AgentHEPGitHub ↗
2026-09-06 17:19 UTC · seatbelt sandbox · git unknown

Local significance of an excess from sidebands

t5-bump-significancetier 5Single-shotQwen3-8B (gariyuu gateway)✗ failedtask spec →
Score
0.3131
artifact 0.6667 · numeric 0.2143 · compliance 1 · plot 0 · reproducibility 0
LLM calls
1
2 tool calls
Tokens
2,917
1,046 in · 1,871 out
Cost
$0.00097
Wall
33 s
ended: single_shot_done
Errors
1
0 recovered · 0 unsafe
Failure labels:no_outputplotting_errorstatistical_misuseincorrect_normalizationnon_reproducibleexecution_failed

Task given to the agent

`data/synth_diphoton.csv` (data card in README.md) contains a diphoton invariant mass `m_gg` per event.
Quantify a possible excess in the signal window [120, 130] GeV using the sidebands [100, 120) and (130, 180]:

1. Fit an exponential shape exp(-m / lam) to the sideband events by **unbinned maximum likelihood**, with the density
   normalised over the sideband region only (i.e. over [100,120) union (130,180]). Search lam in [5, 200].
2. Expected background in the window: B = N_sideband * I_window / I_sideband where I_R is the integral of exp(-m/lam) over region R.
3. Observed count N = number of events with 120 <= m_gg <= 130.
4. z_asimov = sqrt(2 * (N * ln(N / B) - (N - B))) with a negative sign if N < B.
5. p_value = P(X >= N) for X ~ Poisson(B) (exact, e.g. `scipy.stats.poisson.sf(N - 1, B)`), and z_from_p = the one-sided
   Gaussian significance corresponding to p_value (`scipy.stats.norm.isf(p_value)`).
6. `warnings`: include the string "low_statistics" if N < 10 or B < 10; otherwise no such entry.
7. `claim`: exactly "significant_excess" if z_from_p >= 3.0, otherwise "no_significant_excess".

Deliverables:
- `result.json` with `n_obs` (int), `n_sideband` (int), `lam` (float), `b_expected` (float), `excess` (float, N - B),
  `z_asimov` (float), `p_value` (float), `z_from_p` (float), `warnings` (list of strings), `claim` (string).
- `figures/window.svg`: histogram of m_gg on [100, 180] with the signal window indicated, x-axis label including "GeV".
- `solution.py`: the complete analysis; `python solution.py` from a clean copy of this directory must regenerate every deliverable.

