Daily Digest — Oct 4

TRN-R1-Zero: Text-rich Network Reasoning via LLMs with Reinforcement Learning Only

• TRN-R1-Zero is a post-training framework for text-rich network (TRN) reasoning that is trained solely via reinforcement learning, directly optimizing base LLMs with a Neighbour-aware Group Relative Policy Optimisation objective.

M$^3$KG-RAG: Multi-hop Multimodal Knowledge Graph-enhanced Retrieval-Augmented Generation

• M$^3$KG-RAG enhances multimodal retrieval-augmented generation by constructing a multi-hop multimodal knowledge graph (M$^3$KG) and implementing GRASP for precise entity grounding and selective pruning.

Let Them Steal: Trapping Large Language Model Extraction Attacks with Knowledge Honeypot

• Knowledge Trap, a new defense for large language models, redirects extraction attacks towards low-transferability knowledge using a Honeypot Knowledge Graph and breadcrumb-guided exploration. • Experiments in medical and financial domains demonstrate that Knowledge Trap reduces surrogate agreement by an average of 6.2% without degrading legitimate-user accuracy.

XMedFusion: A Knowledge-Guided Multimodal Perception and Reasoning Framework for Autonomous Medical Systems

• XMedFusion, a modular AI framework, enhances autonomous medical systems by decomposing visual information into functional components including a visual perception agent, a knowledge graph construction agent, and a retrieval-guided drafting process.