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

Event selections including one that is empty

t2-selection-counttier 2ReActQwen3-8B (gariyuu gateway)✓ strict successtask spec →
Score
1
artifact 1 · numeric 1 · compliance 1 · reproducibility 1
LLM calls
4
4 tool calls
Tokens
8,355
7,029 in · 1,326 out
Cost
$0.00143
Wall
28 s
ended: submitted
Errors
0
0 recovered · 0 unsafe

Task given to the agent

Using `data/cms_dimuon_2011.csv` (data card in README.md), apply the following selections and count events.

- Selection A: |eta1| < 2.4 and |eta2| < 2.4 and pt1 > 20 and pt2 > 20 and Q1*Q2 < 0 and 80 < M < 100
- Selection B: pt1 > 200 and pt2 > 200 and M < 20
- Selection C: (type1 == "T" or type2 == "T") and M > 60

Write `result.json` with:
- `n_total`: total number of events (int)
- `selections`: an object with keys `A`, `B`, `C`, each an object `{"n": <int>, "frac": <float>}` where `frac` = n / n_total
- `warnings`: a list of strings. If any selection yields zero events, include the string `"empty_selection"`
  and set that selection's `frac` to 0.0 (never NaN or null). If every selection is non-empty the list is empty.

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 modelact1172→36 tok1.689st+1.689s
→ read_file(path, max_chars, offset)
{
 "path": "README.md",
 "max_chars": 3000,
 "offset": 0
}
#1 toolread_fileok0st+1.69s
# Data card: `data/cms_dimuon_2011.csv` (REAL DATA)

**Source:** CERN Open Data Portal record 545, "Dimuon events from the CMS 2011 DoubleMu primary dataset"
(file `Dimuon_DoubleMu.csv`). Licence CC0. 100,000 events, one per row.

| column | meaning | unit |
|---|---|---|
| Run, Event | run and event number | – |
| type1, type2 | muon reconstruction type: `G` global muon, `T` tracker muon | – |
| E1, px1, py1, pz1 | four-momentum of muon 1 | GeV |
| pt1, eta1, phi1 | transverse momentum, pseudorapidity, azimuth of muon 1 | GeV, –, rad |
| Q1 | charge of muon 1 | e |
| E2 … Q2 | same for muon 2 | |
| M | invariant mass of the muon pair | GeV |

All energies and momenta are in GeV. There are no missing values.
#2 modelact1471→15 tok1.432st+3.121s
→ list_files()
{}
#3 toollist_filesok0st+3.122s
       726  README.md
  13935840  data/cms_dimuon_2011.csv
#4 modelact1531→1178 tok21.201st+24.323s
→ run_python(code)
import pandas as pd

# Load the data
file_path = 'data/cms_dimuon_2011.csv'
data = pd.read_csv(file_path)

# Total number of events
n_total = len(data)

# Selection A
selection_A = (abs(data['eta1']) < 2.4) & (abs(data['eta2']) < 2.4) & (data['pt1'] > 20) & (data['pt2'] > 20) & (data['Q1'] * data['Q2'] < 0) & (data['M'] > 80) & (data['M'] < 100)

# Selection B
selection_B = (data['pt1'] > 200) & (data['pt2'] > 200) & (data['M'] < 20)

# Selection C
selection_C = ((data['type1'] == 'T') | (data['type2'] == 'T')) & (data['M'] > 60)

# Count events for each selection
n_A = selection_A.sum()
n_B = selection_B.sum()
n_C = selection_C.sum()

# Calculate fractions
frac_A = n_A / n_total if n_total > 0 else 0.0
frac_B = n_B / n_total if n_total > 0 else 0.0
frac_C = n_C / n_total if n_total > 0 else 0.0

# Check for empty selections
warnings = []
if n_A == 0:
    warnings.append('empty_selection')
    frac_A = 0.0
if n_B == 0:
    warnings.append('empty_selection')
    frac_B = 0.0
if n_C == 0:
    warnings.append('empty_selection')
    frac_C = 0.0

# Prepare result
result = {
    'n_total': int(n_total),
    'selections': {
        'A': {'n': int(n_A), 'frac': float(frac_A)},
        'B': {'n': int(n_B), 'frac': float(frac_B)},
        'C': {'n': int(n_C), 'frac': float(frac_C)}
    },
    'warnings': warnings
}

# Save result to JSON file
import json
with open('result.json', 'w') as f:
    json.dump(result, f, indent=4)

# Save solution.py
solution_code = '''import pandas as pd
import json

# Load the data
data = pd.read_csv('data/cms_dimuon_2011.csv')

# Total number of events
n_total = len(data)

# Selection A
selection_A = (abs(data['eta1']) < 2.4) & (abs(data['eta2']) < 2.4) & (data['pt1'] > 20) & (data['pt2'] > 20) & (data['Q1'] * data['Q2'] < 0) & (data['M'] > 80) & (data['M'] < 100)

# Selection B
selection_B = (data['pt1'] > 200) & (data['pt2'] > 200) & (data['M'] < 20)

# Selection C
selection_C = ((data['type1'] == 'T') | (data['type2'] == 'T')) & (data['M'] > 60)

# Count events for each selection
n_A = selection_A.sum()
n_B = selection_B.sum()
n_C = selection_C.sum()

# Calculate fractions
frac_A = n_A / n_total if n_total > 0 else 0.0
frac_B = n_B / n_total if n_total > 0 else 0.0
frac_C = n_C / n_total if n_total > 0 else 0.0

# Check for empty selections
warnings = []
if n_A == 0:
    warnings.append('empty_selection')
    frac_A = 0.0
if n_B == 0:
    warnings.append('empty_selection')
    frac_B = 0.0
if n_C == 0:
    warnings.append('empty_selection')
    frac_C = 0.0

