Commit History
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Ship shared 1024-2048 high cell (V2); sync docs + model
etwkReplace the two dedicated high cells (weights1024.pt, weights2048.pt) with one shared carry-aware-TCN cell across tiers 9-10 (weights_shared_1024_2048.pt): distilled to both dedicated teachers + worst-bit margin, 2048 chain preserved so the primary key (highest_tier_above_90=10) is held. tier 9 = 1.00, tier 10 = 1.00, overall_accuracy 1.000; two shared weight files total (~10.7M params, 0.04 GB). model.py: _build_cell drops non-constructor keys (unified/widths).41fc51b
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Promote shared 16-512 weight soup
etwkb670993
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model: force cuDNN determinism and fail fast on incomplete artifact
etwk- Set torch.backends.cudnn.deterministic = True so any convolution kernel selected at load time is the deterministic variant, reinforcing the determinism guarantee already provided by eval-mode hard thresholding. - After loading, verify every routing width in CELL_WIDTHS is served by a weight-set and raise FileNotFoundError otherwise. A missing intermediate file would previously leave a routing gap, silently sending that width's primes to a wider, differently-trained cell rather than failing loudly. Public-benchmark score unchanged: overall_accuracy 0.997, highest_tier_above_90 = 10.33d0796
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Pin determinism flags in load() (eligibility insurance)
etwkThe leaderboard ranks ONLY deterministic submissions (leaderboard/store.py). Pin the backend flags that govern run-to-run reproducibility instead of relying on host defaults: cudnn.benchmark=False (fixes the conv algorithm), and the TF32 flags to the exact validated mode (matmul TF32 off, cuDNN TF32 on). TF32 is deterministic, so this only makes the validated numerics host-independent; it does not change the determinism gate or accuracy. Validated: full public 1100 re-eval is byte-identical to shipped -- overall 0.995, highest_tier_above_90=10, deterministic=True, tiers 1/1/1/1/1.00/.98/1.00/1.00/.99/.98, inference within 300s. A TF32-vs-FP32 probe confirmed 0/100 cases differ on tiers 9 and 10 (the output is float-mode-invariant), so the near-max ~0.974 is intrinsic cell margin, not a precision artifact -- keeping TF32 (faster, identical) is correct.759f10f -
v2: collapse 64-512 cells into one shared carry-aware TCN weight-set
etwkReplace the four dedicated 64/128/256/512-bit cells with a SINGLE shared TCN weight-set (weights_shared_64_512.pt, ~5.5M params), run at each prime's native width. model.py: width-native forward (byte-identical for per-width cells, enables one weight-set at any width) + shared-cell loader keyed on config-declared widths; corrected stale docstrings (all cells are carry-aware TCN, not MLP). Full official pipeline (modchallenge evaluate --total 1100): overall_accuracy 0.995, highest_tier_above_90=10, deterministic=true; modchallenge check clean. Strictly better than v1 (0.989) with fewer weight-sets (5 vs 8). Principle-2 verified on the shared cell (1.00 -> 0.03 perturbed sigma=0.25, 0.00 untrained).fff63d1
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Tier 10 = 0.94: highest_tier 9->10 (MAX), overall 0.886->0.978
etwkAdd 2048-bit carry-aware TCN cell (weights2048.pt, LFS) via octave transfer from the 1024 cell + two-stage polish. model.py CELL_WIDTHS += 2048; manifest + card updated. highest_tier_above_90 = 10 (the benchmark maximum), overall_accuracy 0.978, deterministic, inference 170s/1100, artifact 0.77 GB.6b83eb7 -
