3 citations · 4 across the 12 of their papers we have counts for
12 papers
HELIX: Model-Harness Co-evolution for Recursive Self-Improvement
Tianyu Fan, Chao Huang
Scaling agent capability has largely focused on improving the model, yet an interactive agent acts through a runtime harness that mediates context, tools, control flow, and stoppin…
To Answer or to Abstain: Mitigating Search-Agent Hallucinations via Abstention-Aware Reinforcement Learning
Fengji Zhang, Tianyu Fan, Yuxiang Zheng +4
Recent advances in equipping Large Language Models (LLMs) with search tools and outcome-reward reinforcement learning (RL) have achieved new state-of-the-art results on open-domain…
CLI-Anything: Towards Agent-Native Computer Use
Yuhao Yang, Tianyu Fan, Chao Huang
As large language models advance in reasoning and tool use capabilities, researchers increasingly seek to leverage them for computer use agents that can interact with existing soft…
DeepInnovator: Triggering the Innovative Capabilities of LLMs
Tianyu Fan, Fengji Zhang, Yuxiang Zheng +5
The application of Large Language Models (LLMs) in accelerating scientific discovery has garnered increasing attention, with a key focus on constructing research agents endowed wit…
Why Your Deep Research Agent Fails? On Hallucination Evaluation in Full Research Trajectory
Yuhao Zhan, Tianyu Fan, Linxuan Huang +2
Diagnosing failure patterns in Deep Research Agents (DRAs) remains a critical challenge. Existing benchmarks predominantly rely on end-to-end evaluation, obscuring intermediate hal…
Needle in the Web: A Benchmark for Retrieving Targeted Web Pages in the Wild
Yumeng Wang, Tianyu Fan, Lingrui Xu +1
Large Language Models (LLMs) have evolved from simple chatbots into sophisticated agents capable of automating complex real-world tasks, where browsing and reasoning over live web…
AI-Trader: Benchmarking Autonomous Agents in Real-Time Financial Markets
Tianyu Fan, Yuhao Yang, Yangqin Jiang +3
Large Language Models (LLMs) have demonstrated remarkable potential as autonomous agents, approaching human-expert performance through advanced reasoning and tool orchestration. Ho…
Understanding DeepResearch via Reports
Tianyu Fan, Xinyao Niu, Yuxiang Zheng +5
DeepResearch agents represent a transformative AI paradigm, conducting expert-level research through sophisticated reasoning and multi-tool integration. However, evaluating these s…
AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents
Jiabin Tang, Tianyu Fan, Chao Huang
Large Language Model (LLM) Agents have demonstrated remarkable capabilities in task automation and intelligent decision-making, driving the widespread adoption of agent development…
MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation
Tianyu Fan, Jingyuan Wang, Xubin Ren +1
The growing demand for efficient and lightweight Retrieval-Augmented Generation (RAG) systems has highlighted significant challenges when deploying Small Language Models (SLMs) in…
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks
Tianyu Fan, Lirong Wu, Yufei Huang +4
Recent years have witnessed the great success of graph pre-training for graph representation learning. With hundreds of graph pre-training tasks proposed, integrating knowledge acq…
Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns
Yifei Sun, Qi Zhu, Yang Yang +4
Recently, the paradigm of pre-training and fine-tuning graph neural networks has been intensively studied and applied in a wide range of graph mining tasks. Its success is generall…