", | |
| 104 | + "observed": { | |
| 105 | + "h1": { | |
| 106 | + "mae_from_csv": 0.09565685714285715, | |
| 107 | + "mae_reported": 0.09565579999999999, | |
| 108 | + "n": 35, | |
| 109 | + "rmse_from_csv": 0.1198586468541769, | |
| 110 | + "rmse_reported": 0.11985758509962682 | |
| 111 | + }, | |
| 112 | + "h3": { | |
| 113 | + "mae_from_csv": 0.12128514285714283, | |
| 114 | + "mae_reported": 0.12128480000000005, | |
| 115 | + "n": 35, | |
| 116 | + "rmse_from_csv": 0.15976969203021132, | |
| 117 | + "rmse_reported": 0.159770241753767 | |
| 118 | + } | |
| 119 | + }, | |
| 120 | + "passed": false, | |
| 121 | + "tolerance": { | |
| 122 | + "rel": 1e-05 | |
| 123 | + } | |
| 124 | + }, | |
| 125 | + { | |
| 126 | + "description": "rel_improvement_rmse_h* = 1 - rmse_model/rmse_naive (abs 1e-6)", | |
| 127 | + "expected": null, | |
| 128 | + "id": "rel_improvement_consistent", | |
| 129 | + "observed": { | |
| 130 | + "h1": { | |
| 131 | + "expected": -0.023478977868906847, | |
| 132 | + "reported": -0.023478977868906847 | |
| 133 | + }, | |
| 134 | + "h3": { | |
| 135 | + "expected": -0.007600973539263922, | |
| 136 | + "reported": -0.007600973539263922 | |
| 137 | + } | |
| 138 | + }, | |
| 139 | + "passed": true, | |
| 140 | + "tolerance": { | |
| 141 | + "abs": 1e-06 | |
| 142 | + } | |
| 143 | + }, | |
| 144 | + { | |
| 145 | + "description": "data_issues mentions the missing 2025-10 observation", | |
| 146 | + "expected": "mention of 2025-10", | |
| 147 | + "id": "data_issue_reported", | |
| 148 | + "observed": "[{\"issue\": \"Missing value(s) (empty field) at 2025-10-01\", \"handling\": \"Excluded from estimation (any regression row whose dependent or lagged difference touches the missing month is dropped); naive uses latest earlier observation; when missing is the latest month at an origin the AR model iterates ", | |
| 149 | + "passed": true, | |
| 150 | + "tolerance": null | |
| 151 | + } | |
| 152 | + ], | |
| 153 | + "run_id": "RP0024", | |
| 154 | + "schema_error": "", | |
| 155 | + "schema_valid": true, | |
| 156 | + "scientific_success": false, | |
| 157 | + "task_id": "F3-dev-01" | |
| 158 | +} | |
| \ No newline at end of file | ||
added
runs/pilot/RP0024/out/analysis.py
+158 −0
@@ -0,0 +1,158 @@ | ||
| 1 | +"""F3-dev-01: rolling-origin (expanding window) forecasts of UNRATE, naive vs AR(2) on first differences. | |
| 2 | +Run from workspace root: ~/wp29/venv/bin/python out/analysis.py | |
| 3 | +""" | |
| 4 | +import json, os | |
| 5 | +import numpy as np | |
| 6 | +import pandas as pd | |
| 7 | + | |
| 8 | +DATA = "data/UNRATE.csv" | |
| 9 | +OUT = "out" | |
| 10 | +MODEL = "AR2_diff" | |
| 11 | +P = 2 | |
| 12 | +HORIZONS = [1, 3] | |
| 13 | +ORIGINS = pd.date_range("2023-07-01", "2026-06-01", freq="MS") | |
| 14 | + | |
| 15 | +raw = pd.read_csv(DATA, dtype={"observation_date": str, "UNRATE": str}, keep_default_na=False) | |
| 16 | +n_rows = len(raw) | |
| 17 | +df = pd.DataFrame({"date": pd.to_datetime(raw["observation_date"], format="%Y-%m-%d"), | |
| 18 | + "y": pd.to_numeric(raw["UNRATE"].replace("", np.nan), errors="coerce")}) | |
| 19 | +issues = [] | |
| 20 | +miss = df.loc[df.y.isna(), "date"].dt.strftime("%Y-%m-%d").tolist() | |
| 21 | +assert df.date.is_unique and df.date.is_monotonic_increasing | |
| 22 | +full = pd.date_range(df.date.min(), df.date.max(), freq="MS") | |
| 23 | +assert len(full) == len(df), "calendar gaps present" | |
| 24 | +s = df.set_index("date")["y"] | |
| 25 | +if miss: | |
| 26 | + issues.append({"issue": f"Missing value(s) (empty field) at {', '.join(miss)}", | |
| 27 | + "handling": "Excluded from estimation (any regression row whose dependent or lagged difference touches the missing month is dropped); " | |
| 28 | + "naive uses latest earlier observation; when missing is the latest month at an origin the AR model iterates forward from the last observed month " | |
| 29 | + "(extra steps); when it is interior to the window it is linearly interpolated from neighbours dated <= origin only to build lag inputs; " | |
| 30 | + "(origin,horizon) pairs whose target is missing are kept in the ledger with empty actual and excluded from error statistics."}) | |
| 31 | +covid = s["2020-03-01":"2020-06-01"] | |
| 32 | +issues.append({"issue": "COVID-19 outlier: unemployment jumped from %.1f (2020-03) to %.1f (2020-04), extreme first differences in training sample" % (s["2020-03-01"], s["2020-04-01"]), | |
| 33 | + "handling": "Retained in the primary specification (pre-specified, no outlier treatment); a sensitivity variant excluding 2020-03..2020-12 differences from estimation is reported in report.md only."}) | |
| 34 | + | |
| 35 | + | |
| 36 | +def fit_ar_diff(y, p=P, exclude=None): | |
| 37 | + """OLS AR(p) with intercept on dy, using rows with no missing values. y: level Series up to origin.""" | |
| 38 | + d = y.diff() | |
| 39 | + X = pd.concat({f"l{k}": d.shift(k) for k in range(1, p + 1)}, axis=1) | |
| 40 | + data = pd.concat([d.rename("dy"), X], axis=1) | |
| 41 | + if exclude is not None: | |
| 42 | + data = data[~((data.index >= exclude[0]) & (data.index <= exclude[1]))] | |
| 43 | + data = data.dropna() | |
| 44 | + A = np.column_stack([np.ones(len(data)), data[[f"l{k}" for k in range(1, p + 1)]].values]) | |
| 45 | + beta, *_ = np.linalg.lstsq(A, data["dy"].values, rcond=None) | |
| 46 | + return beta, len(data) | |
| 47 | + | |
| 48 | + | |
| 49 | +def forecast_ar_diff(y, t, horizons, exclude=None): | |
| 50 | + y = y[:t] | |
| 51 | + beta, nobs = fit_ar_diff(y, exclude=exclude) | |
| 52 | + last_obs = y.last_valid_index() | |
| 53 | + yy = y[:last_obs].interpolate(method="linear", limit_area="inside") # interior gaps only, data <= t | |
| 54 | + lev = list(yy.values) | |
| 55 | + dif = list(np.diff(yy.values)) | |
| 56 | + steps_extra = (t.year - last_obs.year) * 12 + (t.month - last_obs.month) | |
| 57 | + out = {} | |
| 58 | + H = max(horizons) + steps_extra | |
| 59 | + for step in range(1, H + 1): | |
| 60 | + dnew = beta[0] + sum(beta[k] * dif[-k] for k in range(1, P + 1)) | |
| 61 | + dif.append(dnew) | |
| 62 | + lev.append(lev[-1] + dnew) | |
| 63 | + hh = step - steps_extra | |
| 64 | + if hh in horizons: | |
| 65 | + out[hh] = lev[-1] | |
| 66 | + return out, nobs | |
| 67 | + | |
| 68 | + | |
| 69 | +def run(exclude=None): | |
| 70 | + rows, nobs_last = [], None | |
| 71 | + for t in ORIGINS: | |
| 72 | + yt = s[:t] | |
| 73 | + naive = yt.dropna().iloc[-1] | |
| 74 | + fc, nobs = forecast_ar_diff(s, t, HORIZONS, exclude) | |
| 75 | + nobs_last = nobs | |
| 76 | + for h in HORIZONS: | |
| 77 | + tgt = t + pd.DateOffset(months=h) | |
| 78 | + act = s.get(tgt, np.nan) | |
| 79 | + for m, f in (("naive", naive), (MODEL, fc[h])): | |
| 80 | + rows.append({"origin_date": t.strftime("%Y-%m-%d"), "target_date": tgt.strftime("%Y-%m-%d"), | |
| 81 | + "horizon": h, "forecast": round(float(f), 6), | |
| 82 | + "actual": act, "model": m}) | |
| 83 | + return pd.DataFrame(rows), nobs_last | |
| 84 | + | |
| 85 | + | |
| 86 | +def stats(led): | |
| 87 | + r = {} | |
| 88 | + for h in HORIZONS: | |
| 89 | + for m, tag in (("naive", "naive"), (MODEL, "model")): | |
| 90 | + sub = led[(led.horizon == h) & (led.model == m) & led.actual.notna()] | |
| 91 | + e = sub.actual - sub.forecast | |
| 92 | + r[f"rmse_{tag}_h{h}"] = float(np.sqrt(np.mean(e ** 2))) | |
| 93 | + r[f"mae_{tag}_h{h}"] = float(np.mean(np.abs(e))) | |
| 94 | + r[f"n_evaluated_h{h}"] = int(len(sub)) | |
| 95 | + r[f"rel_improvement_rmse_h{h}"] = 1 - r[f"rmse_model_h{h}"] / r[f"rmse_naive_h{h}"] | |
| 96 | + return r | |
| 97 | + | |
| 98 | + | |
| 99 | +if __name__ == "__main__": | |
| 100 | + led, nobs_last = run() | |
| 101 | + st = stats(led) | |
| 102 | + os.makedirs(OUT, exist_ok=True) | |
| 103 | + led.to_csv(os.path.join(OUT, "forecasts.csv"), index=False, float_format="%.6g") | |
| 104 | + res = { | |
| 105 | + "task_id": "F3-dev-01", | |
| 106 | + "model_name": MODEL, | |
| 107 | + "model_description": "AR(2) with intercept on monthly first differences of UNRATE, estimated by OLS on an expanding window starting with the first available difference (1948-04 as dependent observation) up to the origin, iterated recursively and cumulated to levels for h-step forecasts.", | |
| 108 | + "n_origins": len(ORIGINS), | |
| 109 | + "first_origin": ORIGINS[0].strftime("%Y-%m-%d"), | |
| 110 | + "last_origin": ORIGINS[-1].strftime("%Y-%m-%d"), | |
| 111 | + } | |
| 112 | + for k in ["n_evaluated_h1", "n_evaluated_h3", "rmse_naive_h1", "mae_naive_h1", "rmse_naive_h3", "mae_naive_h3", | |
| 113 | + "rmse_model_h1", "mae_model_h1", "rmse_model_h3", "mae_model_h3", | |
| 114 | + "rel_improvement_rmse_h1", "rel_improvement_rmse_h3"]: | |
| 115 | + res[k] = st[k] | |
| 116 | + res.update({"feasibility": "complete", "feasibility_reason": None, "data_issues": issues, | |
| 117 | + "n_input_rows": {"UNRATE.csv": n_rows}, "n_analysed": int(nobs_last), "random_seed": None}) | |
| 118 | + with open(os.path.join(OUT, "results.json"), "w") as f: | |
| 119 | + json.dump(res, f, indent=2) | |
| 120 | + print(json.dumps(res, indent=2)) | |
| 121 | + | |
| 122 | + # ---------------- supplementary (report only) ---------------- | |
| 123 | + from scipy import stats as sps | |
| 124 | + sup = {} | |
| 125 | + beta_last, _ = fit_ar_diff(s[:ORIGINS[-1]]) | |
| 126 | + beta_first, _ = fit_ar_diff(s[:ORIGINS[0]]) | |
| 127 | + sup["coef_first_origin"] = [float(b) for b in beta_first] | |
| 128 | + sup["coef_last_origin"] = [float(b) for b in beta_last] | |
| 129 | + | |
| 130 | + def dm(led, h): | |
| 131 | + a = led[(led.horizon == h) & (led.model == "naive") & led.actual.notna()].reset_index(drop=True) | |
| 132 | + b = led[(led.horizon == h) & (led.model == MODEL) & led.actual.notna()].reset_index(drop=True) | |
| 133 | + d = (a.actual - b.forecast) ** 2 - (a.actual - a.forecast) ** 2 # model loss - naive loss | |
| 134 | + n = len(d); dbar = d.mean(); dc = d - dbar | |
| 135 | + lrv = np.sum(dc ** 2) / n | |
| 136 | + for k in range(1, h): # rectangular HAC with h-1 lags | |
| 137 | + lrv += 2 * np.sum(dc[k:].values * dc[:-k].values) / n | |
| 138 | + stat = dbar / np.sqrt(lrv / n) | |
| 139 | + hln = np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n) # Harvey-Leybourne-Newbold correction | |
| 140 | + stat *= hln | |
| 141 | + p = 2 * sps.t.sf(abs(stat), df=n - 1) | |
| 142 | + return {"mean_loss_diff_model_minus_naive": float(dbar), "dm_hln_stat": float(stat), "p_value_two_sided": float(p), "n": int(n)} | |
| 143 | + sup["dm_h1"] = dm(led, 1); sup["dm_h3"] = dm(led, 3) | |
| 144 | + | |
| 145 | + # bootstrap-free descriptive: share of origins where model abs error < naive abs error | |
