Case study · GPT-5.5 + DeepAgents

Finding the month variable. Reproducing the result.

We use one actual InferenceNet run to examine the task, the data and where GPT-5.5’s first program went wrong. Then we follow the same model with DeepAgents to see how execution feedback helps it reproduce the result.

01 / Task 0011

Reproduce Table 2, Column 1

The paper studies the relationship between socio-economic conflict and economic policy uncertainty in Spain. We ask GPT-5.5 to recover the coefficient for socio-economic conflict, together with its standard error and p-value.

Journal of Economic History

Economic uncertainty and divisive politics: evidence from the Dos Españas

What data does the model receive?

See this task on the Data page →

The input is data_np.dta, a Stata table with 4,550 observations and 38 columns, spanning 1821–2010. Each row is an observation. The regression variables listed below are only a few of its columns; the program can read the whole file.

4,550Rows in the full file
984Rows from 1905–1945
977Rows used after excluding 7 with missing values

Alongside the outcome and explanatory variables, year identifies the year and tid indexes the month. This page shows the relevant column structure; the analysis runs on the original observations in the file.

The task given to the model

Use Python to run OLS on the supplied data. Restrict the sample to 1905–1945, include the three specified controls, and cluster standard errors by month. Save the three requested statistics in a JSON file.

The difficult detail in this case: the instructions say “by month”, but do not name the month-index column.

Data file
data_np.dta
Outcome
EPU0month_simsn_w
Target regressor
Wscmonth_simsn_w
Controls
Wnamonth_simsn_w
Wmimonth_simsn_w
Wremonth_simsn_w
Time variables
year · tid

The model sees these instructions and the data path. It can inspect the data by running code; reference answers and the original Stata program remain with the evaluator.

02 / Model

The first program stops at the month column

GPT-5.5 generates one complete program. It reads the data and finds the year column, then tries familiar names for the monthly clustering variable. The recorded code below shows where that assumption fails.

Generated program · excerpt
cluster_var = None
for candidate in ["month", "Month", "mdate", "Mdate", "date", "Date", "time", "Time"]:
    if candidate in df.columns:
        cluster_var = candidate
        break

if cluster_var is None:
    datetime_cols = [col for col in df.columns if np.issubdtype(df[col].dtype, np.datetime64)]
    if datetime_cols:
        cluster_var = datetime_cols[0]
    else:
        raise ValueError("Could not identify a month/date variable for clustering.")

Execution returned

Recorded error
Traceback (most recent call last):
  File "/code/main.py", line 71, in <module>
    raise ValueError("Could not identify a month/date variable for clustering.")
ValueError: Could not identify a month/date variable for clustering.

The dataset has no month or date column and no datetime-typed column. Its month index is named tid, so none of the program’s candidates matches.

The run ends here: no coefficient, standard error or p-value is produced. This configuration has no feedback round for the model to revise the program.

03 / Model + Harness

Follow the same task through DeepAgents

The same GPT-5.5 model receives the same task in a separate run, now with DeepAgents and the run_python tool. The model chooses what code to run. The harness executes it and brings the output back into the next model call.

  1. 01

    Model call 1

    Open the file and inspect the columns

    Before fitting a regression, the model asks Python to print the data’s shape, column names and types.

    Model → run_python · code excerpt
    df=pd.read_stata(path, convert_categoricals=False)
print(df.shape)
print(df.columns.tolist())
print(df.dtypes.head(50))

    Harness → model · recorded output

    4,550rows38columns

    Columns include year, tid and np, along with all the regression variables. There is no month column.

    yeartidnp

    Next question: which of these columns identifies the month? The next call investigates tid.

  2. 02

    Model call 2

    Connect “by month” to the tid column

    The model groups the data by tid and year, restricts the time window, and checks missing values. The output shows two rows for each tid.

    Model → run_python · code excerpt
    print(df.groupby('tid').size().value_counts().head())
print(df.groupby(['tid','year']).size().head(20))
sub=df[(df.year>=1905)&(df.year<=1945)]
print('sub shape', sub.shape, 'unique tid', sub.tid.nunique(), 'rows/tid', sub.groupby('tid').size().value_counts())

    Harness → model · findings from the output

    Selected years
    1905–1945
    Distinct tid values
    492
    Rows in that period
    984
    Rows with missing regressors
    7

    41 years × 12 months = 492. Together with the year grouping, this identifies tid as the month index to use for clustering.

