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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented Saturation-Adaptive Prefill Chunking as a dynamics-track chunked-GRU scheduling intervention with the identical rnn_small base architecture. The idea reduced mean MSE from 0.0042593 to 0.0030401, but the paired permutation test was not significant (delta_mean=-0.0012192, p=0.36405). The trained-model activation-ramp signature contradicted the prediction (0.04104 adaptive versus 0.02959 baseline), so there is no confirmed transfer win.", "metrics": { "baseline": "dynamics/rnn_small, best lr=0.01: mean test MSE 0.004259285735315643 over 8 seeds", "idea": "adaptive chunked rnn_small, best lr=0.01: mean test MSE 0.0030400985560845584; paired delta -0.0012191871792310849; 6/8 wins; p=0.36405; activation ramp 0.041038555558770895 versus 0.029594257241114974" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_run.py", "files": [ "bench_run.py", "bench_report.json" ], "limitations": "This is a dynamics-track transfer rather than a real LLM serving benchmark. It measures task MSE and a hardware-independent recurrent-state ramp proxy, not NVML GPU power, TTFT, queueing, throughput, or real prefill scheduling. The report's baseline sweep used learning rates 0.001, 0.003, and 0.01; the idea used the same union and eight paired seeds.", "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.023484921315684915 }, { "cfg": { "lr": 0.003 }, "mean": 0.006196767906658351 }, { "cfg": { "lr": 0.01 }, "mean": 0.0053210142941679806 } ], "full": { "mean": 0.004259285735315643, "std": 0.002730084338516835, "per_seed": [ 0.0023857334163039923, 0.00896634440869093, 0.0018012352520599961, 0.008130744099617004, 0.003042455529794097, 0.0010564422700554132, 0.0035787634551525116, 0.005112567450851202 ], "n": 8 } }, "idea": { "mean": 0.0030400985560845584, "std": 0.00126101653222839, "per_seed": [ 0.0022289918269962072, 0.002270024036988616, 0.001566944643855095, 0.002901123138144612, "0.005441730376332998", "0.003352733328938484", "0.0019949846900999546", "0.004564256407320499" ], "n": 8 }, "comparison": { "delta_mean": -0.0012191871792310849, "idea_wins": 6, "n_pairs": 8, "per_seed_diffs": [ -0.00015674158930778503, -0.006696320371702313, -0.00023429060820490122, -0.005229620961472392, 0.0023992748465389013, 0.002296291058883071, -0.001583778765052557, -0.0005483110435307026 ], "p_value": 0.36405, "mde": 0.002722617253919733, "mde_rel_pct": 63.921920789330834, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "prediction": "adaptive chunking lowers high-load sequential-state ramp while preserving task function", "observed_baseline_state_ramp": 0.029594257241114974, "observed_adaptive_state_ramp": 0.041038555558770895, "relative_reduction": -0.38670672571422005, "confirmed": false, "note": "activation ramp is a hardware-independent proxy; no NVML power available" } }, "system_verdict": "failed", "practical_verdict": "inconclusive", "mechanism_ok": 0, "system_judged": true }