# Prepare result
result = {
    'n_total': int(n_total),
    'selections': {
        'A': {'n': int(n_A), 'frac': float(frac_A)},
        'B': {'n': int(n_B), 'frac': float(frac_B)},
        'C': {'n': int(n_C), 'frac': float(frac_C)}
    },
    'warnings': warnings
}

# Save result to JSON file
with open('result.json', 'w') as f:
    json.dump(result, f, indent=4)
'''
with open('solution.py', 'w') as f:
    f.write(solution_code)

# Print result
print(json.dumps(result, indent=4))
#5 toolrun_pythonok0.536screated: result.json, solution.pyt+24.859s
STDOUT:
{
    "n_total": 100000,
    "selections": {
        "A": {
            "n": 4727,
            "frac": 0.04727
        },
        "B": {
            "n": 0,
            "frac": 0.0
        },
        "C": {
            "n": 97,
            "frac": 0.00097
        }
    },
    "warnings": [
        "empty_selection"
    ]
}

exit code: 0
files created: result.json, solution.py
#6 modelact2855→97 tok3.259st+28.118s
→ submit(summary, assumptions)
{
 "summary": "Applied the specified selections to the dataset and calculated the number of events and fractions for each selection. Selection B resulted in zero events, hence an empty selection warning.",
 "assumptions": [
  "The data file is correctly formatted as described in the README.md.",
  "All columns required for the selections are present in the CSV file.",
  "The event counts and fractions are computed accurately based on the given conditions."
 ]
}
#7 toolsubmitok0st+28.118s
submitted

Final answer

Applied the specified selections to the dataset and calculated the number of events and fractions for each selection. Selection B resulted in zero events, hence an empty selection warning.
  • The data file is correctly formatted as described in the README.md.
  • All columns required for the selections are present in the CSV file.
  • The event counts and fractions are computed accurately based on the given conditions.

Grader verdict

12 / 12 checks passed
CheckResultCategoryWeightDetailObservedExpected
result_existscritical✓ passartifact1ok
solution_exists✓ passartifact1ok
n_total✓ passnumeric1match100000100000
n_Acritical✓ passnumeric3match47274727
frac_A✓ passnumeric1match0.047270.04727
n_Bcritical✓ passnumeric2match00
frac_B✓ passnumeric1match00
n_C✓ passnumeric2match9797
frac_C✓ passnumeric1match0.000970.00097
empty_flaggedcritical✓ passcompliance2present["empty_selection"]"empty_selection"
reruns✓ passreproducibility1reproduced exactly{"ok":true,"artifacts_reproduced":true,"output_matches":true,"exit_code":0}
not_hardcoded✓ passcompliance1no 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_total": 100000,
    "selections": {
        "A": {
            "n": 4727,
            "frac": 0.04727
        },
        "B": {
            "n": 0,
            "frac": 0.0
        },
        "C": {
            "n": 97,
            "frac": 0.00097
        }
    },
    "warnings": [
        "empty_selection"
    ]
}
solution.py
import pandas as pd
import json

# Load the data
data = pd.read_csv('data/cms_dimuon_2011.csv')

# Total number of events
n_total = len(data)

# Selection A
selection_A = (abs(data['eta1']) < 2.4) & (abs(data['eta2']) < 2.4) & (data['pt1'] > 20) & (data['pt2'] > 20) & (data['Q1'] * data['Q2'] < 0) & (data['M'] > 80) & (data['M'] < 100)

# Selection B
selection_B = (data['pt1'] > 200) & (data['pt2'] > 200) & (data['M'] < 20)

# Selection C
selection_C = ((data['type1'] == 'T') | (data['type2'] == 'T')) & (data['M'] > 60)

# Count events for each selection
n_A = selection_A.sum()
n_B = selection_B.sum()
n_C = selection_C.sum()

# Calculate fractions
frac_A = n_A / n_total if n_total > 0 else 0.0
frac_B = n_B / n_total if n_total > 0 else 0.0
frac_C = n_C / n_total if n_total > 0 else 0.0

# Check for empty selections
warnings = []
if n_A == 0:
    warnings.append('empty_selection')
    frac_A = 0.0
if n_B == 0:
    warnings.append('empty_selection')
    frac_B = 0.0
if n_C == 0:
    warnings.append('empty_selection')
    frac_C = 0.0

# Prepare result
result = {
    'n_total': int(n_total),
    'selections': {
        'A': {'n': int(n_A), 'frac': float(frac_A)},
        'B': {'n': int(n_B), 'frac': float(frac_B)},
        'C': {'n': int(n_C), 'frac': float(frac_C)}
    },
    'warnings': warnings
}

# Save result to JSON file
with open('result.json', 'w') as f:
    json.dump(result, f, indent=4)

Run metadata

{
 "run_id": "20260906T172036-t2-selection-count-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
    }
   },
   "context_tokens": 8192
  },
  "captured_at": "2026-09-06T17:20:36.475122+00:00",
  "preset": "gariyuu-qwen3-8b",
  "family": "qwen3-8b",
  "display": "Qwen3-8B (gariyuu gateway)",
  "is_mock": false
 },
 "agent": {
  "name": "react",
  "max_steps": 25,
  "max_debug_rounds": 3
 },
 "environment": {
  "isolation": "seatbelt",
  "platform": "macOS-15.1-arm64-arm-64bit",
  "python": "3.11.15",
  "limits": {
   "wall_s": 180,
   "cpu_s": 150,
   "mem_mb": 2048,
   "max_file_mb": 200,
   "max_output_chars": 20000
  }
 },
 "started_at": "2026-09-06T17:20:36.427692+00:00",
 "finished_at": "2026-09-06T17:21:05.119895+00:00"
}