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20242026
most citedAlphaGo Moment for Model Architecture Discovery

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

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11 papers · 1 filter

cs.AI2026

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…

cs.AI2026

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…

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

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…