RVLM: Recursive Vision-Language Models with Adaptive Depth
Medical AI systems face two fundamental limitations. First, conventional vision-language models (VLMs) perform single-pass inference, yielding black-box predictions that cannot be audited or explained in clinical terms. Second, iterative reasoning systems that expose intermediate steps rely on fixed iteration budgets wasting compute on simple cases while providing insufficient depth for complex ones. We address both limitations with a unified framework. RVLM replaces single-pass inference with an iterative generate-execute loop: at each step, the model writes Python code, invokes vision sub-agents, manipulates images, and accumulates evidence. Every diagnostic claim is grounded in executa...
| Authors | Nicanor Mayumu, Zeenath Khan, Melodena Stephens et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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