4 citations · 9 across the 15 of their papers we have counts for
16 papers
ERPBench: Evaluating LLM Agents for Enterprise Decision-Making Across Competitive Market Ecologies
Xinran Zhang, Pengrui Lu, Lyumanshan Ye +1
Large language model (LLM) agents are increasingly proposed for enterprise workflows, yet existing evaluations rarely test whether business-decision conclusions transfer across com…
Socratic agents for autonomous scientific discovery in high-dimensional physical systems
Xianrui Zeng, Pengfei Liu, Yirui Zang +5
The automation of scientific discovery has reached an inflection point. While AI systems now operate instruments, optimize parameters and generate hypotheses, most remain procedura…
AlphaEval: Evaluating Agents in Production
Pengrui Lu, Bingyu Xu, Wenjun Zhang +24
The rapid deployment of AI agents in commercial settings has outpaced the development of evaluation methodologies that reflect production realities. Existing benchmarks measure age…
ProjDevBench: Benchmarking AI Coding Agents on End-to-End Project Development
Pengrui Lu, Shiqi Zhang, Yunzhong Hou +8
Recent coding agents can generate complete codebases from simple prompts, yet existing evaluations focus on issue-level bug fixing and lag behind end-to-end development. We introdu…
daVinci-Agency: Unlocking Long-Horizon Agency Data-Efficiently
Mohan Jiang, Dayuan Fu, Junhao Shi +8
While Large Language Models (LLMs) excel at short-term tasks, scaling them to long-horizon agentic workflows remains challenging. The core bottleneck lies in the scarcity of traini…
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…
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…
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…
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…
SR-Scientist: Scientific Equation Discovery With Agentic AI
Shijie Xia, Yuhan Sun, Pengfei Liu
Recently, Large Language Models (LLMs) have been applied to scientific equation discovery, leveraging their embedded scientific knowledge for hypothesis generation. However, curren…
DatasetResearch: Benchmarking Agent Systems for Demand-Driven Dataset Discovery
Keyu Li, Mohan Jiang, Dayuan Fu +4
The rapid advancement of large language models has fundamentally shifted the bottleneck in AI development from computational power to data availability-with countless valuable data…
Neural Architecture Discovery via Autonomous Evolution
Yixiu Liu, Yang Nan, Weixian Xu +4
Recent progress in LLM agents has advanced the prospect of autonomous research. Yet whether AI can complete difficult long-horizon tasks, especially those that advance AI research…
ResearcherBench: Evaluating Deep AI Research Systems on the Frontiers of Scientific Inquiry
Tianze Xu, Pengrui Lu, Lyumanshan Ye +2
The emergence of deep research systems presents significant capabilities in problem-solving, extending from basic queries to sophisticated research tasks. However, existing benchma…
Interaction as Intelligence: Deep Research With Human-AI Partnership
Lyumanshan Ye, Xiaojie Cai, Xinkai Wang +23
This paper introduces "Interaction as Intelligence" research series, presenting a reconceptualization of human-AI relationships in deep research tasks. Traditional approaches treat…
DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments
Yuxiang Zheng, Dayuan Fu, Xiangkun Hu +4
Large Language Models (LLMs) equipped with web search capabilities have demonstrated impressive potential for deep research tasks. However, current approaches predominantly rely on…
"Ghost of the past": identifying and resolving privacy leakage from LLM's memory through proactive user interaction
Shuning Zhang, Lyumanshan Ye, Xin Yi +5
Memories, encompassing past inputs in context window and retrieval-augmented generation (RAG), frequently surface during human-LLM interactions, yet users are often unaware of thei…