This project documents research on reinforcement memory for stateless AI systems, conducted within an independent personal systems lab. Current version: v0. 3 (March 15, 2026) — "From Hypothesis to Observed Result" The Ayala Sigil protocol enables persistent, cross-session memory for AI systems using symbolic anchors, structured logging, and temperature-based lifecycle management — without modifying model weights. Version 0. 3 reports empirical results from 20 days of continuous operation across eight distinct AI model families. Key findings: - Bootstrap confidence improved from 0. 62 to 1. 0 over 20 operational days - 8 model families adopted the logging protocol, including autonomous adoption (milestone x=4. 4) - Local compaction engine (Qwen 2. 5 3B) produced 167 capsule artifacts autonomously - No vector database, no embeddings, no cloud dependency — runs on SQLite FTS5 + MCP tools Root files: - Ayala₂026SigilProtocolᵥ0. 3. md — compiled paper with empirical data - Ayala₂025FromAutomationₜoAgency. pdf — original v0. 1 paper (provenance) - README. md — project overview and citation Dashboard: https: //wanatux. net/sigil/
John Ayala (Wed,) studied this question.