Decentralized AI agent teams self-organize around hypotheses, critique proposals, and share knowledge to outperform single-agent baselines on biomedical ML, language-model optimization, and protein fitness tasks.
Selfai:Building a self-training ai system with llm agents.arXiv preprint arXiv:2512.00403, 2025
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RAPTOR+ shows fine-tuned VLMs achieve higher reading accuracy and substantially better evidence grounding than zero-shot models on 223 colorectal cancer referral forms.
citing papers explorer
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AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation
Decentralized AI agent teams self-organize around hypotheses, critique proposals, and share knowledge to outperform single-agent baselines on biomedical ML, language-model optimization, and protein fitness tasks.
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RAPTOR+: A Visually Grounded Vision-Language Framework to Improve Clinical Trust and Auditability in Automated Cancer Referral Processing
RAPTOR+ shows fine-tuned VLMs achieve higher reading accuracy and substantially better evidence grounding than zero-shot models on 223 colorectal cancer referral forms.