Closes the structural gap identified in the 2026-05-17 assessment:
the PropositionGraph was a post-hoc descriptor of what the field walk
already produced. It is now a forward constraint that shapes what the
walk is ALLOWED to produce.
== generate/graph_constraint.py (new) ==
GraphConstraint — converts a PropositionGraph into an AdmissibilityRegion
before generate() runs, not after. The region's allowed_indices are the
intersection of:
- subject versor neighbourhood (top-k by CGA inner product)
- object versor neighbourhood (top-k by CGA inner product)
- any explicitly named node surfaces already in-vocabulary
This is the Pillar 1 → Pillar 2 coupling that was missing:
geometry (CGA) → structure (graph) → propagation (generate)
build_graph_constraint(graph, vocab, *, top_k) is the public entry.
The region label encodes the graph's root node IDs so the admissibility
trace identifies the constraint source.
== generate/stream.py (updated) ==
generate() already accepts an AdmissibilityRegion. No new API needed —
graph_constraint.build_graph_constraint() produces one.
== evals/industry_demos/ (new) ==
Four standalone demo scripts that each make ONE falsifiable claim no
transformer-LLM wrapper can reproduce. Each script runs independently
via `python -m evals.industry_demos.<name>` and exits 0 on pass / 1 on
fail. Each prints structured evidence to stdout.
demo_01_forward_constraint.py
Claim: When the PropositionGraph names subject=light, obj=truth, the
generation walk is constrained to the CGA neighbourhood of those
versors BEFORE any tokens are produced. The allowed_indices set is
computed from geometry, not from a prompt filter. Demonstrated by
showing the AdmissibilityRegion is non-trivial (< full vocab) and
that all generated tokens score positive CGA inner product against
the constraint field.
demo_02_geometry_drives_identity.py
Claim: Swapping the identity pack (precision_first vs generosity_first)
on identical input produces structurally different surfaces via the
manifold alignment path — not via a system-prompt swap. Demonstrated
by running two ChatRuntime instances with different identity_pack IDs
on the same text, showing hedge_rate and identity_score.alignment
differ, and that the manifold alignment_threshold differs at the
algebra level (not just the text level).
demo_03_deterministic_audit.py
Claim: Three independently constructed ChatRuntime instances on the
same input produce byte-identical JSONL audit lines. Demonstrated
by attaching JsonlBufferSink to each, running chat(), and asserting
hash equality of the emitted lines (modulo the 'turn' field which is
per-instance sequential). This is architectural determinism — not
seeded randomness.
demo_04_exact_recall_scale.py
Claim: CGA vault recall is exact (100%) at N=100, N=1_000, N=10_000.
The needle versor is recovered at rank-1 by cga_inner scan regardless
of vault size. No approximate nearest-neighbour index. No FAISS.
No degradation curve. Demonstrated inline with timing so the
linear-scan cost is visible alongside the 100% recall.
== tests/test_graph_constraint.py (new) ==
8 tests:
- build_graph_constraint returns an AdmissibilityRegion
- allowed_indices is a strict subset of vocab (non-trivial constraint)
- all constraint indices score positive cga_inner against at least
one node versor
- empty graph returns unconstrained region (safe fallback)
- two-node graph unions both neighbourhoods
- constraint label encodes root node IDs
- round-trip: constraint region feeds generate() without raising
- forward vs post-hoc: constrained walk produces tokens in the
region; unconstrained walk may not (statistical, seeded vocab)
Co-Authored-By: Perplexity AI
Shay