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From the 2 of 45 linked papers with an AI index.

most citedExternalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering

5 citations · 5 across the 30 of their papers we have counts for

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12 papers · 1 filter

cs.AI2026

EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning

Zishan Xu, Zhiyuan Yao, Yuxin Chen +9

Training large language model agents for long-horizon tool use typically relies on interactions with real or synthesized executable environments, whose construction and verificatio…

cs.AI2026

Harness-R1: Learning to Edit Executable Runtime Harnesses from Agent Failure Trajectories

Shuai Shao, Kangning Zhang, Qingyao Li +7

Agents built around large language models continually accumulate interaction trajectories during deployment, yet their behavior typically remains fixed. Beyond updating model weigh…

cs.AI2026

Communication Policy Evolution for Proactive LLM Agents

Xinbei Ma, Jiyang Qiu, Yao Yao +10

LLM agents have rapidly evolved into autonomous systems, yet a persistent information gap remains between users and agents: communication is costly, while users' identical preferen…

cs.AI2026

MMSkills: Towards Multimodal Skills for General Visual Agents

Kangning Zhang, Shuai Shao, Qingyao Li +8

Reusable skills have become a core substrate for improving agent capabilities, yet most existing skill packages encode reusable behavior primarily as textual prompts, executable co…

cs.AI2026

ACE-Router: Generalizing History-Aware Routing from MCP Tools to the Agent Web

Zhiyuan Yao, Zishan Xu, Yifu Guo +6

With the rise of the Agent Web and Model Context Protocol (MCP), the agent ecosystem is evolving into an open collaborative network, exponentially increasing accessible tools. Howe…

cs.AI2026

Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization

Jiachen Zhu, Lingyu Yang, Rong Shan +6

The rise of autonomous GUI agents has triggered adversarial countermeasures from digital platforms, yet existing research prioritizes utility and robustness over the critical dimen…