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LLM-guided program evolution has enabled discoveries in mathematics and computational optimization, raising the prospect of reinforcement learning (RL) algorithms that self-evolve to improve how agents learn. However, realizing this prospect faces two obstacles. Joint search over coupled algorithmic components is diffi...
This work proposes SCoRE (Selection and Consolidation for Robust Evidence), a unified agent loop for explicit evidence selection and consolidation, which decouples final reasoning from exploratory trial-and-error while ensuring strict visual grounding via indexed claim-to-image linkages.
Janus is introduced, a framework that uses LLMs to co-evolve target programs and executable proxy evaluators to address label scarcity and extend evaluator-guided LLM discovery from tasks with cheap, scalable feedback to scientific domains where trustworthy evaluation is scarce and expensive.
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