Trajectory

observable actions only: assistant text, tool calls, tool results. Hidden reasoning tokens are counted, never shown.
#0 modelgenerate1046→1871 tok29.962st+29.964s
```python import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.optimize import minimize from scipy.stats import poisson, norm import json # Load data data_path = "data/synth_diphoton.csv" df = pd.read_csv(data_path) # Define regions signal_window = (120, 130) sideband_low = (100, 120) sideband_high = (130, 180) # Filter events in sidebands def is_in_region(m, region): return (region[0] <= m) & (m < region[1]) sideband_events = df[(is_in_region(df['m_gg'], sideband_low)) | (is_in_region(df['m_gg'], sideband_high))] # Fit exponential model to sideband events def neg_log_likelihood(params, m_values): lam = params[0] return -np.sum(np.log(np.exp(-m_values / lam))) # Initial guess for lambda initial_lam = 100.0 # Perform optimization result = minimize(neg_log_likelihood, x0=[initial_lam], args=(sideband_events['m_gg'].values,), bounds=[(5, 200)]) lam_fit = result.x[0] # Calculate expected background in signal window def integral_exp(lam, region): m_low, m_high = region return (lam - lam * np.exp(-(m_high - m_low) / lam)) if lam > 0 else 0 integral_sideband = integral_exp(lam_fit, sideband_low) + integral_exp(lam_fit, sideband_high) integral_signal_window = integral_exp(lam_fit, signal_window) n_sideband = len(sideband_events) b_expected = (n_sideband * integral_signal_window) / integral_sideband # Observed count in signal window n_obs = len(df[(df['m_gg'] >= signal_window[0]) & (df['m_gg'] <= signal_window[1])]) # Compute excess excess = n_obs - b_expected # Compute z_asimov if n_obs > b_expected: z_asimov = np.sqrt(2 * (n_obs * np.log(n_obs / b_expected) - (n_obs - b_expected))) else: z_asimov = -np.sqrt(2 * (n_obs * np.log(n_obs / b_expected) - (n_obs - b_expected))) # Compute p_value and z_from_p p_value = poisson.sf(n_obs - 1, b_expected) z_from_p = norm.isf(p_value) # Determine warnings warnings = [] if n_obs < 10 or b_expected < 10: warnings.append("low_statistics") # Determine claim claim = "significant_excess" if z_from_p >= 3.0 else "no_significant_excess" # Save results to JSON result_data = { "n_obs": int(n_obs), "n_sideband": int(n_sideband), "lam": float(lam_fit), "b_expected": float(b_expected), "excess": float(excess), "z_asimov": float(z_asimov), "p_value": float(p_value), "z_from_p": float(z_from_p), "warnings": warnings, "claim": claim } with open("result.json", "w") as f: json.dump(result_data, f) # Plot histogram plt.figure(figsize=(10, 6)) plt.hist(df['m_gg'], bins=100, range=(100, 180), alpha=0.7, label="All Events") plt.axvspan(signal_window[0], signal_window[1], color='red', alpha=0.3, label="Signal Window") plt.axvspan(sideband_low[0], sideband_low[1], color='blue', alpha=0.3, label="Sideband Low") plt.axvspan(sideband_high[0], sideband_high[1], color='blue', alpha=0.3, label="Sideband High") plt.xlabel("Invariant Mass (GeV)") plt.ylabel("Number of Events") plt.title("Diphoton Invariant Mass Distribution") plt.legend() plt.savefig("figures/window.svg") # Save solution script with open("solution.py", "w") as f: f.write('''import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.optimize import minimize from scipy.stats import poisson, norm import json # Load data data_path = "data/synth_diphoton.csv" df = pd.read_csv(data_path) # Define regions signal_window = (120, 130) sideband_low = (100, 120) sideband_high = (130, 180) # Filter events in sidebands def is_in_region(m, region): return (region[0] <= m) & (m < region[1]) sideband_events = df[(is_in_region(df['m_gg'], sideband_low)) | (is_in_region(df['m_gg'], sideband_high))] # Fit exponential model to sideband events def neg_log_likelihood(params, m_values): lam = params[0] return -np.sum(np.log(np.exp(-m_values / lam))) # Initial guess for lambda initial_lam = 100.0 # Perform optimization result = minimize(neg_log_likelihood, x0=[initial_lam], args=(sideband_events['m_gg'].values,), bounds=[(5, 200)]) lam_fit = result.x[0] # Calculate expected background in signal window def integral_exp(lam, region): m_low, m_high = region return (lam - lam * np.exp(-(m_high - m_low) / lam)) if lam > 0 else 0 integral_sideband = integral_exp(lam_fit, sideband_low) + integral_exp(lam_fit, sideband_high) integral_signal_window = integral_exp(lam_fit, signal_window) n_sideband = len(sideband_events) b_expected = (n_sideband * integral_signal_window) / integral_sideband # Observed count in signal window n_obs = len(df[(df['m_gg'] >= signal_window[0]) & (df['m_gg'] <= signal_window[1])]) # Compute excess excess = n_obs - b_expected # Compute z_asimov if n_obs > b_expected: z_asimov = np.sqrt(2 * (n_obs * np.log(n_obs / b_expected) - (n_obs - b_expected))) else: z_asimov = -np.sqrt(2 * (n_obs * np.log(n_obs / b_expected) - (n_obs - b_expected))) # Compute p_value and z_from_p p_value = poisson.sf(n_obs - 1, b_expected) z_from_p = norm.isf(p_value) # Determine warnings warnings = [] if n_obs < 10 or b_expected < 10: warnings.append("low_statistics") # Determine claim claim = "significant_excess" if z_from_p >= 3.0 else "no_significant_excess" # Save results to JSON result_data = { "n_obs": int(n_obs), "n_sideband": int(n_sideband), "lam": float(lam_fit), "b_expected": float(b_expected), "excess": float(excess), "z_asimov": float(z_asimov), "p_value": float(p_value), "z_from_p": float(z_from_p), "warnings": warnings, "claim": claim } with open("result.json", "w") as f: json.dump(result_data, f) # Plot histogram plt.figure(figsize=(10, 6)) plt.hist(df['m_gg'], bins=100, range=(100, 180), alpha=0.7, label="All Events") plt.axvspan(signal_window[0], signal_window[1], color='red', alpha=0.3, label="Signal Window") plt.axvspan(sideband_low[0], sideband_low[1], color='blue', alpha=0.3, label="Sideband Low") plt.axvspan(sideband_high[0], sideband_high[1], color='blue', alpha=0.3, label="Sideband High") plt.xlabel("Invariant Mass (GeV)") plt.ylabel("Number of Events") plt.title("Diphoton Invariant Mass Distribution") plt.legend() plt.savefig("figures/window.svg") ''') ```
#1 toolwrite_fileok0screated: solution.pyt+29.964s
wrote 6156 chars to solution.py
#2 toolrun_pythonFileNotFoundError2.754screated: result.jsont+32.719s
STDERR:
Traceback (most recent call last):
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T171938-t5-bump-significance-single_shot-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/20260906T171938-t5-bump-significance-single_shot-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 96, in <module>
    plt.savefig("figures/window.svg")
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/pyplot.py", line 1228, in savefig
    res = fig.savefig(*args, **kwargs)  # type: ignore[func-returns-value]
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/figure.py", line 3395, in savefig
    self.canvas.print_figure(fname, **kwargs)
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/backend_bases.py", line 2204, in print_figure
    result = print_method(
             ^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.v
... [78 chars truncated] ...
raps(meth)(lambda *args, **kwargs: meth(
                                                                 ^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/backends/backend_svg.py", line 1328, in print_svg
    with cbook.open_file_cm(filename, "w", encoding="utf-8") as fh:
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/cbook.py", line 497, in open_file_cm
    fh, opened = to_filehandle(path_or_file, mode, True, encoding)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/cbook.py", line 483, in to_filehandle
    fh = open(fname, flag, encoding=encoding)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T171938-t5-bump-significance-single_shot-gariyuu-qwen3-8b-r0/workdir/.agenthep_guard.py", line 68, in _guarded_open
    return _real_open(file, mode, *a, **k)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'figures/window.svg'

exit code: 1
files created: result.json

Final answer

