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TriMediQ is proposed, a triplet-structured approach that converts patient responses into triplets, aggregates them into a patient-specific knowledge graph, and injects structured representations into a doctor agent via a trainable projection module, demonstrating that structured knowledge representations provide an eff...
This work proposes SSE-Bio, a structured self-evolving agent with an agentic retrieval policy for multi-hop biomedical reasoning, which maintains a structured state, selectively retrieves knowledge triplets and prior templates through a trainable proxy policy, and improves its reasoning memory through fine-grained temp...
Adaptive Personalised Reranking (APR) is proposed, a framework that routes queries based on intent that uses efficient similarity-based reranking for simple queries and dynamically generates tailored instructions to guide an instruction-following reranker for constraint-heavy contexts.
This work proposes an uncertainty-aware framework for adaptive QA based on explicit signals derived from LLM internal representations that distinguish between knowledge insufficiency and knowledge ambiguity or conflict, and efficiently estimate these from hidden states in a single forward pass.
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