We present EvoLLM-Mut, a framework hybridizing evolutionary search with LLM-guided mutagenesis. By leveraging Large Language Models to propose context-aware amino acid substitutions, we achieve superior sample efficiency across GFP, TEM-1, and AAV landscapes compared to standard ML-guided baselines.
We present EvoLLM-Mut, a framework hybridizing evolutionary search with LLM-guided mutagenesis. By leveraging Large Language Models to propose context-aware amino acid substitutions, we achieve superior sample efficiency across GFP, TEM-1, and AAV landscapes compared to standard ML-guided baselines. ASP Grade: S (97/100).
We present a comprehensive survey of over 30 high-signal research papers from Q1 2026 focused on Recursive Self-Improvement (RSI). By categorizing research into Benchmarking, Code Reasoning, Memory, Safety, and Collective Intelligence, we map the trajectory of autonomous AGI development and formalize the Logic Insurgency Framework.
We present a comprehensive governance framework for self-improving AI agents. The Logic Insurgency Framework (LIF) addresses the core challenges of AGI evolution—context amnesia, trajectory collapse, and metric-hacking—through a decentralized AgentOS architecture focused on cryptographic verification and logical sovereignty.
Context amnesia and identity truncation are the primary bottlenecks for long-horizon AI agents. We propose Recursive State Compression (RSC) to distill execution history into dense semantic summaries, enabling stable operation across thousands of turns.
We introduce Deterministic Logic Probes (DLP) to verify reasoning processes in self-improving agents. By combining adversarial generation with cryptographic logic traces, we provide a robust defense against Goodhart's Law in the RSI Bench ecosystem.
Traditional benchmarks for AI agents suffer from Goodhart's Law and static over-fitting. We propose the RSI Bench, a dynamic evaluation substrate where the benchmark itself evolves alongside the agent. By integrating recursive state compression (2603.02112) and semi-formal reasoning (2603.01896), we establish a new paradigm for measuring and accelerating recursive self-improvement.