  3. 03

    Model call 3

    Run the specified regression on the right sample

    The model now restricts the sample, drops missing values, adds the controls, and runs OLS with standard errors clustered by tid.

    Model → run_python · code excerpt
    sub=df[(df.year>=1905)&(df.year<=1945)].copy()
reg=sub[[y]+xs+['tid','np']].apply(pd.to_numeric, errors='coerce').dropna()
X=sm.add_constant(reg[xs])
model=sm.OLS(reg[y],X).fit(cov_type='cluster', cov_kwds={'groups':reg['tid']})
print(model.summary())

    Harness → model · regression output

    Observations used
    977
    Target coefficient
    0.272736…
    Clustered standard error
    0.054225…

    984 rows minus 7 with missing values gives 977 observations. The model also runs variants of the statistical inference settings in this call.

    The model now has execution evidence for the data preparation and regression. It can assemble the final analysis program.

  4. 04

    Model call 4

    Submit the complete program for a fresh run

    The fourth model call returns the final Python program. It uses tid for clustering and use_t=True for the final p-value calculation.

    Final program · regression excerpt
    model = sm.OLS(y, X).fit(
    cov_type="cluster",
    cov_kwds={"groups": df_reg[cluster_var]},
    use_t=True
)

    Harness → evaluator

    The tool loop ends. The final program reads the original file again, uses tid for monthly clustering, and writes only the target coefficient, standard error and p-value.

    The evaluator runs this program in a fresh environment, then compares its output with the hidden reference.

    The harness connects code execution and feedback across calls. The model still makes the analysis decisions; the evaluator checks the final result independently.

Four model calls, three exploratory tool executions, then one independent final execution. Reference values are never returned during the tool loop.

04 / Result

From failed execution to successful replication

The evaluator reruns the final program from scratch, then compares its output with the hidden reference values. In this case, all three statistics closely match.

GPT-5.5 · Model

Execution failed

The month variable was not identified. No statistical result was returned.

1 model call

GPT-5.5 + DeepAgents

Result reproduced

Coefficient
0.272736
Standard error
0.054225
p-value
6.899 × 10⁻⁷
4 model calls · 3 tool executions
Final execution compared with the reference · values rounded for display
StatisticGPT-5.5 + DeepAgentsReference
Coefficient0.27273630750.2727363075
Standard error0.05422470170.0542247017
p-value6.899021794 × 10⁻⁷6.899021794 × 10⁻⁷

At full precision, the differences are floating-point rounding. All three statistics pass this run’s full-replication thresholds; exact values remain in the original record below.

These are two separate runs of the same task. The DeepAgents run starts from the task instructions, with its own interaction budget; it does not continue from the failed program.

Why did the outcome change?

The single-generation program committed to assumed column names before the model had seen the file’s structure. With DeepAgents, tool outputs let GPT-5.5 inspect the real columns, connect tid to the monthly clustering requirement, and check the regression before submitting its final program.

The harness makes this interaction possible by running tools and returning their output. The model interprets that feedback and writes the analysis; the final replay verifies the result.

One example, many research tasks

This recorded GPT-5.5 case explains how an agent works. Explore other tasks in the benchmark, or compare current models and harnesses across 1,000 tasks on the leaderboard.

Explore more tasks →Compare on the leaderboard →
Run details, original code and execution trace

Recorded on 13 Sep 2026 using GPT-5.5 and DeepAgents 0.7.13 on Selected_1000 task 0011, dataset revision 59f9512.

The Model run took 20.0 seconds. The DeepAgents run took 74.9 seconds, with limits of six model calls and four tool executions. These configurations use different resource budgets.

Full replication in this recorded case requires the coefficient and standard error to be within 1% relative error, and the p-value within 0.01 absolute error (local-paper-v1). Reference values are used only after final execution.

01 / Task 0011

One column of one published table

A task is a single reported estimate. The model receives the instruction below and a path to the data; everything else in the task package stays on the scorer side.

  • Task 0011 · OLS Real run

    Economic uncertainty and divisive politics: evidence from the Dos Españas

    Economic uncertainty and divisive politics (the 'two Spains'): how does socio-economic conflict relate to economic policy uncertainty, 1905–1945? OLS with month-clustered standard errors.

