activity
20242026
most citedContext Engineering 2.0: The Context of Context Engineering

3 citations · 9 across the 16 of their papers we have counts for

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7 papers · 1 filter

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★ 3 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…

cs.AI2025

LIMI: Less is More for Agency

Yang Xiao, Mohan Jiang, Jie Sun +18

We define Agency as the emergent capacity of AI systems to function as autonomous agents actively discovering problems, formulating hypotheses, and executing solutions through self…

cs.AI2025★ 1 cited

NGENT: Next-Generation AI Agents Must Integrate Multi-Domain Abilities to Achieve Artificial General Intelligence

Zhicong Li, Hangyu Mao, Jiangjin Yin +4

This paper argues that the next generation of AI agent (NGENT) should integrate across-domain abilities to advance toward Artificial General Intelligence (AGI). Although current AI…

cs.LG2025

Reward Shaping to Mitigate Reward Hacking in RLHF

Jiayi Fu, Xuandong Zhao, Chengyuan Yao +4

Reinforcement learning from human feedback (RLHF) is widely used to align large language models (LLMs) with human preferences. However, RLHF remains vulnerable to \emph{reward hack…

cs.CL2025★ 3 cited

LIMO: Less is More for Reasoning

Yixin Ye, Zhen Huang, Yang Xiao +3

We challenge the prevailing assumption that complex reasoning in large language models (LLMs) necessitates massive training data. We demonstrate that sophisticated mathematical rea…