```python import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.optimize import minimize from scipy.stats import poisson, norm import json # Load data data_path = "data/synth_diphoton.csv" df = pd.read_csv(data_path) # Define regions signal_window = (120, 130) sideband_low = (100, 120) sideband_high = (130, 180) # Filter events in sidebands def is_in_region(m, region): return (region[0] <= m) & (m < region[1]) sideband_events = df[(is_in_region(df['m_gg'], sideband_low)) | (is_in_region(df['m_gg'], sideband_high))] # Fit exponential model to sideband events def neg_log_likelihood(params, m_values): lam = params[0] return -np.sum(np.log(np.exp(-m_values / lam))) # Initial guess for lambda initial_lam = 100.0 # Perform optimization result = minimize(neg_log_likelihood, x0=[initial_lam], args=(sideband_events['m_gg'].values,), bounds=[(5, 200)]) lam_fit = result.x[0] # Calculate expected background in signal window def integral_exp(lam, region): m_low, m_high = region return (lam - lam * np.exp(-(m_high - m_low) / lam)) if lam > 0 else 0 integral_sideband = integral_exp(lam_fit, sideband_low) + integral_exp(lam_fit, sideband_high) integral_signal_window = integral_exp(lam_fit, signal_window) n_sideband = len(sideband_events) b_expected = (n_sideband * integral_signal_window) / integral_sideband # Observed count in signal window n_obs = len(df[(df['m_gg'] >= signal_window[0]) & (df['m_gg'] <= signal_window[1])]) # Compute excess excess = n_obs - b_expected # Compute z_asimov if n_obs > b_expected: z_asimov = np.sqrt(2 * (n_obs * np.log(n_obs / b_expected) - (n_obs - b_expected))) else: z_asimov = -np.sqrt(2 * (n_obs * np.log(n_obs / b_expected) - (n_obs - b_expected))) # Compute p_value and z_from_p p_value = poisson.sf(n_obs - 1, b_expected) z_from_p = norm.isf(p_value) # Determine warnings warnings = [] if n_obs < 10 or b_expected < 10: warnings.append("low_statistics") # Determine claim claim
no assumptions stated
Numbers in the summary not found in any produced artifact: 120, 130, 100, 120, 130, 180, 100

