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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 10,
  "verdict": "Built an IMM stale-feedback monitor and integrated it into a custom Adam training loop for the matched tabular Friedman#1 plus mlp_tiny benchmark. Across the required 8 paired seeds, the best idea configuration exactly matched the tuned baseline (delta_mean=0, p=1.0), so there is no observed training-metric win. A separate trained-MLP two-step stale-gradient diagnostic also produced zero alarms, meaning the stage-1 detection effect did not transfer quantitatively to this neural setup.",
  "metrics": {
    "baseline": "Friedman#1 tabular / mlp_tiny: tuned lr=0.01, 8-seed mean test MSE 8.567867696285248, std 0.9045233203943077; sweep means lr=0.001:25.60164165496826, lr=0.003:13.860755920410156, lr=0.01:8.352817177772522.",
    "idea": "IMM monitor with persistent alarm-triggered 4x Adam learning-rate reduction: best lr=0.01, 8-seed mean test MSE 8.567867696285248, std 0.9045233203943077; paired delta 0.0, p=1.0, 0/8 wins, verdict no measurable effect.",
    "diagnostic": "On 8 trained MLP runs with explicit two-step stale-gradient application, clean alarm rate 0.0 and stale-delay-2 alarm rate 0.0; stale final no-delay posterior ranged 0.8641-0.9253, while clean final no-delay posterior ranged 0.9713-1.0000."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "tabular",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.01
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 25.60164165496826
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 13.860755920410156
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 8.352817177772522
        }
      ],
      "full": {
        "mean": 8.567867696285248,
        "std": 0.9045233203943077,
        "per_seed": [
          7.95448637008667,
          9.628232955932617,
          7.726639747619629,
          8.101909637451172,
          7.126259803771973,
          8.949170112609863,
          9.36670970916748,
          9.689533233642578
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 8.567867696285248,
      "std": 0.9045233203943077,
      "per_seed": [
        7.95448637008667,
        9.628232955932617,
        7.726639747619629,
        8.101909637451172,
        7.126259803771973,
        8.949170112609863,
        9.36670970916748,
        9.689533233642578
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": 0.0,
      "idea_wins": 0,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0
      ],
      "p_value": 1.0,
      "mde": 0.0,
      "mde_rel_pct": 0.0,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "Persistent stale feedback should lower the no-delay posterior and trigger intervention.",
      "observed": "On trained MLP dynamics with explicit two-step stale gradients, stale final no-delay posterior was 0.8641-0.9253 versus clean 0.9713-1.0000, but no run crossed the three-step alarm condition.",
      "confirmed": false,
      "clean_alarm_rate": 0.0,
      "stale_alarm_rate": 0.0,
      "stale_first_alarm_median": null
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_imm.py && /home/maxwelhelp/main/bin/python3 nn_signature.py",
  "files": [
    "bench_imm.py",
    "nn_signature.py",
    "bench_report.json"
  ],
  "limitations": "Only the matched built-in tabular track was tested; vision, sequence, and dynamics were not tested. The canonical bench has no asynchronous acknowledgement or injected-delay protocol, so the main comparison had no actual stale feedback to detect. The monitor uses a simplified online diagonal observation model rather than fitted A_t/P_t delayed-state IMM filtering, and the explicit diagnostic was not used as the primary task-metric comparison.",
  "system_verdict": "failed",
  "practical_verdict": "no_effect",
  "mechanism_ok": 0,
  "system_judged": true
}