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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented signed spectral attention as an end-to-end replacement for self-attention on the structurally matched sequence forecasting track. The trained-model mechanism signature confirmed the predicted M^-0.5 approximation scaling (observed slope -0.5046) and lower signed than unsigned kernel approximation error. The task MSE improved from 0.6797 to 0.6471, but the paired permutation test gave p=0.0673, so there was no statistically significant win.", "metrics": { "baseline": "Softmax transformer attention; tuned best configuration lr=0.001, temperature=1.0; full 8-seed MSE mean 0.6796555296, std 0.0592304113.", "idea": "Signed RFF attention; best configuration M=32, lr=0.003; full 8-seed MSE mean 0.6470961496, std 0.0759496206.", "paired_delta": "-0.0325593799, 7/8 idea wins, permutation p=0.0673.", "mechanism_signature": "Trained-model signed approximation RMSE: M=8 0.4599075, M=16 0.3563229, M=32 0.2284983; observed slope -0.5045800 versus predicted -0.5; unsigned RMSE at M=32 0.3878027; confirmed=true." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 signed_attention_bench.py", "files": [ "signed_attention_bench.py", "bench_report.json", "run.log" ], "limitations": "Only the sequence track was tested. The run used 400 training samples, 200 test samples, 6 epochs, and a tiny one-block transformer; wall-clock speed, memory, and FLOP scaling were not measured. The baseline used a six-configuration sweep over learning rate and temperature; the idea used a three-configuration learning-rate sweep at M=32, with all idea learning rates included in the baseline union grid.", "bench_report": { "bench_version": 1, "track": "sequence", "model": "transformer_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.001, "temperature": 1.0 }, "sweep": [ { "cfg": { "lr": 0.001, "temperature": 0.7 }, "mean": 0.6538640261 }, { "cfg": { "lr": 0.001, "temperature": 1.0 }, "mean": 0.6523745656 }, { "cfg": { "lr": 0.003, "temperature": 0.7 }, "mean": 0.6653543413 }, { "cfg": { "lr": 0.003, "temperature": 1.0 }, "mean": 0.6606301814 }, { "cfg": { "lr": 0.006, "temperature": 0.7 }, "mean": 0.7325459272 }, { "cfg": { "lr": 0.006, "temperature": 1.0 }, "mean": 0.7683279514 } ], "full_mean": 0.6796555296, "full_std": 0.0592304113, "full_per_seed": [ 0.7301222086, 0.6001890898, 0.6041663885, 0.6750205755, 0.7771981359, 0.6423909664, 0.7304211259, 0.6777357459 ] }, "idea": { "best_cfg": { "lr": 0.003, "m": 32 }, "mean": 0.6470961496, "std": 0.0759496206, "per_seed": [ 0.7749909759, 0.5947428942, 0.5252495408, 0.6463086605, 0.706030488, 0.5773214102, 0.7090907097, 0.6430345178 ] }, "comparison": { "delta_mean": -0.0325593799, "idea_wins": 7, "n_pairs": 8, "p_value": 0.0673, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "trained_model": true, "M": 32, "observed_slope": -0.5045800247, "predicted_slope": -0.5, "signed_rmse": 0.2284983247, "unsigned_rmse": 0.3878027201, "confirmed": true }, "sanity_check": { "observed_slope": -0.4969927921, "predicted_slope": -0.5, "positive_predicted_floor": 0.6262603395, "passed": true } }, "system_verdict": "partial", "practical_verdict": "no_effect", "mechanism_ok": 1, "system_judged": true }