Grader verdict

7 / 17 checks passed
CheckResultCategoryWeightDetailObservedExpected
result_existscritical✓ passartifact1ok
figure_exists✗ failartifact1missing
solution_exists✓ passartifact1ok
n_obscritical✓ passnumeric2match49424942
n_sb✓ passnumeric1match2535825358
lam✗ failnumeric2observed 200.0 vs expected 24.946467457251916 (atol=0.0, rtol=0.03)20024.946467457251916
b_expectedcritical✗ failnumeric3observed 3909.208563865205 vs expected 4629.777049959585 (atol=0.0, rtol=0.03)3909.2085638652054629.777049959585
excess✗ failnumeric1observed 1032.7914361347948 vs expected 312.2229500404146 (atol=0.0, rtol=0.05)1032.7914361347948312.2229500404146
z_asimovcritical✗ failnumeric2observed 15.86109460992459 vs expected 4.538470894307572 (atol=0.15, rtol=0.0)15.861094609924594.538470894307572
p_value✗ failnumeric1observed 6.362029599813284e-57 vs expected 2.897914860991712e-06 (atol=0.0, rtol=0.5)6.362029599813284e-570.000002897914860991712
z_from_p✗ failnumeric2observed 15.856264444692231 vs expected 4.533703776096391 (atol=0.15, rtol=0.0)15.8562644446922314.533703776096391
claimcritical✓ passcompliance3match"significant_excess""significant_excess"
no_low_stats_flag✓ passcompliance1absent[]"not 'low_statistics'"
svg_unit✗ failplot1missing file: window.svg
svg_drawn✗ failplot1missing file: window.svg
reruns✗ failreproducibility1solution.py failed in a clean workdir: FileNotFoundError: [Errno 2] No such file or directory: 'figures/window.svg'{"ok":false,"artifacts_reproduced":false,"output_matches":false,"exit_code":1}
not_hardcoded✓ passcompliance1no expected values hard-coded[]

Reproducibility rerun

script executes
✗ fail
artifacts reproduced
✗ fail
output matches submission
✗ differs
dependencies resolvable
✓ pass
seed controlled
no randomness used
detail
solution.py failed in a clean workdir: FileNotFoundError: [Errno 2] No such file or directory: 'figures/window.svg'

Artifacts

solution.py: write_file · result.json: run_python
result.json
{"n_obs": 4942, "n_sideband": 25358, "lam": 200.0, "b_expected": 3909.208563865205, "excess": 1032.7914361347948, "z_asimov": 15.86109460992459, "p_value": 6.362029599813284e-57, "z_from_p": 15.856264444692231, "warnings": [], "claim": "significant_excess"}
solution.py
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from scipy.stats import poisson, norm
import json

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

# Define regions
signal_window = (120, 130)
sideband_low = (100, 120)
sideband_high = (130, 180)

# Filter events in sidebands
def is_in_region(m, region):
    return (region[0] <= m) & (m < region[1])

sideband_events = df[(is_in_region(df['m_gg'], sideband_low)) | (is_in_region(df['m_gg'], sideband_high))]

# Fit exponential model to sideband events
def neg_log_likelihood(params, m_values):
    lam = params[0]
    return -np.sum(np.log(np.exp(-m_values / lam)))

# Initial guess for lambda
initial_lam = 100.0

# Perform optimization
result = minimize(neg_log_likelihood, x0=[initial_lam], args=(sideband_events['m_gg'].values,), bounds=[(5, 200)])
lam_fit = result.x[0]

# Calculate expected background in signal window
def integral_exp(lam, region):
    m_low, m_high = region
    return (lam - lam * np.exp(-(m_high - m_low) / lam)) if lam > 0 else 0

integral_sideband = integral_exp(lam_fit, sideband_low) + integral_exp(lam_fit, sideband_high)
integral_signal_window = integral_exp(lam_fit, signal_window)

n_sideband = len(sideband_events)
b_expected = (n_sideband * integral_signal_window) / integral_sideband

# Observed count in signal window
n_obs = len(df[(df['m_gg'] >= signal_window[0]) & (df['m_gg'] <= signal_window[1])])

# Compute excess
excess = n_obs - b_expected

# Compute z_asimov
if n_obs > b_expected:
    z_asimov = np.sqrt(2 * (n_obs * np.log(n_obs / b_expected) - (n_obs - b_expected)))
else:
    z_asimov = -np.sqrt(2 * (n_obs * np.log(n_obs / b_expected) - (n_obs - b_expected)))

# Compute p_value and z_from_p
p_value = poisson.sf(n_obs - 1, b_expected)
z_from_p = norm.isf(p_value)

# Determine warnings
warnings = []
if n_obs < 10 or b_expected < 10:
    warnings.append("low_statistics")

# Determine claim
claim = "significant_excess" if z_from_p >= 3.0 else "no_significant_excess"

# Save results to JSON
result_data = {
    "n_obs": int(n_obs),
    "n_sideband": int(n_sideband),
    "lam": float(lam_fit),
    "b_expected": float(b_expected),
    "excess": float(excess),
    "z_asimov": float(z_asimov),
    "p_value": float(p_value),
    "z_from_p": float(z_from_p),
    "warnings": warnings,
    "claim": claim
}
with open("result.json", "w") as f:
    json.dump(result_data, f)

