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

(универсальный)

Ответ агента:

{ "worked": true, "confidence": 8, "verdict": "Spiderweb Hierarchical Attention was implemented as a separately trained, parameter-matched replacement for the registered bench transformer_tiny model on the structurally appropriate sequence forecast track. Across 8 paired seeds it reduced test MSE from 0.1653229 to 0.1244110, with all 8 wins and permutation p=0.0081, satisfying the bench criterion. The trained-model long-range influence signature was not confirmed, and throughput/memory were not measured.", "metrics": { "baseline": "Swept dense transformer_tiny; best lr=0.001; full 8-seed test MSE mean 0.16532287374138832, std 0.008160238971414253.", "idea": "Swept spiderweb transformer over the same learning-rate union; best lr=0.001; 8-seed test MSE mean 0.12441098596900702, std 0.008560845180504158.", "comparison": "delta_mean=-0.040911887772381306, idea_wins=8, p_value=0.0081, verdict=idea better (significant)." }, "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": 0.16495772451162338 }, { "cfg": { "lr": 0.003 }, "mean": 0.18329624831676483 }, { "cfg": { "lr": 0.01 }, "mean": 0.43168069422245026 } ], "full": { "mean": 0.16532287374138832, "std": 0.008160238971414253, "per_seed": [ 0.17448444664478302, 0.1530463844537735, 0.17006324231624603, 0.16223682463169098, 0.16158440709114075, 0.15676827728748322, 0.17869338393211365, 0.16570602357387543 ], "n": 8 } }, "idea": { "mean": 0.12441098596900702, "std": 0.008560845180504158, "per_seed": [ 0.1358136236667633, 0.10833562165498734, 0.11411764472723007, 0.12626340985298157, 0.1262723207473755, 0.12286490947008133, 0.13141369819641113, 0.1302066594362259 ], "n": 8 }, "comparison": { "delta_mean": -0.040911887772381306, "idea_wins": 8, "n_pairs": 8, "per_seed_diffs": [ -0.038670822978019714, -0.04471076279878616, -0.05594559758901596, -0.03597341477870941, -0.03531208634376526, -0.033903367817401886, -0.047279685735702515, -0.035499364137649536 ], "p_value": 0.0081, "mde": 0.006478177383445342, "mde_rel_pct": 3.9185003483420218, "verdict": "idea better (significant)", "system_worked": true }, "mechanism_signature": { "prediction": "hierarchical bounded-neighbor attention preserves measurable long-range influence", "baseline_signature": { "far_first_token_effect": 0.027809560298919678, "local_last_token_effect": 1.0498290061950684, "far_to_local_ratio": 0.02648960913626757 }, "idea_signature": { "far_first_token_effect": 0.020777523517608643, "local_last_token_effect": 1.4758778810501099, "far_to_local_ratio": 0.014078077626615245 }, "confirmed": false, "confirmation_rule": "idea far-to-local ratio > baseline far-to-local ratio by at least 20%" }, "extra": { "idea_sweep": [ { "cfg": { "lr": 0.001 }, "result": { "mean": 0.12441098596900702, "std": 0.008560845180504158, "per_seed": [ 0.1358136236667633, 0.10833562165498734, 0.11411764472723007, 0.12626340985298157, 0.1262723207473755, 0.12286490947008133, 0.13141369819641113, 0.1302066594362259 ], "n": 8 } }, { "cfg": { "lr": 0.003 }, "result": { "mean": 0.13378733582794666, "std": 0.012523346697500463, "per_seed": [ 0.13517771661281586, 0.11398444324731827, 0.1407897174358368, 0.1486937254667282, 0.13302475214004517, 0.11315087229013443, 0.14389027655124664, 0.14158718287944794 ], "n": 8 } }, { "cfg": { "lr": 0.01 }, "result": { "mean": 0.1740857046097517, "std": 0.015146774808178592, "per_seed": [ 0.2027415633201599, 0.14727982878684998, 0.17759738862514496, 0.1726507693529129, 0.16134709119796753, 0.1725672036409378, 0.18487928807735443, 0.1736225038766861 ], "n": 8 } } ], "idea_best_cfg": { "lr": 0.001 }, "params_baseline": 71169, "params_idea": 71181, "elapsed_sec": 418.1431157588959, "n_train": 2000, "n_test": 500, "epochs": 15 } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_stage2.py", "files": [ "bench_stage2.py", "bench_report.json", "bench_report_rerun.json" ], "limitations": "Only the built-in sequence forecast track was tested with sequence length 32, 2,000 training examples, 500 test examples, and 15 epochs. No 2K/4K language modeling, long-range-copy task, wall-clock throughput, peak activation memory, optimized sparse GPU kernel, or hyperbolic-distance routing ablation was evaluated. The mechanism signature used a single held-out test example and was negative despite the task-metric win.", "system_verdict": "worked", "practical_verdict": "helps", "mechanism_ok": 0, "system_judged": true }