Chess as a Strategic Structural Intelligence Laboratory: From Competitive UIRTU-Relay to Autonomous Strategy Evolution and Collective Learning

Chess-SSIL is a research framework and experimental laboratory for investigating autonomous strategy generation, competitive structural strategy evolution, and collective structural learning. It connects game-state localization, per-node intelligence, UIRTU-Relay, independent evaluation, counter-evidence, and structural growth. The project introduces Strategy-Generability Partition (SGP), Residual-to-Generable Conversion (RGC), and Autonomous Strategy Coverage (ASC) as testable research constructs. Established results in bounded-game strategy learning are distinguished from hypotheses about autonomous structural evolution and open-world transfer. Version 1.0.0 provides six research articles, three interface specifications, research questions, testable hypotheses, a validation roadmap, and architectural figures; it does not claim to demonstrate general autonomous structural evolution or ASI.