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

(универсальный)

Ответ агента:

{ "worked": true, "confidence": 8, "verdict": "Implemented an end-to-end conditional mixture transport proxy on the structurally matched eight-token diffusion track, using independently trained transformer systems and identical test MSE evaluation. The idea improved mean MSE from 0.09546 to 0.09005, with 6/8 paired wins, delta -0.00541 and permutation p=0.0468, meeting the benchmark significant-win criterion. The trained model showed separated conditional components with mean spread 1.139, supporting the predicted non-Gaussian conditional mechanism.", "metrics": { "baseline": "Transformer baseline, best lr=0.001: mean test MSE 0.09546016, std 0.00689817 across 8 seeds.", "idea": "Three-component conditional mixture transport proxy, best lr=0.001: mean test MSE 0.09004521, std 0.00461361 across 8 seeds.", "comparison": "Paired delta=-0.00541495, 6/8 idea wins, permutation p=0.0468.", "mechanism_signature": "Mean learned component spread=1.13940798; residual excess kurtosis=1.58704350; confirmed=true.", "math_check": "Normal inverse-CDF PIT sup deviation=0.00660673, below the 0.02 sanity threshold." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 conditional_transport_bench.py", "files": [ "conditional_transport_bench.py", "bench_report.json" ], "limitations": "This is a compact conditional Gaussian-mixture transport proxy rather than a rational-quadratic spline or autoregressive normalizing flow. The custom multi-token track uses 400 training and 200 test examples and 12 epochs; no large-scale DDPM image benchmark, wall-clock comparison, multidimensional Rosenblatt flow, or classifier-based residual AUC was tested.", "bench_report": { "bench_version": 1, "track": "conditional_multitoken_diffusion", "model": "transformer_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.001 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.09234148263931274 }, { "cfg": { "lr": 0.003 }, "mean": 0.10227461904287338 }, { "cfg": { "lr": 0.01 }, "mean": 0.13437412679195404 } ], "full": { "mean": 0.09546016063541174, "std": 0.0068981652474538425, "per_seed": [ 0.08567251265048981, 0.08461520820856094, 0.09914777427911758, 0.10520027577877045, 0.09470903873443604, 0.09319207072257996, 0.1012139692902565 ], "n": 8 } }, "idea": { "mean": 0.09004521276801825, "std": 0.0046136095927630846, "per_seed": [ 0.08257821947336197, 0.08880516141653061, 0.09218183159828186, 0.09180948883295059, 0.0943688154220581, 0.08264964818954468, 0.09331666678190231, 0.09465187042951584 ], "n": 8, "cfg": { "lr": 0.001, "components": 3 } }, "comparison": { "delta_mean": -0.005414947867393494, "idea_wins": 6, "n_pairs": 8, "per_seed_diffs": [ -0.003094293177127838, 0.0041899532079696655, -0.006965942680835724, -0.00812094658613205, -0.010831460356712341, -0.012059390544891357, 0.00012459605932235718, -0.006562098860740662 ], "p_value": 0.0468, "mde": 0.004610854431296922, "mde_rel_pct": 4.830134791944282, "verdict": "idea better (significant)", "system_worked": true }, "mechanism_signature": { "mean_component_spread": 1.1394079849123955, "residual_excess_kurtosis": 1.5870434984444928, "prediction": "multimodal conditional transport has separated learned components", "confirmed": true }, "custom_track": { "name": "conditional_multitoken_diffusion", "file": "conditional_transport_bench.py", "domain": "diffusion-sampling" } }, "system_verdict": "worked", "practical_verdict": "helps", "mechanism_ok": 1, "system_judged": true }