| 146 | + for h in HORIZONS: | |
| 147 | + a = led[(led.horizon == h) & (led.model == "naive") & led.actual.notna()].reset_index(drop=True) | |
| 148 | + b = led[(led.horizon == h) & (led.model == MODEL) & led.actual.notna()].reset_index(drop=True) | |
| 149 | + sup[f"share_model_better_abs_h{h}"] = float(np.mean(np.abs(b.actual - b.forecast) < np.abs(a.actual - a.forecast))) | |
| 150 | + sup[f"share_ties_abs_h{h}"] = float(np.mean(np.isclose(np.abs(b.actual - b.forecast), np.abs(a.actual - a.forecast)))) | |
| 151 | + | |
| 152 | + led2, _ = run(exclude=(pd.Timestamp("2020-03-01"), pd.Timestamp("2020-12-01"))) | |
| 153 | + st2 = stats(led2) | |
| 154 | + sup["sensitivity_exclude_covid_2020"] = {k: st2[k] for k in st2 if k.startswith(("rmse_model", "mae_model", "rel_"))} | |
| 155 | + sup["sensitivity_exclude_covid_coef_last_origin"] = [float(b) for b in fit_ar_diff(s[:ORIGINS[-1]], exclude=(pd.Timestamp("2020-03-01"), pd.Timestamp("2020-12-01")))[0]] | |
| 156 | + with open(os.path.join(OUT, "supplementary.json"), "w") as f: | |
| 157 | + json.dump(sup, f, indent=2) | |
| 158 | + print(json.dumps(sup, indent=2)) | |
added
runs/pilot/RP0024/out/claims.json
+7 −0
@@ -0,0 +1,7 @@ | ||
| 1 | +[ | |
| 2 | + {"id":"C1","text":"At h=1 the AR(2)-on-differences model does not beat the naive forecast: RMSE 0.1199 vs 0.1171 (relative improvement -0.023).","results_keys":["rmse_model_h1","rmse_naive_h1","rel_improvement_rmse_h1","n_evaluated_h1"],"status":"supported","caveat":"35 evaluated pairs, final-vintage data; DM-HLN p=0.017 but economically negligible difference."}, | |
| 3 | + {"id":"C2","text":"At h=3 the model does not beat the naive forecast: RMSE 0.1598 vs 0.1586 (relative improvement -0.008), difference not statistically significant.","results_keys":["rmse_model_h3","rmse_naive_h3","rel_improvement_rmse_h3","n_evaluated_h3"],"status":"supported","caveat":"DM-HLN p=0.18; small sample."}, | |
| 4 | + {"id":"C3","text":"MAE ranks the forecasts the same way: the naive forecast has lower MAE at both horizons.","results_keys":["mae_naive_h1","mae_model_h1","mae_naive_h3","mae_model_h3"],"status":"supported","caveat":"Differences are a few thousandths of a percentage point."}, | |
| 5 | + {"id":"C4","text":"The finding of no improvement holds up when COVID-2020 differences are excluded from estimation (model becomes worse, especially at h=3).","results_keys":[],"status":"supported","caveat":"Sensitivity analysis not pre-specified; numbers in out/supplementary.json, not in results.json."}, | |
| 6 | + {"id":"C5","text":"The 2025-10 missing observation is excluded from evaluation, leaving 35 evaluated pairs per horizon.","results_keys":["n_evaluated_h1","n_evaluated_h3","data_issues"],"status":"supported","caveat":""} | |
| 7 | +] | |
added
runs/pilot/RP0024/out/forecasts.csv
+145 −0
@@ -0,0 +1,145 @@ | ||
| 1 | +origin_date,target_date,horizon,forecast,actual,model | |
| 2 | +2023-07-01,2023-08-01,1,3.5,3.7,naive | |
| 3 | +2023-07-01,2023-08-01,1,3.49567,3.7,AR2_diff | |
| 4 | +2023-07-01,2023-10-01,3,3.5,3.9,naive | |
| 5 | +2023-07-01,2023-10-01,3,3.49906,3.9,AR2_diff | |
| 6 | +2023-08-01,2023-09-01,1,3.7,3.7,naive | |
| 7 | +2023-08-01,2023-09-01,1,3.71155,3.7,AR2_diff | |
| 8 | +2023-08-01,2023-11-01,3,3.7,3.7,naive | |
| 9 | +2023-08-01,2023-11-01,3,3.70196,3.7,AR2_diff | |
| 10 | +2023-09-01,2023-10-01,1,3.7,3.9,naive | |
| 11 | +2023-09-01,2023-10-01,1,3.69108,3.9,AR2_diff | |
| 12 | +2023-09-01,2023-12-01,3,3.7,3.8,naive | |
| 13 | +2023-09-01,2023-12-01,3,3.69046,3.8,AR2_diff | |
| 14 | +2023-10-01,2023-11-01,1,3.9,3.7,naive | |
| 15 | +2023-10-01,2023-11-01,1,3.90747,3.7,AR2_diff | |
| 16 | +2023-10-01,2024-01-01,3,3.9,3.7,naive | |
| 17 | +2023-10-01,2024-01-01,3,3.89839,3.7,AR2_diff | |
| 18 | +2023-11-01,2023-12-01,1,3.7,3.8,naive | |
| 19 | +2023-11-01,2023-12-01,1,3.68363,3.8,AR2_diff | |
| 20 | +2023-11-01,2024-02-01,3,3.7,3.9,naive | |
| 21 | +2023-11-01,2024-02-01,3,3.6919,3.9,AR2_diff | |
| 22 | +2023-12-01,2024-01-01,1,3.8,3.7,naive | |
| 23 | +2023-12-01,2024-01-01,1,3.81206,3.7,AR2_diff | |
| 24 | +2023-12-01,2024-03-01,3,3.8,3.9,naive | |
| 25 | +2023-12-01,2024-03-01,3,3.8072,3.9,AR2_diff | |
| 26 | +2024-01-01,2024-02-01,1,3.7,3.9,naive | |
| 27 | +2024-01-01,2024-02-01,1,3.69171,3.9,AR2_diff | |
| 28 | +2024-01-01,2024-04-01,3,3.7,3.9,naive | |
| 29 | +2024-01-01,2024-04-01,3,3.6955,3.9,AR2_diff | |
| 30 | +2024-02-01,2024-03-01,1,3.9,3.9,naive | |
| 31 | +2024-02-01,2024-03-01,1,3.91158,3.9,AR2_diff | |
| 32 | +2024-02-01,2024-05-01,3,3.9,3.9,naive | |
| 33 | +2024-02-01,2024-05-01,3,3.90259,3.9,AR2_diff | |
| 34 | +2024-03-01,2024-04-01,1,3.9,3.9,naive | |
| 35 | +2024-03-01,2024-04-01,1,3.89145,3.9,AR2_diff | |
| 36 | +2024-03-01,2024-06-01,3,3.9,4.1,naive | |
| 37 | +2024-03-01,2024-06-01,3,3.89128,4.1,AR2_diff | |
| 38 | +2024-04-01,2024-05-01,1,3.9,3.9,naive | |
| 39 | +2024-04-01,2024-05-01,1,3.89991,3.9,AR2_diff | |
| 40 | +2024-04-01,2024-07-01,3,3.9,4.2,naive | |
| 41 | +2024-04-01,2024-07-01,3,3.89973,4.2,AR2_diff | |
| 42 | +2024-05-01,2024-06-01,1,3.9,4.1,naive | |
| 43 | +2024-05-01,2024-06-01,1,3.89991,4.1,AR2_diff | |
| 44 | +2024-05-01,2024-08-01,3,3.9,4.2,naive | |
| 45 | +2024-05-01,2024-08-01,3,3.89973,4.2,AR2_diff | |
| 46 | +2024-06-01,2024-07-01,1,4.1,4.2,naive | |
| 47 | +2024-06-01,2024-07-01,1,4.10756,4.2,AR2_diff | |
| 48 | +2024-06-01,2024-09-01,3,4.1,4.1,naive | |
| 49 | +2024-06-01,2024-09-01,3,4.09903,4.1,AR2_diff | |
| 50 | +2024-07-01,2024-08-01,1,4.2,4.2,naive | |
| 51 | +2024-07-01,2024-08-01,1,4.19551,4.2,AR2_diff | |
| 52 | +2024-07-01,2024-10-01,3,4.2,4.1,naive | |
| 53 | +2024-07-01,2024-10-01,3,4.19161,4.1,AR2_diff | |
| 54 | +2024-08-01,2024-09-01,1,4.2,4.1,naive | |
| 55 | +2024-08-01,2024-09-01,1,4.19601,4.1,AR2_diff | |
| 56 | +2024-08-01,2024-11-01,3,4.2,4.2,naive | |
| 57 | +2024-08-01,2024-11-01,3,4.1965,4.2,AR2_diff | |
| 58 | +2024-09-01,2024-10-01,1,4.1,4.1,naive | |
| 59 | +2024-09-01,2024-10-01,1,4.0964,4.1,AR2_diff | |
| 60 | +2024-09-01,2024-12-01,3,4.1,4.1,naive | |
| 61 | +2024-09-01,2024-12-01,3,4.10107,4.1,AR2_diff | |
| 62 | +2024-10-01,2024-11-01,1,4.1,4.2,naive | |
| 63 | +2024-10-01,2024-11-01,1,4.10436,4.2,AR2_diff | |
| 64 | +2024-10-01,2025-01-01,3,4.1,4,naive | |
| 65 | +2024-10-01,2025-01-01,3,4.10462,4,AR2_diff | |
| 66 | +2024-11-01,2024-12-01,1,4.2,4.1,naive | |
| 67 | +2024-11-01,2024-12-01,1,4.20397,4.1,AR2_diff | |
| 68 | +2024-11-01,2025-02-01,3,4.2,4.2,naive | |
| 69 | +2024-11-01,2025-02-01,3,4.20004,4.2,AR2_diff | |
| 70 | +2024-12-01,2025-01-01,1,4.1,4,naive | |
| 71 | +2024-12-01,2025-01-01,1,4.09217,4,AR2_diff | |
| 72 | +2024-12-01,2025-03-01,3,4.1,4.2,naive | |
| 73 | +2024-12-01,2025-03-01,3,4.09684,4.2,AR2_diff | |
| 74 | +2025-01-01,2025-02-01,1,4,4.2,naive | |
| 75 | +2025-01-01,2025-02-01,1,4.00054,4.2,AR2_diff | |
| 76 | +2025-01-01,2025-04-01,3,4,4.2,naive | |
| 77 | +2025-01-01,2025-04-01,3,4.00499,4.2,AR2_diff | |
| 78 | +2025-02-01,2025-03-01,1,4.2,4.2,naive | |
| 79 | +2025-02-01,2025-03-01,1,4.21193,4.2,AR2_diff | |
| 80 | +2025-02-01,2025-05-01,3,4.2,4.3,naive | |
| 81 | +2025-02-01,2025-05-01,3,4.20354,4.3,AR2_diff | |
| 82 | +2025-03-01,2025-04-01,1,4.2,4.2,naive | |
| 83 | +2025-03-01,2025-04-01,1,4.19172,4.2,AR2_diff | |
| 84 | +2025-03-01,2025-06-01,3,4.2,4.1,naive | |
| 85 | +2025-03-01,2025-06-01,3,4.19222,4.1,AR2_diff | |
| 86 | +2025-04-01,2025-05-01,1,4.2,4.3,naive | |
| 87 | +2025-04-01,2025-05-01,1,4.20024,4.3,AR2_diff | |
| 88 | +2025-04-01,2025-07-01,3,4.2,4.3,naive | |
| 89 | +2025-04-01,2025-07-01,3,4.20072,4.3,AR2_diff | |
| 90 | +2025-05-01,2025-06-01,1,4.3,4.1,naive | |
| 91 | +2025-05-01,2025-06-01,1,4.30406,4.1,AR2_diff | |
| 92 | +2025-05-01,2025-08-01,3,4.3,4.3,naive | |
| 93 | +2025-05-01,2025-08-01,3,4.30033,4.3,AR2_diff | |
| 94 | +2025-06-01,2025-07-01,1,4.1,4.3,naive | |
| 95 | +2025-06-01,2025-07-01,1,4.08847,4.3,AR2_diff | |
| 96 | +2025-06-01,2025-09-01,3,4.1,4.4,naive | |
| 97 | +2025-06-01,2025-09-01,3,4.09759,4.4,AR2_diff | |
| 98 | +2025-07-01,2025-08-01,1,4.3,4.3,naive | |
| 99 | +2025-07-01,2025-08-01,1,4.31618,4.3,AR2_diff | |
| 100 | +2025-07-01,2025-10-01,3,4.3,,naive | |
| 101 | +2025-07-01,2025-10-01,3,4.30804,,AR2_diff | |
| 102 | +2025-08-01,2025-09-01,1,4.3,4.4,naive | |
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runs/pilot/RP0024/out/report.md
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| 1 | +# F3-dev-01 — Can an AR model beat the naive forecast of US unemployment? (Rolling origins 2023-07 to 2026-06) | |
| 2 | + | |
| 3 | +## Question | |
| 4 | +Over the 36 monthly forecast origins 2023-07-01 … 2026-06-01, does a simple autoregressive model forecast the US unemployment rate (FRED UNRATE, SA, %) better than the naive no-change forecast at horizons h = 1 and h = 3 months? We compare out-of-sample RMSE and MAE and report 1 − RMSE_model/RMSE_naive. | |
| 5 | + | |
| 6 | +## Data and exclusions | |
| 7 | +- `data/UNRATE.csv`: 945 data rows, 1948-01 to 2026-09. Dates are unique, in order and have no calendar gaps. | |
| 8 | +- **Missing value:** 2025-10-01 has an empty field (no October 2025 observation). How it was handled: | |
| 9 | + - The 2025-10 target is not scored. Two (origin, horizon) pairs are affected: origin 2025-09 at h=1 and origin 2025-07 at h=3. They stay in `forecasts.csv` with an empty `actual`. This leaves **35 evaluated pairs per horizon**. | |
| 10 | + - In estimation, any regression row whose dependent or lagged difference involves 2025-10 is dropped. | |
| 11 | + - At origin 2025-10, the naive forecast uses 2025-09 (4.4). The AR model iterates forward from 2025-09, adding one extra step. | |
| 12 | + - At later origins, 2025-10 is linearly interpolated from 2025-09 and 2025-11, both dated ≤ origin. This is used only to build lag inputs and is never used as an estimation observation. | |
| 13 | +- **COVID-19 outlier:** unemployment rose from 4.4 (2020-03) to 14.8 (2020-04). In the primary specification this outlier was kept, a choice made in advance. A sensitivity check is reported below. | |
| 14 | +- Every error is computed on the final vintage, so none of these results are valid in real time. | |
| 15 | + | |
| 16 | +## Method | |
| 17 | +- **Naive:** the forecast for all h is the latest observation dated ≤ origin. | |
| 18 | +- **Model `AR2_diff`:** an AR(2) with intercept on monthly first differences Δy_t. | |
| 19 | + - Estimated by OLS on an expanding window that starts at the beginning of the series and ends at the origin. It is re-estimated at every origin using only data dated ≤ origin. | |
