13 papers
Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation
Yi Yang, Cong Qin, Xiaodan Liu +8
Large language model agents are commonly trained through reinforcement learning with sparse trajectory-level rewards, which offer limited guidance on how strongly individual tokens…
ToolVerse: Unlocking Massive Environments and Long-Horizon Tasks for Agentic Reinforcement Learning
Shuaiyu Zhou, Fengpeng Yue, Zengjie Hu +5
While LLM agents demonstrate strong reasoning abilities in compact and well-defined scenarios, they struggle to maintain robustness and effectiveness when faced with large-scale, d…
Factorized Latent Reasoning for LLM-based Recommendation
Tianqi Gao, Chengkai Huang, Zihan Wang +3
Large language models (LLMs) have recently been adopted for recommendation by framing user preference modeling as a language generation problem. However, existing latent reasoning…
Purifying Multimodal Retrieval: Fragment-Level Evidence Selection for RAG
Xihang Wang, Zihan Wang, Chengkai Huang +4
Multimodal Retrieval-Augmented Generation (MRAG) is widely adopted for Multimodal Large Language Models (MLLMs) with external evidence to reduce hallucinations. Despite its success…
Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation
Yubo Jiang, Xin Yang, Abudukelimu Wuerkaixi +7
Vision-Language Models (VLMs) are frequently undermined by object hallucination--generating content that contradicts visual reality--due to an over-reliance on linguistic priors. W…
Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding
Yubo Jiang, Yitong An, Xin Yang +7
Vision-Language Models (VLMs) are frequently undermined by object hallucination, generating content that contradicts visual reality, due to an over-reliance on linguistic priors. W…