{"slug": "inference-time-agentic-decision-rules-beat-longer-evolving-search-for-multi", "title": "Inference-Time Agentic Decision Rules Beat Longer Evolving Search for Multi-Image Medical Reasoning", "summary": "A new arXiv study (2607.27564v1) finds that a simple order-vote policy outperforms more complex agentic strategies for multi-image medical visual question answering, achieving 57.89% ± 0.65% final-test accuracy on MedFrameQA, compared to 52.73% ± 0.42% for the fixed baseline and 55.79% ± 0.43% for the order-rerank variant. Extending the evolutionary search budget from 50 to 100 generations did not improve generalization, with final-test accuracy dropping to 56.02%, indicating that the choice of agentic decision rule matters more than search budget.", "body_md": "arXiv:2607.27564v1 Announce Type: new\nAbstract: Multi-image medical VQA is not merely a prompt-length problem; it is a fundamental challenge of agentic decision-making. Medical vision-language agents must aggregate evidence across ordered images, remain robust to answer-order perturbations, and avoid overfitting to noisy search-time feedback. We study MedFrameQA through a controlled comparison of five inference-time agentic strategies, optimized using the same high-budget ShinkaEvolve configuration and evaluated on a reproducible internal frozen split (1,331 evolution, 665 holdout, 855 final test). Across five independent repeated runs, the strongest method emerges as the simplest robust aggregator: the \\textbf{order-vote} policy achieves $57.89 \\pm 0.65\\%$ final-test accuracy, significantly outperforming the fixed baseline ($52.73 \\pm 0.42\\%$) and the more complex, albeit brittle, order-rerank variant ($55.79 \\pm 0.43\\%$). Paired bootstrap analysis confirms these significant gains. Extending the evolutionary search budget from 50 to 100 generations yields no generalization benefit: while holdout performance marginally increases, final-test accuracy drops from $57.89\\%$ to $56.02\\%$. Our findings suggest that for multi-image medical reasoning, defining the correct agentic decision rule is substantially more impactful than expanding the optimization search budget.", "url": "https://wpnews.pro/news/inference-time-agentic-decision-rules-beat-longer-evolving-search-for-multi", "canonical_source": "https://arxiv.org/abs/2607.27564", "published_at": "2026-07-31 04:00:00+00:00", "updated_at": "2026-07-31 04:39:29.104336+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "computer-vision", "ai-research"], "entities": ["arXiv", "MedFrameQA", "ShinkaEvolve"], "alternates": {"html": "https://wpnews.pro/news/inference-time-agentic-decision-rules-beat-longer-evolving-search-for-multi", "markdown": "https://wpnews.pro/news/inference-time-agentic-decision-rules-beat-longer-evolving-search-for-multi.md", "text": "https://wpnews.pro/news/inference-time-agentic-decision-rules-beat-longer-evolving-search-for-multi.txt", "jsonld": "https://wpnews.pro/news/inference-time-agentic-decision-rules-beat-longer-evolving-search-for-multi.jsonld"}}