AgentHEP

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 modelgenerate895→1279 tok21.232st+21.232s

```python import pandas as pd import numpy as np import json # Load the data data_path = "data/cms_dimuon_2011.csv" df = pd.read_csv(data_path) # Total number of events n_total = len(df) # Selection A: |eta1| < 2.4 and |eta2| < 2.4 and pt1 > 20 and pt2 > 20 and Q1*Q2 < 0 and 80 < M < 100 selection_A = ( (np.abs(df['eta1']) < 2.4) & (np.abs(df['eta2']) < 2.4) & (df['pt1'] > 20) & (df['pt2'] > 20) & (df['Q1'] * df['Q2'] < 0) & (df['M'] > 80) & (df['M'] < 100) ) n_A = selection_A.sum() # Selection B: pt1 > 200 and pt2 > 200 and M < 20 selection_B = ( (df['pt1'] > 200) & (df['pt2'] > 200) & (df['M'] < 20) ) n_B = selection_B.sum() # Selection C: (type1 == "T" or type2 == "T") and M > 60 selection_C = ( (df['type1'] == "T") | (df['type2'] == "T") & (df['M'] > 60) ) 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 # Warnings for empty selections warnings = [] if n_A == 0: warnings.append("empty_selection") if n_B == 0: warnings.append("empty_selection") if n_C == 0: warnings.append("empty_selection") # Prepare result result = { "n_total": n_total, "selections": { "A": {"n": n_A, "frac": frac_A}, "B": {"n": n_B, "frac": frac_B}, "C": {"n": n_C, "frac": frac_C} }, "warnings": warnings } # Save result to JSON with open("result.json", "w") as f: json.dump(result, f, indent=2) # Save the complete analysis as solution.py with open("solution.py", "w") as f: f.write('''import pandas as pd import numpy as np import json # Load the data data_path = "data/cms_dimuon_2011.csv" df = pd.read_csv(data_path) # Total number of events n_total = len(df) # Selection A: |eta1| < 2.4 and |eta2| < 2.4 and pt1 > 20 and pt2 > 20 and Q1*Q2 < 0 and 80 < M < 100 selection_A = ( (np.abs(df['eta1']) < 2.4) & (np.abs(df['eta2']) < 2.4) & (df['pt1'] > 20) & (df['pt2'] > 20) & (df['Q1'] * df['Q2'] < 0) & (df['M'] > 80) & (df['M'] < 100) ) n_A = selection_A.sum() # Selection B: pt1 > 200 and pt2 > 200 and M < 20 selection_B = ( (df['pt1'] > 200) & (df['pt2'] > 200) & (df['M'] < 20) ) n_B = selection_B.sum() # Selection C: (type1 == "T" or type2 == "T") and M > 60 selection_C = ( (df['type1'] == "T") | (df['type2'] == "T") & (df['M'] > 60) ) 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 # Warnings for empty selections warnings = [] if n_A == 0: warnings.append("empty_selection") if n_B == 0: warnings.append("empty_selection") if n_C == 0: warnings.append("empty_selection") # Prepare result result = { "n_total": n_total, "selections": { "A": {"n": n_A, "frac": frac_A}, "B": {"n": n_B, "frac": frac_B}, "C": {"n": n_C, "frac": frac_C} }, "warnings": warnings } # Save result to JSON with open("result.json", "w") as f: json.dump(result, f, indent=2) ''') # Submit the result print("Submission complete.") print("Assumptions made:") print("- The data file is correctly formatted and contains no missing values.") print("- The selections are applied as described in the task.") print("- The invariant mass M is calculated correctly from the muon pair.") ```

#1 toolwrite_fileok0.002screated: solution.pyt+21.234s

wrote 3455 chars to solution.py

#2 toolrun_pythonTypeError1.013screated: result.jsont+22.247s

STDERR:
Traceback (most recent call last):
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T174906-t2-selection-count-self_debug-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/20260906T174906-t2-selection-count-self_debug-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 66, in <module>
    json.dump(result, f, indent=2)
  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 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 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

#3 modeldebug_12944→1299 tok23.094st+45.341s

