5 papers · 1 filter
When Does In-Context Search Help? A Sampling-Complexity Theory of Reflection-Driven Reasoning
Yotam Wolf, Noam Wies, Amnon Shashua
Training large language models (LLMs) with extended reasoning has enabled in-context search, in which models iteratively generate, critique, and revise solution attempts. We provid…
From Reasoning to Super-Intelligence: A Search-Theoretic Perspective
Shai Shalev-Shwartz, Amnon Shashua
Chain-of-Thought (CoT) reasoning has emerged as a powerful tool for enhancing the problem-solving capabilities of large language models (LLMs). However, the theoretical foundations…
FormulaOne: Measuring the Depth of Algorithmic Reasoning Beyond Competitive Programming
Gal Beniamini, Yuval Dor, Alon Vinnikov +10
Frontier AI models demonstrate formidable breadth of knowledge. But how close are they to true human -- or superhuman -- expertise? Genuine experts can tackle the hardest problems…
Compositional Hardness of Code in Large Language Models -- A Probabilistic Perspective
Yotam Wolf, Binyamin Rothberg, Dorin Shteyman +1
A common practice in large language model (LLM) usage for complex analytical tasks such as code generation, is to sample a solution for the entire task within the model's context w…
Artificial Expert Intelligence through PAC-reasoning
Shai Shalev-Shwartz, Amnon Shashua, Gal Beniamini +6
Artificial Expert Intelligence (AEI) seeks to transcend the limitations of both Artificial General Intelligence (AGI) and narrow AI by integrating domain-specific expertise with cr…