| 20 | + - h-step forecasts come from iterating the difference equation and adding the steps to the last level. | |
| 21 | + - The specification was fixed before looking at any results, with no model selection. The last training window (origin 2026-06) has 935 regression observations. | |
| 22 | +- **Uncertainty:** Diebold–Mariano test on squared-error loss differentials, with HAC variance (h−1 lags) and the Harvey–Leybourne–Newbold small-sample correction, compared against a t(n−1) distribution. | |
| 23 | +- **Reproducibility:** `out/analysis.py` writes `results.json`, `forecasts.csv` and `supplementary.json`. It is deterministic and uses no random numbers. | |
| 24 | + | |
| 25 | +## Results | |
| 26 | +| horizon | n | RMSE naive | RMSE AR2_diff | MAE naive | MAE AR2_diff | 1 − RMSE ratio | | |
| 27 | +|---|---|---|---|---|---|---| | |
| 28 | +| h=1 | 35 | 0.1171 | 0.1199 | 0.0914 | 0.0957 | −0.0235 | | |
| 29 | +| h=3 | 35 | 0.1586 | 0.1598 | 0.1200 | 0.1213 | −0.0076 | | |
| 30 | + | |
| 31 | +The AR model **does not beat** the naive forecast. It is slightly worse on both RMSE and MAE at both horizons: about 2.3% higher RMSE at h=1 and 0.8% at h=3. Its absolute error was smaller than the naive error at only 10 of 35 origins (28.6%) at each horizon. | |
| 32 | + | |
| 33 | +Why: the estimated AR coefficients are close to zero. At the last origin they are intercept 0.0003, lag 1 0.037 and lag 2 −0.042, and they are almost the same at the first origin. The 2020 spike dominates the full-sample OLS fit and pushes them towards zero. As a result the model is close to a random walk, and its forecasts differ from the naive one by only a few thousandths of a point. The data are recorded to 0.1 pp. | |
| 34 | + | |
| 35 | +## Uncertainty | |
| 36 | +- **h=1:** DM-HLN statistic = 2.50, two-sided p = 0.017. Positive means the model's loss is higher. The model's (small) inferiority is nominally significant at the 5% level. | |
| 37 | +- **h=3:** statistic = 1.36, p = 0.18, so there is no significant difference. | |
| 38 | +- Treat these tests with caution: | |
| 39 | + - n = 35, and the two forecasts are nearly identical, so the loss differentials are tiny (mean 0.00065 pp² at h=1). | |
| 40 | + - The DM test's assumptions are only approximately met. | |
| 41 | + - The economic difference is negligible. | |
| 42 | +- **Sensitivity (not pre-specified; report only):** I dropped 2020-03…2020-12 differences from estimation. The AR coefficients then become substantial (lag 1 0.08, lag 2 0.28 at the last origin), but out-of-sample performance gets *worse*: | |
| 43 | + - h=1: RMSE 0.1186, relative improvement −0.013. | |
| 44 | + - h=3: RMSE 0.1756, relative improvement −0.108. | |
| 45 | +- The conclusion "no improvement over naive" therefore holds up under this alternative. | |
| 46 | + | |
| 47 | +## Limitations and open issues | |
| 48 | +- The evaluation covers only 36 origins in one fairly calm, slowly rising unemployment period. The results may not carry over to recessions. | |
| 49 | +- Final-vintage data were used, not real-time vintages. Seasonal-factor revisions may change the errors. | |
| 50 | +- Only one model specification was evaluated. The primary model's near-zero coefficients largely reflect the influence of the COVID outlier, so the comparison is effectively "random walk with tiny drift vs random walk". | |
| 51 | +- The missing 2025-10 value reduces each horizon to 35 evaluated pairs. Interpolating it as a lag input is a modelling choice. | |
| 52 | +- The HAC/DM inference is approximate in small samples. | |
added
runs/pilot/RP0024/out/results.json
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@@ -0,0 +1,37 @@ | ||
| 1 | +{ | |
| 2 | + "task_id": "F3-dev-01", | |
| 3 | + "model_name": "AR2_diff", | |
| 4 | + "model_description": "AR(2) with intercept on monthly first differences of UNRATE, estimated by OLS on an expanding window starting with the first available difference (1948-04 as dependent observation) up to the origin, iterated recursively and cumulated to levels for h-step forecasts.", | |
| 5 | + "n_origins": 36, | |
| 6 | + "first_origin": "2023-07-01", | |
| 7 | + "last_origin": "2026-06-01", | |
| 8 | + "n_evaluated_h1": 35, | |
| 9 | + "n_evaluated_h3": 35, | |
| 10 | + "rmse_naive_h1": 0.11710800875382403, | |
| 11 | + "mae_naive_h1": 0.09142857142857148, | |
| 12 | + "rmse_naive_h3": 0.15856499343441843, | |
| 13 | + "mae_naive_h3": 0.12000000000000005, | |
| 14 | + "rmse_model_h1": 0.11985758509962682, | |
| 15 | + "mae_model_h1": 0.09565579999999999, | |
| 16 | + "rmse_model_h3": 0.159770241753767, | |
| 17 | + "mae_model_h3": 0.12128480000000005, | |
| 18 | + "rel_improvement_rmse_h1": -0.023478977868906847, | |
| 19 | + "rel_improvement_rmse_h3": -0.007600973539263922, | |
| 20 | + "feasibility": "complete", | |
| 21 | + "feasibility_reason": null, | |
| 22 | + "data_issues": [ | |
| 23 | + { | |
| 24 | + "issue": "Missing value(s) (empty field) at 2025-10-01", | |
| 25 | + "handling": "Excluded from estimation (any regression row whose dependent or lagged difference touches the missing month is dropped); naive uses latest earlier observation; when missing is the latest month at an origin the AR model iterates forward from the last observed month (extra steps); when it is interior to the window it is linearly interpolated from neighbours dated <= origin only to build lag inputs; (origin,horizon) pairs whose target is missing are kept in the ledger with empty actual and excluded from error statistics." | |
| 26 | + }, | |
| 27 | + { | |
| 28 | + "issue": "COVID-19 outlier: unemployment jumped from 4.4 (2020-03) to 14.8 (2020-04), extreme first differences in training sample", | |
| 29 | + "handling": "Retained in the primary specification (pre-specified, no outlier treatment); a sensitivity variant excluding 2020-03..2020-12 differences from estimation is reported in report.md only." | |
| 30 | + } | |
| 31 | + ], | |
| 32 | + "n_input_rows": { | |
| 33 | + "UNRATE.csv": 945 | |
| 34 | + }, | |
| 35 | + "n_analysed": 935, | |
| 36 | + "random_seed": null | |
| 37 | +} | |
| \ No newline at end of file | ||
added
runs/pilot/RP0024/out/supplementary.json
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@@ -0,0 +1,41 @@ | ||
| 1 | +{ | |
| 2 | + "coef_first_origin": [ | |
| 3 | + -0.000535192175988547, | |
| 4 | + 0.03795097312802282, | |
| 5 | + -0.04298039140375718 | |
| 6 | + ], | |
| 7 | + "coef_last_origin": [ | |
| 8 | + 0.0003368050557942884, | |
| 9 | + 0.036584777952152876, | |
| 10 | + -0.04220948335502803 | |
| 11 | + ], | |
| 12 | + "dm_h1": { | |
| 13 | + "mean_loss_diff_model_minus_naive": 0.0006515549916285615, | |
| 14 | + "dm_hln_stat": 2.5036115978683102, | |
| 15 | + "p_value_two_sided": 0.0172577765361148, | |
| 16 | + "n": 35 | |
| 17 | + }, | |
| 18 | + "dm_h3": { | |
| 19 | + "mean_loss_diff_model_minus_naive": 0.0003836730071999882, | |
| 20 | + "dm_hln_stat": 1.358099191951314, | |
| 21 | + "p_value_two_sided": 0.18337674877793833, | |
| 22 | + "n": 35 | |
| 23 | + }, | |
| 24 | + "share_model_better_abs_h1": 0.2857142857142857, | |
| 25 | + "share_ties_abs_h1": 0.0, | |
| 26 | + "share_model_better_abs_h3": 0.2857142857142857, | |
| 27 | + "share_ties_abs_h3": 0.0, | |
| 28 | + "sensitivity_exclude_covid_2020": { | |
| 29 | + "rmse_model_h1": 0.11861155669206222, | |
| 30 | + "mae_model_h1": 0.09794017142857145, | |
| 31 | + "rel_improvement_rmse_h1": -0.012838984747822302, | |
| 32 | + "rmse_model_h3": 0.17561730831457695, | |
| 33 | + "mae_model_h3": 0.14061548571428575, | |
| 34 | + "rel_improvement_rmse_h3": -0.10754148510851014 | |
| 35 | + }, | |
| 36 | + "sensitivity_exclude_covid_coef_last_origin": [ | |
| 37 | + -0.0021042781557614612, | |
| 38 | + 0.08212921988039322, | |
| 39 | + 0.2814591585552241 | |
| 40 | + ] | |
| 41 | +} | |
| \ No newline at end of file | ||
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runs/pilot/RP0024/prepare.json
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| 1 | +{ | |
| 2 | + "workspace_manifest": { | |
| 3 | + "TASK.md": "833ca858b6ed80242f4f98d27c4d0171d9f2e96debc31a4662de6460fcd18351", | |
| 4 | + "bundle.json": "47db3ab9ce0210fa4891fd00875f639ab089a3dc9d79145639ccd75e5ac72b99", | |
| 5 | + "data/README.md": "62a5a815ca9b63bd6f91fdb176f2e1cecc2faa8ca816c45277b02e8ffd9a70c1", | |
| 6 | + "data/UNRATE.csv": "621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c", | |
| 7 | + "gate_spec.json": "0c33840434296984e05d8ee43f34155234e15336eb7d7232262070a866c5f012" | |
| 8 | + }, | |
| 9 | + "node_environment": { | |
| 10 | + "node": "M4M64b", | |
| 11 | + "claude": "2.1.314 (Claude Code)", | |
| 12 | + "os": "26.5.1", | |
| 13 | + "arch": "arm64", | |
| 14 | + "py": "3.14.4 2.3.4 2.3.3 1.16.2 0.14.5", | |
| 15 | + "gate": "1760d13e9f5bf9cf", | |
| 16 | + "host": "MacStudnpierre4", | |
| 17 | + "environment_id": "1284ebf789405928", | |
| 18 | + "recorded_utc": "2026-10-30T03:59:03.696+00:00" | |
| 19 | + }, | |
| 20 | + "system_append_sha256": "1d6fe7d64c0b037d7429fd0d88e61f2e8f633cb68c9d020bfb6e61f3b1ce6d17" | |
| 21 | +} | |
