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

(универсальный)

Ответ агента:

{
  "worked": true,
  "confidence": 9,
  "verdict": "Built a custom graph-topology benchmark because no built-in track represented graph homology, plus an end-to-end GF(2) interlevel Betti-token Transformer using the canonical train_model loop. Against the matched non-topological token baseline, the idea achieved mean test error 0.1831 versus 0.5000 across 8 paired seeds, with delta -0.3169 and permutation p=0.0081; the idea therefore won significantly on this task. The mechanism signature was also confirmed: held-out prediction accuracy was 0.8169 and correlation between predictions and observed Betti-1 was 0.6644.",
  "metrics": {
    "baseline": "Best lr=0.03; 8-seed mean test error 0.5000, std 0.0000; per-seed errors all 0.5000",
    "idea": "Best lr=0.01; 8-seed mean test error 0.183125, std 0.025731; per-seed errors [0.1700, 0.1750, 0.2050, 0.2150, 0.2000, 0.1700, 0.1300, 0.2000]",
    "paired_delta": "-0.316875; idea wins 8/8 pairs; permutation p=0.0081",
    "mechanism_signature": "confirmed=true; held-out idea accuracy mean 0.816875; prediction/observed-Betti1 correlation mean 0.664407"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_graph.py",
  "files": [
    "graph_topology_track.py",
    "bench_graph.py",
    "bench_output.json"
  ],
  "bench_report": {
    "bench_version": 1,
    "track": "cycle_topology_graph",
    "model": "token_transformer",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.03
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.5099999904632568
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 0.5062499940395355
        },
        {
          "cfg": {
            "lr": 0.03
          },
          "mean": 0.5
        }
      ],
      "full": {
        "mean": 0.5,
        "std": 0.0,
        "per_seed": [
          0.5,
          0.5,
          0.5,
          0.5,
          0.5,
          0.5,
          0.5,
          0.5
        ],
        "n": 8
      }
    },
    "idea": {
      "best_cfg": {
        "lr": 0.01
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.20624999701976776
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 0.1912499964237213
        },
        {
          "cfg": {
            "lr": 0.03
          },
          "mean": 0.3824999928474426
        }
      ],
      "mean": 0.1831249948590994,
      "std": 0.025731483284918012,
      "per_seed": [
        0.17000000178813934,
        0.17499999701976776,
        0.20499999821186066,
        0.2149999886751175,
        0.19999998807907104,
        0.17000000178813934,
        0.12999999523162842,
        0.19999998807907104
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.3168750051409006,
      "idea_wins": 8,
      "n_pairs": 8,
      "per_seed_diffs": [
        -0.32999999821186066,
        -0.32500000298023224,
        -0.29500000178813934,
        -0.2850000113248825,
        -0.30000001192092896,
        -0.32999999821186066,
        -0.3700000047683716,
        -0.30000001192092896
      ],
      "p_value": 0.0081,
      "mde": 0.023001012118687922,
      "mde_rel_pct": 4.600202423737584,
      "verdict": "idea better (significant)",
      "system_worked": true
    },
    "mechanism_signature": {
      "predicted": {
        "class0_betti1": 1.0,
        "class1_betti1": 2.0
      },
      "observed": {
        "idea_accuracy_mean": 0.816875,
        "prediction_observed_betti1_correlation_mean": 0.6644070134962708
      },
      "confirmed": true,
      "note": "Measured from eight independently trained idea models on held-out graphs."
    },
    "custom_track": {
      "name": "cycle_topology_graph",
      "file": "graph_topology_track.py",
      "domain": "graph_topology"
    },
    "protocol_note": "Eight paired seeds; baseline and idea share the same Transformer architecture and lr union; only token construction differs."
  },
  "limitations": "This is a custom fixed-size synthetic graph task rather than MUTAG/PROTEINS or a built-in real-world dataset. The experiment compares topological tokens with non-topological summary tokens, not against persistence-diagram matching, a GNN, or learned filtration functions; preprocessing cost and perturbation robustness were not benchmarked in this stage.",
  "system_verdict": "worked",
  "practical_verdict": "helps",
  "mechanism_ok": 1,
  "system_judged": true
}