```python import pandas as pd import numpy as np import json # Load the data data_path = "data/cms_dimuon_2011.csv" df = pd.read_csv(data_path) # Total number of events n_total = len(df) # Selection A: |eta1| < 2.4 and |eta2| < 2.4 and pt1 > 20 and pt2 > 20 and Q1*Q2 < 0 and 80 < M < 100 selection_A = ( (np.abs(df['eta1']) < 2.4) & (np.abs(df['eta2']) < 2.4) & (df['pt1'] > 20) & (df['pt2'] > 20) & (df['Q1'] * df['Q2'] < 0) & (df['M'] > 80) & (df['M'] < 100) ) n_A = selection_A.sum() # Selection B: pt1 > 200 and pt2 > 200 and M < 20 selection_B = ( (df['pt1'] > 200) & (df['pt2'] > 200) & (df['M'] < 20) ) n_B = selection_B.sum() # Selection C: (type1 == "T" or type2 == "T") and M > 60 selection_C = ( (df['type1'] == "T") | (df['type2'] == "T") & (df['M'] > 60) ) 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 # Warnings for empty selections warnings = [] if n_A == 0: warnings.append("empty_selection") if n_B == 0: warnings.append("empty_selection") if n_C == 0: warnings.append("empty_selection") # 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 with open("result.json", "w") as f: json.dump(result, f, indent=2) # Save the complete analysis as solution.py with open("solution.py", "w") as f: f.write('''import pandas as pd import numpy as np import json # Load the data data_path = "data/cms_dimuon_2011.csv" df = pd.read_csv(data_path) # Total number of events n_total = len(df) # Selection A: |eta1| < 2.4 and |eta2| < 2.4 and pt1 > 20 and pt2 > 20 and Q1*Q2 < 0 and 80 < M < 100 selection_A = ( (np.abs(df['eta1']) < 2.4) & (np.abs(df['eta2']) < 2.4) & (df['pt1'] > 20) & (df['pt2'] > 20) & (df['Q1'] * df['Q2'] < 0) & (df['M'] > 80) & (df['M'] < 100) ) n_A = selection_A.sum() # Selection B: pt1 > 200 and pt2 > 200 and M < 20 selection_B = ( (df['pt1'] > 200) & (df['pt2'] > 200) & (df['M'] < 20) ) n_B = selection_B.sum() # Selection C: (type1 == "T" or type2 == "T") and M > 60 selection_C = ( (df['type1'] == "T") | (df['type2'] == "T") & (df['M'] > 60) ) 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 # Warnings for empty selections warnings = [] if n_A == 0: warnings.append("empty_selection") if n_B == 0: warnings.append("empty_selection") if n_C == 0: warnings.append("empty_selection") # 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 with open("result.json", "w") as f: json.dump(result, f, indent=2) ''') # Submit the result print("Submission complete.") print("Assumptions made:") print("- The data file is correctly formatted and contains no missing values.") print("- The selections are applied as described in the task.") print("- The invariant mass M is calculated correctly from the muon pair.") ```

#4 toolwrite_fileok0.002screated: solution.pyt+45.343s

wrote 3537 chars to solution.py

#5 toolrun_pythonok1.046st+46.389s

STDOUT:
Submission complete.
Assumptions made:
- The data file is correctly formatted and contains no missing values.
- The selections are applied as described in the task.
- The invariant mass M is calculated correctly from the muon pair.

