REFLEX: Reflective Evolution from LLM Experience
Large multimodal language models (MLLMs) have emerged as powerful tools for guiding evolutionary search toward interpretable programmatic policies. In existing program-evolution systems, however, reusable knowledge is usually carried by whole programs in the population, and it is difficult to trace how a visual observation led to a particular code change and its measured outcome. We present REFLEX, a train-free evolutionary framework that links these steps in one loop. A vision-enabled Critic turns task-specific behavioral evidence into a structured diagnosis; the diagnosis retrieves executable code snippets from a persistent Skill Memory; a text-only Actor writes the child program; and t...
| Authors | Pan Wang |
|---|---|
| Primary category | cs.CL |
| Published | 15 June 2026 |
| Last updated at source | 5 October 2026 |
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