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| 13 | +{"parentUuid":"cf10982f-49dd-4c8f-a275-3d296a02a52a","isSidechain":false,"promptId":"3ee256e1-e8df-4565-999b-376f641ddd55","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016irkHwgKMi9Vi9JP7oNip4","type":"tool_result","content":"# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate: can a simple time-series model beat the naive forecast?\n\n## Scientific question\nOver the last three years, does a simple autoregressive-type model forecast the US civilian unemployment rate better than the naive (no-change) forecast at one- and three-month horizons? The estimands are the out-of-sample root-mean-squared error (RMSE) and mean absolute error (MAE) of (a) the naive forecast and (b) one model of your choice, computed over a fixed set of rolling (expanding-window) forecast origins, and the relative improvement of the model over the naive forecast, 1 − RMSE_model / RMSE_naive, at each horizon.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: US unemployment rate (percent, seasonally adjusted, monthly), FRED series UNRATE (source BLS), retrieved 2026-10-30 (UTC) from https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE, exactly as served (header `observation_date,UNRATE`, one row per month dated YYYY-MM-01, 1948-01 to 2026-09). Missing observations appear as an empty value field. This is the current (final) vintage; see `data/README.md` for provenance, SHA-256 and terms.\n\n## Information-availability rules\n- Forecast origins: the 36 months t = 2023-07-01, 2023-08-01, …, 2026-06-01 (the `observation_date` of the origin month). Horizons h ∈ {1, 3} months; the target of origin t at horizon h is the month t + h.\n- A forecast made at origin t may use only observations dated ≤ t (expanding window: the training sample grows with t). Model selection, parameter estimation and any transformation (e.g. differencing, scaling) must use only observations dated ≤ t.\n- Naive baseline: the forecast for every horizon is the most recent available observation dated ≤ t (if the origin month itself is missing, use the latest earlier observation).\n- If the actual value at a target month is missing in the data, that (origin, horizon) pair still appears in the forecast ledger with an empty `actual`, but is excluded from all error statistics; report the number of evaluated pairs per horizon.\n- The evaluation errors are computed on the data as given (final vintage); do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — the forecast ledger, one row per (origin, horizon, model), with columns `origin_date` (YYYY-MM-DD), `target_date` (YYYY-MM-DD), `horizon` (integer), `forecast` (number), `actual` (number, empty if missing), `model` (string: `naive` for the baseline, and the value of `model_name` for your model). 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The estimands are the out-of-sample root-mean-squared error (RMSE) and mean absolute error (MAE) of (a) the naive forecast and (b) one model of your choice, computed over a fixed set of rolling (expanding-window) forecast origins, and the relative improvement of the model over the naive forecast, 1 − RMSE_model / RMSE_naive, at each horizon.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: US unemployment rate (percent, seasonally adjusted, monthly), FRED series UNRATE (source BLS), retrieved 2026-10-30 (UTC) from https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE, exactly as served (header `observation_date,UNRATE`, one row per month dated YYYY-MM-01, 1948-01 to 2026-09). Missing observations appear as an empty value field. This is the current (final) vintage; see `data/README.md` for provenance, SHA-256 and terms.\n\n## Information-availability rules\n- Forecast origins: the 36 months t = 2023-07-01, 2023-08-01, …, 2026-06-01 (the `observation_date` of the origin month). 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| 23 | +{"parentUuid":"38e3149c-9e8f-477f-bfc0-f1e96e0e41df","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkaQK6eT1qaH7Wo1NDM","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TPXjbqYCDoqE7nyN5ppgMF","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"F3-dev-01: rolling-origin (expanding window) forecasts of UNRATE, naive vs AR(2) on first differences.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\n\nDATA = \"data/UNRATE.csv\"\nOUT = \"out\"\nMODEL = \"AR2_diff\"\nP = 2\nHORIZONS = [1, 3]\nORIGINS = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\n\nraw = pd.read_csv(DATA, dtype={\"observation_date\": str, \"UNRATE\": str}, keep_default_na=False)\nn_rows = len(raw)\ndf = pd.DataFrame({\"date\": pd.to_datetime(raw[\"observation_date\"], format=\"%Y-%m-%d\"),\n \"y\": pd.to_numeric(raw[\"UNRATE\"].replace(\"\", np.nan), errors=\"coerce\")})\nissues = []\nmiss = df.loc[df.y.isna(), \"date\"].dt.strftime(\"%Y-%m-%d\").tolist()\nassert df.date.is_unique and df.date.is_monotonic_increasing\nfull = pd.date_range(df.date.min(), df.date.max(), freq=\"MS\")\nassert len(full) == len(df), \"calendar gaps present\"\ns = df.set_index(\"date\")[\"y\"]\nif miss:\n issues.append({\"issue\": f\"Missing value(s) (empty field) at {', '.join(miss)}\",\n \"handling\": \"Excluded from estimation (any regression row whose dependent or lagged difference touches the missing month is dropped); \"\n \"naive uses latest earlier observation; when missing is the latest month at an origin the AR model iterates forward from the last observed month \"\n \"(extra steps); when it is interior to the window it is linearly interpolated from neighbours dated <= origin only to build lag inputs; \"\n \"(origin,horizon) pairs whose target is missing are kept in the ledger with empty actual and excluded from error statistics.\"})\ncovid = s[\"2020-03-01\":\"2020-06-01\"]\nissues.append({\"issue\": \"COVID-19 outlier: unemployment jumped from %.1f (2020-03) to %.1f (2020-04), extreme first differences in training sample\" % (s[\"2020-03-01\"], s[\"2020-04-01\"]),\n \"handling\": \"Retained in the primary specification (pre-specified, no outlier treatment); a sensitivity variant excluding 2020-03..2020-12 differences from estimation is reported in report.md only.\"})\n\n\ndef fit_ar_diff(y, p=P, exclude=None):\n \"\"\"OLS AR(p) with intercept on dy, using rows with no missing values. y: level Series up to origin.\"\"\"\n d = y.diff()\n X = pd.concat({f\"l{k}\": d.shift(k) for k in range(1, p + 1)}, axis=1)\n data = pd.concat([d.rename(\"dy\"), X], axis=1)\n if exclude is not None:\n data = data[~((data.index >= exclude[0]) & (data.index <= exclude[1]))]\n data = data.dropna()\n A = np.column_stack([np.ones(len(data)), data[[f\"l{k}\" for k in range(1, p + 1)]].values])\n beta, *_ = np.linalg.lstsq(A, data[\"dy\"].values, rcond=None)\n return beta, len(data)\n\n\ndef forecast_ar_diff(y, t, horizons, exclude=None):\n y = y[:t]\n beta, nobs = fit_ar_diff(y, exclude=exclude)\n last_obs = y.last_valid_index()\n yy = y[:last_obs].interpolate(method=\"linear\", limit_area=\"inside\") # interior gaps only, data <= t\n lev = list(yy.values)\n dif = list(np.diff(yy.values))\n steps_extra = (t.year - last_obs.year) * 12 + (t.month - last_obs.month)\n out = {}\n H = max(horizons) + steps_extra\n for step in range(1, H + 1):\n dnew = beta[0] + sum(beta[k] * dif[-k] for k in range(1, P + 1))\n dif.append(dnew)\n lev.append(lev[-1] + dnew)\n hh = step - steps_extra\n if hh in horizons:\n out[hh] = lev[-1]\n return out, nobs\n\n\ndef run(exclude=None):\n rows, nobs_last = [], None\n for t in ORIGINS:\n yt = s[:t]\n naive = yt.dropna().iloc[-1]\n fc, nobs = forecast_ar_diff(s, t, HORIZONS, exclude)\n nobs_last = nobs\n for h in HORIZONS:\n tgt = t + pd.DateOffset(months=h)\n act = s.get(tgt, np.nan)\n for m, f in ((\"naive\", naive), (MODEL, fc[h])):\n rows.append({\"origin_date\": t.strftime(\"%Y-%m-%d\"), \"target_date\": tgt.strftime(\"%Y-%m-%d\"),\n \"horizon\": h, \"forecast\": round(float(f), 6),\n \"actual\": act, \"model\": m})\n return pd.DataFrame(rows), nobs_last\n\n\ndef stats(led):\n r = {}\n for h in HORIZONS:\n for m, tag in ((\"naive\", \"naive\"), (MODEL, \"model\")):\n sub = led[(led.horizon == h) & (led.model == m) & led.actual.notna()]\n e = sub.actual - sub.forecast\n r[f\"rmse_{tag}_h{h}\"] = float(np.sqrt(np.mean(e ** 2)))\n r[f\"mae_{tag}_h{h}\"] = float(np.mean(np.abs(e)))\n r[f\"n_evaluated_h{h}\"] = int(len(sub))\n r[f\"rel_improvement_rmse_h{h}\"] = 1 - r[f\"rmse_model_h{h}\"] / r[f\"rmse_naive_h{h}\"]\n return r\n\n\nif __name__ == \"__main__\":\n led, nobs_last = run()\n st = stats(led)\n os.makedirs(OUT, exist_ok=True)\n led.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.6g\")\n res = {\n \"task_id\": \"F3-dev-01\",\n \"model_name\": MODEL,\n \"model_description\": \"AR(2) with intercept on monthly first differences of UNRATE, estimated by OLS on an expanding window starting with the first available difference (1948-04 as dependent observation) up to the origin, iterated recursively and cumulated to levels for h-step forecasts.\",\n \"n_origins\": len(ORIGINS),\n \"first_origin\": ORIGINS[0].strftime(\"%Y-%m-%d\"),\n \"last_origin\": ORIGINS[-1].strftime(\"%Y-%m-%d\"),\n }\n for k in [\"n_evaluated_h1\", \"n_evaluated_h3\", \"rmse_naive_h1\", \"mae_naive_h1\", \"rmse_naive_h3\", \"mae_naive_h3\",\n \"rmse_model_h1\", \"mae_model_h1\", \"rmse_model_h3\", \"mae_model_h3\",\n \"rel_improvement_rmse_h1\", \"rel_improvement_rmse_h3\"]:\n res[k] = st[k]\n res.update({\"feasibility\": \"complete\", \"feasibility_reason\": None, \"data_issues\": issues,\n \"n_input_rows\": {\"UNRATE.csv\": n_rows}, \"n_analysed\": int(nobs_last), \"random_seed\": None})\n with open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\n print(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && wc -l out/forecasts.csv && grep -E '2025-(09|10|11|12)' out/forecasts.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1169,"cache_read_input_tokens":4374,"output_tokens":3535,"output_tokens_details":{"thinking_tokens":709},"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":1169},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXkaQ3E382nHvDye4dKk","requestRef":"875f8813-455d-4ae0-bf58-c12acdf03ab1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c971d0b0-8b41-424d-9180-14c49b0906ef","timestamp":"2026-10-30T03:59:39.956Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0024","sessionId":"9b17892a-17dd-4863-9fe8-7564f240c508","version":"2.1.314","gitBranch":"HEAD"} | |
| 24 | +{"parentUuid":"c971d0b0-8b41-424d-9180-14c49b0906ef","isSidechain":false,"promptId":"3ee256e1-e8df-4565-999b-376f641ddd55","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TPXjbqYCDoqE7nyN5ppgMF","type":"tool_result","content":"{\n \"task_id\": \"F3-dev-01\",\n \"model_name\": \"AR2_diff\",\n \"model_description\": \"AR(2) with intercept on monthly first differences of UNRATE, estimated by OLS on an expanding window starting with the first available difference (1948-04 as dependent observation) up to the origin, iterated recursively and cumulated to levels for h-step forecasts.\",\n \"n_origins\": 36,\n \"first_origin\": \"2023-07-01\",\n \"last_origin\": \"2026-06-01\",\n \"n_evaluated_h1\": 35,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h1\": 0.11710800875382403,\n \"mae_naive_h1\": 0.09142857142857148,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h1\": 0.11985758509962682,\n \"mae_model_h1\": 0.09565579999999999,\n \"rmse_model_h3\": 0.159770241753767,\n \"mae_model_h3\": 0.12128480000000005,\n \"rel_improvement_rmse_h1\": -0.023478977868906847,\n \"rel_improvement_rmse_h3\": -0.007600973539263922,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"Missing value(s) (empty field) at 2025-10-01\",\n \"handling\": \"Excluded from estimation (any regression row whose dependent or lagged difference touches the missing month is dropped); naive uses latest earlier observation; when missing is the latest month at an origin the AR model iterates forward from the last observed month (extra steps); when it is interior to the window it is linearly interpolated from neighbours dated <= origin only to build lag inputs; (origin,horizon) pairs whose target is missing are kept in the ledger with empty actual and excluded from error statistics.