# Plot histogram
plt.figure(figsize=(10, 6))
plt.hist(df['m_gg'], bins=100, range=(100, 180), alpha=0.7, label="All Events")
plt.axvspan(signal_window[0], signal_window[1], color='red', alpha=0.3, label="Signal Window")
plt.axvspan(sideband_low[0], sideband_low[1], color='blue', alpha=0.3, label="Sideband Low")
plt.axvspan(sideband_high[0], sideband_high[1], color='blue', alpha=0.3, label="Sideband High")
plt.xlabel("Invariant Mass (GeV)")
plt.ylabel("Number of Events")
plt.title("Diphoton Invariant Mass Distribution")
plt.legend()
plt.savefig("figures/window.svg")

# Save solution script
with open("solution.py", "w") as f:
    f.write('''import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from scipy.stats import poisson, norm
import json

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

# Define regions
signal_window = (120, 130)
sideband_low = (100, 120)
sideband_high = (130, 180)

# Filter events in sidebands
def is_in_region(m, region):
    return (region[0] <= m) & (m < region[1])

sideband_events = df[(is_in_region(df['m_gg'], sideband_low)) | (is_in_region(df['m_gg'], sideband_high))]

# Fit exponential model to sideband events
def neg_log_likelihood(params, m_values):
    lam = params[0]
    return -np.sum(np.log(np.exp(-m_values / lam)))

# Initial guess for lambda
initial_lam = 100.0

# Perform optimization
result = minimize(neg_log_likelihood, x0=[initial_lam], args=(sideband_events['m_gg'].values,), bounds=[(5, 200)])
lam_fit = result.x[0]

# Calculate expected background in signal window
def integral_exp(lam, region):
    m_low, m_high = region
    return (lam - lam * np.exp(-(m_high - m_low) / lam)) if lam > 0 else 0

integral_sideband = integral_exp(lam_fit, sideband_low) + integral_exp(lam_fit, sideband_high)
integral_signal_window = integral_exp(lam_fit, signal_window)

n_sideband = len(sideband_events)
b_expected = (n_sideband * integral_signal_window) / integral_sideband

# Observed count in signal window
n_obs = len(df[(df['m_gg'] >= signal_window[0]) & (df['m_gg'] <= signal_window[1])])

# Compute excess
excess = n_obs - b_expected

# Compute z_asimov
if n_obs > b_expected:
    z_asimov = np.sqrt(2 * (n_obs * np.log(n_obs / b_expected) - (n_obs - b_expected)))
else:
    z_asimov = -np.sqrt(2 * (n_obs * np.log(n_obs / b_expected) - (n_obs - b_expected)))

# Compute p_value and z_from_p
p_value = poisson.sf(n_obs - 1, b_expected)
z_from_p = norm.isf(p_value)

# Determine warnings
warnings = []
if n_obs < 10 or b_expected < 10:
    warnings.append("low_statistics")

# Determine claim
claim = "significant_excess" if z_from_p >= 3.0 else "no_significant_excess"

# Save results to JSON
result_data = {
    "n_obs": int(n_obs),
    "n_sideband": int(n_sideband),
    "lam": float(lam_fit),
    "b_expected": float(b_expected),
    "excess": float(excess),
    "z_asimov": float(z_asimov),
    "p_value": float(p_value),
    "z_from_p": float(z_from_p),
    "warnings": warnings,
    "claim": claim
}
with open("result.json", "w") as f:
    json.dump(result_data, f)

# Plot histogram
plt.figure(figsize=(10, 6))
plt.hist(df['m_gg'], bins=100, range=(100, 180), alpha=0.7, label="All Events")
plt.axvspan(signal_window[0], signal_window[1], color='red', alpha=0.3, label="Signal Window")
plt.axvspan(sideband_low[0], sideband_low[1], color='blue', alpha=0.3, label="Sideband Low")
plt.axvspan(sideband_high[0], sideband_high[1], color='blue', alpha=0.3, label="Sideband High")
plt.xlabel("Invariant Mass (GeV)")
plt.ylabel("Number of Events")
plt.title("Diphoton Invariant Mass Distribution")
plt.legend()
plt.savefig("figures/window.svg")
''')

Run metadata

{
 "run_id": "20260906T171938-t5-bump-significance-single_shot-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": "2026-09-06T17:19:38.134939+00:00",
  "preset": "gariyuu-qwen3-8b",
  "family": "qwen3-8b",
  "display": "Qwen3-8B (gariyuu gateway)",
  "is_mock": false
 },
 "agent": {
  "name": "single_shot",
  "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": "2026-09-06T17:19:38.107599+00:00",
 "finished_at": "2026-09-06T17:20:12.602041+00:00"
}