closed-loop-reasoning

XiangqiBench is an executable benchmark for closed-loop evaluation of LLM agents in Xiangqi endgames, measuring whether models can convert forced-mate positions into verified wins against an engine defender, and revealing large gaps between static move-finding competence and actual game-winning performance across 12 frontier LLMs.

Introduces CLVR (Closed-Loop Visual Reasoning), a framework that reformulates text-to-image generation from a single-step process into a closed-loop, multi-step visual reasoning approach using a VLM controller and diffusion models, achieving improved performance on compositional prompts.