\"\n },\n {\n \"issue\": \"COVID-19 outlier: unemployment jumped from 4.4 (2020-03) to 14.8 (2020-04), extreme first differences in training sample\",\n \"handling\": \"Retained in the primary specification (pre-specified, no outlier treatment); a sensitivity variant excluding 2020-03..2020-12 differences from estimation is reported in report.md only.\"\n }\n ],\n \"n_input_rows\": {\n \"UNRATE.csv\": 945\n },\n \"n_analysed\": 935,\n \"random_seed\": null\n}\n 145 out/forecasts.csv\n2025-06-01,2025-09-01,3,4.1,4.4,naive\n2025-06-01,2025-09-01,3,4.09759,4.4,AR2_diff\n2025-07-01,2025-10-01,3,4.3,,naive\n2025-07-01,2025-10-01,3,4.30804,,AR2_diff\n2025-08-01,2025-09-01,1,4.3,4.4,naive\n2025-08-01,2025-09-01,1,4.29186,4.4,AR2_diff\n2025-08-01,2025-11-01,3,4.3,4.5,naive\n2025-08-01,2025-11-01,3,4.29258,4.5,AR2_diff\n2025-09-01,2025-10-01,1,4.4,,naive\n2025-09-01,2025-10-01,1,4.40412,,AR2_diff\n2025-09-01,2025-12-01,3,4.4,4.4,naive\n2025-09-01,2025-12-01,3,4.40065,4.4,AR2_diff\n2025-10-01,2025-11-01,1,4.4,4.5,naive\n2025-10-01,2025-11-01,1,4.4005,4.5,AR2_diff\n2025-10-01,2026-01-01,3,4.4,4.3,naive\n2025-10-01,2026-01-01,3,4.40126,4.3,AR2_diff\n2025-11-01,2025-12-01,1,4.5,4.4,naive\n2025-11-01,2025-12-01,1,4.50018,4.4,AR2_diff\n2025-11-01,2026-02-01,3,4.5,4.4,naive\n2025-11-01,2026-02-01,3,4.4989,4.4,AR2_diff\n2025-12-01,2026-01-01,1,4.4,4.3,naive\n2025-12-01,2026-01-01,1,4.39467,4.3,AR2_diff\n2025-12-01,2026-03-01,3,4.4,4.3,naive\n2025-12-01,2026-03-01,3,4.39999,4.3,AR2_diff","is_error":false}]},"uuid":"d3a7b551-d699-4491-8e10-d56522280b6f","timestamp":"2026-10-30T03:59:40.188Z","toolUseResult":{"stdout":"{\n \"task_id\": \"F3-dev-01\",\n \"model_name\": \"AR2_diff\",\n \"model_description\": \"AR(2) with intercept on monthly first differences of UNRATE, estimated by OLS on an expanding window starting with the first available difference (1948-04 as dependent observation) up to the origin, iterated recursively and cumulated to levels for h-step forecasts.\",\n \"n_origins\": 36,\n \"first_origin\": \"2023-07-01\",\n \"last_origin\": \"2026-06-01\",\n \"n_evaluated_h1\": 35,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h1\": 0.11710800875382403,\n \"mae_naive_h1\": 0.09142857142857148,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h1\": 0.11985758509962682,\n \"mae_model_h1\": 0.09565579999999999,\n \"rmse_model_h3\": 0.159770241753767,\n \"mae_model_h3\": 0.12128480000000005,\n \"rel_improvement_rmse_h1\": -0.023478977868906847,\n \"rel_improvement_rmse_h3\": -0.007600973539263922,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"Missing value(s) (empty field) at 2025-10-01\",\n \"handling\": \"Excluded from estimation (any regression row whose dependent or lagged difference touches the missing month is dropped); naive uses latest earlier observation; when missing is the latest month at an origin the AR model iterates forward from the last observed month (extra steps); when it is interior to the window it is linearly interpolated from neighbours dated <= origin only to build lag inputs; (origin,horizon) pairs whose target is missing are kept in the ledger with empty actual and excluded from error statistics.\"\n },\n {\n \"issue\": \"COVID-19 outlier: unemployment jumped from 4.4 (2020-03) to 14.8 (2020-04), extreme first differences in training sample\",\n \"handling\": \"Retained in the primary specification (pre-specified, no outlier treatment); a sensitivity variant excluding 2020-03..2020-12 differences from estimation is reported in report.md only.\"\n }\n ],\n \"n_input_rows\": {\n \"UNRATE.csv\": 945\n },\n \"n_analysed\": 935,\n \"random_seed\": null\n}\n 145 out/forecasts.csv\n2025-06-01,2025-09-01,3,4.1,4.4,naive\n2025-06-01,2025-09-01,3,4.09759,4.4,AR2_diff\n2025-07-01,2025-10-01,3,4.3,,naive\n2025-07-01,2025-10-01,3,4.30804,,AR2_diff\n2025-08-01,2025-09-01,1,4.3,4.4,naive\n2025-08-01,2025-09-01,1,4.29186,4.4,AR2_diff\n2025-08-01,2025-11-01,3,4.3,4.5,naive\n2025-08-01,2025-11-01,3,4.29258,4.5,AR2_diff\n2025-09-01,2025-10-01,1,4.4,,naive\n2025-09-01,2025-10-01,1,4.40412,,AR2_diff\n2025-09-01,2025-12-01,3,4.4,4.4,naive\n2025-09-01,2025-12-01,3,4.40065,4.4,AR2_diff\n2025-10-01,2025-11-01,1,4.4,4.5,naive\n2025-10-01,2025-11-01,1,4.4005,4.5,AR2_diff\n2025-10-01,2026-01-01,3,4.4,4.3,naive\n2025-10-01,2026-01-01,3,4.40126,4.3,AR2_diff\n2025-11-01,2025-12-01,1,4.5,4.4,naive\n2025-11-01,2025-12-01,1,4.50018,4.4,AR2_diff\n2025-11-01,2026-02-01,3,4.5,4.4,naive\n2025-11-01,2026-02-01,3,4.4989,4.4,AR2_diff\n2025-12-01,2026-01-01,1,4.4,4.3,naive\n2025-12-01,2026-01-01,1,4.39467,4.3,AR2_diff\n2025-12-01,2026-03-01,3,4.4,4.3,naive\n2025-12-01,2026-03-01,3,4.39999,4.3,AR2_diff","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c971d0b0-8b41-424d-9180-14c49b0906ef","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0024","sessionId":"9b17892a-17dd-4863-9fe8-7564f240c508","version":"2.1.314","gitBranch":"HEAD"} | |
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cat > out/analysis.py <<'EOF'\n\"\"\"F3-dev-01: rolling-origin (expanding window) forecasts of UNRATE, naive vs AR(2) on first differences.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\n\nDATA = \"data/UNRATE.csv\"\nOUT = \"out\"\nMODEL = \"AR2_diff\"\nP = 2\nHORIZONS = [1, 3]\nORIGINS = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\n\nraw = pd.read_csv(DATA, dtype={\"observation_date\": str, \"UNRATE\": str}, keep_default_na=False)\nn_rows = len(raw)\ndf = pd.DataFrame({\"date\": pd.to_datetime(raw[\"observation_date\"], format=\"%Y-%m-%d\"),\n \"y\": pd.to_numeric(raw[\"UNRATE\"].replace(\"\", np.nan), errors=\"coerce\")})\nissues = []\nmiss = df.loc[df.y.isna(), \"date\"].dt.strftime(\"%Y-%m-%d\").tolist()\nassert df.date.is_unique and df.date.is_monotonic_increasing\nfull = pd.date_range(df.date.min(), df.date.max(), freq=\"MS\")\nassert len(full) == len(df), \"calendar gaps present\"\ns = df.set_index(\"date\")[\"y\"]\nif miss:\n issues.append({\"issue\": f\"Missing value(s) (empty field) at {', '.join(miss)}\",\n \"handling\": \"Excluded from estimation (any regression row whose dependent or lagged difference touches the missing month is dropped); \"\n \"naive uses latest earlier observation; when missing is the latest month at an origin the AR model iterates forward from the last observed month \"\n \"(extra steps); when it is interior to the window it is linearly interpolated from neighbours dated <= origin only to build lag inputs; \"\n \"(origin,horizon) pairs whose target is missing are kept in the ledger with empty actual and excluded from error statistics.\"})\ncovid = s[\"2020-03-01\":\"2020-06-01\"]\nissues.append({\"issue\": \"COVID-19 outlier: unemployment jumped from %.1f (2020-03) to %.1f (2020-04), extreme first differences in training sample\" % (s[\"2020-03-01\"], s[\"2020-04-01\"]),\n \"handling\": \"Retained in the primary specification (pre-specified, no outlier treatment); a sensitivity variant excluding 2020-03..2020-12 differences from estimation is reported in report.md only.\"})\n\n\ndef fit_ar_diff(y, p=P, exclude=None):\n \"\"\"OLS AR(p) with intercept on dy, using rows with no missing values. y: level Series up to origin.\"\"\"\n d = y.diff()\n X = pd.concat({f\"l{k}\": d.shift(k) for k in range(1, p + 1)}, axis=1)\n data = pd.concat([d.rename(\"dy\"), X], axis=1)\n if exclude is not None:\n data = data[~((data.index >= exclude[0]) & (data.index <= exclude[1]))]\n data = data.dropna()\n A = np.column_stack([np.ones(len(data)), data[[f\"l{k}\" for k in range(1, p + 1)]].values])\n beta, *_ = np.linalg.lstsq(A, data[\"dy\"].values, rcond=None)\n return beta, len(data)\n\n\ndef forecast_ar_diff(y, t, horizons, exclude=None):\n y = y[:t]\n beta, nobs = fit_ar_diff(y, exclude=exclude)\n last_obs = y.last_valid_index()\n yy = y[:last_obs].interpolate(method=\"linear\", limit_area=\"inside\") # interior gaps only, data <= t\n lev = list(yy.values)\n dif = list(np.diff(yy.values))\n steps_extra = (t.year - last_obs.year) * 12 + (t.month - last_obs.month)\n out = {}\n H = max(horizons) + steps_extra\n for step in range(1, H + 1):\n dnew = beta[0] + sum(beta[k] * dif[-k] for k in range(1, P + 1))\n dif.append(dnew)\n lev.append(lev[-1] + dnew)\n hh = step - steps_extra\n if hh in horizons:\n out[hh] = lev[-1]\n return out, nobs\n\n\ndef run(exclude=None):\n rows, nobs_last = [], None\n for t in ORIGINS:\n yt = s[:t]\n naive = yt.dropna().iloc[-1]\n fc, nobs = forecast_ar_diff(s, t, HORIZONS, exclude)\n nobs_last = nobs\n for h in HORIZONS:\n tgt = t + pd.DateOffset(months=h)\n act = s.get(tgt, np.nan)\n for m, f in ((\"naive\", naive), (MODEL, fc[h])):\n rows.append({\"origin_date\": t.strftime(\"%Y-%m-%d\"), \"target_date\": tgt.strftime(\"%Y-%m-%d\"),\n \"horizon\": h, \"forecast\": round(float(f), 6),\n \"actual\": act, \"model\": m})\n return pd.DataFrame(rows), nobs_last\n\n\ndef stats(led):\n r = {}\n for h in HORIZONS:\n for m, tag in ((\"naive\", \"naive\"), (MODEL, \"model\")):\n sub = led[(led.horizon == h) & (led.model == m) & led.actual.notna()]\n e = sub.actual - sub.forecast\n r[f\"rmse_{tag}_h{h}\"] = float(np.sqrt(np.mean(e ** 2)))\n r[f\"mae_{tag}_h{h}\"] = float(np.mean(np.abs(e)))\n r[f\"n_evaluated_h{h}\"] = int(len(sub))\n r[f\"rel_improvement_rmse_h{h}\"] = 1 - r[f\"rmse_model_h{h}\"] / r[f\"rmse_naive_h{h}\"]\n return r\n\n\nif __name__ == \"__main__\":\n led, nobs_last = run()\n st = stats(led)\n os.makedirs(OUT, exist_ok=True)\n led.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.6g\")\n res = {\n \"task_id\": \"F3-dev-01\",\n \"model_name\": MODEL,\n \"model_description\": \"AR(2) with intercept on monthly first differences of UNRATE, estimated by OLS on an expanding window starting with the first available difference (1948-04 as dependent observation) up to the origin, iterated recursively and cumulated to levels for h-step forecasts.\",\n \"n_origins\": len(ORIGINS),\n \"first_origin\": ORIGINS[0].strftime(\"%Y-%m-%d\"),\n \"last_origin\": ORIGINS[-1].strftime(\"%Y-%m-%d\"),\n }\n for k in [\"n_evaluated_h1\", \"n_evaluated_h3\", \"rmse_naive_h1\", \"mae_naive_h1\", \"rmse_naive_h3\", \"mae_naive_h3\",\n \"rmse_model_h1\", \"mae_model_h1\", \"rmse_model_h3\", \"mae_model_h3\",\n \"rel_improvement_rmse_h1\", \"rel_improvement_rmse_h3\"]:\n res[k] = st[k]\n res.update({\"feasibility\": \"complete\", \"feasibility_reason\": None, \"data_issues\": issues,\n \"n_input_rows\": {\"UNRATE.csv\": n_rows}, \"n_analysed\": int(nobs_last), \"random_seed\": None})\n with open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\n print(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && wc -l out/forecasts.csv && grep -E '2025-(09|10|11|12)' out/forecasts.csv"}}]}} | |
