ADR-0020 next-level: close the parity-gate hole on the four remaining
ungated Rust surfaces.
Gates landed (subprocess-based, raw f32/f64 byte equality):
cga_inner — 14/14 bit-identical (random + basis blades + self-norm)
geometric_product — 15/15 bit-identical (random + basis blades + scalar identity)
versor_condition — 9/9 bit-identical AFTER kernel fix
versor_apply — 8/8 intentionally skipped (see below)
Kernel fix: versor_condition_raw
The Python source-of-truth (algebra.versor.versor_unit_residual) folds
the geometric product + identity subtraction + Frobenius norm in f64.
The Rust kernel was folding in f32, drifting by 1 ULP on out-of-shell
inputs. Rewrote versor_condition_raw to promote inputs to f64, use the
existing geometric_product_f64/reverse_f64 building blocks, and cast
only the final scalar back to f32. Python is canonical per CLAUDE.md
sequencing rule 5.
Honest disable: versor_apply
The Rust versor_apply_closed diverges structurally:
(1) precision — f32 sandwich vs Python's f64 throughout
(2) closure form — Rust has a null-vector early branch + no
post-unitize condition recheck; Python is the
inverse (no null branch; recheck + seed-rotor
fallback)
Per ADR-0020 "default-off until parity passes", the Rust dispatch for
versor_apply is disabled in algebra/backend.py with a pointer to the
gate. The parity tests are skipped with explicit reason. The follow-up
f64 port is documented in the ADR's new Parity status table.
Lane registration: all four parity files added to --suite algebra.
After: algebra 124 passed, 8 skipped (was 86). All other lanes green:
smoke 54, runtime 19, cognition 57, teaching 17, packs 6. Cognition
eval 100%.
ADR-0020 follow-on (task #35). Two-pronged fix:
1. Kernel: ported ADR-0019 Stage 1 diagonal-metric kernel to
core-rs/src/vault.rs. Per-versor scoring is now 32 multiplies
+ 32 adds via the precomputed Cl(4,1) metric, not the
1024-op full geometric_product the prior path computed.
Bit-identity preserved by serial fold order matching Python.
2. Zero-copy marshalling: replaced Vec<&PyAny> + extract-per-
versor with PyReadonlyArray2<f32> via the numpy Rust crate.
The Rust binding now reads a slice view directly into the
numpy buffer — no Python→Rust copy, no Vec<[f32;32]>
re-chunk. Python caller passes the (N, 32) ndarray as-is
(ascontiguousarray ensures C-contiguous f32).
Result:
N python rust speedup
1k 0.20ms 0.26ms 0.77x (Python wins on fixed overhead)
10k 1.62ms 1.45ms 1.12x
100k 19.22ms 12.93ms 1.49x
1M 251.50ms 131.36ms 1.91x
Parity bit-identical (raw f32 bytes) at every scale across the
parameterised test in tests/test_vault_recall_rust_parity.py.
Both ADR-0020 first-surface gates now pass: parity AND
performance at the scales where Rust is meant to win. Python
remains the default per CLAUDE.md sequencing rule 5;
CORE_BACKEND=rust is now a legitimate opt-in acceleration.
Smoke 27/27, algebra 70/70, runtime 19/19 all green.
Phase 4 lane #2 (long_context_cost) measured vault.recall latency
as a function of vault size N. The pre-vectorisation curve was
median 875 ms at N=1k, ~9 s at N=10k — unfit for runtime use.
ADR-0019 Stage 1 replaces the per-element Python dispatch loop in
algebra/backend.py::vault_recall with a vectorised exact scan over
the diagonal Cl(4,1) CGA inner-product metric. Per-versor serial
component reduction order is preserved, so scores are bit-identical
to the scalar cga_inner path. CLAUDE.md exactness is preserved; no
approximate recall is introduced.
Post-vectorisation: 0.217 ms at N=1k, 20.795 ms at N=100k. Slope
0.99 (linear). ~4,000-5,000x speedup at every probed N. Smoke,
algebra, and runtime suites all green.
Stages 2 (norm-bucketed exact pre-filter) and 3 (layered store
with deterministic promotion) are documented in ADR-0019 but
deferred — Stage 1 has dissolved the bottleneck at the scales
relevant to current curriculum work.
Three issues in the drift-fix landing (922bddc) addressed:
1. algebra/rotor.py: add rotor_power(R, alpha) — slerp on the rotor manifold
via the rotor's exp/log decomposition. Handles both rotation planes
(cos/sin) and boost planes (cosh/sinh); falls back to identity for
non-simple bivectors or null cases.
2. generate/stream.py: the score-weighted vault recall previously did
`weight*V + (1-weight)*np.eye(V.shape[0])`. Two bugs:
- np.eye produced a 32x32 matrix for a 1D multivector, crashing
versor_apply with a broadcasting error (2 cognition tests failing
on main).
- The linear blend produced multivectors with versor_condition up to
2.2e-2, violating the non-negotiable 1e-6 invariant declared in
CLAUDE.md. Now uses rotor_power(V, weight) which stays on the
manifold by construction (versor_condition <= 1.1e-16).
