v0.3: Object-specific truth and query negation

v0.3 experiment: object-specific truth and query negation

Two new analytically compiled checkpoints, v10a and v11a, extend the existing
offline transformer. Each contains exactly 250,000,000 float32 parameters and
1,000,000,000 raw weight bytes. No training, pretrained weights, downloaded
knowledge or runtime answer generator is used.

• v10a: exact subject/predicate/object retrieval, three-valued truth,
  per-object corrections and separation of questions from assertions.
• v11a (current): all seven existing predicates, copular and auxiliary
  inversion, and a separate query-negation bit. Missing evidence stays unknown.

Development suite	v9a	v10a	v11a
Legacy	185/185	185/185	185/185
Graph	75/79	75/79	75/79
Arithmetic	115/120	115/120	115/120
Polarity	227/233	229/233	229/233
Signed operands	153/159	153/159	153/159
Object-specific truth	45/239	229/239	234/239
Query scope	Not run	80/269	262/269

Seeds selected after freezing reproduce v10a truth 229/239 and v11a query scope
262/269 on both CPU and CUDA. These are fresh combinations under known grammar,
not independent general-intelligence tests. Exact outputs, timings, source
commits, hashes, failures and 22 matched-suite comparisons are in results/.

The runtime is unchanged. Causal-prefix, cached/full decoding, file-integrity,
offline-guard and neural ablation checks pass. Removing proposition retrieval,
object identity or query negation changes the associated answers. The archived
v11a compiler reproduces raw weight and config hashes exactly.

Still an experiment: most parameters are zero (v11a has 744,215 nonzero
parameters). Open-ended conversation, creative writing and code generation remain
unachieved. Conjunction, general pronouns, repeated negation and instruction scope
fail. v10a adds one later-instruction regression; v11a adds none across the matched
suites. The earlier open-question multiple-action regression is still present.
No failing case was removed and no broad research milestone is declared complete.

Extract autoregressive-v11a.zip into the repository root and run:

python -m chat1gb_autoregressive.chat --device auto

It supplies the default model directory. The v10a archive uses
versions/autoregressive_v10a/model; select that path with --model-dir.
Check archive hashes against SHA256SUMS.txt, then raw/config hashes against
the included manifest. The small ZIPs reflect sparse/repetitive weights; every
unpacked checkpoint is exactly 1 GB. NumPy/PyTorch must already be installed;
construction and inference perform no downloads.