ReLoop closes the feasibility-correctness gap in LLM optimization code via structured generation and behavioral verification with parameter perturbations, reaching 100% executability and accuracy gains on benchmarks while releasing RetailOpt-190.
Reflexion: Language agents with verbal reinforcement learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Bilevel optimization with outer-loop MCTS for skill structure and inner-loop LLM refinement improves agent accuracy on an operations-research question-answering dataset.
citing papers explorer
-
ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimization
ReLoop closes the feasibility-correctness gap in LLM optimization code via structured generation and behavioral verification with parameter perturbations, reaching 100% executability and accuracy gains on benchmarks while releasing RetailOpt-190.
-
Bilevel Optimization of Agent Skills via Monte Carlo Tree Search
Bilevel optimization with outer-loop MCTS for skill structure and inner-loop LLM refinement improves agent accuracy on an operations-research question-answering dataset.