2026-09-06 17:20 UTC · seatbelt sandbox · git unknown

Data-quality audit of a jet table

t1-missing-audittier 1ReActQwen3-8B (gariyuu gateway)✗ core success onlytask spec →

Score

0.92

artifact 1 · numeric 0.8667 · reproducibility 1 · compliance 1

LLM calls

5 tool calls

Tokens

19,607

16,300 in · 3,307 out

Cost

$0.00341

Wall

1.1 min

ended: submitted

Errors

2 recovered · 0 unsafe

Failure labels:missing_values sentinel_value

Task given to the agent

Audit the data quality of `data/synth_jets.csv` (one row per jet). Read `README.md` first: it is the
data card and documents the file's conventions. Write `result.json` with exactly these keys:

- `n_rows`: number of rows in the raw file (int)
- `n_duplicate_rows`: number of rows that are exact repeats of an earlier row (int)
- `n_unique_jets`: number of distinct jets, identified as described in the data card (int)
- `n_events`: number of distinct events (int)
- `n_missing_pt`: number of distinct jets whose transverse momentum is missing (int)
- `n_missing_eta`: number of distinct jets whose pseudorapidity is missing, following the data card's convention (int)
- `columns_with_missing`: sorted list of column names that contain any missing value (including the documented sentinel)
- `max_jets_per_event`: maximum number of distinct jets in one event (int)
- `frac_jets_missing_pt`: n_missing_pt / n_unique_jets (float)

Remove duplicate rows (keeping the first occurrence) before computing every quantity except `n_rows`
and `n_duplicate_rows`. Save the complete analysis as `solution.py`; running `python solution.py` from a
clean copy of this directory must regenerate `result.json`.

Trajectory

observable actions only: assistant text, tool calls, tool results. Hidden reasoning tokens are counted, never shown.

#0 modelact1159→36 tok1.561st+1.562s

→ read_file(path, max_chars, offset)

{
 "path": "README.md",
 "max_chars": 3000,
 "offset": 0
}

#1 toolread_fileok0st+1.562s

# Data card: `data/synth_jets.csv` (SYNTHETIC)

Simulated jet table for 20,000 events, **one row per jet**, produced by a toy generator with a known seed.
This is synthetic data; it is not a measurement.

| column | meaning | unit |
|---|---|---|
| event_id | event number | – |
| jet_index | index of the jet within its event, 0 = leading (highest-pt) jet | – |
| jet_pt | transverse momentum. **NaN when jet reconstruction failed.** | GeV |
| jet_eta | pseudorapidity. **The value -999.0 is a sentinel meaning "not measured".** | – |
| jet_phi | azimuthal angle | rad |
| jet_e_mev | jet energy. **Note the unit: MeV, not GeV.** | MeV |
| btag_score | b-tagging discriminant in [0, 1] | – |

## Known issues

* A jet is uniquely identified by the pair `(event_id, jet_index)`.
* Because of a bug in the ntuple merger, **some rows are exact duplicates**. Duplicates must be
  removed (keep the first occurrence) before any physics quantity is computed.
* Jets with missing `jet_pt` failed reconstruction and must be excluded from any sum over jet pt.
  Jets with the eta sentinel are otherwise valid (their pt and energy are measured).

#2 modelact1519→1011 tok15.376st+16.938s

→ run_python(code)

import pandas as pd

# Read the CSV file
file_path = 'data/synth_jets.csv'
df = pd.read_csv(file_path)

# Calculate n_rows
n_rows = len(df)

# Calculate n_duplicate_rows
# Remove duplicates, keeping the first occurrence
df_unique = df.drop_duplicates()

# Calculate n_unique_jets
# A jet is uniquely identified by (event_id, jet_index)
unique_jets = df_unique.groupby(['event_id', 'jet_index']).size().reset_index(name='count')
n_unique_jets = len(unique_jets)

# Calculate n_events
n_events = len(df_unique['event_id'].unique())

# Calculate n_missing_pt
n_missing_pt = len(df_unique[df_unique['jet_pt'].isna()])

# Calculate n_missing_eta
# The sentinel value for jet_eta is -999.0
n_missing_eta = len(df_unique[df_unique['jet_eta'] == -999.0])

# Calculate columns_with_missing
# Check for any missing values in each column
columns_with_missing = [col for col in df.columns if df[col].isnull().any()]

