most citedEmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety

2 citations · 3 across the 7 of their papers we have counts for

collaborators

10 papers

cs.CV2025

Zoom in, Click out: Unlocking and Evaluating the Potential of Zooming for GUI Grounding

Zhiyuan Jiang, Shenghao Xie, Wenyi Li +8

Grounding is a fundamental capability for building graphical user interface (GUI) agents. Although existing approaches rely on large-scale bounding box supervision, they still face…

cs.AI2025

Alita-G: Self-Evolving Generative Agent for Agent Generation

Jiahao Qiu, Xuan Qi, Hongru Wang +9

Large language models (LLMs) have been shown to perform better when scaffolded into agents with memory, tools, and feedback. Beyond this, self-evolving agents have emerged, but cur…

cs.CL2025

On the Role of Preference Variance in Preference Optimization

Jiacheng Guo, Zihao Li, Jiahao Qiu +2

Direct Preference Optimization (DPO) has emerged as an important approach for learning from human preferences in aligning large language models (LLMs). However, collecting human pr…

cs.AI20251 cited

On Path to Multimodal Historical Reasoning: HistBench and HistAgent

Jiahao Qiu, Fulian Xiao, Yimin Wang +96

Recent advances in large language models (LLMs) have led to remarkable progress across domains, yet their capabilities in the humanities, particularly history, remain underexplored…

cs.AI2025

AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes

Jiahao Qiu, Xinzhe Juan, Yimin Wang +11

While knowledge distillation has become a mature field for compressing large language models (LLMs) into smaller ones by aligning their outputs or internal representations, the dis…

cs.AI2025

Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution

Jiahao Qiu, Xuan Qi, Tongcheng Zhang +15

Recent advances in large language models (LLMs) have enabled agents to autonomously perform complex, open-ended tasks. However, many existing frameworks depend heavily on manually…