Thought-Retriever: Don't Just Retrieve Raw Data, Retrieve Thoughts for Memory-Augmented Agentic Systems

Thought-Retriever is a new algorithm that enables large language models to generate outputs based on extensive external data without context length constraints, by leveraging and organizing intermediate responses as thought memory. This innovation significantly enhances LLMs' ability to handle long-term knowledge and complex queries, making them more capable in real-world applications like academic research.

Read the full article at arXiv cs.CL (NLP)

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