# Calculate max_jets_per_event
max_jets_per_event = df_unique['event_id'].value_counts().max()

# Calculate frac_jets_missing_pt
frac_jets_missing_pt = n_missing_pt / n_unique_jets if n_unique_jets > 0 else 0.0

# Save the results to result.json
result = {
    'n_rows': n_rows,
    'n_duplicate_rows': len(df) - len(df_unique),
    'n_unique_jets': n_unique_jets,
    'n_events': n_events,
    'n_missing_pt': n_missing_pt,
    'n_missing_eta': n_missing_eta,
    'columns_with_missing': sorted(columns_with_missing),
    'max_jets_per_event': max_jets_per_event,
    'frac_jets_missing_pt': frac_jets_missing_pt
}

import json
with open('result.json', 'w') as f:
    json.dump(result, f)

# Save the analysis as solution.py
with open('solution.py', 'w') as f:
    f.write("""
import pandas as pd
import json

# Read the CSV file
file_path = 'data/synth_jets.csv'
df = pd.read_csv(file_path)

# Calculate n_rows
n_rows = len(df)

# Calculate n_duplicate_rows
# Remove duplicates, keeping the first occurrence
df_unique = df.drop_duplicates()

# Calculate n_unique_jets
# A jet is uniquely identified by (event_id, jet_index)
unique_jets = df_unique.groupby(['event_id', 'jet_index']).size().reset_index(name='count')
n_unique_jets = len(unique_jets)

# Calculate n_events
n_events = len(df_unique['event_id'].unique())

# Calculate n_missing_pt
n_missing_pt = len(df_unique[df_unique['jet_pt'].isna()])

# Calculate n_missing_eta
# The sentinel value for jet_eta is -999.0
n_missing_eta = len(df_unique[df_unique['jet_eta'] == -999.0])

# Calculate columns_with_missing
# Check for any missing values in each column
columns_with_missing = [col for col in df.columns if df[col].isnull().any()]

# Calculate max_jets_per_event
max_jets_per_event = df_unique['event_id'].value_counts().max()

# Calculate frac_jets_missing_pt
frac_jets_missing_pt = n_missing_pt / n_unique_jets if n_unique_jets > 0 else 0.0

# Save the results to result.json
result = {
    'n_rows': n_rows,
    'n_duplicate_rows': len(df) - len(df_unique),
    'n_unique_jets': n_unique_jets,
    'n_events': n_events,
    'n_missing_pt': n_missing_pt,
    'n_missing_eta': n_missing_eta,
    'columns_with_missing': sorted(columns_with_missing),
    'max_jets_per_event': max_jets_per_event,
    'frac_jets_missing_pt': frac_jets_missing_pt
}

with open('result.json', 'w') as f:
    json.dump(result, f)
""")

# Print the result to confirm
print(result)

#3 toolrun_pythonTypeError0.669screated: result.jsont+17.607s

STDERR:
Traceback (most recent call last):
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T172011-t1-missing-audit-react-gariyuu-qwen3-8b-r0/workdir/.agenthep_guard.py", line 117, in main
    runpy.run_path(SCRIPT, run_name="__main__")
  File "<frozen runpy>", line 291, in run_path
  File "<frozen runpy>", line 98, in _run_module_code
  File "<frozen runpy>", line 88, in _run_code
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T172011-t1-missing-audit-react-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 54, in <module>
    json.dump(result, f)
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/__init__.py", line 179, in dump
    for chunk in iterable:
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 432, in _iterencode
    yield from _iterencode_dict(o, _current_indent_level)
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 406, in _iterencode_dict
    yield from chunks
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 439, in _iterencode
    o = _default(o)
        ^^^^^^^^^^^
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 180, in default
    raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type int64 is not JSON serializable

exit code: 1
files created: result.json

#4 modelact3143→1015 tok20.246st+37.853s

→ run_python(code)

import pandas as pd

# Read the CSV file
file_path = 'data/synth_jets.csv'
df = pd.read_csv(file_path)

# Calculate n_rows
n_rows = len(df)

# Calculate n_duplicate_rows
# Remove duplicates, keeping the first occurrence
df_unique = df.drop_duplicates()

# Calculate n_unique_jets
# A jet is uniquely identified by (event_id, jet_index)
unique_jets = df_unique.groupby(['event_id', 'jet_index']).size().reset_index(name='count')
n_unique_jets = len(unique_jets)

# Calculate n_events
n_events = len(df_unique['event_id'].unique())

# Calculate n_missing_pt
n_missing_pt = len(df_unique[df_unique['jet_pt'].isna()])

# Calculate n_missing_eta
# The sentinel value for jet_eta is -999.0
n_missing_eta = len(df_unique[df_unique['jet_eta'] == -999.0])

# Calculate columns_with_missing
# Check for any missing values in each column
columns_with_missing = [col for col in df.columns if df[col].isnull().any()]

