Abductive Bridge-Finding in Epistemically Structured Knowledge Graphs

We present a mechanism for abductive combinatorics in epistemically structured knowledge graphs, implemented as part of the Bastian A-000 cognitive architecture. The mechanism operates on pairs of Symbolats — atomic knowledge units with triadic SSS structure (BIO/PHY/ABS) — that exhibit intuitional resonance without an established causal relation. Rather than enumerating combinations blindly, the system employs Bayesian abduction to identify bridge candidates: third Symbolats that maximally explain the observed resonance. The scoring function integrates intentional alignment (bio_bridge), factual grounding (phy_anchor), semantic proximity (semantic_fit), and session-accumulated Bayesian posterior. Results are surfaced as proto-intentions requiring Architect ratification before entering the causal graph as permanent A→C→B relations. This constitutes a targeted hypothesis generation mechanism grounded in epistemic structure — distinct from stochastic combinatorial approaches that operate without accumulated knowledge context.