Closing the Epistemic Loop Across the Five Layers of Agent Memory: Cryptographic Provenance, Paraconsistent Bilattices, and Z3-Quarantined Procedural Induction

Contemporary agent architectures decompose persistent state into five layers (Working, Episodic, Semantic, Procedural, and Meta-Memory), yet connect them via unconstrained asynchronous extraction and reflection jobs that launder unverified model narration into grounded facts and reusable skills. We present the unified five-layer epistemic architecture of Project Lucca (v1.47.0, Work Order 39) and evaluate every inter-layer promotion boundary on local consumer silicon. At the Layer 2 -> Layer 3 boundary, six HKDF HMAC-SHA256 domain signers and SQLite triggers cut multi-session self-poisoning from 10/10 (Naive RAG) to 0/10 (p = 0.00195) across four Gemma-4 parameter scales (26B-A4B, 12B, E4B, E2B). Within Layer 3, a zero-VRAM 22M OpenVINO MiniLM-L6 cross-encoder isolates semantic contradictions in 12.23 ms mean CPU latency (0.975/0.975 contradiction P/R, 0/100 timeouts) into Belnap's four-valued paraconsistent state B (BOTH) paired with OpenNARS Beta(w+, w-) evidence weights and append-only supersede() lineage, while hybrid RRF + Allen temporal retrieval lifts LongMemEval (N=30, 12B) accuracy from 23.3% to 70.0% (p = 0.0001, 8.1 ms mean retrieval). At the Layer 4 & Layer 5 boundary, a live 52-turn waking + 16-turn held-out rule-induction baseline (IN-D) demonstrates 4/4 structured tool rules verified by Z3 SMT and 50-turn historical shadow replay, achieving 8/8 (100.0%) held-out recall at 0.14 ms reflex latency and 0/8 (0.0%) distractor misfires under a single-strike circuit breaker, while documenting a 0/2 admission boundary on free-form S-expression macro anti-unification.