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cs.AI2026
From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge
Wenkang Wei, Yuan Fang, Renhe Jiang +2
How does a language model's dependence on query-routing information and target knowledge change as it answers a question? We study this question through layerwise interventions on…
cs.AI2026
GraphReAct: Reasoning and Acting for Multi-step Graph Inference
Xingtong Yu, Zhongwei Kuai, Chang Zhou +6
Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, extending this paradigm to gra…