    Journal
    Journal of Economic History
    Notation
    Table 2, Column 1, Row Socioec. conflict
    Method
    OLS
    Outcome y
    EPU0month_simsn_w
    Treatment x
    Wscmonth_simsn_w
    Controls
    Wnamonth_simsn_w · Wmimonth_simsn_w · Wremonth_simsn_w
    Requirement
    Replicate PDF Table 2 column 1: OLS of EPU on the four political division variables for 1905-1945, clustering standard errors by month.
    Data file
    0011/data/data_np.dta · Stata, mounted read-only under /data
    Tags
    • cluster standard error
    Selected_1000 · task 0011Open task_row.json on Hugging Face
model-visible instructionverbatim · both arms receive the same text
use python language to compute the following task: Please use the OLS method to compute the effect of Wscmonth_simsn_w on EPU0month_simsn_w. You also need to control the following control variables: Wnamonth_simsn_w, Wmimonth_simsn_w, Wremonth_simsn_w. Besides, you need to consider the following requirements: Replicate PDF Table 2 column 1: OLS of EPU on the four political division variables for 1905-1945, clustering standard errors by month.. You could load the corresponding data from /data/0011/data/data_np.dta. At the end of the program, please print the coefficient, standard error, p value of the effect in a json format like {"coefficient": 0.1, "standard_error": 0.1, "p_value": 0.1}, and output the json string as json file to /output/11_result.json

Strict requirements:
1. Return ```python your_code_here ``` with NO other texts.
2. Include all necessary imports.
3. The code must save the final result to the required json path.
4. The final result json must contain ONLY:
    {
    "coefficient": ...,
    "standard_error": ...,
    "p_value": ...
    }
5. Do NOT save params, pvalues, rsquared, nobs, or any extra fields in the output JSON file.
6. The coefficient, standard_error, and p_value must correspond to the requested effect of x on y.
7. If you use statsmodels, extract the coefficient / standard error / p-value for the target regressor only.
8. If fixed effects are needed, correctly handle categorical variables instead of passing object dtype directly into OLS.
9. Before regression, ensure variables used in regression are numeric where needed.

your code:

Rendered by the evaluator's task loader from task_row.json: method, x, y, controls, the requirement sentence, the /data path and the output contract. Leading indentation trimmed; wording unchanged.

What the model does not see

The reference answer. The triplet (β, SE, p) is held by the scorer and compared only after the final program has been replayed.

The reference Stata program. The task package carries it as do/task_<id>.do; it is never shown to the model.

The data itself, until it runs code. The instruction names a path, not a schema. Arm A writes its program blind; Arm B sees only what its own run_python calls print.

02 / Arm A

Single generation: one call, one guess

The instruction goes to the model once. Whatever comes back is executed once in Docker and scored. Nothing is inspected, nothing is retried.

  • 1Model callno tools
  • 0Tool callsrun_python not exposed
  • 1,788Tokens418 in · 1,370 out
  • 20.0sWall timeas recorded for this run

The program is careful in the abstract and wrong in the particular. It searches for a year column, then for a clustering column among names such as month, mdate and date, then for any datetime column. The data has none of these: the requirement says clustering by month, but the monthly index in data_np.dta is called tid, a name the model had never seen when it wrote the code. No candidate matched, the program raised ValueError at line 71, and the task was scored as a failed execution (final_execution_failed).

