# Стенд-проверка (stage-2) · промт оператора:

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built and benchmarked an end-to-end semantic pushforward system using a shared MLP backbone, nine response continuations, deterministic state aggregation, and trainable calibration parameters. The exact pushforward checks passed, and trained models showed conserved state mass (mean error 1.19e-7) plus a nontrivial calibration change, confirming the mechanism signature. However, eight paired seeds produced identical mean test error for baseline and idea (0.2784375), delta 0.0, permutation p=1.0; therefore the idea did not improve the standard task metric.",
  "metrics": {
    "baseline": "Test classification error mean 0.2784375, std 0.0445452; best config lr=0.003, temperature=1.0, epochs=18, weight_decay=0.",
    "idea": "Test classification error mean 0.2784375, std 0.0458758; selected cfg lr=0.003, temperature=1.0, epochs=18, weight_decay=0. Idea wins on 5/8 seeds but paired delta is 0.0 and p=1.0.",
    "paired_delta": "0.0 lower-is-better error units; permutation p-value 1.0; verdict no measurable effect",
    "mechanism_signature": "Confirmed: aggregation error 0.0, mass conservation error 4.44e-16 in the direct check; trained-model observed mass error mean 1.19e-7, calibrated-vs-raw probability difference 0.00750, learned calibration slopes [1.0525, 1.0695, 1.0371]."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 run_bench.py",
  "files": [
    "semantic_track.py",
    "run_bench.py",
    "bench_report.json"
  ],
  "limitations": "This custom benchmark uses synthetic evidence and response logits rather than a real language model with token-level constrained decoding, malformed generations, prompt sensitivity, or semantic-map errors. The primary bench metric is classification error, so calibration benefits may not appear in it; NLL, Brier score, ECE, prediction-set coverage, compute overhead, and distribution-shift calibration were not included in the final harness report. The baseline and idea share the same trained response architecture and differ only by the calibration head, while the full baseline sweep covered nine configurations and the idea evaluated the same temperature and learning-rate union at three settings.",
  "bench_report": {
    "bench_version": 1,
    "track": "semantic_pushforward_classification",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.003,
        "epochs": 18,
        "temperature": 1.0,
        "weight_decay": 0.0
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "epochs": 18,
            "temperature": 0.8,
            "weight_decay": 0.0
          },
          "mean": 0.285625
        },
        {
          "cfg": {
            "lr": 0.001,
            "epochs": 18,
            "temperature": 1.0,
            "weight_decay": 0.0
          },
          "mean": 0.29
        },
        {
          "cfg": {
            "lr": 0.001,
            "epochs": 18,
            "temperature": 1.2,
            "weight_decay": 0.0
          },
          "mean": 0.290625
        },
        {
          "cfg": {
            "lr": 0.003,
            "epochs": 18,
            "temperature": 0.8,
            "weight_decay": 0.0
          },
          "mean": 0.28125
        },
        {
          "cfg": {
            "lr": 0.003,
            "epochs": 18,
            "temperature": 1.0,
            "weight_decay": 0.0
          },
          "mean": 0.278125
        },
        {
          "cfg": {
            "lr": 0.003,
            "epochs": 18,
            "temperature": 1.2,
            "weight_decay": 0.0
          },
          "mean": 0.278125
        },
        {
          "cfg": {
            "lr": 0.006,
            "epochs": 18,
            "temperature": 0.8,
            "weight_decay": 0.0
          },
          "mean": 0.29625
        },
        {
          "cfg": {
            "lr": 0.006,
            "epochs": 18,
            "temperature": 1.0,
            "weight_decay": 0.0
          },
          "mean": 0.2975
        },
        {
          "cfg": {
            "lr": 0.006,
            "epochs": 18,
            "temperature": 1.2,
            "weight_decay": 0.0
          },
          "mean": 0.28625
        }
      ],
      "full": {
        "mean": 0.2784374934,
        "std": 0.044545225,
        "per_seed": [
          0.2825,
          0.325,
          0.33,
          0.175,
          0.28,
          0.275,
          0.2675,
          0.2925
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.2784374934,
      "std": 0.0458758041,
      "per_seed": [
        0.28,
        0.3225,
        0.335,
        0.1725,
        0.2775,
        0.2725,
        0.27,
        0.2975
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": 0.0,
      "idea_wins": 5,
      "n_pairs": 8,
      "per_seed_diffs": [
        -0.0025,
        -0.0025,
        0.005,
        -0.0025,
        -0.0025,
        -0.0025,
        0.0025,
        0.005
      ],
      "p_value": 1.0,
      "mde": 0.0029562472,
      "mde_rel_pct": 1.0617274,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "Pushforward conserves state mass; calibration learns a nontrivial correction when raw response probabilities are distorted.",
      "predicted_vs_observed": {
        "predicted_mass_error": 0.0,
        "observed_mass_error_mean": 1.1920929e-07,
        "observed_abs_calibrated_vs_raw_mean": 0.0074999,
        "learned_a_mean_by_state": [
          1.0525091,
          1.0694604,
          1.0370568
        ]
      },
      "confirmed": true
    },
    "custom_track": {
      "name": "semantic_pushforward_classification",
      "file": "semantic_track.py",
      "domain": "uncertainty_calibration"
    }
  },
  "system_verdict": "partial",
  "practical_verdict": "no_effect",
  "mechanism_ok": 1,
  "system_judged": true
}