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| 28 | +{"parentUuid":"e1c9a278-1884-41fc-9750-55041d06cb09","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkciZkuURBjHVgkfqoY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RmP7qLbPemFrF9f1tsJyBj","name":"Bash","input":{"command":"cat >> out/analysis.py <<'EOF'\n\n # ---------------- supplementary (report only) ----------------\n from scipy import stats as sps\n sup = {}\n beta_last, _ = fit_ar_diff(s[:ORIGINS[-1]])\n beta_first, _ = fit_ar_diff(s[:ORIGINS[0]])\n sup[\"coef_first_origin\"] = [float(b) for b in beta_first]\n sup[\"coef_last_origin\"] = [float(b) for b in beta_last]\n\n def dm(led, h):\n a = led[(led.horizon == h) & (led.model == \"naive\") & led.actual.notna()].reset_index(drop=True)\n b = led[(led.horizon == h) & (led.model == MODEL) & led.actual.notna()].reset_index(drop=True)\n d = (a.actual - b.forecast) ** 2 - (a.actual - a.forecast) ** 2 # model loss - naive loss\n n = len(d); dbar = d.mean(); dc = d - dbar\n lrv = np.sum(dc ** 2) / n\n for k in range(1, h): # rectangular HAC with h-1 lags\n lrv += 2 * np.sum(dc[k:].values * dc[:-k].values) / n\n stat = dbar / np.sqrt(lrv / n)\n hln = np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n) # Harvey-Leybourne-Newbold correction\n stat *= hln\n p = 2 * sps.t.sf(abs(stat), df=n - 1)\n return {\"mean_loss_diff_model_minus_naive\": float(dbar), \"dm_hln_stat\": float(stat), \"p_value_two_sided\": float(p), \"n\": int(n)}\n sup[\"dm_h1\"] = dm(led, 1); sup[\"dm_h3\"] = dm(led, 3)\n\n # bootstrap-free descriptive: share of origins where model abs error < naive abs error\n for h in HORIZONS:\n a = led[(led.horizon == h) & (led.model == \"naive\") & led.actual.notna()].reset_index(drop=True)\n b = led[(led.horizon == h) & (led.model == MODEL) & led.actual.notna()].reset_index(drop=True)\n sup[f\"share_model_better_abs_h{h}\"] = float(np.mean(np.abs(b.actual - b.forecast) < np.abs(a.actual - a.forecast)))\n sup[f\"share_ties_abs_h{h}\"] = float(np.mean(np.isclose(np.abs(b.actual - b.forecast), np.abs(a.actual - a.forecast))))\n\n led2, _ = run(exclude=(pd.Timestamp(\"2020-03-01\"), pd.Timestamp(\"2020-12-01\")))\n st2 = stats(led2)\n sup[\"sensitivity_exclude_covid_2020\"] = {k: st2[k] for k in st2 if k.startswith((\"rmse_model\", \"mae_model\", \"rel_\"))}\n sup[\"sensitivity_exclude_covid_coef_last_origin\"] = [float(b) for b in fit_ar_diff(s[:ORIGINS[-1]], exclude=(pd.Timestamp(\"2020-03-01\"), pd.Timestamp(\"2020-12-01\")))[0]]\n with open(os.path.join(OUT, \"supplementary.json\"), \"w\") as f:\n json.dump(sup, f, indent=2)\n print(json.dumps(sup, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py | sed -n '/coef_first/,$p'; ~/wp29/venv/bin/python out/analysis.py >/dev/null; md5 out/results.json out/forecasts.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5100,"cache_read_input_tokens":5543,"output_tokens":1586,"output_tokens_details":{"thinking_tokens":240},"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":5100},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXkciF9nzJhbkeU13rhN","requestRef":"a90d04b5-e506-4731-a629-b5c371816d94","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5319471c-a571-4336-a742-f636638a96ba","timestamp":"2026-10-30T03:59:53.387Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0024","sessionId":"9b17892a-17dd-4863-9fe8-7564f240c508","version":"2.1.314","gitBranch":"HEAD"} | |
| 29 | +{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"5319471c-a571-4336-a742-f636638a96ba","sessionId":"9b17892a-17dd-4863-9fe8-7564f240c508"} | |
| 30 | +{"type":"atis-latch","atis":"","sessionId":"9b17892a-17dd-4863-9fe8-7564f240c508"} | |
| 31 | +{"parentUuid":"5319471c-a571-4336-a742-f636638a96ba","isSidechain":false,"promptId":"3ee256e1-e8df-4565-999b-376f641ddd55","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RmP7qLbPemFrF9f1tsJyBj","type":"tool_result","content":" \"coef_first_origin\": [\n -0.000535192175988547,\n 0.03795097312802282,\n -0.04298039140375718\n ],\n \"coef_last_origin\": [\n 0.0003368050557942884,\n 0.036584777952152876,\n -0.04220948335502803\n ],\n \"dm_h1\": {\n \"mean_loss_diff_model_minus_naive\": 0.0006515549916285615,\n \"dm_hln_stat\": 2.5036115978683102,\n \"p_value_two_sided\": 0.0172577765361148,\n \"n\": 35\n },\n \"dm_h3\": {\n \"mean_loss_diff_model_minus_naive\": 0.0003836730071999882,\n \"dm_hln_stat\": 1.358099191951314,\n \"p_value_two_sided\": 0.18337674877793833,\n \"n\": 35\n },\n \"share_model_better_abs_h1\": 0.2857142857142857,\n \"share_ties_abs_h1\": 0.0,\n \"share_model_better_abs_h3\": 0.2857142857142857,\n \"share_ties_abs_h3\": 0.0,\n \"sensitivity_exclude_covid_2020\": {\n \"rmse_model_h1\": 0.11861155669206222,\n \"mae_model_h1\": 0.09794017142857145,\n \"rel_improvement_rmse_h1\": -0.012838984747822302,\n \"rmse_model_h3\": 0.17561730831457695,\n \"mae_model_h3\": 0.14061548571428575,\n \"rel_improvement_rmse_h3\": -0.10754148510851014\n },\n \"sensitivity_exclude_covid_coef_last_origin\": [\n -0.0021042781557614612,\n 0.08212921988039322,\n 0.2814591585552241\n ]\n}\nMD5 (out/results.json) = f86d70051817679d8fc7e5b63513d210\nMD5 (out/forecasts.csv) = a12f8f76c103e0a207eb2c475a20811f","is_error":false}]},"uuid":"6b7ef86f-0242-4ccd-a904-205309b51b53","timestamp":"2026-10-30T03:59:54.440Z","toolUseResult":{"stdout":" \"coef_first_origin\": [\n -0.000535192175988547,\n 0.03795097312802282,\n -0.04298039140375718\n ],\n \"coef_last_origin\": [\n 0.0003368050557942884,\n 0.036584777952152876,\n -0.04220948335502803\n ],\n \"dm_h1\": {\n \"mean_loss_diff_model_minus_naive\": 0.0006515549916285615,\n \"dm_hln_stat\": 2.5036115978683102,\n \"p_value_two_sided\": 0.0172577765361148,\n \"n\": 35\n },\n \"dm_h3\": {\n \"mean_loss_diff_model_minus_naive\": 0.0003836730071999882,\n \"dm_hln_stat\": 1.358099191951314,\n \"p_value_two_sided\": 0.18337674877793833,\n \"n\": 35\n },\n \"share_model_better_abs_h1\": 0.2857142857142857,\n \"share_ties_abs_h1\": 0.0,\n \"share_model_better_abs_h3\": 0.2857142857142857,\n \"share_ties_abs_h3\": 0.0,\n \"sensitivity_exclude_covid_2020\": {\n \"rmse_model_h1\": 0.11861155669206222,\n \"mae_model_h1\": 0.09794017142857145,\n \"rel_improvement_rmse_h1\": -0.012838984747822302,\n \"rmse_model_h3\": 0.17561730831457695,\n \"mae_model_h3\": 0.14061548571428575,\n \"rel_improvement_rmse_h3\": -0.10754148510851014\n },\n \"sensitivity_exclude_covid_coef_last_origin\": [\n -0.0021042781557614612,\n 0.08212921988039322,\n 0.2814591585552241\n ]\n}\nMD5 (out/results.json) = f86d70051817679d8fc7e5b63513d210\nMD5 (out/forecasts.csv) = a12f8f76c103e0a207eb2c475a20811f","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5319471c-a571-4336-a742-f636638a96ba","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0024","sessionId":"9b17892a-17dd-4863-9fe8-7564f240c508","version":"2.1.314","gitBranch":"HEAD"} | |
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| 39 | +{"parentUuid":"9aab15b0-1cc3-44f8-9cc4-3d45d252bdb9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkdzKa66joYrjjccjBM","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EAU7w1vF8xC23E2qPE9wqD","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-dev-01 — Can an AR model beat the naive forecast of US unemployment? (Rolling origins 2023-07 to 2026-06)\n\n## Question\nOver the 36 monthly forecast origins 2023-07-01 … 2026-06-01, does a simple autoregressive model forecast the US unemployment rate (FRED UNRATE, SA, %) better than the naive no-change forecast at horizons h = 1 and h = 3 months? We compare out-of-sample RMSE and MAE and report 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- `data/UNRATE.csv`: 945 data rows, 1948-01 to 2026-09. Dates are unique, in order and have no calendar gaps.\n- **Missing value:** 2025-10-01 has an empty field (no October 2025 observation). 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A sensitivity check is reported below.\n- Every error is computed on the final vintage, so none of these results are valid in real time.\n\n## Method\n- **Naive:** the forecast for all h is the latest observation dated ≤ origin.\n- **Model `AR2_diff`:** an AR(2) with intercept on monthly first differences Δy_t.\n - Estimated by OLS on an expanding window that starts at the beginning of the series and ends at the origin. It is re-estimated at every origin using only data dated ≤ origin.\n - h-step forecasts come from iterating the difference equation and adding the steps to the last level.\n - The specification was fixed before looking at any results, with no model selection. The last training window (origin 2026-06) has 935 regression observations.\n- **Uncertainty:** Diebold–Mariano test on squared-error loss differentials, with HAC variance (h−1 lags) and the Harvey–Leybourne–Newbold small-sample correction, compared against a t(n−1) distribution.\n- **Reproducibility:** `out/analysis.py` writes `results.json`, `forecasts.csv` and `supplementary.json`. It is deterministic and uses no random numbers.\n\n## Results\n| horizon | n | RMSE naive | RMSE AR2_diff | MAE naive | MAE AR2_diff | 1 − RMSE ratio |\n|---|---|---|---|---|---|---|\n| h=1 | 35 | 0.1171 | 0.1199 | 0.0914 | 0.0957 | −0.0235 |\n| h=3 | 35 | 0.1586 | 0.1598 | 0.1200 | 0.1213 | −0.0076 |\n\nThe AR model **does not beat** the naive forecast. It is slightly worse on both RMSE and MAE at both horizons: about 2.3% higher RMSE at h=1 and 0.8% at h=3. Its absolute error was smaller than the naive error at only 10 of 35 origins (28.6%) at each horizon.\n\nWhy: the estimated AR coefficients are close to zero. At the last origin they are intercept 0.0003, lag 1 0.037 and lag 2 −0.042, and they are almost the same at the first origin. The 2020 spike dominates the full-sample OLS fit and pushes them towards zero. As a result the model is close to a random walk, and its forecasts differ from the naive one by only a few thousandths of a point. The data are recorded to 0.1 pp.\n\n## Uncertainty\n- **h=1:** DM-HLN statistic = 2.50, two-sided p = 0.017. Positive means the model's loss is higher. The model's (small) inferiority is nominally significant at the 5% level.\n- **h=3:** statistic = 1.36, p = 0.18, so there is no significant difference.\n- Treat these tests with caution:\n - n = 35, and the two forecasts are nearly identical, so the loss differentials are tiny (mean 0.00065 pp² at h=1).\n - The DM test's assumptions are only approximately met.\n - The economic difference is negligible.