Author

Zhuo-Ran Li

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Preprint Aug 2026

When Personal Memory Has No Single Answer: Evaluating LLM Agents under Irreducible Conflict

LLM agents increasingly maintain personal memory across sessions, but it can conflict. Preferences depend on context, behavior evolves, and sources can conflict. When a query lacks context, time, or source authority to interpret conflict, treating one memory as definitive converts unresolved conflict into an unjustifie...

Lulu Yang, Shusheng Xu, Zhuo-Ran Li et al. · 1 citation

#artificial intelligence Preprint Oct 2026

Flowing Faster to Coordinate: One-Step Online Multi-Agent Flow Policies

Multi-agent reinforcement learning (MARL) provides a powerful framework for learning coordinated behaviors through interactions with the environment. Developing MARL policies requires balancing expressive modeling of complex and multimodal action distributions with efficient training and execution. Generative policies,...

Zhuo-Ran Li, Yun-Zhan Li, Xun Wang et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Optimal Skill Selection for LLM Agents with Provable Bicriteria Guarantees

Loading reusable skill documents into a bounded context window has become a primary way large language model (LLM) agents acquire task-specific capabilities, which makes skill selection a first-order determinant of task performance and token cost. Yet current agents score skills independently by semantic relevance and...

Yu Chen, Rui-Shuo Chen, Xun Wang et al. · 2 citations

#artificial intelligence Preprint Sep 2026

FlexLoop: Depth-Elastic Looped Policies for Adaptive Test-Time Computation in Deep RL

FlexLoop is proposed, a novel post-training framework that converts pretrained fixed-depth looped policies into depth-elastic policies that supports reliable inference across recurrent depths and enables state-wise adaptive inference through recurrent-depth consistency.

Xun Wang, Rui-Shuo Chen, Yu Chen et al. · 0 citations

#artificial intelligence Preprint Sep 2026

The Router Within: Eliciting Native Skill Routing from a Frozen LLM

It is shown that the frozen agent LLM already carries the routing signal in its own forward passes, and that two linear maps suffice to read it out with no skill text in the context, and Gavel (Glance And Verdict from a frozen LLM) reads it in two steps.

Rui-Shuo Chen, Xun Wang, Yu Chen et al. · 2 citations

#artificial intelligence Preprint Aug 2026

MLREF: Efficient Module Reuse for Reward Design in Reinforcement Learning via Large Language Models

The proposed Module Level Reward Evolution Framework integrates three mechanisms: reflection-based refinement, hybrid credit assignment, and a merge strategy with rollback, which together improve the effectiveness and robustness of reward optimization.

Chenglin Liu, Xun Wang, Ruishuo Chen et al. · 0 citations