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cs.CL2026
ATTUNER: Recomputation-Free Agentic Context KV Reuse
Xinghao Chen, Junnan Dong, Cai Ke +8
Large language model (LLM) agents repeatedly load reusable content, such as skills, documents, and memory entries, into the current context. Re-encoding this content for every requ…
cs.CL2026
From Final Artifacts to Trajectories: Retrospective Process Supervision for Evidence-Grounded Long-Form Generation
Junjie Huang, Jiarui Qin, Di Yin +4
Trajectory data is getting more vital for training large language models for boosting the agentic abilities. Unlike the verifiable domains such as coding or mathematics, scaling tr…
cs.CL2026
MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation
Zheng Yuan, Chuang Zhou, Linhao Luo +4
Retrieval-augmented generation is intensively studied to ground large language models on external evidence. However, retrieving from a unified knowledge base could inevitably intro…