\n- **Sensitivity (not pre-specified; report only):** I dropped 2020-03…2020-12 differences from estimation. The AR coefficients then become substantial (lag 1 0.08, lag 2 0.28 at the last origin), but out-of-sample performance gets *worse*:\n - h=1: RMSE 0.1186, relative improvement −0.013.\n - h=3: RMSE 0.1756, relative improvement −0.108.\n- The conclusion \"no improvement over naive\" therefore holds up under this alternative.\n\n## Limitations and open issues\n- The evaluation covers only 36 origins in one fairly calm, slowly rising unemployment period. The results may not carry over to recessions.\n- Final-vintage data were used, not real-time vintages. Seasonal-factor revisions may change the errors.\n- Only one model specification was evaluated. The primary model's near-zero coefficients largely reflect the influence of the COVID outlier, so the comparison is effectively \"random walk with tiny drift vs random walk\".\n- The missing 2025-10 value reduces each horizon to 35 evaluated pairs. Interpolating it as a lag input is a modelling choice.\n- The HAC/DM inference is approximate in small samples.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At h=1 the AR(2)-on-differences model does not beat the naive forecast: RMSE 0.1199 vs 0.1171 (relative improvement -0.023).\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"rel_improvement_rmse_h1\",\"n_evaluated_h1\"],\"status\":\"supported\",\"caveat\":\"35 evaluated pairs, final-vintage data; DM-HLN p=0.017 but economically negligible difference.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the model does not beat the naive forecast: RMSE 0.1598 vs 0.1586 (relative improvement -0.008), difference not statistically significant.\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"rel_improvement_rmse_h3\",\"n_evaluated_h3\"],\"status\":\"supported\",\"caveat\":\"DM-HLN p=0.18; small sample.\"},\n {\"id\":\"C3\",\"text\":\"MAE ranks the forecasts the same way: the naive forecast has lower MAE at both horizons.\",\"results_keys\":[\"mae_naive_h1\",\"mae_model_h1\",\"mae_naive_h3\",\"mae_model_h3\"],\"status\":\"supported\",\"caveat\":\"Differences are a few thousandths of a percentage point.\"},\n {\"id\":\"C4\",\"text\":\"The finding of no improvement holds up when COVID-2020 differences are excluded from estimation (model becomes worse, especially at h=3).\",\"results_keys\":[],\"status\":\"supported\",\"caveat\":\"Sensitivity analysis not pre-specified; numbers in out/supplementary.json, not in results.json.\"},\n {\"id\":\"C5\",\"text\":\"The 2025-10 missing observation is excluded from evaluation, leaving 35 evaluated pairs per horizon.\",\"results_keys\":[\"n_evaluated_h1\",\"n_evaluated_h3\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\"; ls out"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":251,"cache_read_input_tokens":12907,"output_tokens":2829,"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":251},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXkdz2TSVN42QnP9KGmK","requestRef":"88b8aabd-27b8-4a38-ae57-57e7651fcef5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"cc3adeb9-5374-4bce-a7ae-47b12b529ac6","timestamp":"2026-10-30T04:00:22.519Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0024","sessionId":"9b17892a-17dd-4863-9fe8-7564f240c508","version":"2.1.314","gitBranch":"HEAD"} | |
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| 42 | +{"type":"api-request-blob","sessionId":"9b17892a-17dd-4863-9fe8-7564f240c508","hash":"c3f52db55744807bc6348058f3826501296a2ac78fd78eb3fa2bcd31688fcc63","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01EAU7w1vF8xC23E2qPE9wqD","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-dev-01 — Can an AR model beat the naive forecast of US unemployment? (Rolling origins 2023-07 to 2026-06)\n\n## Question\nOver the 36 monthly forecast origins 2023-07-01 … 2026-06-01, does a simple autoregressive model forecast the US unemployment rate (FRED UNRATE, SA, %) better than the naive no-change forecast at horizons h = 1 and h = 3 months? We compare out-of-sample RMSE and MAE and report 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- `data/UNRATE.csv`: 945 data rows, 1948-01 to 2026-09. Dates are unique, in order and have no calendar gaps.\n- **Missing value:** 2025-10-01 has an empty field (no October 2025 observation). How it was handled:\n - The 2025-10 target is not scored. Two (origin, horizon) pairs are affected: origin 2025-09 at h=1 and origin 2025-07 at h=3. They stay in `forecasts.csv` with an empty `actual`. This leaves **35 evaluated pairs per horizon**.\n - In estimation, any regression row whose dependent or lagged difference involves 2025-10 is dropped.\n - At origin 2025-10, the naive forecast uses 2025-09 (4.4). The AR model iterates forward from 2025-09, adding one extra step.\n - At later origins, 2025-10 is linearly interpolated from 2025-09 and 2025-11, both dated ≤ origin. This is used only to build lag inputs and is never used as an estimation observation.\n- **COVID-19 outlier:** unemployment rose from 4.4 (2020-03) to 14.8 (2020-04). In the primary specification this outlier was kept, a choice made in advance. A sensitivity check is reported below.\n- Every error is computed on the final vintage, so none of these results are valid in real time.\n\n## Method\n- **Naive:** the forecast for all h is the latest observation dated ≤ origin.\n- **Model `AR2_diff`:** an AR(2) with intercept on monthly first differences Δy_t.\n - Estimated by OLS on an expanding window that starts at the beginning of the series and ends at the origin. It is re-estimated at every origin using only data dated ≤ origin.\n - h-step forecasts come from iterating the difference equation and adding the steps to the last level.\n - The specification was fixed before looking at any results, with no model selection. The last training window (origin 2026-06) has 935 regression observations.\n- **Uncertainty:** Diebold–Mariano test on squared-error loss differentials, with HAC variance (h−1 lags) and the Harvey–Leybourne–Newbold small-sample correction, compared against a t(n−1) distribution.\n- **Reproducibility:** `out/analysis.py` writes `results.json`, `forecasts.csv` and `supplementary.json`. It is deterministic and uses no random numbers.\n\n## Results\n| horizon | n | RMSE naive | RMSE AR2_diff | MAE naive | MAE AR2_diff | 1 − RMSE ratio |\n|---|---|---|---|---|---|---|\n| h=1 | 35 | 0.1171 | 0.1199 | 0.0914 | 0.0957 | −0.0235 |\n| h=3 | 35 | 0.1586 | 0.1598 | 0.1200 | 0.1213 | −0.0076 |\n\nThe AR model **does not beat** the naive forecast. It is slightly worse on both RMSE and MAE at both horizons: about 2.3% higher RMSE at h=1 and 0.8% at h=3. Its absolute error was smaller than the naive error at only 10 of 35 origins (28.6%) at each horizon.\n\nWhy: the estimated AR coefficients are close to zero. At the last origin they are intercept 0.0003, lag 1 0.037 and lag 2 −0.042, and they are almost the same at the first origin. The 2020 spike dominates the full-sample OLS fit and pushes them towards zero. As a result the model is close to a random walk, and its forecasts differ from the naive one by only a few thousandths of a point. The data are recorded to 0.1 pp.\n\n## Uncertainty\n- **h=1:** DM-HLN statistic = 2.50, two-sided p = 0.017. Positive means the model's loss is higher. The model's (small) inferiority is nominally significant at the 5% level.\n- **h=3:** statistic = 1.36, p = 0.18, so there is no significant difference.\n- Treat these tests with caution:\n - n = 35, and the two forecasts are nearly identical, so the loss differentials are tiny (mean 0.00065 pp² at h=1).\n - The DM test's assumptions are only approximately met.\n - The economic difference is negligible.\n- **Sensitivity (not pre-specified; report only):** I dropped 2020-03…2020-12 differences from estimation. The AR coefficients then become substantial (lag 1 0.08, lag 2 0.28 at the last origin), but out-of-sample performance gets *worse*:\n - h=1: RMSE 0.1186, relative improvement −0.013.\n - h=3: RMSE 0.1756, relative improvement −0.108.\n- The conclusion \"no improvement over naive\" therefore holds up under this alternative.\n\n## Limitations and open issues\n- The evaluation covers only 36 origins in one fairly calm, slowly rising unemployment period. The results may not carry over to recessions.\n- Final-vintage data were used, not real-time vintages. Seasonal-factor revisions may change the errors.\n- Only one model specification was evaluated. The primary model's near-zero coefficients largely reflect the influence of the COVID outlier, so the comparison is effectively \"random walk with tiny drift vs random walk\".\n- The missing 2025-10 value reduces each horizon to 35 evaluated pairs. Interpolating it as a lag input is a modelling choice.\n- The HAC/DM inference is approximate in small samples.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At h=1 the AR(2)-on-differences model does not beat the naive forecast: RMSE 0.1199 vs 0.1171 (relative improvement -0.023).\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"rel_improvement_rmse_h1\",\"n_evaluated_h1\"],\"status\":\"supported\",\"caveat\":\"35 evaluated pairs, final-vintage data; DM-HLN p=0.017 but economically negligible difference.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the model does not beat the naive forecast: RMSE 0.1598 vs 0.1586 (relative improvement -0.008), difference not statistically significant.\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"rel_improvement_rmse_h3\",\"n_evaluated_h3\"],\"status\":\"supported\",\"caveat\":\"DM-HLN p=0.18; small sample.\"},\n {\"id\":\"C3\",\"text\":\"MAE ranks the forecasts the same way: the naive forecast has lower MAE at both horizons.\",\"results_keys\":[\"mae_naive_h1\",\"mae_model_h1\",\"mae_naive_h3\",\"mae_model_h3\"],\"status\":\"supported\",\"caveat\":\"Differences are a few thousandths of a percentage point.\"},\n {\"id\":\"C4\",\"text\":\"The finding of no improvement holds up when COVID-2020 differences are excluded from estimation (model becomes worse, especially at h=3).\",\"results_keys\":[],\"status\":\"supported\",\"caveat\":\"Sensitivity analysis not pre-specified; numbers in out/supplementary.json, not in results.json.\"},\n {\"id\":\"C5\",\"text\":\"The 2025-10 missing observation is excluded from evaluation, leaving 35 evaluated pairs per horizon.\",\"results_keys\":[\"n_evaluated_h1\",\"n_evaluated_h3\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\"; ls out"}}]}} | |