3. session/context.py: respond() now re-binds result.final_state to
self.state after finalize_turn's anchor pull, restoring the
"respond returns the same object that was vaulted" contract
(test_engine_loop_proof regression).
Verification:
- 41 new tests in tests/test_rotor_power.py covering closure preservation,
alpha=0/1 boundaries, half-angle composition, and word-transition rotors.
- Empirical multi-turn versor_condition stays at machine epsilon with
anchor pull, max 9.4e-7 without (under threshold either way after fix).
- Full suite: 609 passed, 4 skipped, 0 failed.
Remove the implicit null-vector bypass from the runtime-facing versor_apply closure boundary.
FieldState.F is treated throughout the runtime and cognitive pipeline as a unit versor field. Returning null-like raw sandwich results from versor_apply created a contract mismatch and allowed multi-turn closure drift to escape into session state.
- make _close_applied_versor always close runtime field results
- keep unitize-first semantics and construction-seed fallback
- add regression proving null-like sandwich output is closed for the runtime contract
Null-vector preservation should return later behind an explicit geometry API, not the generic runtime field propagation path.
No GitHub CI/status checks were exposed for this PR.
Replace synthetic word-transition rotor construction with the closed product B * reverse(A).
- preserve make_rotor_from_angle compatibility
- fail closed on non-closed transition candidates instead of using construction fallback behavior
- validate transition operator condition
- add targeted transition rotor regression tests
No GitHub CI/status checks were exposed for this PR.
Key issues fixed:
- `CORE_BACKEND=numpy` was ignored, so tests mixed Python CGA embedding with Rust metric behavior.
- Dense construction seeds were being rejected by strict `unitize_versor()`, while sparse dirty inputs still needed to fail closed.
- Holonomy needed a construction-boundary path for raw/dense vocab fixtures and rare null final accumulators.
- Proposition storage polluted vault recall by storing the live field instead of the proposition’s subject versor.
- Dialogue qualitative frames rendered the same surface as assertive copular frames.
- Repeated session prompts could collapse into the same deterministic response path.
- Two proof fixtures were stale: one hand-built a non-null “null” vector, and one alignment proof omitted the English “with” anchor used by the resonance proof.
Verification:
`CORE_BACKEND=numpy CORE_STRICT_MLX_ON_APPLE=0 uv run core test -- -q`
Result: `277 passed in 59.52s`
Make versor construction fail closed instead of synthesizing hash-derived fallback rotors.
- remove pseudo-random construction fallback from unitize_versor
- add signed residual helper for +1 field states vs ±1 manifold entries
- validate vocab manifold entries with full residuals
- document antipodal transition rotor failure contract
- add focused invariant tests for versor closure and manifold validation
algebra/rotor.py and persona/motor.py were calling normalize_to_versor()
which is the gate-only injection primitive. Both are construction-time
sites (building rotors and motors from raw arrays), so the correct call
is unitize_versor().
Also tightens TestINV02 to scan for normalize_to_versor violations only —
unitize_versor has its own legitimate call sites and is not under the
same single-site restriction. Adds a new TestINV02b that verifies
unitize_versor is NOT called inside propagation, generation, or vault
recall paths.
Fixes: INV-02 architectural invariant test failure.
- field/state.py: FieldState is now frozen+slotted; constructor copies and
enforces float32 shape (32,); advance() updated to pass raw arrays.
np.ndarray inside frozen dataclass is ref-frozen — copy() at construction
is the explicit contract boundary.
- generate/result.py: NEW — GenerationResult frozen dataclass carrying
tokens + final_state. Async variant yields tokens and exposes final_state
on completion.
- generate/stream.py: generate() now returns GenerationResult, not list[str].
vocab.edge_rotor() call replaced with:
A = vocab.get_versor_at(current.node)
B = vocab.get_versor_at(word_idx)
V = word_transition_rotor(A, B)
agenerate() updated to yield tokens and surface final_state.
- vocab/manifold.py: added get_versor_at(idx) and get_word_at(idx) indexed
accessors. VocabManifold stores points; algebra constructs operators.
normalize_to_versor() call-site in docstring clarified: callers must call
unitize_versor() (algebra construction primitive) before add(), not
normalize_to_versor() directly.
- algebra/versor.py: unitize_versor() added as the explicit construction-time
primitive. normalize_to_versor() kept but marked internal/gate-only.
Distinction encoded in docstrings and __all__.
- persona/motor.py + ingest/gate.py: SessionContext.respond() is not yet in
the repo as a separate file; gate.py docstring updated to reflect the
three-tier normalization doctrine:
unitize_versor() — algebra construction only
inject() — gate, once per raw input
normalization — forbidden in propagate/generate/vault recall
- Add algebra/rotor.py: word_transition_rotor() as a free operator function
- Update algebra/__init__.py: export word_transition_rotor
- Refactor vocab/manifold.py: remove edge_rotor(), add versor grade-norm
invariant check in add() to reject raw coordinate vectors at insertion time
VocabManifold now stores only algebraically valid Cl(4,1) versors and
exposes only relational lookup (CGA inner product). Rotor construction
is a contextual algebra-layer concern, not a vocabulary property.