# Calculate max_jets_per_event
max_jets_per_event = df_unique['event_id'].value_counts().max()

# Calculate frac_jets_missing_pt
frac_jets_missing_pt = n_missing_pt / n_unique_jets if n_unique_jets > 0 else 0.0

# Save the results to result.json
result = {
    'n_rows': n_rows,
    'n_duplicate_rows': len(df) - len(df_unique),
    'n_unique_jets': n_unique_jets,
    'n_events': n_events,
    'n_missing_pt': n_missing_pt,
    'n_missing_eta': n_missing_eta,
    'columns_with_missing': sorted(columns_with_missing),
    'max_jets_per_event': max_jets_per_event,
    'frac_jets_missing_pt': frac_jets_missing_pt
}

import json
with open('result.json', 'w') as f:
    json.dump(result, f)

# Save the analysis as solution.py
with open('solution.py', 'w') as f:
    f.write("""
import pandas as pd
import json

# Read the CSV file
file_path = 'data/synth_jets.csv'
df = pd.read_csv(file_path)

# Calculate n_rows
n_rows = len(df)

# Calculate n_duplicate_rows
# Remove duplicates, keeping the first occurrence
df_unique = df.drop_duplicates()

# Calculate n_unique_jets
# A jet is uniquely identified by (event_id, jet_index)
unique_jets = df_unique.groupby(['event_id', 'jet_index']).size().reset_index(name='count')
n_unique_jets = len(unique_jets)

# Calculate n_events
n_events = len(df_unique['event_id'].unique())

# Calculate n_missing_pt
n_missing_pt = len(df_unique[df_unique['jet_pt'].isna()])

# Calculate n_missing_eta
# The sentinel value for jet_eta is -999.0
n_missing_eta = len(df_unique[df_unique['jet_eta'] == -999.0])

# Calculate columns_with_missing
# Check for any missing values in each column
columns_with_missing = [col for col in df.columns if df[col].isnull().any()]

# Calculate max_jets_per_event
max_jets_per_event = df_unique['event_id'].value_counts().max()

# Calculate frac_jets_missing_pt
frac_jets_missing_pt = n_missing_pt / n_unique_jets if n_unique_jets > 0 else 0.0

# Save the results to result.json
result = {
    'n_rows': n_rows,
    'n_duplicate_rows': len(df) - len(df_unique),
    'n_unique_jets': n_unique_jets,
    'n_events': n_events,
    'n_missing_pt': n_missing_pt,
    'n_missing_eta': n_missing_eta,
    'columns_with_missing': sorted(columns_with_missing),
    'max_jets_per_event': max_jets_per_event,
    'frac_jets_missing_pt': frac_jets_missing_pt
}

with open('result.json', 'w') as f:
    json.dump(result, f)
""")

# Print the result to confirm
print(result)

#5 toolrun_pythonTypeError0.555st+38.408s

STDERR:
Traceback (most recent call last):
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T172011-t1-missing-audit-react-gariyuu-qwen3-8b-r0/workdir/.agenthep_guard.py", line 117, in main
    runpy.run_path(SCRIPT, run_name="__main__")
  File "<frozen runpy>", line 291, in run_path
  File "<frozen runpy>", line 98, in _run_module_code
  File "<frozen runpy>", line 88, in _run_code
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T172011-t1-missing-audit-react-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 54, in <module>
    json.dump(result, f)
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/__init__.py", line 179, in dump
    for chunk in iterable:
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 432, in _iterencode
    yield from _iterencode_dict(o, _current_indent_level)
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 406, in _iterencode_dict
    yield from chunks
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 439, in _iterencode
    o = _default(o)
        ^^^^^^^^^^^
  File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 180, in default
    raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type int64 is not JSON serializable

exit code: 1
files modified: result.json

#6 modelact4767→1047 tok21.311st+59.718s

→ run_python(code)

import pandas as pd

# Read the CSV file
file_path = 'data/synth_jets.csv'
df = pd.read_csv(file_path)

# Calculate n_rows
n_rows = len(df)

# Calculate n_duplicate_rows
# Remove duplicates, keeping the first occurrence
df_unique = df.drop_duplicates()

# Calculate n_unique_jets
# A jet is uniquely identified by (event_id, jet_index)
unique_jets = df_unique.groupby(['event_id', 'jet_index']).size().reset_index(name='count')
n_unique_jets = len(unique_jets)

# Calculate n_events
n_events = len(df_unique['event_id'].unique())

# Calculate n_missing_pt
n_missing_pt = len(df_unique[df_unique['jet_pt'].isna()])

# Calculate n_missing_eta
# The sentinel value for jet_eta is -999.0
n_missing_eta = len(df_unique[df_unique['jet_eta'] == -999.0])

