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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented an end-to-end categorical multi-token benchmark with a shared MLP, independent categorical decoding baseline, and persistent Hamiltonian momentum sampler intervention. Across 8 paired seeds, the best idea configuration achieved error 0.48641 versus baseline 0.49445, but the paired delta was not significant (p=0.3411). The mechanism signature was not confirmed: observed sign correlation was 0.065 versus predicted 0.540.",
  "metrics": {
    "baseline": "test err mean=0.494453125, std=0.0157818301, best_cfg={lr:0.003,temp:0.8}",
    "idea": "test err mean=0.48640625, std=0.0130644063, best_cfg={lr:0.01,temp:1.0,rho:0.0}",
    "comparison": "delta_mean=-0.008046875, idea_wins=4/8, p_value=0.3411, verdict=no significant win",
    "mechanism_signature": "predicted sign correlation=0.5398931; observed mean=0.0651730; mean absolute error=0.4747211; confirmed=false"
  },
  "bench_report": {
    "bench_version": 1,
    "track": "categorical_persistent_sequences",
    "model": "mlp_token_logits",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.003,
        "temp": 0.8
      },
      "full": {
        "mean": 0.494453125,
        "std": 0.015781830125475786,
        "per_seed": [
          0.460625,
          0.498125,
          0.514375,
          0.489375,
          0.483125,
          0.50875,
          0.501875,
          0.499375
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.48640625,
      "std": 0.013064406260427612,
      "per_seed": [
        0.480625,
        0.503125,
        0.48625,
        0.49875,
        0.47375,
        0.464375,
        0.4825,
        0.501875
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.008046875000000002,
      "idea_wins": 4,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.020000000000000018,
        0.00500000000000006,
        -0.02812500000000001,
        0.009375000000000022,
        -0.009375000000000022,
        -0.04437500000000005,
        -0.019374999999999976,
        0.0024999999999999467
      ],
      "p_value": 0.3411,
      "verdict": "no significant win",
      "system_worked": false
    },
    "custom_track": {
      "name": "categorical_persistent_sequences",
      "file": "categorical_track.py",
      "domain": "diffusion-sampling"
    },
    "idea_config": {
      "lr": 0.01,
      "temp": 1.0,
      "rho": 0.0
    },
    "mechanism_signature": {
      "prediction": "Gaussian refresh sign correlation is 2 asin(rho)/pi",
      "rho": 0.75,
      "observed_sign_corr_mean": 0.0651729827586207,
      "predicted_sign_corr": 0.5398930876747683,
      "mean_abs_error": 0.4747211049161476,
      "tolerance": 0.04,
      "confirmed": false
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "categorical_track.py",
    "stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "The custom benchmark uses a small synthetic binary multi-token classification task and an approximate fixed-step sampler rather than exact continuous-time Gillespie dynamics. No larger categorical VAE, real-language token sequence, multimodal target, or FLOP-normalized production-scale evaluation was tested.",
  "system_verdict": "failed",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 0,
  "system_judged": true
}