#llm-reasoning2 articles

2 articles

AIBullisharXiv – CS AI · 5d ago7/105

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Expressive Power of Implicit Models: Rich Equilibria and Test-Time Scaling

Researchers provide mathematical proof that implicit models can achieve greater expressive power through increased test-time computation, explaining how these memory-efficient architectures can match larger explicit networks. The study validates this scaling property across image reconstruction, scientific computing, operations research, and LLM reasoning domains.

AINeutralarXiv – CS AI · 5d ago5/104

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Learning Global Hypothesis Space for Enhancing Synergistic Reasoning Chain

Researchers propose GHS-TDA, a new method to improve large language model reasoning by using global hypothesis graphs and topological data analysis. The approach addresses limitations in Chain-of-Thought reasoning by providing error correction mechanisms and filtering redundant reasoning paths.