# Calculate columns_with_missing
# Check for any missing values in each column
columns_with_missing = [col for col in df.columns if df[col].isnull().any()]

# Calculate max_jets_per_event
max_jets_per_event = df_unique['event_id'].value_counts().max()

# Calculate frac_jets_missing_pt
frac_jets_missing_pt = n_missing_pt / n_unique_jets if n_unique_jets > 0 else 0.0

# Save the results to result.json
result = {
    'n_rows': int(n_rows),
    'n_duplicate_rows': int(len(df) - len(df_unique)),
    'n_unique_jets': int(n_unique_jets),
    'n_events': int(n_events),
    'n_missing_pt': int(n_missing_pt),
    'n_missing_eta': int(n_missing_eta),
    'columns_with_missing': sorted(columns_with_missing),
    'max_jets_per_event': int(max_jets_per_event),
    'frac_jets_missing_pt': float(frac_jets_missing_pt)
}

import json
with open('result.json', 'w') as f:
    json.dump(result, f)

# Save the analysis as solution.py
with open('solution.py', 'w') as f:
    f.write("""
import pandas as pd
import json

# Read the CSV file
file_path = 'data/synth_jets.csv'
df = pd.read_csv(file_path)

# Calculate n_rows
n_rows = len(df)

# Calculate n_duplicate_rows
# Remove duplicates, keeping the first occurrence
df_unique = df.drop_duplicates()

# Calculate n_unique_jets
# A jet is uniquely identified by (event_id, jet_index)
unique_jets = df_unique.groupby(['event_id', 'jet_index']).size().reset_index(name='count')
n_unique_jets = len(unique_jets)

# Calculate n_events
n_events = len(df_unique['event_id'].unique())

# Calculate n_missing_pt
n_missing_pt = len(df_unique[df_unique['jet_pt'].isna()])

# Calculate n_missing_eta
# The sentinel value for jet_eta is -999.0
n_missing_eta = len(df_unique[df_unique['jet_eta'] == -999.0])

# Calculate columns_with_missing
# Check for any missing values in each column
columns_with_missing = [col for col in df.columns if df[col].isnull().any()]

# Calculate max_jets_per_event
max_jets_per_event = df_unique['event_id'].value_counts().max()

# Calculate frac_jets_missing_pt
frac_jets_missing_pt = n_missing_pt / n_unique_jets if n_unique_jets > 0 else 0.0

# Save the results to result.json
result = {
    'n_rows': int(n_rows),
    'n_duplicate_rows': int(len(df) - len(df_unique)),
    'n_unique_jets': int(n_unique_jets),
    'n_events': int(n_events),
    'n_missing_pt': int(n_missing_pt),
    'n_missing_eta': int(n_missing_eta),
    'columns_with_missing': sorted(columns_with_missing),
    'max_jets_per_event': int(max_jets_per_event),
    'frac_jets_missing_pt': float(frac_jets_missing_pt)
}

with open('result.json', 'w') as f:
    json.dump(result, f)
""")

# Print the result to confirm
print(result)

#7 toolrun_pythonok0.505screated: solution.pyt+60.224s

STDOUT:
{'n_rows': 65779, 'n_duplicate_rows': 1915, 'n_unique_jets': 63864, 'n_events': 20000, 'n_missing_pt': 1277, 'n_missing_eta': 957, 'columns_with_missing': ['jet_pt'], 'max_jets_per_event': 6, 'frac_jets_missing_pt': 0.01999561568332707}

exit code: 0
files created: solution.py
files modified: result.json

#8 modelact5712→198 tok5.512st+65.736s

→ submit(summary, assumptions)

{
 "summary": "Completed data-quality audit of `data/synth_jets.csv`. Key findings: 65779 rows in total, 1915 duplicate rows, 63864 unique jets, 20000 distinct events, 1277 jets missing PT, 957 jets missing ETA, only `jet_pt` column has missing values, maximum 6 jets per event, and 1.99956% of jets have missing PT.",
 "assumptions": [
  "The sentinel value for jet_eta is -999.0 as documented in the data card.",
  "Duplicate rows are identified by exact repetition.",
  "Missing values in jet_pt are represented as NaN.",
  "Missing values in jet_eta are represented by the sentinel value -999.0.",
  "Jet uniqueness is determined by (event_id, jet_index)."
 ]
}

#9 toolsubmitok0st+65.736s

submitted

Final answer

Completed data-quality audit of `data/synth_jets.csv`. Key findings: 65779 rows in total, 1915 duplicate rows, 63864 unique jets, 20000 distinct events, 1277 jets missing PT, 957 jets missing ETA, only `jet_pt` column has missing values, maximum 6 jets per event, and 1.99956% of jets have missing PT.

