most citedDeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments

1 citations · 2 across the 4 of their papers we have counts for

collaborators

9 papers

cs.SE2026

daVinci-Dev: Agent-native Mid-training for Software Engineering

Ji Zeng, Dayuan Fu, Tiantian Mi +14

Recently, the frontier of Large Language Model (LLM) capabilities has shifted from single-turn code generation to agentic software engineering-a paradigm where models autonomously…

cs.AI2025

DeepPersona: A Generative Engine for Scaling Deep Synthetic Personas

Zhen Wang, Yufan Zhou, Zhongyan Luo +5

Simulating human profiles by instilling personas into large language models (LLMs) is rapidly transforming research in agentic behavioral simulation, LLM personalization, and human…

cs.AI2025

Interaction as Intelligence Part II: Asynchronous Human-Agent Rollout for Long-Horizon Task Training

Dayuan Fu, Yunze Wu, Xiaojie Cai +13

Large Language Model (LLM) agents have recently shown strong potential in domains such as automated coding, deep research, and graphical user interface manipulation. However, train…

cs.AI2025

InnovatorBench: Evaluating Agents' Ability to Conduct Innovative LLM Research

Yunze Wu, Dayuan Fu, Weiye Si +13

AI agents could accelerate scientific discovery by automating hypothesis formation, experiment design, coding, execution, and analysis, yet existing benchmarks probe narrow skills…

cs.AI2025

Context Engineering 2.0: The Context of Context Engineering

Qishuo Hua, Lyumanshan Ye, Dayuan Fu +6

Karl Marx once wrote that ``the human essence is the ensemble of social relations'', suggesting that individuals are not isolated entities but are fundamentally shaped by their int…

cs.AI20251 cited

AlphaGo Moment for Model Architecture Discovery

Yixiu Liu, Yang Nan, Weixian Xu +4

While AI systems demonstrate exponentially improving capabilities, the pace of AI research itself remains linearly bounded by human cognitive capacity, creating an increasingly sev…