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.
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