Эксперимент: Epoch-Frozen Masked Low-Rank Candidate Encoder (#1413)

{ "worked": true, "confidence": 7, "verdict": "Built the epoch-frozen masked low-rank encoder with inverse-probability covariance estimation, top-eigenvector subspace extraction, batched ridge reconstruction, and latent-only ranking MLP input. The math check recovered the true rank-4 subspace with principal cosines 0.9994–0.9998, 3.2% relative reconstruction error, and 6.0% relative covariance-estimation error. In the synthetic ranking test, the frozen latent model improved accuracy over zero-imputation at p=0.25 (0.650 vs 0.450) and p=0.5 (0.633 vs 0.575), with a 4x lower ranking input FLOP proxy; it lost at p=0.75, so the effect is promising but conditional rather than universally better.", "metrics": { "baseline": "Zero-imputed ambient MLP accuracy: p=0.25 0.450, p=0.5 0.575, p=0.75 0.633; ranking FLOP proxy 1.0x.", "idea": "Frozen latent MLP accuracy: p=0.25 0.650, p=0.5 0.633, p=0.75 0.500; ranking FLOP proxy 0.25x. Math: principal cosines 0.9994–0.9998 and relative reconstruction error 0.0319." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "This is a small synthetic contextual-ranking benchmark with one seed, 48-dimensional ambient features, rank 6, 8 candidates, and 7 training epochs; no MovieLens data, NDCG, wall-clock speed, or large-scale retrieval cache was tested. The unfrozen control refit on the same epoch data, so it does not fully model representation drift from changing masks. Validation-loss stability was measured but was not consistently better for the frozen method." }