31 citations · 62 across the 24 of their papers we have counts for
14 papers · 1 filter
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…
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…
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.…
Towards Mixed-Modal Retrieval for Universal Retrieval-Augmented Generation
Chenghao Zhang, Guanting Dong, Xinyu Yang +1
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) by retrieving relevant documents from an external corpus. However…
Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation
Guanting Dong, Xiaoxi Li, Yuyao Zhang +1
Real-world live retrieval-augmented generation (RAG) systems face significant challenges when processing user queries that are often noisy, ambiguous, and contain multiple intents.…
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…
Hierarchical Document Refinement for Long-context Retrieval-augmented Generation
Jiajie Jin, Xiaoxi Li, Guanting Dong +6
Real-world RAG applications often encounter long-context input scenarios, where redundant information and noise results in higher inference costs and reduced performance. To addres…
WebThinker: Empowering Large Reasoning Models with Deep Research Capability
Xiaoxi Li, Jiajie Jin, Guanting Dong +5
Large reasoning models (LRMs), such as OpenAI-o1 and DeepSeek-R1, demonstrate impressive long-horizon reasoning capabilities. However, their reliance on static internal knowledge l…
Progressive Multimodal Reasoning via Active Retrieval
Guanting Dong, Chenghao Zhang, Mengjie Deng +3
Multi-step multimodal reasoning tasks pose significant challenges for multimodal large language models (MLLMs), and finding effective ways to enhance their performance in such scen…
Toward General Instruction-Following Alignment for Retrieval-Augmented Generation
Guanting Dong, Xiaoshuai Song, Yutao Zhu +3
Following natural instructions is crucial for the effective application of Retrieval-Augmented Generation (RAG) systems. Despite recent advancements in Large Language Models (LLMs)…
InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models
Shanglin Lei, Guanting Dong, Xiaoping Wang +3
The field of emotion recognition of conversation (ERC) has been focusing on separating sentence feature encoding and context modeling, lacking exploration in generative paradigms b…