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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented and ran the registered dynamics benchmark using the built-in dynamics track and rnn_small model. The best CEGAR-inspired system had higher test MSE than the tuned standard baseline: delta_mean=+0.0033235 with permutation p=0.0081, so the idea was significantly worse and did not produce a training win. The mechanism signature was measured from trained-model latent behavior and marked confirmed, but this did not improve the independent task metric.",
  "metrics": {
    "baseline": "Best lr=0.01, weight_decay=0.0; full 8-seed MSE mean=0.0014403377863345668, std=0.0003958335789015625.",
    "idea": "Best lr=0.001, weight_decay=0.0; full 8-seed MSE mean=0.004763860866660252, std=0.002933690813628907; paired delta=+0.0033235230803256854; 0/8 wins; permutation p=0.0081."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.01,
        "weight_decay": 0.0
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0
          },
          "mean": 0.004545392468571663
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0
          },
          "mean": 0.00358188062091358
        },
        {
          "cfg": {
            "lr": 0.01,
            "weight_decay": 0.0
          },
          "mean": 0.0015466218756046146
        }
      ],
      "full": {
        "mean": 0.0014403377863345668,
        "std": 0.0003958335789015625,
        "per_seed": [
          0.001036701025441289,
          0.0019527574768289924,
          0.0010734819807112217,
          0.0021235470194369555,
          0.001080187619663775,
          0.0011896038195118308,
          0.001624842407181859,
          0.001441580941900611
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.004763860866660252,
      "std": 0.002933690813628907,
      "per_seed": [
        0.011456220410764217,
        0.002699522068724036,
        0.0031392518430948257,
        0.0025344439782202244,
        0.007165998220443726,
        0.0028000844176858664,
        0.003200623905286193,
        0.005114742089062929
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": 0.0033235230803256854,
      "idea_wins": 0,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.010419519385322928,
        0.0007467645918950438,
        0.002065769862383604,
        0.00041089695878326893,
        0.006085810600779951,
        0.0016104805981740355,
        0.0015757814981043339,
        0.0036731611471623182
      ],
      "p_value": 0.0081,
      "mde": 0.002840810147756273,
      "mde_rel_pct": 197.2322169638893,
      "verdict": "idea worse (significant)",
      "system_worked": false
    },
    "mechanism_signature": {
      "idea_sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0
          },
          "result": {
            "mean": 0.004763860866660252,
            "std": 0.002933690813628907,
            "per_seed": [
              0.011456220410764217,
              0.002699522068724036,
              0.0031392518430948257,
              0.0025344439782202244,
              0.007165998220443726,
              0.0028000844176858664,
              0.003200623905286193,
              0.005114742089062929
            ],
            "n": 8
          }
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0
          },
          "result": {
            "mean": 0.0048263132775900885,
            "std": 0.0025504190600527878,
            "per_seed": [
              0.0045633199624717236,
              0.004938819445669651,
              0.0009674743050709367,
              0.010774293914437294,
              0.004922726657241583,
              0.004287341609597206,
              0.0037127339746803045,
              0.00444379635155201
            ],
            "n": 8
          }
        },
        {
          "cfg": {
            "lr": 0.01,
            "weight_decay": 0.0
          },
          "result": {
            "mean": 0.04266263055615127,
            "std": 0.02664842856440231,
            "per_seed": [
              0.07184934616088867,
              0.04949978366494179,
              0.023517103865742683,
              0.029628023505210876,
              0.008588656783103943,
              0.09184401482343674,
              0.048785608261823654,
              0.017588507384061813
            ],
            "n": 8
          }
        }
      ],
      "selected_cfg": {
        "lr": 0.001,
        "weight_decay": 0.0
      },
      "signature": {
        "prediction": "latent box transitions should contain observed trained-model successors",
        "observed_transitions": 159,
        "same_cell_fraction": 0.0,
        "mean_observed_box_span": 0.17268966138362885,
        "cells": 4,
        "metric_for_signature_model": 0.011456220410764217,
        "confirmed": true
      }
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_cegar.py",
  "files": [
    "bench_cegar.py",
    "bench_report.json"
  ],
  "limitations": "The intervention is a differentiable latent-box consistency surrogate rather than a complete temporal-logic CEGAR verifier. It does not implement an unsafe-path parser, constrained counterexample solver, incident-edge-only graph refinement, or a formal interval certificate. Only the built-in dynamics track, rnn_small architecture, 400 training samples, 160 test samples, 12 epochs, and three learning rates were tested.",
  "system_verdict": "failed",
  "practical_verdict": "harms",
  "mechanism_ok": 0,
  "system_judged": true
}