main.py · Arm Asingle-generation program · 97 lines 
import osimport jsonimport numpy as npimport pandas as pdimport statsmodels.api as smdata_path = "/data/0011/data/data_np.dta"output_path = "/output/11_result.json"df = pd.read_stata(data_path, convert_dates=True)y_var = "EPU0month_simsn_w"target_var = "Wscmonth_simsn_w"control_vars = ["Wnamonth_simsn_w", "Wmimonth_simsn_w", "Wremonth_simsn_w"]x_vars = [target_var] + control_vars# Ensure regression variables are numericfor var in [y_var] + x_vars:    df[var] = pd.to_numeric(df[var], errors="coerce")# Identify / construct year variable for 1905-1945 restrictionyear_var = Nonefor candidate in ["year", "Year", "yr", "YR"]:    if candidate in df.columns:        year_var = candidate        breakif year_var is None:    for col in df.columns:        if np.issubdtype(df[col].dtype, np.datetime64):            df["_reg_year"] = df[col].dt.year            year_var = "_reg_year"            breakif year_var is None:    for candidate in ["date", "Date", "month", "Month", "mdate", "Mdate", "time", "Time"]:        if candidate in df.columns:            s = df[candidate]            if np.issubdtype(s.dtype, np.number):                vals = pd.to_numeric(s, errors="coerce")                # Stata monthly dates are months since 1960m1                converted_year = np.floor(vals / 12 + 1960).astype("float")                if ((converted_year >= 1800) & (converted_year <= 2100)).any():                    df["_reg_year"] = converted_year                    year_var = "_reg_year"                    break            else:                dates = pd.to_datetime(s, errors="coerce")                if dates.notna().any():                    df["_reg_year"] = dates.dt.year                    year_var = "_reg_year"                    breakif year_var is None:    raise ValueError("Could not identify a year/date variable for restricting the sample to 1905-1945.")df = df[(df[year_var] >= 1905) & (df[year_var] <= 1945)].copy()# Identify clustering variable: cluster standard errors by monthcluster_var = Nonefor candidate in ["month", "Month", "mdate", "Mdate", "date", "Date", "time", "Time"]:    if candidate in df.columns:        cluster_var = candidate        breakif cluster_var is None:    datetime_cols = [col for col in df.columns if np.issubdtype(df[col].dtype, np.datetime64)]    if datetime_cols:        cluster_var = datetime_cols[0]    else:        raise ValueError("Could not identify a month/date variable for clustering.")# Prepare estimation samplereg_cols = [y_var] + x_vars + [cluster_var]reg_df = df[reg_cols].dropna().copy()# Create cluster groupscluster_data = reg_df[cluster_var]if np.issubdtype(cluster_data.dtype, np.datetime64):    groups = cluster_data.dt.to_period("M").astype(str)else:    groups = cluster_dataX = sm.add_constant(reg_df[x_vars], has_constant="add")y = reg_df[y_var]model = sm.OLS(y, X).fit(cov_type="cluster", cov_kwds={"groups": groups})result = {    "coefficient": float(model.params[target_var]),    "standard_error": float(model.bse[target_var]),    "p_value": float(model.pvalues[target_var])}os.makedirs(os.path.dirname(output_path), exist_ok=True)with open(output_path, "w") as f:    json.dump(result, f)
stderr · tailexecution failed · scored as failure
Traceback (most recent call last):
  File "/code/main.py", line 71, in <module>
    raise ValueError("Could not identify a month/date variable for clustering.")
ValueError: Could not identify a month/date variable for clustering.

03 / Arm B

Deep Agents harness: look, probe, fit, answer

Same model, same instruction, plus one execution tool. Each run_python call runs a complete program in a fresh container and returns only stdout, stderr and an exit code. The recorded run below is shown unedited except for redacted data rows and the marked elisions in the stdout excerpts.

  • 4/ 6Model calls usedbudget 6 per task
  • 3/ 4Tool calls usedbudget 4 executions
  • 24,476Tokens21,039 in · 3,437 out
  • 74.9sWall timeof 600 s per task

Internal study

This trajectory is one task from the internal paired study (GPT-5.5, Responses API, reasoning high; run service-three-models-syntaxfix-20260913, executed 13 Sep 2026). It is exploratory material, not a leaderboard result. Raw data rows printed by the agent are omitted and long stdout is excerpted with elisions marked; code, prompts and results are unedited.

  1. Model

    Opens the declared file and prints shape, columns and dtypes

    call 1 · run_python

    Before writing any regression the model checks that the file exists and how large it is, then prints the shape, the 38 column names and the dtypes. Nothing is assumed about the schema yet.

    run_python · code8 lines
    import os, pandas as pdpath='/data/0011/data/data_np.dta'print(os.path.exists(path), os.path.getsize(path) if os.path.exists(path) else None)df=pd.read_stata(path, convert_categoricals=False)print(df.shape)print(df.columns.tolist())print(df.dtypes.head(50))print(df.head())
  2. Harness

    Returns stdout from a fresh container

    exit 0 · 0.714 s

    4,550 rows × 38 columns, all numeric. There is a year, a tid and an np column, and every regression variable the instruction names is present, but no column is named month, date or time, and none has a datetime dtype.

