most citedContext Engineering 2.0: The Context of Context Engineering

3 citations · 3 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.AI2026

AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts

Keyu Li, Junhao Shi, Yang Xiao +11

Large Language Models (LLMs) based autonomous agents demonstrate multifaceted capabilities to contribute substantially to economic production. However, existing benchmarks remain f…

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

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…