Automated Discovery

🧬 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.