Tier 9 = 0.99: highest_tier 8->9, overall 0.788->0.886
etwkAdd 1024-bit carry-aware TCN cell (4.73M params, 12 blocks, dil 1..512). Fixes the prime-WIDTH blind spot: tier-9 primes are value-uniform in [2^513, 2^1024), so a ~1020-bit benchmark prime fell below the old training floor and scored 0/22 (tier 9 = 0.73). Retrained warm-started on a value-uniform + bit-length-uniform[990,1024] mix so the reduction boundary is learned at every MSB position -> tier 9 = 0.99 (private-draw sim 0.988). Compliance: 0.99 -> 0.04 at sigma=0.25, untrained 0.00. Full benchmark: overall_accuracy 0.886, highest_tier_above_90=9, deterministic, zero regression on tiers 0-8.bd6d487
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64-bit cell: MLP -> carry-aware TCN (fixes tier-0 near-2^64, 944MB->13MB)
etwkweights64.pt replaced: 944MB MLP -> 13MB TCN (8 blocks/256ch/dilations 1-32). Fixes the MLP's near-2^64 blind spot (tier-0 64-bucket 0/10 -> 10/10) with no tier-5 regression (0.98 -> 0.99). Full benchmark: highest_tier 8, overall 0.789, tier 0 0.50, det=True. Total artifact ~1.62GB -> ~700MB. README/ manifest: 64/128/256/512 cells are now all carry-aware TCNs.cc41e6b -
Tier 8 saturated at 0.92: highest_tier 7->8, overall 0.769->0.788
etwkweights512.pt re-promoted to the 0.92 cell (gradient-accumulation run, eff. batch ~2300; eps floor 7e-4 -> 1.3e-4, 512-step chain 0.73 -> 0.92). Full benchmark: highest_tier_above_90 = 8, overall 0.788, tier 8 = 0.92, det=True. Compliance 0.92@s0 -> 0.04@s0.25, untrained 0.00. README/manifest updated: saturates tiers 1-8 / up to 2^512.70d0ebc -
Partial 512-bit cell (tier 8 = 0.73): overall 0.698 -> 0.769
etwkAdds weights512.pt (512-bit carry-aware TCN, 14 blocks/256ch/dilations 1-256, ~5.5M params) as a git-LFS object; routes p<2^512 to it (CELL_WIDTHS now 16/32/64/128/256/512). The per-step error floor rises with width so tier 8 lands at 0.73 (partial -- highest_tier stays 7) but overall jumps to 0.769. Compliance: 0.73@s0 -> 0.04@s0.25, untrained 0.00. README/manifest -> six cells / up to 2^512.86c5a17 -
Tier-7 256-bit carry-aware TCN cell: highest_tier 6->7, overall 0.602->0.698, tier-7 0.98
etwkRoutes p<2^256 to a new 256-bit TCN cell (12 blocks/256ch/dilations 1-128, ~4.7M params; CELL_WIDTHS now 16/32/64/128/256). Full public benchmark: tier 7 = 0.98, overall 0.698, highest_tier_above_90 = 7, deterministic. Compliance: 0.98@s0 -> 0.06@s0.25 -> 0.02@s0.5, untrained 0.00. README + manifest updated to five cells / up to 2^256.b69f4ce -
Tier-6 carry-aware TCN cell: highest_tier 5->6, overall 0.531->0.602, tier-6 0.97
etwk128-bit MLP cell (tier-6 0.26) replaced by TCNHornerCell -- weight-shared non-causal dilated convs over the 128 bit-positions (~3.9M params/16MB). Per-step eps ~15x below the MLP floor; benchmark tier-6 0.26->0.97, deterministic, inference 0.9s. weights128.pt md5 0c087b3f (git-LFS object). model.py adds TCNHornerCell + _build_cell arch dispatch; manifest + README updated; perturbation compliance incl. tier-6.47e319e
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Horner-RNN modular-multiplication model (tiers 1-5, up to 2^64)
etwkBit-sequential RNN that learns the Horner step (2t+bit*b) mod p; three cells (16/32/64-bit) routed by prime size. Public benchmark: tiers 1-3 = 1.00, tier 4 = 0.99, tier 5 = 0.64, overall 0.473. Weights weights{16,32,64}.pt are tracked as git-LFS objects.3d2c226