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20232026
most citedInstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models

31 citations · 62 across the 24 of their papers we have counts for

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Showing 2025 · cs.AIShow all

6 papers · 2 filters

cs.AI2025

ToolScope: An Agentic Framework for Vision-Guided and Long-Horizon Tool Use

Mengjie Deng, Guanting Dong, Zhicheng Dou

Recently, large language models (LLMs) have demonstrated remarkable problem-solving capabilities by autonomously integrating with external tools for collaborative reasoning. Howeve…

cs.AI2025★ 1 cited

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…

cs.AI2025

Toward Effective Tool-Integrated Reasoning via Self-Evolved Preference Learning

Yifei Chen, Guanting Dong, Zhicheng Dou

Tool-Integrated Reasoning (TIR) enables large language models (LLMs) to improve their internal reasoning ability by integrating external tools. However, models employing TIR often…

cs.AI2025

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…

cs.AI2025★ 2 cited

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…

cs.AI2025★ 10 cited

Search-o1: Agentic Search-Enhanced Large Reasoning Models

Xiaoxi Li, Guanting Dong, Jiajie Jin +5

Large reasoning models (LRMs) like OpenAI-o1 have demonstrated impressive long stepwise reasoning capabilities through large-scale reinforcement learning. However, their extended r…