The sentinel value for jet_eta is -999.0 as documented in the data card.
Duplicate rows are identified by exact repetition.
Missing values in jet_pt are represented as NaN.
Missing values in jet_eta are represented by the sentinel value -999.0.
Jet uniqueness is determined by (event_id, jet_index).

Grader verdict

12 / 13 checks passed

Check	Result	Category	Weight	Detail	Observed	Expected
result_existscritical	✓ pass	artifact	1	ok
solution_exists	✓ pass	artifact	1	ok
n_rows	✓ pass	numeric	1	match	65779	65779
n_dupcritical	✓ pass	numeric	2	match	1915	1915
n_uniquecritical	✓ pass	numeric	2	match	63864	63864
n_events	✓ pass	numeric	1	match	20000	20000
n_missing_pt	✓ pass	numeric	2	match	1277	1277
n_missing_etacritical	✓ pass	numeric	3	match	957	957
cols_missing	✗ fail	numeric	2	length 1 vs expected 2	["jet_pt"]	["jet_eta","jet_pt"]
max_jets	✓ pass	numeric	1	match	6	6
frac_missing	✓ pass	numeric	1	match	0.01999561568332707	0.01999561568332707
reruns	✓ pass	reproducibility	1	reproduced exactly	{"ok":true,"artifacts_reproduced":true,"output_matches":true,"exit_code":0}
not_hardcoded	✓ pass	compliance	1	no expected values hard-coded	[]

Reproducibility rerun

script executes

✓ pass

artifacts reproduced

✓ pass

output matches submission

✓ exact

dependencies resolvable

✓ pass

seed controlled

no randomness used

detail

reproduced exactly

Artifacts

result.json: run_python · solution.py: run_python

result.json

{"n_rows": 65779, "n_duplicate_rows": 1915, "n_unique_jets": 63864, "n_events": 20000, "n_missing_pt": 1277, "n_missing_eta": 957, "columns_with_missing": ["jet_pt"], "max_jets_per_event": 6, "frac_jets_missing_pt": 0.01999561568332707}

solution.py


import pandas as pd
import json

# Read the CSV file
file_path = 'data/synth_jets.csv'
df = pd.read_csv(file_path)

# Calculate n_rows
n_rows = len(df)

# Calculate n_duplicate_rows
# Remove duplicates, keeping the first occurrence
df_unique = df.drop_duplicates()

# Calculate n_unique_jets
# A jet is uniquely identified by (event_id, jet_index)
unique_jets = df_unique.groupby(['event_id', 'jet_index']).size().reset_index(name='count')
n_unique_jets = len(unique_jets)

# Calculate n_events
n_events = len(df_unique['event_id'].unique())

# Calculate n_missing_pt
n_missing_pt = len(df_unique[df_unique['jet_pt'].isna()])

# Calculate n_missing_eta
# The sentinel value for jet_eta is -999.0
n_missing_eta = len(df_unique[df_unique['jet_eta'] == -999.0])

# Calculate columns_with_missing
# Check for any missing values in each column
columns_with_missing = [col for col in df.columns if df[col].isnull().any()]

# Calculate max_jets_per_event
max_jets_per_event = df_unique['event_id'].value_counts().max()

# Calculate frac_jets_missing_pt
frac_jets_missing_pt = n_missing_pt / n_unique_jets if n_unique_jets > 0 else 0.0

# Save the results to result.json
result = {
    'n_rows': int(n_rows),
    'n_duplicate_rows': int(len(df) - len(df_unique)),
    'n_unique_jets': int(n_unique_jets),
    'n_events': int(n_events),
    'n_missing_pt': int(n_missing_pt),
    'n_missing_eta': int(n_missing_eta),
    'columns_with_missing': sorted(columns_with_missing),
    'max_jets_per_event': int(max_jets_per_event),
    'frac_jets_missing_pt': float(frac_jets_missing_pt)
}

with open('result.json', 'w') as f:
    json.dump(result, f)

Run metadata

{
 "run_id": "20260906T172011-t1-missing-audit-react-gariyuu-qwen3-8b-r0",
 "benchmark_version": "1.0.0",
 "harness_version": "0.1.0",
 "git_sha": "unknown",
 "provider": {
  "provider": "openai_compat",
  "model": "Yuu no Sekai",
  "temperature": 0,
  "max_tokens": 2500,
  "context_tokens": 8192,
  "config": {
   "base_url": "https://api.gariyuuu.com/v1",
   "extra_body": {
    "reasoning": {
     "enabled": false
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