collaborators

7 papers

cs.MA2026

When Does Communication Help? Beyond Spectral Descriptions of Collective Intelligence

Xuening Wu

Communication can bring agents into agreement while making their decisions worse. We identify two limits of aggregate descriptions of communication gain in distributed inference. F…

cs.LG2026

IncentRL: The Trade-Off Between Preference Guidance and Task Performance

Xuening Wu, Yanlan Kang, Shenqin Yin

Preference-based reward shaping can guide reinforcement learning, but adding preference signals to the reward may unintentionally change the task being optimized. We address this p…

cs.LG2026

Closed-Loop Knowledge Dynamics: An Operational Framework for Saturation and Escape

Xuening Wu, Shan Yu, Shenqin Yin

Feedback-driven loops support iterative improvement in large language models, reinforcement learning, and autonomous discovery, yet their gains often diminish under repeated intern…

cs.AI2026

BayesEvolve: Explicit Belief States for Autonomous Scientific Discovery

Xuening Wu, Shan Yu, Qianya Xu +1

Autonomous scientific discovery systems increasingly use large language models (LLMs) to propose new hypotheses, but many such systems condition primarily on experimental memory: a…

cs.AI2026

Do Language Models Converge to Themselves? Recursive Self-Refinement as Textual Relaxation

Xuening Wu, Qianya Xu, Yanlan Kang +3

Large language models are increasingly used in recursive refinement workflows, where an initial draft is repeatedly revised by the same model. Despite their growing use, the long-t…

cs.HC2026

Human-AI Co-Evolution and Epistemic Collapse: A Dynamical Systems Perspective

Xuening Wu, Yanlan Kang, Qianya Xu +5

Large language models (LLMs) are reshaping how knowledge is produced, with increasing reliance on AI systems for generation, summarization, and reasoning. While prior work has stud…

cs.LG2025

SGM: A Statistical Godel Machine for Risk-Controlled Recursive Self-Modification

Xuening Wu, Shenqin Yin, Yanlan Kang +4

Recursive self-modification is increasingly central in AutoML, neural architecture search, and adaptive optimization, yet no existing framework ensures that such changes are made s…