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Direct-weight chat: negative facts and signed operands
Pre-release
Negative facts and signed operands
Current checkpoint: v9a. This release adds three archived versions to the
initial release: v8a, v8b and v9a. Every model still has exactly 250,000,000
float32 parameters / 1,000,000,000 weight bytes, analytically constructed without
training, pretrained weights or online construction/inference.
| Version | Legacy /185 | Graph /79 | Arithmetic /120 | Polarity /233 | Signed /159 |
|---|---|---|---|---|---|
| v8a | 185 | 75 | 114 | 226 | not run |
| v8b | 185 | 75 | 114 | 227 | 46 |
| v9a | 185 | 75 | 115 | 227 | 153 |
The polarity baseline was v7a at 53/233. A fresh seed after freezing v8b
reproduced 227/233 on CPU and CUDA. A fresh signed-operand seed after freezing
v9a reproduced 153/159 on both, including 120 new random combinations. The
generators and grammatical structures were known: this is restricted
compositional evidence, not an independent general-language benchmark.
Negative facts, contractions, corrections and inherited denials are represented
inside neural fact slots. Negative class assertions do not license positive
inheritance. Signed addition/subtraction accepts two operands in [-999,999].
Unary signs are distinguished from binary operators; a neural count check
rejects tested expressions with too many or too few operands.
Retained limits and regression
One multiple-action case regresses from v7a: after X can fly. X cannot swim.,
the open question selects the last denied action. The current answer retrieval
selects one object per predicate. Yes/no questions, query/instruction scope,
double negation, longer graph chains, creative writing, coding and useful
open-ended conversation remain unresolved.
Malformed expressions such as 5 + * 3 still produce numbers. The count check
is not a full parser. Multiplication, division, parentheses and multi-step
mathematical answers still fail. Old expected answers were not changed to hide
these failures; separate abstention tests measure a different requirement.
Artifacts and checks
Extract autoregressive-v9a.zip in the source repository root, then use an
existing offline environment with PyTorch:
python -m chat1gb_autoregressive.chat --device auto
Rebuild from the frozen compiler in an environment already containing NumPy:
python -m tools.build_version v9a --output build/reproduced
A separate rebuild reproduced the exact raw weight and config hashes. All
three archives passed decompressed SHA-256 and CRC checks. The v8a/v8b archives
extract under versions/autoregressive_VERSION/model; select them using
--model-dir. Earlier checkpoints remain in the v0.1.0 experimental release.
v9a contains 720,343 nonzero parameters (0.2881372%); most connections remain
zero. Small ZIP sizes reflect sparsity. It is a limited compiled neural system,
not a general-purpose language model.
v9a raw weight SHA-256:
397b76ee15d876d6d28d4f30ce59dd4d40069029018887133a173a4a67a74f53.