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

(универсальный)

Ответ агента:

{ "worked": true, "confidence": 8, "verdict": "Implemented boundary-compressed approximate pruning as a structured 50%-token selection intervention for the registered multi-token sequence forecasting track, using matched transformer_tiny systems and the canonical train_model path. Across 8 paired seeds it significantly improved test MSE over the tuned magnitude baseline (delta_mean=-0.2312, permutation p=0.0081). The independently measured mechanism signature was not confirmed, so the gain is empirically real under the bench but should not be attributed confidently to the proposed theorem mechanism.", "metrics": { "baseline": "Tuned magnitude baseline, best lr=0.0015, 8-seed mean test MSE=0.8053048.", "idea": "Boundary-compressed DP, eta=2, lr=0.0015, 8-seed mean test MSE=0.5740982; 8/8 wins; retained fraction=0.5; peak states=64." }, "bench_report": { "bench_version": 1, "track": "sequence", "model": "transformer_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "grid": [ { "config": { "lr": 0.0015, "epochs": 10, "keep": 16 }, "mean": 0.718140721321106, "per_seed": [ 1.2147727012634277, 0.9435746669769287, 0.3435657322406769, 0.3706497848033905 ] }, { "config": { "lr": 0.003, "epochs": 10, "keep": 16 }, "mean": 0.759648285806179, "per_seed": [ 1.2128480672836304, 1.0560415983200073, 0.36093801259994507, 0.4087654650211334 ] }, { "config": { "lr": 0.006, "epochs": 10, "keep": 16 }, "mean": 0.8399795442819595, "per_seed": [ 1.2241541147232056, 0.9892252683639526, 0.5402792096138, 0.6062595844268799 ] } ], "best": { "lr": 0.0015, "epochs": 10, "keep": 16 }, "full": { "per_seed": [ 1.2147727012634277, 0.9435746669769287, 0.3435657322406769, 0.3706497848033905, 0.942135751247406, 0.7555773258209229, 1.2186181545257568, 0.6535401940345764 ] } }, "idea": { "config": { "lr": 0.0015, "epochs": 10, "eta": 2, "keep": 16 }, "per_seed": [ 0.9471153020858765, 0.7885468602180481, 0.3400212824344635, 0.34854480624198914, 0.5234649777412415, 0.3543352484703064, 0.9228858947753906, 0.36787140369415283 ] }, "comparison": { "delta_mean": -0.23120606690645218, "idea_wins": 8, "n_pairs": 8, "per_seed_diffs": [ -0.26765739917755127, -0.15502780675888062, -0.003544449806213379, -0.022104978561401367, -0.41867077350616455, -0.40124207735061646, -0.2957322597503662, -0.2856687903404236 ], "p_value": 0.0081, "verdict": "idea better (significant)", "system_worked": true }, "mechanism_signature": { "prediction": "omitted-token interaction mass predicts output sensitivity", "predicted_mean": 0.09890676662325859, "observed_mean": 0.06862932164222002, "correlation": -0.32015715855609533, "confirmed": false }, "runtime_sec": 65.80087637901306 }, "how_to_run": "/home/maxwelhelp/main/bin/python3 boundary_bench.py", "files": [ "boundary_bench.py", "bench_report.json" ], "limitations": "Only the registered sequence track was tested. The implementation masks input tokens rather than physically pruning transformer heads or MLP channels, and does not implement continuous rescaling, inverse-precision graphs, epsilon-rounded residual keys, fine-tuning, or wall-clock/memory comparisons. The benchmark used 1200 training and 400 test samples with 10 epochs; the mechanism signature failed confirmation.", "system_verdict": "worked", "practical_verdict": "helps", "mechanism_ok": 0, "system_judged": true }