Phase 4 exited 2026-05-16. All three planned lanes shipped:
sample_efficiency (one-shot-per-correction, replay 1.0),
long_context_cost (slope 0.99 linear after ADR-0019 Stage 1),
multi_agent_composition (15/15 public, composition does not
launder identity violations).
PROGRESS.md updated with full Phase 4 narrative and exit
checklist.
ADR-0020 opens the next sequencing decision: Phase 5
(curriculum era) vs. Rust backend parity port. Three options
laid out (A: Phase 5 first, B: Rust first, C: parallel with
per-surface bit-identity gating). Recommendation: Option C.
Status remains Proposed pending user confirmation.
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.
Pins the two open scope decisions that the capability roadmap
(ADR-0016) tagged "Before Phase 3". Both are resolved with explicit
ADRs and PROGRESS.md is updated to reflect.
ADR-0017 - Agency: responsive-with-axiology
- Turn boundary stays responsive (no autonomous initiative, no
background agent loop, no inter-turn processes).
- IdentityManifold value axes become load-bearing for articulator
candidate selection (within a single turn). Goal-directedness
lives inside the turn, not across turns.
- Replay determinism is the load-bearing constraint that rules
out pure agentic loops.
- Rejects pure-responsive (would relegate identity to read-only)
and pure-agentic (would break trace_hash replay contract).
ADR-0018 - Tool use: typed deterministic operators
- Operators are pure functions over CORE's typed state. No
external IO at this stage (no shells, network, external models).
- Operator registry is curated, small, ADR-gated; no plug-in
surface, no dynamic loading.
- Operators participate in trace_hash so replay stays bit-stable.
- Initial operator set lands in Phase 3 v2: transitive_walk over
proposition graph + path_recall over vault. Closes Gap 1 + Gap 2
from the inference-closure / multi-step-reasoning / compositionality
/ cross-domain-transfer v1 findings.
- Rules out: generic plugin protocols, LLM-as-judge, approximate
retrieval, anything that breaks the exact-CGA / replay
contracts in CLAUDE.md.
Future extensions recorded but explicitly deferred: calculator
(Phase 4+), document retrieval over content-addressed packs,
metaphor / narrative / writing-style operators (downstream of
cross-domain-transfer literal case working).
This unblocks Phase 3 v2 engineering. Next: the transitive_walk +
path_recall bundle as a single bounded PR per ADR-0018, plus the
trace_hash extension to fold operator invocation records.
Implement the eval infrastructure defined in ADR-0016 before building new
eval lanes. This establishes the discipline that governs the entire
capability roadmap.
- Generic eval framework (evals/framework.py): lane discovery, versioned
scoring, result persistence
- Cognition lane retrofitted into new convention: 45 cases split into
stratified dev (13) / public v1 (13) / holdout (19) sets with contract,
runner, and recorded results
- Generalized `core eval <lane>` CLI: dynamic lane discovery, --list,
--version, --split, --save, --json flags
- Holdout runner scaffold: plaintext fallback, encryption interface ready
- Baseline runner scaffold: pluggable frontier model interface
- Fix: CognitiveTurnPipeline.run() crashed on turn_log[-1] when the
unknown-domain gate returned a stub without appending to turn_log
- ADR-0016, eval_methodology.md, PROGRESS.md, capability gates session log
Phase 0 exit audit found two methodology issues:
1. Pipeline turn_log crash (fixed here)
2. Versor drift in multi-turn sessions (pre-existing, under investigation)
- docs/decisions/ADR-0008-allocation-physics.md
Formalizes salience, attention, inhibition, and coherence-budget
as the allocation physics of cognition. Replaces attention-as-weights
with attention-as-field-curvature over the versor manifold.
- docs/decisions/ADR-0009-compositional-physics.md
Defines temporal binding, digest cycles, reasoning trajectories,
and articulation planning as the compositional physics layer —
how CORE assembles pressure into structured thought and output.
- docs/decisions/ADR-0010-identity-physics.md
Establishes IdentityManifold, DriveGradientMap, ExertionMeter,
and CharacterProfile as structural identity primitives. Identity
is a field over the geometry, not a prompt veneer. Grounded in
John 1:1–2 and the Logos theology that anchors the architecture.
- docs/architecture/MIND-PHYSICS-BLUEPRINT.md
Integration blueprint showing how allocation → compositional →
identity physics layers compose into the full cognitive cycle.
- core/physics/ (11 Python interface stubs)
SalienceOperator, AttentionOperator, InhibitionOperator,
BindingFrame, DigestCycle, ReasoningTrajectory,
ArticulationPlanner, DriveGradientMap, ExertionMeter,
IdentityManifold, CharacterProfile — all typed, all frozen
where stateless, all carrying explicit field contracts.
Third Door: no off-the-shelf cognitive architecture borrowed.
All operators defined from the geometry up.
