6 papers
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…
Tradeoffs Between Alignment and Helpfulness in Language Models with Steering Methods
Yotam Wolf, Noam Wies, Dorin Shteyman +3
Language model alignment has become an important component of AI safety, allowing safe interactions between humans and language models, by enhancing desired behaviors and inhibitin…
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…