v0.8 experiment: ground conditional reasoning inside the weights

v0.8 experiment: ground conditional reasoning inside the weights

v16c adds signed ground implications to the analytically compiled decoder.
Rules supplied during a conversation can connect unfamiliar subjects through
up to three links. Neural attention retrieves a rule by its conclusion, checks
the premise against current signed facts, and propagates proof through the
existing truth/query circuit. Missing or false premises remain unknown.

The checkpoint remains exactly 250,000,000 float32 parameters / 1,000,000,000
bytes, with no padding. It uses 995,881 nonzero parameters (0.3983524%).
Construction uses no training, optimization, pretrained weights, downloaded
data or internet. Inference uses the unchanged generic transformer runtime;
there is no host rule engine, answer fallback, completed-response bank or
weight update. GitHub is used to publish the experiment and evidence.

CUDA development check	v15a	v16a	v16b	v16c
Ground conditionals	152/293	269/293	269/293	269/293
Additional scope/dialogue guards	19/36	27/36	29/36	29/36
Original narrow cases	185/185	182/185	185/185	185/185
Coordinated assertions and stress	382/390	368/390	383/390	383/390
Object-specific truth	236/239	229/239	235/239	236/239

All 33 matched CUDA suites retain every v15a pass: 5,499/5,619 cases,
130 improvements and zero regressions. Historical suites repeat some cases
and structures. The new conditional suite was committed before compiler
changes; the guard suite is explicitly author-designed development evidence.
CPU matches the conditional 269/293 and guard 29/36 scores. Exact prompts,
outputs, expected answers, timings and hashes are in the
version table
and comparison.

The failed v16a/v16b sources, weights and measurements are retained. v16a
allowed proof state to interfere with ordinary open answers. v16b repaired
that isolation but could treat an atomic fact bit as a rule coordinate,
creating a false rule at an entity token. v16c adds a type penalty derived
from the maximum possible coordinate spillover. Read-only activation traces
and a penalty-removal intervention preserve evidence of the cause and fix.

Eight conditional interventions pass on CPU and CUDA, covering rule links, premise
evidence, record validity, object identity, polarity and the typed-record
guard. The initial object-matching intervention was ineffective because it
also removed the positive match score; its failed report remains public.
The corrected diagnostic retains that reward while removing identity
dependence. All interventions use temporary tensor copies and restore them;
the saved model is unchanged. Archived source rebuilds reproduce the exact
raw/config hashes. Both devices pass the existing integrity/intervention suite.
A 31-turn conversation after long declarative distractors scores 31/31 CUDA
and 28/31 CPU. CPU misses three later conditional conclusions while retaining
all 23 earlier turns. This device/long-context difference is unresolved and is
the next numerical investigation. It is not hidden by the matching short-suite
scores or presented as a passed conversation test.

After source freeze commit 9c84c6ad4c4202764358ab0c48d4d2a4d7cc25e7,
seed 1711846539 scores 269/293 on CPU and CUDA, including 98/98
positive/signed applications and all tested chains through three links.
These are fresh combinations from the known generator; fixed structures and
boundaries repeat. They are not independent general-language evidence. No
frozen source or parameters were retuned after this evaluation.

Useful open-ended conversation remains unachieved. This is a bounded
single-path proof circuit. It fails chains longer than three rules and misses
older successful alternatives when a newer rule has no proof. Variables,
compound rule premises and open answers from rules are unsupported. Some
paraphrases, including implies and whenever, can be mistaken for assertions.
Creative writing, code generation, unrestricted English and broader arithmetic
remain failures. The experimental goal stays active.

Download autoregressive-v16c.zip, verify SHA256SUMS.txt, and extract it into
the repository root. The smaller archive reflects sparse/repetitive weights;
the decompressed parameter file is exactly 1 GB. NumPy/PyTorch must already be
installed; the project downloads no dependencies or model data.

python -m chat1gb_autoregressive.chat --device auto

The v16a/v16b archives extract to their historical version directories.
Every earlier release remains available. v16c raw SHA-256:
385dff256910dec6eba802d3930452c2963e85223bbaa54251d2931b9b86b1dc.

Fresh-download validation verifies all three published archive sizes/digests,
the checksum file, decompressed raw/config hashes, ZIP CRC and frozen compiler
source against a clean tagged clone. CPU and CUDA each pass integrity and all
eight conditional interventions from that checkout. Its conversation replay
reproduces 31/31 CUDA and 28/31 CPU, with the same three CPU failures.
Publication evidence
records those failures explicitly. No source or saved weights were retuned.