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The paper introduces ε-MemEvo, a framework for cross-task knowledge transfer in LLM-based program evolution, storing tactic memories as natural-language summaries and using an adaptive injection gate. It reports consistent gains on 8 optimization benchmarks with less than 1% computational overhead.
Introduces ELMER, an evolutionary language model that searches over natural-language policy descriptions and compiles them into executable programs, using fine-tuned Qwen3-8B with Direct Preference Optimization to control mutation strength and improve search efficiency.
FunL2O presents a unified framework that uses LLM-driven program evolution to automate feature function design for learning-to-optimize methods, showing consistent improvements over hand-crafted features across continuous and discrete optimization tasks.
The paper introduces MILP-Evo, a closed-loop framework that uses LLM-guided program evolution to automatically design white-box MILP solver components (cut selectors and branching rules) by iteratively generating and evaluating candidate programs via end-to-end solver performance on MILP instances.
SMCEvolve introduces a principled framework for LLM-driven program evolution by reformulating it as sampling from a reward-tilted distribution using Sequential Monte Carlo. It provides convergence guarantees and outperforms existing methods across multiple scientific discovery benchmarks.