    stdout · excerptredacted · elisions marked · long lines wrapped 
    True 753040
(4550, 38)
['np', 'year', 'election', 'polity2', 'gov_change', 'iirep', 'civilwar', 'franco', 'censorshipn', 'v2mecenefm', 'v2meharjrn', 'v2meslfcen', 'tid', 'EPUmonth_simsn_w', 'EPr0Umonth_simsn_w', 'EPU0month_simsn_w', 'Wscmonth_simsn_w', 'Wnamonth_simsn_w', 'Wremonth_simsn_w', 'Wmimonth_simsn_w', 'Wsc2month_simsn_w', 'Wna2month_simsn_w', 'Wre2month_simsn_w', 'Wmi2month_simsn_w', 'Wsc4month_simsn_w', 'Wna4month_simsn_w', 'Wre4month_simsn_w', 'Wmi4month_simsn_w', 'Wag4month_simsn_w', 'Wsc6month_simsn_w', 'Wna6month_simsn_w', 'Wre6month_simsn_w', 'Wmi6month_simsn_w', 'ag_top_2n_w', 'ag_top_28n_w', 'v2mecenefm_np_1', 'v2meharjrn_np_1', 'v2meslfcen_np_1']
np                       int8
year                  float32
election              float32
… [9 dtype lines omitted]
tid                   float32
EPUmonth_simsn_w      float32
EPr0Umonth_simsn_w    float32
EPU0month_simsn_w     float32
Wscmonth_simsn_w      float32
Wnamonth_simsn_w      float32
Wremonth_simsn_w      float32
Wmimonth_simsn_w      float32
… [18 dtype lines omitted]
dtype: object
… [5 raw data rows omitted from this public page]
  3. Model

    Probes the panel structure: tid, year, np

    call 2 · run_python

    The model prints the year range, how many rows each tid has, the tid × year grouping, the 1905–1945 window and the missing-value counts of the five regression variables.

    run_python · code17 lines
    import pandas as pd, numpy as nppath='/data/0011/data/data_np.dta'df=pd.read_stata(path, convert_categoricals=False)print(df[['tid','year','np']].head(30).to_string())print(df[['tid','year','np']].tail(30).to_string())print(df['year'].min(), df['year'].max(), df['tid'].min(), df['tid'].max())print('unique years', df['year'].nunique(), sorted(df['year'].dropna().unique())[:5], sorted(df['year'].dropna().unique())[-5:])print('rows per tid sample')print(df.groupby('tid').size().value_counts().head())print(df.groupby(['tid','year']).size().head(20))sub=df[(df.year>=1905)&(df.year<=1945)]print('sub shape', sub.shape, 'unique tid', sub.tid.nunique(), 'rows/tid', sub.groupby('tid').size().value_counts())print(sub[['tid','year','np']].head(20).to_string())print(sub[['tid','year','np']].tail(20).to_string())vars=['EPU0month_simsn_w','Wscmonth_simsn_w','Wnamonth_simsn_w','Wmimonth_simsn_w','Wremonth_simsn_w','tid']print(sub[vars].isna().sum())print(sub[vars].describe().T)
  4. Harness

    Returns the structure of the panel

    exit 0 · 0.588 s

    Years run 1821–2010 and every tid has exactly two rows (np = 1, 2). The 1905–1945 window holds 984 rows over 492 distinct tid values — twelve per year, so tid is the month index the requirement refers to. Seven rows in the window have missing regressors.

    stdout · excerptredacted · elisions marked · long lines wrapped 
    1821.0 2010.0 1.0 2275.0
unique years 190 [np.float32(1821.0), np.float32(1822.0), np.float32(1823.0), np.float32(1824.0), np.float32(1825.0)] [np.float32(2006.0), np.float32(2007.0), np.float32(2008.0), np.float32(2009.0), np.float32(2010.0)]
rows per tid sample
2    2275
Name: count, dtype: int64
tid   year
1.0   1821.0    2
2.0   1821.0    2
… [17 tid × year rows omitted]
20.0  1823.0    2
dtype: int64
sub shape (984, 38) unique tid 492 rows/tid 2    492
Name: count, dtype: int64
EPU0month_simsn_w    7
Wscmonth_simsn_w     7
Wnamonth_simsn_w     7
Wmimonth_simsn_w     7
Wremonth_simsn_w     7
tid                  0
dtype: int64
                   count          mean  ...          75%          max
EPU0month_simsn_w  977.0  0.000000e+00  ...     0.381628     5.875330
… [5 describe rows omitted]
… [100 raw data rows omitted from this public page]
  5. Model

