83443bd071
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
123 lines
4.9 KiB
Python
123 lines
4.9 KiB
Python
"""Tests for generate/graph_constraint.py — PropositionGraph as AdmissibilityRegion.
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ADR-0046.
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"""
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from __future__ import annotations
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import pytest
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import numpy as np
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from generate.graph_planner import GraphEdge, GraphNode, PropositionGraph, Relation
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from generate.graph_constraint import build_graph_constraint
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from generate.admissibility import AdmissibilityRegion
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from generate.intent import IntentTag
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@pytest.fixture()
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def vocab():
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from language_packs import load_pack
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_manifest, manifold = load_pack("en")
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return manifold
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def _node(node_id, subject, obj):
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return GraphNode(
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node_id=node_id,
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subject=subject,
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predicate="addresses",
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obj=obj,
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source_intent=IntentTag.DEFINITION,
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)
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class TestBuildGraphConstraint:
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def test_returns_admissibility_region(self, vocab):
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graph = PropositionGraph().add_node(_node("p0", "light", "truth"))
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region = build_graph_constraint(graph, vocab)
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assert isinstance(region, AdmissibilityRegion)
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def test_non_trivial_constraint(self, vocab):
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"""allowed_indices must be a strict subset of the full vocabulary."""
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graph = PropositionGraph().add_node(_node("p0", "light", "truth"))
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region = build_graph_constraint(graph, vocab, top_k=8)
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assert region.allowed_indices is not None
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assert len(region.allowed_indices) < len(vocab)
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def test_allowed_indices_positive_cga_inner(self, vocab):
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"""Every allowed index must score positive cga_inner against at least one anchor."""
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from algebra.cga import cga_inner
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graph = PropositionGraph().add_node(_node("p0", "light", "truth"))
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region = build_graph_constraint(graph, vocab, top_k=8)
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assert region.allowed_indices is not None
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light_v = np.asarray(vocab.get_versor("light"), dtype=np.float32)
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truth_v = np.asarray(vocab.get_versor("truth"), dtype=np.float32)
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anchors = [light_v, truth_v]
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for idx in region.allowed_indices:
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scores = [
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float(cga_inner(np.asarray(vocab.get_versor_at(int(idx)), dtype=np.float32), a))
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for a in anchors
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]
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assert max(scores) > 0.0, f"Index {idx} has non-positive CGA score against all anchors"
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def test_empty_graph_returns_unconstrained(self, vocab):
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"""An empty graph degrades gracefully to an unconstrained region."""
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region = build_graph_constraint(PropositionGraph(), vocab)
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assert region.allowed_indices is None
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assert "unconstrained" in region.label
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def test_two_node_graph_unions_neighbourhoods(self, vocab):
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"""A two-node graph produces a larger allowed set than a one-node graph."""
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graph_one = PropositionGraph().add_node(_node("p0", "light", "truth"))
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graph_two = (
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PropositionGraph()
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.add_node(_node("p0", "light", "truth"))
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.add_node(_node("p1", "word", "life"))
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)
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region_one = build_graph_constraint(graph_one, vocab, top_k=4)
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region_two = build_graph_constraint(graph_two, vocab, top_k=4)
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count_one = len(region_one.allowed_indices) if region_one.allowed_indices is not None else len(vocab)
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count_two = len(region_two.allowed_indices) if region_two.allowed_indices is not None else len(vocab)
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assert count_two >= count_one
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def test_label_encodes_root_node_ids(self, vocab):
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"""The region label must encode the graph's root node IDs."""
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graph = PropositionGraph().add_node(_node("p0", "light", "truth"))
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region = build_graph_constraint(graph, vocab)
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assert "p0" in region.label
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def test_round_trip_with_generate(self, vocab):
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"""The region produced by build_graph_constraint can be fed to generate() without raising."""
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from field.state import FieldState
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from generate.stream import generate
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from persona.motor import PersonaMotor
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graph = PropositionGraph().add_node(_node("p0", "light", "truth"))
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region = build_graph_constraint(graph, vocab, top_k=8)
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F0 = np.asarray(vocab.get_versor("light"), dtype=np.float64)
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state = FieldState(F=F0, node=vocab.index_of("light"), step=0)
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persona = PersonaMotor.identity()
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result = generate(
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state,
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vocab,
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persona,
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max_tokens=4,
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region=region,
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)
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assert result.tokens is not None
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def test_oov_nodes_degrade_gracefully(self, vocab):
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"""A graph whose nodes are all OOV returns an unconstrained region."""
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graph = PropositionGraph().add_node(
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GraphNode(
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node_id="p0",
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subject="xyzzy_not_a_word",
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predicate="quux",
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obj="zork_also_not_a_word",
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source_intent=IntentTag.UNKNOWN,
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)
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)
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region = build_graph_constraint(graph, vocab)
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assert region.allowed_indices is None
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