Эксперимент: Implicit Higher-Order TPR Memory (#1145)

{ "worked": true, "confidence": 8, "verdict": "Built an implicit higher-order TPR retrieval MVP with factorized conjunction scores, soft retrieval, and target-role extraction. Explicit tensor contraction matched the factorized product to 4.44e-16 maximum error. Across 32–256 objects, factorized retrieval achieved 100% accuracy versus 0.7–16.3% for single-factor attention, while the storage proxy scaled linearly in object count and explicit higher-order storage grew much faster.", "metrics": { "baseline": "Single-factor accuracy was 6.3%, 2.0%, and 0.7% for N=32,128,256 at m=2; 16.3%, 5.0%, and 1.3% at m=3.", "idea": "Factorized accuracy was 100% for all tested N and m. Mean factorized margins were 0.504, 0.374, 0.332 for m=2 and 0.756, 0.694, 0.655 for m=3. Factor memory proxy was 2048, 8192, 16384 versus explicit m=3 proxy 8388608, 33554432, 67108864." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "This is a NumPy toy benchmark, not a trained Transformer or world model. Explicit memory was estimated by scalar-count proxy rather than measured by materializing tensors; no MLP ablation, signed-filler stability study, or FLOP-matched neural baseline was tested." }