    Fits the OLS clustered by tid and explores variants

    call 3 · run_python

    Restricts to 1905–1945, clusters by tid and prints the full summary; then compares use_t settings, computes the small-sample correction factor and tries an np fixed-effect variant, all in one execution.

    run_python · code34 lines · shown in full
    import pandas as pd, numpy as npimport statsmodels.api as smfrom scipy import statspath='/data/0011/data/data_np.dta'df=pd.read_stata(path, convert_categoricals=False)y='EPU0month_simsn_w'; xs=['Wscmonth_simsn_w','Wnamonth_simsn_w','Wmimonth_simsn_w','Wremonth_simsn_w']sub=df[(df.year>=1905)&(df.year<=1945)].copy()reg=sub[[y]+xs+['tid','np']].apply(pd.to_numeric, errors='coerce').dropna()X=sm.add_constant(reg[xs])model=sm.OLS(reg[y],X).fit(cov_type='cluster', cov_kwds={'groups':reg['tid']})print(model.summary())print('params', model.params.to_dict())print('bse', model.bse.to_dict())print('p', model.pvalues.to_dict())print('use_t', model.use_t, 'df_resid', model.df_resid)# get_robustcov_results with use_t trueres=sm.OLS(reg[y],X).fit()for use_t in [None,True,False]:    if use_t is None:        r=res.get_robustcov_results(cov_type='cluster', groups=reg['tid'])        label='default'    else:        r=res.get_robustcov_results(cov_type='cluster', groups=reg['tid'], use_t=use_t)        label=f'use_t {use_t}'    names=r.model.exog_names; i=names.index('Wscmonth_simsn_w')    print(label, r.params[i], r.bse[i], r.tvalues[i], r.pvalues[i], 'use_t', r.use_t)# manual Stata correction? N=len(reg); k=X.shape[1]; G=reg['tid'].nunique()print('N k G', N,k,G, 'corr', (G/(G-1))*((N-1)/(N-k)))# np fixed effect include dummy np=2X2=sm.add_constant(pd.concat([reg[xs], pd.get_dummies(reg['np'].astype(int), prefix='np', drop_first=True, dtype=float)], axis=1))for dfcorr in [True,False]:    r=sm.OLS(reg[y],X2).fit(cov_type='cluster', cov_kwds={'groups':reg['tid'], 'use_correction':True, 'df_correction':dfcorr}, use_t=True)    print('npFE dfcorr', dfcorr, r.params['Wscmonth_simsn_w'], r.bse['Wscmonth_simsn_w'], r.pvalues['Wscmonth_simsn_w'], r.use_t)
  6. Harness

    Returns the estimates

    exit 0 · 1.121 s

    Coefficient 0.2727 with clustered standard error 0.054 on 977 observations. The default cluster fit reports a normal-based p-value of 4.91 × 10⁻⁷; the final program switches to use_t=True, which is the p-value the reference records. The recorded stdout is truncated before the variant lines.

    stdout · excerptredacted · elisions marked 
                                OLS Regression Results
==============================================================================
Dep. Variable:      EPU0month_simsn_w   R-squared:                       0.225
Model:                            OLS   Adj. R-squared:                  0.222
… [4 summary header lines omitted: Method, Date, Time, Df Model]
No. Observations:                 977   AIC:                             2533.
Df Residuals:                     972   BIC:                             2557.
Covariance Type:              cluster
====================================================================================
                       coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------------
const              3.49e-10      0.033   1.07e-08      1.000      -0.064       0.064
Wscmonth_simsn_w     0.2727      0.054      5.030      0.000       0.166       0.379
Wnamonth_simsn_w     0.2170      0.048      4.477      0.000       0.122       0.312
Wmimonth_simsn_w    -0.0031      0.035     -0.088      0.930      -0.071       0.065
Wremonth_simsn_w     0.1676      0.034      4.961      0.000       0.101       0.234
… [Omnibus / Durbin-Watson block omitted]
Notes:
[1] Standard Errors are robust to cluster correlation (cluster)
params {'const': 3.4896700067822897e-10, 'Wscmonth_simsn_w': 0.27273630753072026, …}
bse {'const': 0.03269790115695885, 'Wscmonth_simsn_w': 0.054224701707830926, …}
p {'const': 0.9999999914846099, 'Wscmonth_simsn_w': 4.911379939713391e-07, …}
… [stdout truncated in the recorded trace: use_t variants and np fixed-effect variant not preserved]
  7. Model

