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cs.AI2026
AutoRAS: Learning Robust Agentic Systems with Primitive Representations
Yang Yue, Xuancheng Zhu, Yuyang Ma +7
The automated design of agentic systems offers a promising pathway for scaling large language models (LLMs) beyond single-agent reasoning. While prior work has advanced task perfor…
cs.AI2026
ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL
Zelin He, Haotian Lin, Boran Han +6
Agentic reinforcement learning (RL) enables LLM agents to improve continuously from environment rewards, yet the resulting policies do not systematically accumulate reusable strate…
cs.AI2026
Can LLM Agents Sustain Long-Horizon Organizational Dynamics?
Xuancheng Zhu, Yang Yue, Shuaibing Wan +4
Large language agents are increasingly used for social simulation, yet it remains unclear whether they can sustain coherent behavior in structured organizations, where goals must p…