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TOPIC · ENTITY #22854

multi-agent system

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2

RESEARCH · 1 SOURCE · arXiv cs.AI

GEMS-RAG: graph-grounded multimodal RAG for engineering standards

GEMS-RAG is a framework that combines hierarchical parsing, fine-grained visual entity extraction, multimodal knowledge-graph construction, and graph-grounded hybrid retrieval to enable grounded reasoning over complex, multimodal engineering standards. Evaluated on the Manual on Uniform Traffic Control Devices (MUTCD), GEMS-RAG improves retrieval effectiveness and question-answering performance compared with text-only, multimodal, and graph-based RAG baselines.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

Evidence-driven multi-agent AI (DRL + LLM) for head & neck proton PBS radiotherapy planning (arXiv:2610.29010v1)

This arXiv preprint proposes a compound AI system that combines a PPO-trained policy network to generate multiple proton PBS treatment plans with an LLM-based multi-agent conversational refinement layer. The planning agent (based on Qwen3-14B and post-trained with SFT and GRPO), a chatbot interface, and an evaluation agent were tested on 21 head-and-neck cancer patients (5 PPO plans each; 50 plans selected for instruction-guided refinement), and the post-trained Qwen3-14B improved instruction-following, retrieval and domain reasoning to produce clinically more appropriate trade-offs during user-guided plan adjustments.

6.0