🧬 Revolutionary framework that uses Large Language Models to automatically design Large Neighborhood Search operators for combinatorial optimization. Through synergy-aware co-evolution of destroy and repair operators, achieves near-optimal solutions with reduced computational budgets on TSP and CVRP benchmarks.
🧬 Revolutionary LLM-guided framework for automated algorithmic discovery in gravitational wave detection. Combines evolutionary optimization with Monte Carlo Tree Search to achieve 20.2% improvement over domain-specific methods and 59.1% over LLM-based optimization frameworks on MLGWSC-1 benchmark.
🌊 AI-powered gravitational wave detection system using Model Context Protocol (MCP) for efficient parameter space exploration. Features optimization client, analysis server, and automated parameter exploration for LIGO GW150914 data with LLM-agent validation.