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Search-o1 is a newly introduced framework that enhances large reasoning models (LRMs) like OpenAI's o1 by integrating an agentic search workflow. This framework combines reasoning with a retrieval-augmented generation (RAG) mechanism and a specialized knowledge refinement module, aimed at improving the accuracy and reliability of LRMs. The integration allows for dynamic retrieval of knowledge documents, enabling these models to address knowledge gaps encountered during complex problem-solving tasks. Recent literature, including a book titled 'A Pattern Language for Large Reasoning Models', outlines prompting strategies for LRMs, emphasizing a chain-of-thought (CoT) reasoning approach that encourages models to think before answering.
Meet Search-o1: An AI Framework that Integrates the Agentic Search Workflow into the o1-like Reasoning Process of LRM for Achieving Autonomous Knowledge Supplementation The framework integrates task instructions, questions, and dynamically retrieved knowledge documents into a… https://t.co/I9ccVqgryP
1/n Search-o1: Bridging Knowledge Gaps in Large Reasoning Models Recent advances in Large Reasoning Models have demonstrated impressive capabilities in complex problem-solving through stepwise reasoning. However, these models frequently encounter knowledge gaps during extended… https://t.co/VAqDCKOqt1
Search-o1 is a framework that enhances LRMs with an agentic retrieval-augmented generation (RAG) mechanism and a Reason-in-Documents module for refining retrieved documents. Search-o1 integrates an agentic search workflow into the reasoning process, enabling dynamic retrieval of… https://t.co/CsIRybSyPO
