{"ID":2829813,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.06037","arxiv_id":"2601.06037","title":"TeleMem: Building Long-Term and Multimodal Memory for Agentic AI","abstract":"Large language models (LLMs) excel at many NLP tasks but struggle to sustain long-term interactions due to limited attention over extended dialogue histories. Retrieval-augmented generation (RAG) mitigates this issue but lacks reliable mechanisms for updating or refining stored memories, leading to schema-driven hallucinations, inefficient write operations, and minimal support for multimodal reasoning.To address these challenges, we propose TeleMem, a unified long-term and multimodal memory system that maintains coherent user profiles through narrative dynamic extraction, ensuring that only dialogue-grounded information is preserved. TeleMem further introduces a structured writing pipeline that batches, retrieves, clusters, and consolidates memory entries, substantially improving storage efficiency, reducing token usage, and accelerating memory operations. Additionally, a multimodal memory module combined with ReAct-style reasoning equips the system with a closed-loop observe, think, and act process that enables accurate understanding of complex video content in long-term contexts. Experimental results show that TeleMem surpasses the state-of-the-art Mem0 baseline with 19% higher accuracy, 43% fewer tokens, and a 2.1x speedup on the ZH-4O long-term role-play gaming benchmark.","short_abstract":"Large language models (LLMs) excel at many NLP tasks but struggle to sustain long-term interactions due to limited attention over extended dialogue histories. Retrieval-augmented generation (RAG) mitigates this issue but lacks reliable mechanisms for updating or refining stored memories, leading to schema-driven halluc...","url_abs":"https://arxiv.org/abs/2601.06037","url_pdf":"https://arxiv.org/pdf/2601.06037v4","authors":"[\"Chunliang Chen\",\"Ming Guan\",\"Xiao Lin\",\"Jiaxu Li\",\"Luxi Lin\",\"Qiyi Wang\",\"Xiangyu Chen\",\"Jixiang Luo\",\"Changzhi Sun\",\"Dell Zhang\",\"Xuelong Li\"]","published":"2025-12-12T11:24:52Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.CV\"]","methods":"[\"RAG\",\"Large Language Model\",\"Language Model\"]","has_code":false}