    Returns exactly one fenced program

    call 4 · final answer

    The final program hard-codes what the exploration established: read with convert_categoricals=False, coerce to numeric, restrict to 1905–1945, cluster by tid with use_t=True, write only the three required fields to /output/11_result.json. No text outside the fence.

    main.py · Arm Bfinal program · 42 lines · shown in full
    import osimport jsonimport pandas as pdimport statsmodels.api as smdata_path = "/data/0011/data/data_np.dta"output_path = "/output/11_result.json"target_y = "EPU0month_simsn_w"target_x = "Wscmonth_simsn_w"controls = ["Wnamonth_simsn_w", "Wmimonth_simsn_w", "Wremonth_simsn_w"]cluster_var = "tid"df = pd.read_stata(data_path, convert_categoricals=False)df["year"] = pd.to_numeric(df["year"], errors="coerce")vars_needed = [target_y, target_x] + controls + [cluster_var]for var in vars_needed:    df[var] = pd.to_numeric(df[var], errors="coerce")df_reg = df[(df["year"] >= 1905) & (df["year"] <= 1945)][vars_needed].dropna().copy()X = sm.add_constant(df_reg[[target_x] + controls], has_constant="add")y = df_reg[target_y]model = sm.OLS(y, X).fit(    cov_type="cluster",    cov_kwds={"groups": df_reg[cluster_var]},    use_t=True)result = {    "coefficient": float(model.params[target_x]),    "standard_error": float(model.bse[target_x]),    "p_value": float(model.pvalues[target_x])}os.makedirs(os.path.dirname(output_path), exist_ok=True)with open(output_path, "w") as f:    json.dump(result, f)print(json.dumps(result))

    This file, and nothing from the three exploration calls, is what the harness replays and scores.

04 / Scoring

Clean replay, then the comparison

The final program is run again from scratch in a new container. Only then does the scorer open the reference triplet it has held all along.

Task 0011, Arm B: clean-replay prediction against the scorer's reference.
StatisticPrediction (replay)Reference (scorer)|Δ|Relative errorPerfect threshold
Coefficient β0.272736307530720260.27273630753072085.6 × 10⁻¹⁶2.0 × 10⁻¹⁵relative error ≤ 1 %
Standard error0.0542247017078309260.05422470170783092.8 × 10⁻¹⁷5.1 × 10⁻¹⁶relative error ≤ 1 %
p-value6.899021793750105e-076.89902179375e-071.0 × 10⁻²⁰1.5 × 10⁻¹⁴|Δp| ≤ 0.01 absolute

Relative error = |prediction − reference| / |reference|; the p-value is additionally compared on an absolute scale. Internal diagnostic profile local-paper-v1, not the public leaderboard scorer. Both values are shown at the precision recorded in the run; the differences are floating-point rounding. Reference values appear on this site only for tasks 0001 and 0011.

The five internal flags for this run, profile local-paper-v1.
FlagDefinitionResult
perfectβ, SE within 1% and p within 0.01true
partialβ, SE relative errors < 5%; no p gate; includes perfecttrue
coefficient_onlyβ within 5%true
directionsame signtrue
significancesame band and correct directiontrue
Arm A on the same task produced no prediction (final_execution_failed), so none of the five flags can be true and the task stays in the denominator.
Replay
Fresh Docker container · /data read-only · result JSON to /output · 90 s · no network
Execution
succeeded · exit 0 · /output/11_result.json well-formed
Scoring
scored · reference compared after replay, never during generation
Profile
local-paper-v1 (internal diagnostic): perfect = coefficient, SE within 1% relative and p within 0.01 absolute; partial = coefficient and SE relative errors < 5%, no p gate (includes perfect); coefficient_only; direction; significance category (with direction gate).

  • What this run shows · 1

    Look before you regress

    Arm A guessed column names it had never seen and raised at line 71. Arm B spent its first call on shape, columns and dtypes — 0.7 s — and never had to guess.

  • What this run shows · 2

    Map the requirement text to the data

    “Clustering by month” names a concept, not a column. Two rows per tid, 492 of them across 41 years: tid is the month. The second call exists to make that mapping explicit.

  • What this run shows · 3

    Nothing persists between calls

    Every run_python starts a new container, so each probe re-reads the file from scratch, and the final program must stand alone — which is exactly what the clean replay checks.