Implements the coupled forward-correction loop that separates CORE from
a nearest-neighbour lookup engine:
per iteration:
state, Δ_fwd = diffusion_op.forward(state) # spread context
state, Δ_corr = correction_op.adjoint_pass(state) # enforce intent
converged when both Δ_fwd < ε and Δ_corr < ε
field/operators.py:
- Add ConstraintCorrectionOperator(target_versor, correction_rate, node_index)
- adjoint_pass() builds an incremental correction rotor from the current
output-node versor toward the intent target using the exponential map
(same _unitize_f32 path, same boost/rotation blade classification).
This is a non-self-adjoint operator: it has a preferred direction.
- forward() is identity (correction acts only on the output node via adjoint_pass).
- The target is the prompt centroid versor — same geometry that seeds the
output node, so the correction restores coherence broken by diffusion.
scripts/run_pulse.py (V4):
- Build target_versor from prompt centroid before the loop (exposed from
_build_manifold as a second return value alongside state + labels).
- Instantiate GraphDiffusionOperator + ConstraintCorrectionOperator.
- Coupled convergence: loop until both Δ_fwd < ε AND Δ_corr < ε.
- Print both deltas each step for observability.
- --correction-rate flag (default 0.3) to tune correction strength.
- --no-correction flag to reproduce V3 pure-diffusion behaviour.
tests/test_pulse_integration.py:
- test_correction_pulls_toward_target: verifies output node moves closer
to target versor under correction than without it.
- test_coupled_loop_converges: full V4 pulse with correction converges.
- test_correction_rate_zero_is_identity: rate=0 leaves the field unchanged.
- test_different_inputs_produce_different_correction_targets: correction
targets differ for semantically distinct inputs.
Replace the divergent rotation-based diffusion operator with a linear
blend + exponential-map re-unitization approach that converges in ~28
steps while maintaining vc < 1e-6.
Key changes:
- GraphDiffusionOperator now averages neighbors in multivector space and
re-projects via per-plane exponentials (cos/sin for rotations, cosh/sinh
for boosts in Cl(4,1))
- run_pulse V3: per-token graph topology with input-driven output node,
recall via VocabManifold.nearest(), --no-glove flag for compiled pack
- Tests updated for V3 API
Different inputs now produce different recall rankings from the compiled
en_core_cognition_v1 vocabulary, completing Threshold 1 (Semantic Encoding).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add ManifoldState (N,32) versor field over graph edges, GraphDiffusionOperator
with damped convergence via construction_seed_versor closure, deterministic
hash-to-versor stub, and run_pulse.py end-to-end script proving injection →
propagation → vault recall → token output. 24 new tests, zero regressions
on architectural invariants.
