4 citations · 5 across the 9 of their papers we have counts for
10 papers
Harness Policy Distillation: Aligning Agent Policies with Runtime Semantics
Junze Zhu, Weihao Chen, Zhiyang Zhou +2
Deploying a general-purpose model as an agent means running it inside a product-specific runtime harness that decides which actions are executable, how state advances, and when the…
Recognize Your Orchestrator: An Entropy Dynamics Perspective for LLM Multi-Agent Systems
Junze Zhu, Weihao Chen, Xuanwang Zhang +2
The transition from single-turn models to Multi-Agent Systems (MAS) promises enhanced problem-solving capabilities, yet the centralized orchestration topology remains a critical po…
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
Yuhang Wu, Xiangqing Shen, Fanfan Wang +4
Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation. However, current reranking models are typically optimized on static human annotated…
WebNavigator: Global Web Navigation via Interaction Graph Retrieval
Xuanwang Zhang, Yuteng Han, Jinnan Qi +5
Despite significant advances in autonomous web navigation, current methods remain far from human-level performance in complex web environments. We argue that this limitation stems…
TrustJudge: Inconsistencies of LLM-as-a-Judge and How to Alleviate Them
Yidong Wang, Yunze Song, Tingyuan Zhu +11
The adoption of Large Language Models (LLMs) as automated evaluators (LLM-as-a-judge) has revealed critical inconsistencies in current evaluation frameworks. We identify two fundam…
RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios
Fei Zhao, Chengqiang Lu, Yufan Shen +9
While various multimodal multi-image evaluation datasets have been emerged, but these datasets are primarily based on English, and there has yet to be a Chinese multi-image dataset…
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
Xuanwang Zhang, Yunze Song, Yidong Wang +10
Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. However, even the most advanced LLMs face challenges such as hall…
AutoSurvey: Large Language Models Can Automatically Write Surveys
Yidong Wang, Qi Guo, Wenjin Yao +10
This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial…
Knowledge-aware Dual-side Attribute-enhanced Recommendation
Taotian Pang, Xingyu Lou, Fei Zhao +5
\textit{Knowledge-aware} recommendation methods (KGR) based on \textit{graph neural networks} (GNNs) and \textit{contrastive learning} (CL) have achieved promising performance. How…
Dr3: Ask Large Language Models Not to Give Off-Topic Answers in Open Domain Multi-Hop Question Answering
Yuan Gao, Yiheng Zhu, Yuanbin Cao +6
Open Domain Multi-Hop Question Answering (ODMHQA) plays a crucial role in Natural Language Processing (NLP) by aiming to answer complex questions through multi-step reasoning over…