Mathematical
DeepSeek-Prover-V2: Bridging the Gap Between Informal and Formal Mathematical Reasoning
While DeepSeek-R1 has significantly advanced AI’s capabilities in informal reasoning, formal mathematical reasoning has remained a challenging task for AI. This is primarily because producing verifiable mathematical proof requires both deep conceptual understanding and the ability to construct precise, step-by-step logical arguments. Recently, however, significant advancement is made in this direction as researchers at DeepSeek-AI…
FunSearch: Making new discoveries in mathematical sciences using Large Language Models
Research Published 14 December 2023 Authors Alhussein Fawzi and Bernardino Romera Paredes By searching for “functions” written in computer code, FunSearch made the first discoveries in open problems in mathematical sciences using LLMs Update: In December 2024, we published a report on arXiv showing how our method can be used to amplify human performance in…
Why AI Needs Large Numerical Models (LNMs) for Mathematical Mastery • AI Blog
The availability and structure of mathematical training data, combined with the unique characteristics of mathematics itself, suggest that training a Large Numerical Model (LNM) is feasible and may require less data than training a general-purpose LLM. Here’s a detailed look: Availability of Mathematical Training Data Structure of Mathematics and Data Efficiency Mathematics’ highly structured nature…