activity
20242026
most citedDeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments

1 citations · 2 across the 8 of their papers we have counts for

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9 papers · 1 filter

cs.AI2026

ASI-Evolve: AI Accelerates AI

Weixian Xu, Tiantian Mi, Yixiu Liu +6

Can AI accelerate the development of AI itself? While recent agentic systems have shown strong performance on well-scoped tasks with rapid feedback, it remains unclear whether they…

cs.AI2026

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…

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.AI20253 cited

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…