Cat-PO: Cross-modal Adaptive Token-rewards for Preference Optimization in Truthful Multimodal LLMs
Zhixiao Zheng · Zheren Fu · Zhiyuan Yao · Dongming Zhang · Zhendong Mao
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Zhixiao Zheng · Zheren Fu · Zhiyuan Yao · Dongming Zhang · Zhendong Mao
Hou Xia · Zheren Fu · Fangcan Ling · Jiajun Li · Yi Tu · Zhendong Mao · Yongdong Zhang
Mingxuan Du · Benfeng Xu · Chiwei Zhu · Licheng Zhang · Xiaorui Wang · Zhendong Mao
Weinan Jia · Yuning Lu · Mengqi Huang · Hualiang Wang · Binyuan Huang · Nan Chen · weihao zhou · Jidong Jiang · Zhendong Mao
Huatian Zhang · Zhendong Mao · Lei Zhang · Yongdong Zhang
Jingjing Zhang · Lei Zhang · Zheren Fu · Bo Hu · Zhendong Mao
Chiwei Zhu · Benfeng Xu · Mingxuan Du · Shaohan Wang · Xiaorui Wang · Zhendong Mao · Yongdong Zhang
Deep research is emerging as a representative long-horizon task for large language model (LLM) agents. However, long trajectories in deep research often exceed model context limits, compressing token budgets for both evidence collection and report writing, and preventing effective test-time scali...
Xin Wang · Jiahao Li · Licheng Zhang · Zhendong Mao
Large Language Models (LLMs) suffer from hallucinations, severely undermining their reliability. While white-box hallucination detection methods that leverage hidden states prevail, they fail to identify and focus on fact-critical information when analyzing token sequences. To address this, we pr...
Shixuan Ma · Jiahao Li · Zhendong Mao · Quan Wang
The widespread deployment of Large Language Models (LLMs) has spurred significant progress in the detection of LLM-generated text. However, existing detection methods often rely on statistical features that are insufficient for reliable detection; for example, even though LLM-generated and human-...
Xingyu Yao · Zhendong Mao · Quan Wang
Detecting LLM-generated code is crucial for ensuring software provenance, security, reliability, and licensing compliance. Existing training-free detectors, mostly adapted from text-based methods, rely on global statistics of the Token Perplexity Sequence (TPS) and struggle with code. We reveal a...
Pengyu Wang · Benfeng Xu · Licheng Zhang · Shaohan Wang · Mingxuan Du · Chiwei Zhu · Zhendong Mao
Graph-based Retrieval-Augmented Generation (GraphRAG) organizes external knowledge as a hierarchical graph, enabling efficient retrieval and aggregation of scattered evidence across multiple documents. However, many existing benchmarks for GraphRAG rely on short, curated passages as external know...
Jiaang Li · Zhendong Mao · Quan Wang · Yuning Wan · Yongdong Zhang
Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) by incorporating retrieved documents and/or generated context. However, LLMs often exhibit a stylistic bias when presented with mixed contexts, favoring fluent but hallucinated generated content over fact...
Chiwei Zhu · Mingxuan Du · Benfeng Xu · Shengzhuo zhang · Xiaorui Wang · Zhendong Mao
Web browsing—widely used for information retrieval and fact verification—has become a fundamental capability of recently emerged large language model (LLM) agents, which is often elicited by training on complex questions requiring web search. However, this task faces challenges with respect to da...
Dengcan Liu · Fengkai Yang · Xiaohan Wang · Shurui Yan · Jiajun Chai · Jiahao Li · Yikun Ban · Zhendong Mao · Shuai Chen · Guojun Yin
Fengyi Fu · Mengqi Huang · Lei Zhang · Zhendong Mao
Zikang Guo · Benfeng Xu · Chiwei Zhu · Wentao Hong · Xiaorui Wang · Zhendong Mao
Dengcan Liu · Jiahao Li · Zheren Fu · Yi Tu · Jiajun Li · Zhendong Mao · Yongdong Zhang