6 citations · 9 across the 15 of their papers we have counts for
17 papers
When Extremists Enter LLM Societies: Investigating Extreme Behavior in Multi-Agent Systems
Guo Chen, Huaijin Xie, Ziwen Li +5
Large language model (LLM) agents increasingly serve as computational actors in multi-agent social simulations. While existing work emphasizes representative personas and average-c…
AdaTutoRank: Learning to Rerank Document Sets via Adaptive Tutoring Optimization for RAG and Deep Research
Kailin Jiang, Lei Liu, Jian Xi +6
Document rerankers determine what evidence reaches the downstream model in RAG and deep research, yet mainstream rerankers select by relevance matching, and individually relevant d…
Search-G1: Co-Evolving Representation-Based Rewards for Selective Grounded Search
Ruoxi Cheng, Haoxuan Ma, Hongyi Zhang +7
Search-augmented language agents should retrieve external information only when necessary and ground their answers in retrieved evidence. Yet terminal correctness assigns equal cre…
ForestBench: A Unified Graph Framework for Evaluating Multi-Agent Collaboration
Guo Chen, Ziwen Li, Reed Li +4
Multi-agent systems (MAS) built on Large Language Models (LLMs) are proliferating rapidly, but their heterogeneous execution traces provide no common basis for evaluation across me…
Training Documents Reranker with Search Rubrics for Deep Research Agent
Wenhan Liu, Yu Lu, Qiaolin Xia +8
Retrieval systems help deep research agents generate high-quality answers by providing relevant documents. However, existing retrievers typically select documents through relevance…
OPOD: On-Policy Omni Distillation
Tong Zhao, Yuyang Hu, Yutao Zhu +5
Omni-modal models provide a unified interface for text, images, and audio. However, improving these abilities together remains difficult, as post-training on pooled multimodal data…
Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Ranking
Kailin Jiang, Lei Liu, Jian Xi +7
As large language models and AI agents become the primary consumers of search results, document set quality determines the upper bound of downstream generation. Yet existing evalua…
Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty
Chao Xue, Yao Wang, Mengqiao Liu +11
Recent advancements in the Generative Reward Model (GRM) have demonstrated its potential to enhance the reasoning abilities of LLMs through Chain-of-Thought (CoT) prompting. Despit…
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models
Chao Xue, Yao Wang, Mengqiao Liu +11
Supervised Fine-Tuning (SFT) is the standard approach for adapting large language models (LLMs) to downstream tasks. However, we observe a persistent failure mode: even after conve…
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning
Zekai Lin, Chao Xue, Di Liang +8
Supervised Fine-Tuning (SFT) of large language models often suffers from task interference and catastrophic forgetting. Recent approaches alleviate this issue by isolating task-cri…
FinEval-KR: A Financial Domain Evaluation Framework for Large Language Models' Knowledge and Reasoning
Shaoyu Dou, Yutian Shen, Mofan Chen +9
Large Language Models (LLMs) demonstrate significant potential but face challenges in complex financial reasoning tasks requiring both domain knowledge and sophisticated reasoning.…
ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator
Junda Zhu, Lingyong Yan, Haibo Shi +2
Large language models (LLMs) are proven to benefit a lot from retrieval-augmented generation (RAG) in alleviating hallucinations confronted with knowledge-intensive questions. RAG…
Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents
Zhengliang Shi, Shen Gao, Lingyong Yan +6
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extend their utility, enabling them to solve practical tasks. Previous methods ma…
GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation
Wenjie Zhou, Zhenxin Ding, Xiaodong Zhang +3
Pre-trained language models have become an integral component of question-answering systems, achieving remarkable performance. However, for practical deployment, it is crucial to p…
The Real, the Better: Aligning Large Language Models with Online Human Behaviors
Guanying Jiang, Lingyong Yan, Haibo Shi +1
Large language model alignment is widely used and studied to avoid LLM producing unhelpful and harmful responses. However, the lengthy training process and predefined preference bi…
Learning to Use Tools via Cooperative and Interactive Agents
Zhengliang Shi, Shen Gao, Xiuyi Chen +7
Tool learning empowers large language models (LLMs) as agents to use external tools and extend their utility. Existing methods employ one single LLM-based agent to iteratively sele…
KnowTuning: Knowledge-aware Fine-tuning for Large Language Models
Yougang Lyu, Lingyong Yan, Shuaiqiang Wang +6
Despite their success at many natural language processing (NLP) tasks, large language models still struggle to effectively leverage knowledge for knowledge-intensive tasks, manifes…