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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "Implemented RG Pyramid Flow Matching as a dyadic pooled coarse-conditioning model with a local fine residual field, compared against a matched radius-1 local convolutional baseline on the registered multitoken_diffusion track. The idea achieved MSE 0.54740 versus baseline 0.54884, delta_mean -0.00144, but p=0.40545, so this is no measurable effect rather than a significant win. The behavioral signature also failed: distant sensitivity was 0.0 for both systems, so the claimed long-range transfer was not quantitatively confirmed.", "metrics": { "baseline": "mean MSE 0.5488443524; std 0.0252618988; best lr 0.006, epochs 12, hidden 32; 8 paired seeds", "idea": "mean MSE 0.5474011526; std 0.0256864126; best lr 0.006, epochs 12, hidden 32; 8 paired seeds", "delta_mean": "-0.0014431998 (idea lower is better)", "p_value": "0.40545", "idea_wins": "4/8", "mechanism_signature": "confirmed=false; observed distant sensitivity idea 0.0, baseline 0.0" }, "bench_report": { "bench_version": 1, "track": "multitoken_diffusion", "model": "custom_local_vs_rg_pyramid", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "epochs": 12, "hidden": 32 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 12, "hidden": 32 }, "mean": 0.5760585516691208 }, { "cfg": { "lr": 0.003, "epochs": 12, "hidden": 32 }, "mean": 0.5570157170295715 }, { "cfg": { "lr": 0.006, "epochs": 12, "hidden": 32 }, "mean": 0.552870586514473 } ], "full": { "mean": 0.5488443523645401, "std": 0.02526189879, "n": 8 } }, "idea": { "best_cfg": { "lr": 0.006, "epochs": 12, "hidden": 32 }, "mean": 0.5474011525511742, "std": 0.025686412604992322, "per_seed": [ 0.547066330909729, 0.5185491442680359, 0.5782044529914856, 0.5547541379928589, 0.5519959330558777, 0.5247784852981567, 0.5898048281669617, 0.5140559077262878 ], "n": 8 }, "comparison": { "delta_mean": -0.0014431998133659363, "idea_wins": 4, "n_pairs": 8, "per_seed_diffs": [ 0.002116858959197998, -0.0090712308883667, 4.8041343688964844e-05, -0.006001949310302734, 0.00014024972915649414, -0.0014293789863586426, -0.00023895502090454102, 0.00289076566696167 ], "p_value": 0.40545, "mde": 0.0034127227373531473, "mde_rel_pct": 0.6218015586113622, "verdict": "no measurable effect", "system_worked": false }, "mechanism_signature": { "prediction": "coarse pooled conditioning transmits distant-token influence beyond a radius-1 local field", "predicted_vs_observed": { "predicted": "idea distant sensitivity > baseline", "observed_idea_mean_distant_sensitivity": 0.0, "observed_baseline_mean_distant_sensitivity": 0.0 }, "confirmed": false }, "custom_track": { "name": "multitoken_diffusion", "file": "/home/maxwelhelp/all/math2nn/bench/custom_tracks/multitoken_diffusion.py", "domain": "diffusion-sampling" } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json" ], "limitations": "The benchmark used the registered synthetic multitoken diffusion track rather than CIFAR-10 or FFHQ, and tested only DIM=8 with a compact radius-1 convolutional architecture. No FID, ODE sampling wall-clock, activation-memory, or resolution-scaling study was performed. Evaluation was forced to CPU after a shared-GPU Conv1d execution-engine failure; training still used the benchmark's GPU-to-CPU fallback.", "system_verdict": "failed", "practical_verdict": "no_effect", "mechanism_ok": 0, "system_judged": true }