Commit History
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Use HF default .gitattributes (comprehensive LFS rules)
etwkba38bb5 -
Add model-card YAML frontmatter (clears HF 'missing yaml metadata' warning)
etwklicense apache-2.0, library_name pytorch, descriptive tags + accuracy metric.84d4071
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Shrink: 16/32-bit MLPs -> width-matched TCN. Artifact 0.77GB -> 0.13GB
etwkAll eight cells are now the same carry-aware TCN. weights16.pt 202MB->9.5MB, weights32.pt 456MB->12.6MB (both LFS). tier 4 0.99->1.00, overall 0.988->0.989, tiers 1-3 hold 1.00, no regression. Fixes the tier-4 width blind spot (audit). Card + manifest updated.9294ab9 -
Tier 8 re-polish: 0.92 -> 0.98, overall 0.982 -> 0.988
etwkWidth-matched re-polish of the 512 cell (value-uniform [257,512) + bitlen band [480,512], accum 16, lr 8e-5, worst-bit margin) closes the short-width gap and sharpens precision: real public tier-8 0.92->0.98, robustness sim [257,512] = 0.985. overall 0.988, every scored tier >=0.97, highest_tier 10, deterministic. weights512.pt updated (LFS).ffa1be7 -
Harden tier 10: 0.94 -> 0.98, overall 0.978 -> 0.982
etwkHardening tail on the 2048 cell (worst-bit margin, accum 24, lr 4e-5) lifts tier 10 0.94->0.98 (2047 27/27, 2048 71/73). overall_accuracy 0.982, highest_tier 10, deterministic. weights2048.pt updated (LFS), manifest + card updated.e258f44 -
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 -
Commit all 5 cell weights as git-LFS objects (16/32/64/128/256-bit)
etwkAll five cell weight files are tracked as proper LFS objects. md5s: weights16 96549e1a, weights32 3c3cf174, weights64 07851d3d (tier-5 0.98), weights128 0c087b3f (tier-6 TCN 0.97), weights256 cb1bf338 (tier-7 TCN 0.98). Repo is now complete.845fdfb -
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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Update card + manifest: tier 5 0.74 -> 0.98 (trajectory training), highest_tier_above_90 5
etwkweights64.pt is the trajectory-trained 0.98 cell (md5 07851d3d..., git-LFS object). model.py/train.py unchanged.6b82250 -
Update tier-5 metrics to 0.74 (overall 0.483); refresh 64-bit compliance row
etwkfaa14cf -
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