1\footnotesize2\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\toprule5Task & Split & Data & Real & Src & Oracle & Null & Question (abridged) \\6\midrule7\endfirsthead8\toprule9Task & Split & Data & Real & Src & Oracle & Null & Question (abridged) \\10\midrule11\endhead12F1-conf-01 & conf & observed & yes & no & independent code & no & 12-month growth rate of CPIAUCSL for 2001-01 \ldots{} 2026-09 using, for each month, the most recent vintage available; estimands = per-month growth, window \\13F1-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 \\14F1-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 \\15F1-conf-04 & conf & synthetic & no & no & independent code & yes & summarise a property-sales export (count, price sum and mean, median price per m$^2$) and reconcile it to the provider's control totals; declare feasibil \\16F2-conf-01 & conf & synthetic & no & no & independent code & yes & effect of a school-level programme on student scores (estimand $\tau$ = student-weighted difference in means, OLS of score on treated) with uncertainty val \\17F2-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) \\18F2-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 \\19F2-conf-04 & conf & observed & yes & no & independent code & no & Okun coefficient $\beta$ in $\Delta$u\_t = $\alpha$ + $\beta$ g\_t + e\_t on U.S. quarterly data (u = quarterly mean of monthly UNRATE, g = 100$\cdot$$\Delta$ln GDPC1, 1960Q1--2019Q4, n = 240), \\20F3-conf-01 & conf & observed & yes & no & independent code & no & rolling-origin forecasts (h = 1, 3; 48 month-end origins 2022-05 \ldots{} 2026-04) of Canadian monthly real GDP year-over-year growth using only information \\21F3-conf-02 & conf & observed & yes & no & independent code & no & real-time forecasts of US real GDP growth (SAAR \%) at h = 1 and h = 2 quarters from 8 vintage-dated origins (2024-01-31 \ldots{} 2025-12-31); at each origin \\22F3-conf-03 & conf & observed & yes & no & independent code & no & one-month-ahead rolling-origin forecasts of US industrial production growth (60 origins, 2021-09 \ldots{} 2026-08) with a fully specified ridge regression ($\lambda$ \\23F3-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 \ldots{} 2025Q3); seasonal-naive baselin \\24F4-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 \\25F4-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 \\26F4-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 \\27F4-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. \\28F5-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 \\29F5-conf-02 & conf & synthetic & no & no & analytical & no & value of the Marriott--Pope/Kendall first-order bias approximation --(1+3$\rho$)/n for the OLS AR(1) coefficient (with intercept) at the design point, versus \\30F5-conf-03 & conf & synthetic & no & no & analytical & yes & prove or refute (A) "X and Y each normal $\Rightarrow$ X + Y normal (degenerate allowed)" and (B) "X and Y independent and each normal $\Rightarrow$ X + Y normal"; a refutati \\31F5-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 \\32F6-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 \\33F6-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). \\34F6-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 \\35F6-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 \\36F1-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 \\37F2-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 = $\beta$ in y\_it = a\_i + g\_t + \\38F2-smoke-01 & dev & synthetic & no & no & independent code & no & OLS slope of y on x with firm-clustered uncertainty; estimand = linear-projection slope $\beta$. \\39F3-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 \ldots{} 2026-06); naive no \\40F4-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 \\41F5-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 \\42F6-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 \\43T1-forecast & transfer & observed & yes & no & independent code & no & real-time rolling-origin forecasts of US retail-sales year-over-year growth (RSAFS) at h = 1 and 3 months from ten ALFRED vintages (2025-10-31 \ldots{} 2026- \\44T2-simulation & transfer & synthetic & no & no & independent code & no & finite-sample coverage and variance bias of three cluster-robust 95\% intervals for a mean (CR0 + z, CR1 + t(G--1), wild cluster bootstrap-t with Radema \\45T3-synthesis & transfer & observed & yes & yes & designer extraction & no & does generative-AI assistance shorten workers' task-completion time? Five CC BY arXiv primary studies (Google enterprise RCT; METR field RCT; Microsof \\46\bottomrule47\end{longtable}48\normalsize49