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…
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…
V-tableR1: Process-Supervised Multimodal Table Reasoning with Critic-Guided Policy Optimization
Yubo Jiang, Yitong An, Xin Yang +7
We introduce V-tableR1, a process-supervised reinforcement learning framework that elicits rigorous, verifiable reasoning from multimodal large language models (MLLMs). Current MLL…
Silo-Bench: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems
Yuzhe Zhang, Feiran Liu, Yi Shan +8
Large language models are increasingly deployed in multi-agent systems to overcome context limitations by distributing information across agents. Yet whether agents can reliably co…
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…
AutothinkRAG: Complexity-Aware Control of Retrieval-Augmented Reasoning for Image-Text Interaction
Jiashu Yang, Chi Zhang, Abudukelimu Wuerkaixi +5
Multimodal document question answering requires retrieving dispersed evidence from visually rich long documents and performing reliable reasoning over heterogeneous information. Ex…
Towards Self-Robust LLMs: Intrinsic Prompt Noise Resistance via CoIPO
Xin Yang, Letian Li, Abudukelimu Wuerkaixi +5
Large language models (LLMs) have demonstrated remarkable and steadily improving performance across a wide range of tasks. However, LLM performance may be highly sensitive to promp…
TRIP-Bench: A Benchmark for Long-Horizon Interactive Agents in Real-World Scenarios
Yuanzhe Shen, Zisu Huang, Zhengyuan Wang +14
As LLM-based agents are deployed in increasingly complex real-world settings, existing benchmarks underrepresent key challenges such as enforcing global constraints, coordinating m…
DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding
Jiaming Zhou, Xuxin Cheng, Shiwan Zhao +5
Autoregressive (AR) large audio language models (LALMs) such as Qwen-2.5-Omni have achieved strong performance on audio understanding and interaction, but scaling them remains cost…
Reflecting Twice before Speaking with Empathy: Self-Reflective Alternating Inference for Empathy-Aware End-to-End Spoken Dialogue
Yuhang Jia, Pei Liu, Haoqin Sun +6
End-to-end Spoken Language Models (SLMs) hold great potential for paralinguistic perception, and numerous studies have aimed to enhance their capabilities, particularly for empathe…