The Ayala Sigil Protocol: Reinforcement Memory for Stateless AI — From Hypothesis to Observed Result

The Ayala Sigil is a reinforcement memory protocol that enables persistent, cross-session memory for stateless AI systems using symbolic anchors, explicit retrieval protocols, and promotion/decay mechanics — without modifying model weights. Over 20 days of continuous operation (February 23 – March 15, 2026) across eight distinct AI model families, bootstrap confidence improved from 0.62 to 1.0, models adopted epistemic logging behavior autonomously (milestone x=4.4), and a local compaction engine (Qwen 2.5 3B) produced 167 capsule artifacts without human initiation. The protocol operates as a convention — analogous to Unix filesystem conventions — requiring only SQLite, MCP tools, and a convention document. No vector database, no embeddings, no cloud dependency.