🧬 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 framework using LLMs to automatically design Large Neighborhood Search operators through synergy-aware co-evolution. Achieves near-optimal solutions on TSP and CVRP benchmarks with strong generalization.
A framework for automated discovery of gravitational-wave detection algorithms, combining LLM guidance with Evolutionary Monte Carlo Tree Search, enabling efficient and creative pipeline discovery.
🧬 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.
A review of modern simulation-based inference techniques for gravitational wave data analysis, highlighting methodological advances, practical applications, and future outlook.