From the 1 of 5 linked papers with an AI index.
5 papers
Closed-Loop Knowledge Dynamics: An Operational Framework for Saturation and Escape
Xuening Wu, Shan Yu, Shenqin Yin
The paper proposes a framework for understanding why feedback loops in AI systems like large language models and reinforcement learning eventually stop improving, and how external…
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…
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…
Denoising Iterative Self-Correction: Structured Verification Loops for Reliable LLM Reasoning
Shen Yin, David Ken, Joel Stremmel
Large language models produce fluent but often incorrect multi-step reasoning, and naive correction methods risk degrading already-correct answers. We introduce Denoising Iterative…
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…