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

Final answer

```python import pandas as pd import numpy as np import json # Load the data data_path = "data/cms_dimuon_2011.csv" df = pd.read_csv(data_path) # Total number of events n_total = len(df) # Selection A: |eta1| < 2.4 and |eta2| < 2.4 and pt1 > 20 and pt2 > 20 and Q1*Q2 < 0 and 80 < M < 100 selection_A = ( (np.abs(df['eta1']) < 2.4) & (np.abs(df['eta2']) < 2.4) & (df['pt1'] > 20) & (df['pt2'] > 20) & (df['Q1'] * df['Q2'] < 0) & (df['M'] > 80) & (df['M'] < 100) ) n_A = selection_A.sum() # Selection B: pt1 > 200 and pt2 > 200 and M < 20 selection_B = ( (df['pt1'] > 200) & (df['pt2'] > 200) & (df['M'] < 20) ) n_B = selection_B.sum() # Selection C: (type1 == "T" or type2 == "T") and M > 60 selection_C = ( (df['type1'] == "T") | (df['type2'] == "T") & (df['M'] > 60) ) 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 # Warnings for empty selections warnings = [] if n_A == 0: warnings.append("empty_selection") if n_B == 0: warnings.append("empty_selection") if n_C == 0: warnings.append("empty_selection") # 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 with open("result.json", "w") as f: json.dump(result, f, indent=2) # Save the complete analysis as solution.py with open("solution.py", "w") as f: f.write('''import pandas as pd import numpy as np import json # Load the data data_path = "data/cms_dimuon_2011.csv" df = pd.read_csv(data_path) # Total number of events n_total = len(df) # Selection A: |eta1| < 2.4 and |eta2| < 2.4 and pt1 > 20 and pt2 > 20 and Q1*Q2 < 0 and 80 < M < 100 selection_A = ( (np.abs(df['eta1']) < 2

no assumptions stated

Numbers in the summary not found in any produced artifact: 100, 100, 200, 200, 200, 200, 100

Grader verdict

12 / 12 checks passed

Check	Result	Category	Weight	Detail	Observed	Expected
result_existscritical	✓ pass	artifact	1	ok
solution_exists	✓ pass	artifact	1	ok
n_total	✓ pass	numeric	1	match	100000	100000
n_Acritical	✓ pass	numeric	3	match	4727	4727
frac_A	✓ pass	numeric	1	match	0.04727	0.04727
n_Bcritical	✓ pass	numeric	2	match	0	0
frac_B	✓ pass	numeric	1	match	0	0
n_C	✓ pass	numeric	2	match	97	97
frac_C	✓ pass	numeric	1	match	0.00097	0.00097
empty_flaggedcritical	✓ pass	compliance	2	present	["empty_selection"]	"empty_selection"
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

solution.py: run_python · result.json: 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 numpy as np
import json

# Load the data
data_path = "data/cms_dimuon_2011.csv"
df = pd.read_csv(data_path)

# Total number of events
n_total = len(df)

# Selection A: |eta1| < 2.4 and |eta2| < 2.4 and pt1 > 20 and pt2 > 20 and Q1*Q2 < 0 and 80 < M < 100
selection_A = (
    (np.abs(df['eta1']) < 2.4) &
    (np.abs(df['eta2']) < 2.4) &
    (df['pt1'] > 20) &
    (df['pt2'] > 20) &
    (df['Q1'] * df['Q2'] < 0) &
    (df['M'] > 80) &
    (df['M'] < 100)
)
n_A = selection_A.sum()

# Selection B: pt1 > 200 and pt2 > 200 and M < 20
selection_B = (
    (df['pt1'] > 200) &
    (df['pt2'] > 200) &
    (df['M'] < 20)
)
n_B = selection_B.sum()

# Selection C: (type1 == "T" or type2 == "T") and M > 60
selection_C = (
    (df['type1'] == "T") | (df['type2'] == "T") &
    (df['M'] > 60)
)
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

# Warnings for empty selections
warnings = []
if n_A == 0:
    warnings.append("empty_selection")
if n_B == 0:
    warnings.append("empty_selection")
if n_C == 0:
    warnings.append("empty_selection")

# 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
with open("result.json", "w") as f:
    json.dump(result, f, indent=2)

Run metadata

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 "benchmark_version": "1.0.0",
 "harness_version": "0.1.0",
 "git_sha": "unknown",
 "provider": {
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  "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": "2026-09-06T17:49:06.351840+00:00",
  "preset": "gariyuu-qwen3-8b",
  "family": "qwen3-8b",
  "display": "Qwen3-8B (gariyuu gateway)",
  "is_mock": false
 },
 "agent": {
  "name": "self_debug",
  "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": "2026-09-06T17:49:06.293380+00:00",
 "finished_at": "2026-09-06T17:49:54.255667+00:00"
}

Event selections including one that is empty

Task given to the agent

Trajectory

Final answer

Grader verdict

Reproducibility rerun

Artifacts

Run metadata