31 citations · 62 across the 24 of their papers we have counts for
13 papers · 1 filter
WorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution
Hang He, Linting Wang, Guanming Liu +14
Scaling interactive environments is important for training long-horizon agents through reinforcement learning. These environments often require further testing and refinement after…
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…
WEFT: Scaling Tool-Use Post-Training for General-Purpose Agents
Bo Mao, Hang He, Linting Wang +17
Recent efforts to scale tool-use post-training have largely centered on the synthesis of executable environments, which constitute only one component of a broader agentic interacti…
EvoIn: Bridging Evolution and Internalization for Agent Fine-Tuning
Shihan Dou, Shaofan Liu, Zhonghang Lu +8
Recent work has explored improving agents by jointly evolving their harnesses and models, but often takes a ''potpourri'' approach that bundles together new tools, new decision-mak…
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…
Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation
Chenghao Zhang, Guanting Dong, Yufan Liu +3
Large Language Models (LLMs) have advanced autonomous agents from deep search, which retrieves concise factual answers, to deep research, which synthesizes scattered evidence into…
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…
OmniGAIA: Towards Native Omni-Modal AI Agents
Xiaoxi Li, Wenxiang Jiao, Jiarui Jin +10
Human intelligence naturally intertwines omni-modal perception -- spanning vision, audio, and language -- with complex reasoning and tool usage to interact with the world. However,…
ET-Agent: Incentivizing Effective Tool-Integrated Reasoning Agent via Behavior Calibration
Yifei Chen, Guanting Dong, Zhicheng Dou
Large Language Models (LLMs) can extend their parameter knowledge limits by adopting the Tool-Integrated Reasoning (TIR) paradigm. However, existing LLM-based agent training framew…
SmartSearch: Process Reward-Guided Query Refinement for Search Agents
Tongyu Wen, Guanting Dong, Zhicheng Dou
Large language model (LLM)-based search agents have proven promising for addressing knowledge-intensive problems by incorporating information retrieval capabilities. Existing works…
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.…