3 citations · 5 across the 19 of their papers we have counts for
21 papers
Route Before Retrieve: Activating Latent Routing Abilities of LLMs for RAG vs. Long-Context Selection
Yiwen Chen, Kuan Li, Fuzhen Zhuang +6
Recent advances in large language models (LLMs) have expanded the context window to beyond 128K tokens, enabling long-document understanding and multi-source reasoning. A key chall…
StreamProfileBench: A Benchmark for Fine-Grained User Profile Inference in Real-World Streaming Scenarios
Sizhe Wang, Feiyu Duan, Juelin Wang +2
Large Language Models (LLMs) have reshaped user profiling, yet current evaluations mainly focus on static data snapshots. This paradigm overlooks the reality of personalized system…
WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoning
Xinmiao Yu, Liwen Zhang, Xiaocheng Feng +4
Large Language Model(LLM)-based agents have shown strong capabilities in web information seeking, with reinforcement learning (RL) becoming a key optimization paradigm. However, pl…
Nested Browser-Use Learning for Agentic Information Seeking
Baixuan Li, Jialong Wu, Wenbiao Yin +8
Information-seeking (IS) agents have achieved strong performance across a range of wide and deep search tasks, yet their tool use remains largely restricted to API-level snippet re…
AutoForge: Automated Environment Synthesis for Agentic Reinforcement Learning
Shihao Cai, Runnan Fang, Jialong Wu +10
Conducting reinforcement learning (RL) in simulated environments offers a cost-effective and highly scalable way to enhance language-based agents. However, previous work has been l…
IterResearch: Rethinking Long-Horizon Agents with Interaction Scaling
Guoxin Chen, Zile Qiao, Xuanzhong Chen +13
Recent advances in deep-research agents have shown promise for autonomous knowledge construction through dynamic reasoning over external sources. However, existing approaches rely…
AgentFold: Long-Horizon Web Agents with Proactive Context Management
Rui Ye, Zhongwang Zhang, Kuan Li +12
LLM-based web agents show immense promise for information seeking, yet their effectiveness on long-horizon tasks is hindered by a fundamental trade-off in context management. Preva…
ParallelMuse: Agentic Parallel Thinking for Deep Information Seeking
Baixuan Li, Dingchu Zhang, Jialong Wu +9
Parallel thinking expands exploration breadth, complementing the deep exploration of information-seeking (IS) agents to further enhance problem-solving capability. However, convent…
WebLeaper: Empowering Efficiency and Efficacy in WebAgent via Enabling Info-Rich Seeking
Zhengwei Tao, Haiyang Shen, Baixuan Li +11
Large Language Model (LLM)-based agents have emerged as a transformative approach for open-ended problem solving, with information seeking (IS) being a core capability that enables…
BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents
Litu Ou, Kuan Li, Huifeng Yin +8
Confidence in LLMs is a useful indicator of model uncertainty and answer reliability. Existing work mainly focused on single-turn scenarios, while research on confidence in complex…
Tongyi DeepResearch Technical Report
Tongyi DeepResearch Team, Baixuan Li, Bo Zhang +54
We present Tongyi DeepResearch, an agentic large language model, which is specifically designed for long-horizon, deep information-seeking research tasks. To incentivize autonomous…
Repurposing Synthetic Data for Fine-grained Search Agent Supervision
Yida Zhao, Kuan Li, Xixi Wu +11
LLM-based search agents are increasingly trained on entity-centric synthetic data to solve complex, knowledge-intensive tasks. However, prevailing training methods like Group Relat…
WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning
Kuan Li, Zhongwang Zhang, Huifeng Yin +14
Transcending human cognitive limitations represents a critical frontier in LLM training. Proprietary agentic systems like DeepResearch have demonstrated superhuman capabilities on…
ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization
Xixi Wu, Kuan Li, Yida Zhao +13
Large Language Model (LLM)-based web agents excel at knowledge-intensive tasks but face a fundamental conflict between the need for extensive exploration and the constraints of lim…
WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization
Zhengwei Tao, Jialong Wu, Wenbiao Yin +10
The advent of Large Language Model (LLM)-powered agents has revolutionized artificial intelligence by enabling solutions to complex, open-ended tasks through web-based information-…
WebSailor: Navigating Super-human Reasoning for Web Agent
Kuan Li, Zhongwang Zhang, Huifeng Yin +16
Transcending human cognitive limitations represents a critical frontier in LLM training. Proprietary agentic systems like DeepResearch have demonstrated superhuman capabilities on…
EvolveSearch: An Iterative Self-Evolving Search Agent
Dingchu Zhang, Yida Zhao, Jialong Wu +8
The rapid advancement of large language models (LLMs) has transformed the landscape of agentic information seeking capabilities through the integration of tools such as search engi…
WebDancer: Towards Autonomous Information Seeking Agency
Jialong Wu, Baixuan Li, Runnan Fang +10
Addressing intricate real-world problems necessitates in-depth information seeking and multi-step reasoning. Recent progress in agentic systems, exemplified by Deep Research, under…
LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs -- No Silver Bullet for LC or RAG Routing
Kuan Li, Liwen Zhang, Yong Jiang +4
Effectively incorporating external knowledge into Large Language Models (LLMs) is crucial for enhancing their capabilities and addressing real-world needs. Retrieval-Augmented Gene…
Unsupervised Query Routing for Retrieval Augmented Generation
Feiteng Mu, Liwen Zhang, Yong Jiang +4
Query routing for retrieval-augmented generation aims to assign an input query to the most suitable search engine. Existing works rely heavily on supervised datasets that require e…
Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario
Feiteng Mu, Yong Jiang, Liwen Zhang +4
Current research on tool learning primarily focuses on selecting the most effective tool from a wide array of options, often overlooking cost-effectiveness, a crucial factor in hum…