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From the 1 of 14 linked papers with an AI index.

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20242026
most citedReinforcement World Model Learning for LLM-based Agents

1 citations · 1 across the 5 of their papers we have counts for

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cs.AI2026

OpenForgeRL: Train Harness-native Agents in Any Environment

Xiao Yu, Baolin Peng, Ruize Xu +7

Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems. While power…

cs.AI2026

OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks

Mengqi Yuan, Zilong Zhou, Xinzhuang Xiong +33

The paper presents OSWorld 2.0, a benchmark consisting of 108 long‑horizon, real‑world computer‑use workflows designed to evaluate how well AI agents can handle complex, multi‑step…

cs.AI2026

Orchard: An Open-Source Agentic Modeling Framework

Baolin Peng, Wenlin Yao, Qianhui Wu +11

Agentic modeling aims to transform LLMs into autonomous agents capable of solving complex tasks through planning, reasoning, tool use, and multi-turn interaction with external envi…

cs.AI2025

Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents

Xiao Yu, Baolin Peng, Ruize Xu +5

Recent progress in reasoning with large language models (LLMs), such as DeepSeek-R1, demonstrates impressive capabilities in domains like mathematics and coding, by exhibiting comp…

cs.AI2025

Strategize Globally, Adapt Locally: A Multi-Turn Red Teaming Agent with Dual-Level Learning

Si Chen, Xiao Yu, Ninareh Mehrabi +3

The exploitation of large language models (LLMs) for malicious purposes poses significant security risks as these models become more powerful and widespread. While most existing re…