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This paper presents the first systematic evaluation of large language models for post-OCR correction in Hindi and Marathi, comparing in-context learning retrieval strategies and demonstrating the effectiveness of CharBM25.
A developer shares a prototype memory architecture for multi-agent systems with separate storage for episodic, semantic, and procedural memories and intent-based retrieval, seeking feedback on scalability and design choices.