v12 Design Document: Conductance-Gated Abductive Inference

Hypothesis

Resource-bounded abductive inference improves in both efficiency and epistemic quality when hypothesis selection is gated by a learned 4-layer attention landscape (adaptive SPH kernel, conductance permeability, contradiction veto, utility drift) rather than by geometric proximity or uniform budget allocation alone.

Architecture

Transport Equation

delta_STI(i->j) = W(dist, h_i) * g(e_ij) * gate(e_ij) * (STI_j - STI_i + alpha*(U_j - U_i))

Layer 1 — SPH Adaptive Kernel W(dist, h_i)

Layer 2 — Conductance g(e_ij)

Layer 3 — Contradiction Gate gate(e_ij)

Layer 4 — Utility Drift U_j - U_i

Abduction Pipeline (from v20_rev3)

  1. SPH convergence on cyclic KB (v16: 2 iterations)
  2. Budget-gated flat abduction: bc-step reads conductance as budget
  3. Recursive backward chaining with TV composition
  4. Auto-revision merging multi-path evidence

Validated Components

Component File Key Result
v9c conductance+gate ecan_v9c_assert.metta 47x selectivity
v10 SPH transport ecan_v10_sph.metta A->B=-0.155
v11 adaptive kernel ecan_v11_adaptive.metta h=1.2 to 2.6
v11b full 4-layer ecan_v11b_transport.metta 47x same-distance diff
v16 SPH+abduction abd_tv11_v16.metta Cyclic convergence 2 iters
v20_rev3 full pipeline abd_tv11_v20_rev3.metta Recursive BC+revision

Open Questions

  1. Uncertainty source for adaptive radius: NAL confidence or variance of incoming signals?
  2. How does adaptive radius affect abduction breadth vs depth tradeoff?
  3. Utility drift interaction with contradiction gate under cyclic evidence
  4. Convergence guarantees when conductance field changes during inference
  5. Scaling: does 4-layer gating maintain selectivity on 100+ node KBs?

Experimental Plan

Core Principle (Kevin Machiels, 2026-04-23)

Attention flows when target is nearby, permeable, compatible, and locally more valuable — not just geometrically close.