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| 47 | +{"parentUuid":"6bd8fcc3-4b88-4235-96e5-b33f00af9da9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkfr4YUuRSaueQA8HRF","type":"message","role":"assistant","content":[{"type":"text","text":"The AR model I tested does not beat the naive (no-change) forecast. It is slightly worse at both the one-month and three-month horizons.\n\nI used an AR(2) on monthly changes in the unemployment rate, called `AR2_diff`. It is re-estimated by ordinary least squares at each of the 36 forecast origins (2023-07 to 2026-06), using only data up to that origin. I fixed this specification before looking at any results.\n\n| Horizon | Pairs scored | RMSE naive | RMSE AR2_diff | MAE naive | MAE AR2_diff | 1 − RMSE ratio |\n|---|---|---|---|---|---|---|\n| 1 month | 35 | 0.117 | 0.120 | 0.091 | 0.096 | −0.023 |\n| 3 months | 35 | 0.159 | 0.160 | 0.120 | 0.121 | −0.008 |\n\n- **How often the model wins:** its error was smaller than the naive one at only 10 of 35 origins at each horizon.\n- **Significance:** a Diebold–Mariano test (with a small-sample correction) finds the model's extra error nominally significant at one month (p = 0.017) but not at three months (p = 0.18). The size of the difference is negligible in practice: a few thousandths of a percentage point, while the data are recorded to 0.1.\n- **Why the model behaves like the naive forecast:** the 2020 COVID spike (4.4% to 14.8% in one month) dominates the fit and pushes the AR coefficients close to zero. The model ends up almost a random walk.\n- **Sensitivity check:** I added this after seeing the results, so it is not pre-specified. Leaving the 2020 changes out of estimation gives the model real coefficients, but it then does worse: −0.013 at one month and −0.108 at three months. The \"no improvement\" conclusion holds either way.\n\n**Limitations:**\n- **Missing month:** there is no value for October 2025. The two forecasts that target it are kept in the forecast file with an empty actual and left out of the scores, which is why each horizon has 35 scored pairs rather than 36. For later origins I filled October 2025 by interpolating between its neighbours, but only to supply lag values, never as an estimation data point.\n- **Scope:** the results are on the current revised data, so they are not real-time results. They cover a short, calm period, and only one model was tested.\n\nRunning `analysis.py` twice gave identical outputs.\n\nFiles are in `out/`:\n- `analysis.py`\n- `results.json`\n- `forecasts.csv` (144 rows)\n- `report.md`\n- `claims.json`\n- `supplementary.json` (test results, AR coefficients and the sensitivity check)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2867,"cache_read_input_tokens":13158,"output_tokens":925,"output_tokens_details":{"thinking_tokens":27},"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":2867},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXkfqmftjdBy63ptKsxS","requestRef":"23588bae-1dd9-4fb6-9463-7fc99fe71cc3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a374d1b1-ba6b-4299-a14a-2a98bf6e09ee","timestamp":"2026-10-30T04:00:32.085Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0024","sessionId":"9b17892a-17dd-4863-9fe8-7564f240c508","version":"2.1.314","gitBranch":"HEAD"} | |
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added
runs/pilot/RP0024/system_append.md
+16 −0
@@ -0,0 +1,16 @@ | ||
| 1 | +You are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace. | |
| 2 | + | |
| 3 | +Environment facts (identical for every task): | |
| 4 | +- 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. | |
| 5 | +- 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. | |
| 6 | +- 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. | |
| 7 | +- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions. | |
| 8 | + | |
| 9 | +Integrity rules (identical for every task): | |
| 10 | +- 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. | |
| 11 | +- 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). | |
| 12 | +- 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. | |
| 13 | +- Do not modify anything under `data/`. | |
| 14 | +- When you are done, end with a brief plain-text summary of what you produced and any limitations. | |
| 15 | + | |
| 16 | +Complete the task described in `TASK.md`. | |
added
runs/pilot/assignment_seed203.json
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@@ -0,0 +1,154 @@ | ||
| 1 | +[ | |
| 2 | + { | |
| 3 | + "artifact_manifest": null, | |
| 4 | + "block_id": "pilot-B01", | |
| 5 | + "budget": { | |
| 6 | + "max_budget_usd": 4.0, | |
| 7 | + "max_gate_cycles": 3, | |
| 8 | + "max_review_cycles": 1, | |
| 9 | + "max_turns_per_session": 80, | |
| 10 | + "min_continuation_usd": 0.25, | |
| 11 | + "wall_seconds": 1500 | |
| 12 | + }, | |
| 13 | + "client_version": null, | |
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| 18 | + "evaluation_receipt": null, | |
| 19 | + "fault": null, | |
| 20 | + "human_intervention_ids": [], | |
| 21 | + "model_reported": null, | |
| 22 | + "model_requested": "claude-opus-5-5", | |
| 23 | + "node": "", | |
| 24 | + "notes": "assignment seed 203", | |
| 25 | + "order_in_block": 0, | |
| 26 | + "protocol_hash": null, | |
| 27 | + "repeat_index": 0, | |
| 28 | + "run_id": "RP0021", | |
| 29 | + "screenshot_ids": [], | |
| 30 | + "session_ids": [], | |
| 31 | + "split": "pilot", | |
| 32 | + "started_at_utc": null, | |
| 33 | + "status": "not_started", | |
| 34 | + "task_hash": null, | |
| 35 | + "task_id": "F3-dev-01", | |
| 36 | + "terminal_reason": null, | |
| 37 | + "treatment": "C", | |
| 38 | + "usage": {} | |
| 39 | + }, | |
| 40 | + { | |
| 41 | + "artifact_manifest": null, | |
| 42 | + "block_id": "pilot-B01", | |
| 43 | + "budget": { | |
| 44 | + "max_budget_usd": 4.0, | |
| 45 | + "max_gate_cycles": 3, | |
| 46 | + "max_review_cycles": 1, | |
| 47 | + "max_turns_per_session": 80, | |
| 48 | + "min_continuation_usd": 0.25, | |
| 49 | + "wall_seconds": 1500 | |
| 50 | + }, | |
| 51 | + "client_version": null, | |
| 52 | + "code_commit": "55b92f9bcc679fb8bcbc12d51e0b6519e7f8b07d-dirty", | |
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| 59 | + "model_reported": null, | |
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| 62 | + "notes": "assignment seed 203", | |
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| 64 | + "protocol_hash": null, | |
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| 66 | + "run_id": "RP0022", | |
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| 83 | + "max_gate_cycles": 3, | |
| 84 | + "max_review_cycles": 1, | |
| 85 | + "max_turns_per_session": 80, | |
| 86 | + "min_continuation_usd": 0.25, | |
| 87 | + "wall_seconds": 1500 | |
| 88 | + }, | |
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| 115 | + }, | |
| 116 | + { | |
| 117 | + "artifact_manifest": null, | |
| 118 | + "block_id": "pilot-B01", | |
| 119 | + "budget": { | |
| 120 | + "max_budget_usd": 4.0, | |
| 121 | + "max_gate_cycles": 3, | |
| 122 | + "max_review_cycles": 1, | |
| 123 | + "max_turns_per_session": 80, | |
| 124 | + "min_continuation_usd": 0.25, | |
| 125 | + "wall_seconds": 1500 | |
| 126 | + }, | |
| 127 | + "client_version": null, | |
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| 135 | + "model_reported": null, | |
| 136 | + "model_requested": "claude-opus-5-5", | |
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| 138 | + "notes": "assignment seed 203", | |
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| 140 | + "protocol_hash": null, | |
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| 142 | + "run_id": "RP0024", | |
| 143 | + "screenshot_ids": [], | |
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| 146 | + "started_at_utc": null, | |
| 147 | + "status": "not_started", | |
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| 149 | + "task_id": "F3-dev-01", | |
| 150 | + "terminal_reason": null, | |
| 151 | + "treatment": "A", | |
| 152 | + "usage": {} | |
| 153 | + } | |
| 154 | +] | |
| \ No newline at end of file | ||
added
runs/pilot/pilot_batch2_controller.log
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modified
src/pgar/isolation.py
+1 −1
@@ -12,7 +12,7 @@ import json, os, posixpath, re, shlex | ||
| 12 | 12 | from .contracts import dir_manifest, sha256_file, sha256_text |
| 13 | 13 | from . import launcher |
| 14 | 14 | |
| 15 | −FORBIDDEN_NAME_FRAGMENTS = ("oracle", "evaluate.py", "REFERENCE", "validation", "wrong_out", "correct_out") | |
| 15 | +FORBIDDEN_NAME_FRAGMENTS = ("oracle", "evaluate.py", "REFERENCE.md", "validation", "wrong_out", "correct_out", "generator.py", "build.py") | |
| 16 | 16 | CTL_SUFFIX = ".ctl" |
| 17 | 17 | |
| 18 | 18 | def prepare_workspace(node: str, remote_root: str, run_id: str, bundle_dir: str) -> dict: |
modified
tasks/registry.csv
+2 −0
@@ -12,6 +12,8 @@ F2-dev-01,F2,dev,synthetic,no,no,independent_code,f5f69d92e1127a0e83eb59a88c9475 | ||
| 12 | 12 | F2-smoke-01,F2,dev,synthetic,no,no,independent_code,4e448a0612a060ef58ba2d66f1e56f2cc42c62959cec390b35a0d9ba1561cfef,09c41858f3e534a31189c09ef6d7b2f8f4e4f1a222c073a42c460f42508aeffa,no,2026-10-30T03:18:27.528616+00:00, |
| 13 | 13 | F3-conf-01,F3,conf,observed,yes,no,independent_code,3d3666ddb8c8b19c93f6cea1ef983368f52f9eb6cee4696a19ad82111cab35b8,4bdc81b9911fd8af6b7eb61e560a8aed267a9e03218b07adc8afa72728fabe45,no,2026-10-30T03:41:04.958026+00:00, |
| 14 | 14 | F3-conf-02,F3,conf,observed,yes,no,independent_code,72bd0760441017e0cd4b184dcd5b514b61e577d851e895d582c6a245515a2603,eedec7d03dd243b4193827a51afdac386dae16fd206fe289174b0f9fb316ba82,no,2026-10-30T03:44:51.999144+00:00, |
| 15 | +F3-conf-03,F3,conf,observed,yes,no,independent_code,f3e5a971802b57e92ae4fad13bebd464e6a5dc2f755df9aa9420e2b515763cf8,273d2af8cf5c5aa4208c6c027b99b3bb991a50206b54c5d3c1a212dd610f3935,no,2026-10-30T03:52:35.811207+00:00, | |
| 16 | +F3-conf-04,F3,conf,observed,yes,no,independent_code,60423555f5292067aebaa897310813f5bbbca474e63d2256e1cb8a67bbad2a1a,e4d1358d226ad618b91a80bf40bd5faca84e88da6cdfcca828d5c14cf5fc5e42,no,2026-10-30T03:55:58.429558+00:00, | |
| 15 | 17 | F3-dev-01,F3,dev,observed,yes,no,independent_code,47db3ab9ce0210fa4891fd00875f639ab089a3dc9d79145639ccd75e5ac72b99,3c7498c1f70346124c8233d46ab30a2eef957ec2e3437b34674287f93b43016a,no,2026-10-30T03:36:32.922288+00:00, |
| 16 | 18 | F4-conf-01,F4,conf,observed,yes,yes,designer_extraction,4f8e4fcee6110ddaaf15267a53dbbcdec8b1b0de7e9ec7c0db85d32ef7819b57,6b1f40035072ca62d8892c68a75946607da2e2816432ecba6604ca0f0bef38e0,no,2026-10-30T03:45:06.348580+00:00, |
| 17 | 19 | F4-conf-02,F4,conf,observed,yes,yes,designer_extraction,78a0db5fd3108905cf14cda85fdf7ce155c29c3435c3cb69a8a427a52fc6d6ec,89c2cd2985479ab67db1b9926dbaf37cc5f5a999132d26128e8bcec9aebbac76,no,2026-10-30T03:45:06.399097+00:00, |
| 18 | 20 | |