core/notes/spine_unification_design_2026-05-19.md

Cognitive-Spine Unification — Deferred RFC

Date: 2026-05-19 Status: Design proposal — NOT implemented. Deferred from the 2026-05-19 fluency push because the change crosses public-API entrypoints and depends on the SurfaceSelector landing. Companion RFC: notes/surface_selector_design_2026-05-19.md

Motivation

The 2026-05-19 design review's Finding P0 #2:

The live cognitive spine is fragmented across public entrypoints.

Entrypoint	Today's spine	Affected by intent fixes?	Affected by pipeline fixes?
core chat (REPL)	ChatRuntime.chat() direct	✓ yes	✗ no
core trace (single turn)	ChatRuntime.chat() direct	✓ yes	✗ no
core pulse (research)	scripts/run_pulse.py graph diffusion + GloVe-seeded	✗ no	✗ no
evals/cognition/runner.py	CognitiveTurnPipeline.run()	✓ yes	✓ yes
evals/cold_start_grounding/runner.py	ChatRuntime.chat() direct	✓ yes	✗ no
evals/warmed_session_consistency/runner.py	CognitiveTurnPipeline.run()	✓ yes	✓ yes
evals/deterministic_fluency/runner.py	ChatRuntime.chat() direct	✓ yes	✗ no

Three separate cognitive spines exist:

1. ChatRuntime.chat() direct — the simplest path, used by core chat and core trace.
2. CognitiveTurnPipeline.run() — wraps ChatRuntime.chat() and adds a graph-realizer override step + transitive walk + frame compose.
3. scripts/run_pulse.py — independent path with GloVe seeding, graph constraint correction, top-k recall.

Effects of fragmentation:

• A fix to the pipeline's override behaviour does not reach the user via core chat.
• A fix to the runtime reaches the user but is masked under the pipeline-wrapped eval lanes.
• Pulse can "prove" capabilities the user never experiences.
• Tests can be green while user behaviour is broken (and vice versa).

Proposed direction

One canonical chat spine

ChatRuntime.chat() becomes the single canonical entrypoint. The pipeline's value-add (transitive walks, frame composition) moves INSIDE the runtime as opt-in passes consulted by the selector:

class ChatRuntime:
    def chat(self, text, *, max_tokens=None, mode="full"):
        # mode="full"   — runtime + pipeline-equivalent passes
        # mode="bridge" — runtime only (today's bridge path)
        # mode="walk"   — walk evidence only (research / introspection)
        ...

The pipeline becomes a thin convenience wrapper that selects a mode:

class CognitiveTurnPipeline:
    def run(self, text, *, max_tokens=None):
        # Equivalent to ChatRuntime.chat(text, mode="full").
        # Retained as the API the cognition eval harness was built
        # against; new code calls ChatRuntime.chat() directly.
        ...

Pulse demoted to research harness

scripts/run_pulse.py keeps existing for the geometry-research path but is labeled non-canonical. It does not contribute to "fluent chat" claims; the eval lanes that rely on it (if any) are renamed.

Single selector consumed everywhere

The SurfaceSelector (companion RFC) is the only path that emits the user-facing surface. All entrypoints route through it:

user input
  ─▶ ChatRuntime.chat(text, mode=…)
        ─▶ collect_candidates(intent, subject, field_state, mode)
        ─▶ SurfaceSelector.select(candidates, context)
        ─▶ ChatResponse(surface=chosen.surface, …)

core chat, core trace, every eval lane, and the pipeline shim all call ChatRuntime.chat() with different modes. One emission point, one telemetry record, one trace hash.

What this fixes

Today	After
Pipeline override invisible to core chat	Pipeline's value-add is opt-in modes inside the runtime; visible everywhere or nowhere
Eval-vs-user behaviour drift	Same code path; can't drift
Pulse "proves" things the user doesn't see	Pulse explicitly labeled non-canonical
Tests asserting r.surface == r.walk_surface	Surface is the selector's output; walk_surface remains audit telemetry
Three places to add a fluency surface	One: register a SurfaceProvider

Sequencing

This RFC is dependent on surface_selector_design_2026-05-19.md. Land in this order:

1. SurfaceSelector + provider registry (the companion RFC)
2. Wrap each existing dispatcher branch as a provider
3. Re-implement pipeline override as a provider (or remove if the selector handles it via ordering)
4. Move core chat to ChatRuntime.chat(mode="full")
5. Move core trace to ChatRuntime.chat(mode="full") with trace instrumentation
6. Audit eval lanes — every lane explicitly declares its mode
7. Label scripts/run_pulse.py as non-canonical in its docstring and any eval lane that depends on it

What does NOT change

• Pack content (already correct authoring path)
• Teaching chains (already correct authoring path)
• Intent classification (already canonical via generate.intent)
• Telemetry schema (one emitter, one shape)
• Trace-hash stability (intra-session; hashes are still per-run)

Risk register

• Public API stability — CognitiveTurnPipeline.run() cannot be removed without a deprecation cycle. Migration step is a wrapper, not a removal.
• Mode semantics — the three modes (full / bridge / walk) must be documented in docs/runtime_contracts.md BEFORE the refactor so users can rely on them.
• Eval invariant — cognition eval expects pipeline-level behaviour. The wrapper preserves that; verifiable byte-identity on the eval is a hard prerequisite to commit.

When to land

After the SurfaceSelector RFC. Spine unification without the selector would just move the fragmentation; with the selector it collapses the spines onto a single, observable, replayable path.