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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.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…