MEGA: Self-Evolving Agent Optimization Infrastructure via Wisdom Graph

A recent submission to arXiv (2608.10504) presents MEGA (Meta Evaluation-Grounded Adaptation), a self-evolving framework designed to enhance coding agents. The authors contend that as these agents take on more implementation tasks, the primary challenge lies in developing a systematic infrastructure for their improvement. Existing methods struggle to gather transferable knowledge, lack compositional reasoning, and do not facilitate self-evolution of knowledge through operational evidence. MEGA fills these voids by ensuring that each optimization cycle yields lasting assets, while compositional reasoning informs future optimizations. Operational evidence further sharpens both the accumulated insights and the reasoning behind them. Layer 1 extracts reusable insights via behavioral-pattern clustering and empirical A/B testing, creating durable assets, while Layer 2 breaks these assets into atomic PCR (Probabilistic Causal Rules) components.