765 citations · 1.1k across the 60 of their papers we have counts for
66 papers
Agent-Reflex: Teaching Language Agents to Act Reflectively
Guanting Dong, Xinyu Yang, Jiajie Jin +9
Large language models have evolved from single-turn chatbots into interactive agents capable of long-horizon reasoning. In real deployments, MCP tool services can return noisy feed…
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…
WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
Xiaoshuai Song, Liancheng Zhang, Kangzhi Zhao +8
Large language model (LLM)-based web search agents are transforming information seeking from simple factoid question answering into complex, deep-and-wide search and research-orien…
VeriGraph: Towards Verifiable Data-Analytic Agents
Jiajie Jin, Zhao Yang, Wenle Liao +5
LLM-based agents have demonstrated strong capabilities in data-intensive analytical tasks, yet their outputs are rarely verifiable: a reliance on linear text trajectories makes the…
Toward Generalist Autonomous Research via Hypothesis-Tree Refinement
Jiajie Jin, Yuyang Hu, Kai Qiu +15
Scientific progress depends on a repeated loop of exploration, experimentation, and abstraction. Researchers test candidate directions, interpret the evidence, and carry the result…
RAG: Retriever Routing for Retrieval-Augmented Generation
Tong Zhao, Yutao Zhu, Yucheng Tian +1
Retrieval-augmented generation (RAG) has become a cornerstone for knowledge-intensive tasks. However, the efficacy of RAG is often bottlenecked by the ``one-size-fits-all'' retriev…
ATIR: Towards Audio-Text Interleaved Contextual Retrieval
Tong Zhao, Chenghao Zhang, Yutao Zhu +1
Audio carries richer information than text, including emotion, speaker traits, and environmental context, while also enabling lower-latency processing compared to speech-to-text pi…
Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Guanting Dong, Junting Lu, Junjie Huang +17
Large language models are increasingly expected to serve as general-purpose agents that interact with external, stateful tool environments. The Model Context Protocol (MCP) and bro…
RecThinker: An Agentic Framework for Tool-Augmented Reasoning in Recommendation
Haobo Zhang, Yutao Zhu, Kelong Mao +2
Large Language Models (LLMs) have revolutionized recommendation agents by providing superior reasoning and flexible decision-making capabilities. However, existing methods mainly f…
LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrieval
Jiajie Jin, Yanzhao Zhang, Mingxin Li +4
LLMs have fundamentally transformed dense retrieval, upgrading backbones from discriminative encoders to generative architectures. However, a critical disconnect remains: while LLM…
GISA: A Benchmark for General Information-Seeking Assistant
Yutao Zhu, Xingshuo Zhang, Maosen Zhang +9
The advancement of large language models (LLMs) has significantly accelerated the development of search agents capable of autonomously gathering information through multi-turn web…
DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories
Chenlong Deng, Mengjie Deng, Junjie Wu +10
Existing multimodal retrieval systems excel at semantic matching but implicitly assume that query-image relevance can be measured in isolation. This paradigm overlooks the rich dep…
Agentic-R: Learning to Retrieve for Agentic Search
Wenhan Liu, Xinyu Ma, Yutao Zhu +4
Agentic search has recently emerged as a powerful paradigm, where an agent interleaves multi-step reasoning with on-demand retrieval to solve complex questions. Despite its success…
Memory in the Age of AI Agents
Yuyang Hu, Shichun Liu, Yanwei Yue +44
Memory has emerged, and will continue to remain, a core capability of foundation model-based agents. As research on agent memory rapidly expands and attracts unprecedented attentio…
Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning
Yuyang Hu, Jiongnan Liu, Jiejun Tan +2
Large language models (LLMs) are increasingly deployed as intelligent agents that reason, plan, and interact with their environments. To effectively scale to long-horizon scenarios…
EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis
Xiaoshuai Song, Haofei Chang, Guanting Dong +3
Large language models (LLMs) are expected to be trained to act as agents in various real-world environments, but this process relies on rich and varied tool-interaction sandboxes.…
FinSight: Towards Real-World Financial Deep Research
Jiajie Jin, Yuyao Zhang, Yimeng Xu +3
Generating professional financial reports is a labor-intensive and intellectually demanding process that current AI systems struggle to fully automate. To address this challenge, w…
Agentic Entropy-Balanced Policy Optimization
Guanting Dong, Licheng Bao, Zhongyuan Wang +11
Recently, Agentic Reinforcement Learning (Agentic RL) has made significant progress in incentivizing the multi-turn, long-horizon tool-use capabilities of web agents. While mainstr…
DeepAgent: A General Reasoning Agent with Scalable Toolsets
Xiaoxi Li, Wenxiang Jiao, Jiarui Jin +8
Large reasoning models have demonstrated strong problem-solving abilities, yet real-world tasks often require external tools and long-horizon interactions. Existing agent framework…
Revisiting RAG Ensemble: A Theoretical and Mechanistic Analysis of Multi-RAG System Collaboration
Yifei Chen, Guanting Dong, Yutao Zhu +1
Retrieval-Augmented Generation (RAG) technology has been widely applied in recent years. However, despite the emergence of various RAG frameworks, a single RAG framework still cann…
ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability
Wenhan Liu, Xinyu Ma, Weiwei Sun +4
Large Language Model (LLM) based listwise ranking has shown superior performance in many passage ranking tasks. With the development of Large Reasoning Models (LRMs), many studies…
Agentic Reinforced Policy Optimization
Guanting Dong, Hangyu Mao, Kai Ma +11
Large-scale reinforcement learning with verifiable rewards (RLVR) has demonstrated its effectiveness in harnessing the potential of large language models (LLMs) for single-turn rea…
HiRA: A Hierarchical Reasoning Framework for Decoupled Planning and Execution in Deep Search
Jiajie Jin, Xiaoxi Li, Guanting Dong +5
Complex information needs in real-world search scenarios demand deep reasoning and knowledge synthesis across diverse sources, which traditional retrieval-augmented generation (RAG…
Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning
Guanting Dong, Yifei Chen, Xiaoxi Li +7
Recently, large language models (LLMs) have shown remarkable reasoning capabilities via large-scale reinforcement learning (RL). However, leveraging the RL algorithm to empower eff…