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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 10,
  "verdict": "Implemented a response-sufficient bottleneck for the structurally matched sequence forecasting track, with an auxiliary future-response prediction loss and trained-model intervention signature. The mathematical sanity check matched the predicted heterogeneity gap with 0.26% relative error, and the neural intervention response correlated strongly with the learned response prediction (0.957), confirming the mechanism signal. However, the idea significantly worsened test MSE: 1.09184 versus the tuned baseline's 0.30664, paired delta +0.78520 with permutation p=0.0081, so it does not give a benchmark win.",
  "metrics": {
    "baseline": "sequence/transformer_tiny, lr=0.001 selected by sweep; 8-seed test MSE mean 0.3066449799, std 0.0326071854",
    "idea": "response bottleneck, lr=0.001 and auxiliary weight alpha=0.2 selected from shared-lr sweep; 8-seed test MSE mean 1.0918433443, std 0.0919865673; paired delta +0.7851983644, p=0.0081, 0/8 wins",
    "math_sanity": "Predicted G=10.3808250, observed G=10.3542673, relative error=0.00255834",
    "trained_model_signature": "Event intervention observed/predicted response correlation=0.9570617; observed std=0.00484657, predicted std=0.00489357; confirmed=true"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_response_memory.py",
  "files": [
    "stage2_response_memory.py",
    "bench_report.json"
  ],
  "limitations": "Only the built-in sequence forecasting track was tested, using a transformer rather than an RNN/SSM. The auxiliary response target was the future scalar observable at every position rather than explicit event-counterfactual rollouts or intensity-weighted martingale residuals; no long-horizon memory-compression, wall-clock, or parameter/memory savings study was performed. The runner uses a compact 1,200/400 train/test subset and 8 epochs for the required time budget.",
  "bench_report": {
    "bench_version": 1,
    "track": "sequence",
    "model": "transformer_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.001
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 0.2947123125195503
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.3950112909078598
        },
        {
          "cfg": {
            "lr": 0.006
          },
          "mean": 0.6112398952245712
        }
      ],
      "full": {
        "mean": 0.3066449798643589,
        "std": 0.032607185412887076,
        "per_seed": [
          0.34020674228668213,
          0.267076700925827,
          0.2600124776363373,
          0.31155332922935486,
          0.3084578514099121,
          0.3220774829387665,
          0.36002540588378906,
          0.28374984860420227
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 1.091843344271183,
      "std": 0.0919865672886275,
      "per_seed": [
        1.2080531120300293,
        0.9245380759239197,
        1.118348479270935,
        1.0414921045303345,
        1.1348344087600708,
        1.1197314262390137,
        1.1946824789047241,
        0.993066668510437
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": 0.7851983644068241,
      "idea_wins": 0,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.8678463697433472,
        0.6574613749980927,
        0.8583360016345978,
        0.7299387753009796,
        0.8263765573501587,
        0.7976539433002472,
        0.8346570730209351,
        0.7093168199062347
      ],
      "p_value": 0.0081,
      "mde": 0.06446300514098574,
      "mde_rel_pct": 21.022031913010643,
      "verdict": "idea worse (significant)",
      "system_worked": false
    },
    "mechanism_signature": {
      "math_sanity": {
        "predicted_G": 10.380825000000002,
        "observed_G": 10.354267303384185,
        "relative_error": 0.002558341616953959,
        "scaling_prediction": "G proportional to lambda^2 and (1-rho^2)"
      },
      "trained_model_intervention": {
        "event_position": 16,
        "observed_intervention_std": 0.004846571013331413,
        "predicted_response_std": 0.004893567878752947,
        "observed_predicted_correlation": 0.9570617378060089,
        "confirmed": true,
        "test_task_mse": 1.0759129524230957
      },
      "method": "response bottleneck z with auxiliary q(z,position) future-observable loss"
    },
    "idea_sweep": [
      {
        "cfg": {
          "lr": 0.001,
          "alpha": 0.2
        },
        "result": {
          "mean": 1.091843344271183,
          "std": 0.0919865672886275,
          "per_seed": [
            1.2080535163879396,
            0.9245380759239197,
            1.118348479270935,
            1.0414921045303345,
            1.1348344087600708,
            1.1197314262390137,
            1.1946824789047241,
            0.993066668510437
          ],
          "n": 8
        }
      },
      {
        "cfg": {
          "lr": 0.003,
          "alpha": 0.2
        },
        "result": {
          "mean": 1.0942725539207458,
          "std": 0.0937933969335425,
          "per_seed": [
            1.208382248878479,
            0.9236506223678589,
            1.118179440498352,
            1.0507817268371582,
            1.1268558502197266,
            1.1211200952529907,
            1.211830735206604,
            0.9933797121047974
          ],
          "n": 8
        }
      },
      {
        "cfg": {
          "lr": 0.006,
          "alpha": 0.2
        },
        "result": {
          "mean": 1.1014602482318878,
          "std": 0.0956785143000178,
          "per_seed": [
            1.2139562368392944,
            0.9385022521018982,
            1.1244070529937744,
            1.0431501865386963,
            1.152730107307434,
            1.1348258256912231,
            1.2168488502502441,
            0.9872614741325378
          ],
          "n": 8
        }
      }
    ]
  },
  "system_verdict": "failed",
  "practical_verdict": "harms",
  "mechanism_ok": 0,
  "system_judged": true
}