CarMem: Enhancing Long-Term Memory in LLM Voice Assistants through Category-Bounding (2025.coling-industry)
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| Challenge: | Large Language Models (LLMs) are stateless and present all relevant memories during each interaction, resulting in repetitive user requests and disengagement. |
| Approach: | They propose a long-term memory system for voice assistants structured around predefined categories that leverages Large Language Models to extract, store, and retrieve preferences within these categories. |
| Outcome: | The proposed system achieves an F1-score of .78 to .95 in preference extraction, depending on category granularity, and is suitable for industrial applications. |
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| Challenge: | Existing methods for integrating long-term memory do not provide dynamic and personalized memory refinement. |
| Approach: | They propose a long-term memory update mechanism that enables dynamic and personalized memory refinement. |
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RecMem: Recurrence-based Memory Consolidation for Efficient and Effective Long-Running LLM Agents (2026.findings-acl)
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| Challenge: | Existing memory systems invoke LLMs to extract episodic and semantic memory, and this leads to substantial token consumption. |
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Zhen Tan, Jun Yan, I-Hung Hsu, Rujun Han, Zifeng Wang, Long Le, Yiwen Song, Yanfei Chen, Hamid Palangi, George Lee, Anand Rajan Iyer, Tianlong Chen, Huan Liu, Chen-Yu Lee, Tomas Pfister
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| Challenge: | Existing dialogue systems focus on brief single-session interactions, neglecting real-world needs for long-term companionship and personalized interactions. |
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| Challenge: | Large Language Models (LLMs) are becoming increasingly prevalent in various domains, requiring a one-off input of overly long texts to maintain a degree of memory. |
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LLM-based Medical Assistant Personalization with Short- and Long-Term Memory Coordination (2024.naacl-long)
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| Challenge: | Existing studies have focused on learning and enhancing large language models to understand and generate natural language. |
| Approach: | They propose a computational bionic memory mechanism equipped with a parameter-efficient fine-tuning schema to personalize medical assistants. |
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HyperMem: Hypergraph Memory for Long-Term Conversations (2026.acl-long)
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| Challenge: | Existing approaches to long-term memory management rely on pairwise relations, causing fragmented retrieval. |
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Exploring the Potential of LLMs as Personalized Assistants: Dataset, Evaluation, and Analysis (2025.acl-long)
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| Challenge: | Personalized AI assistants are a challenging application that intertwines multiple problems in LLM research. |
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Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models (2025.findings-acl)
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| Challenge: | Recent advances in multi-turn voice interaction models have improved user-model communication, but whether open-source models share this ability remains unexplored. |
| Approach: | They propose to use ContextDialog to evaluate open-source interaction models' ability to recall past utterances to identify key limitations. |
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Evaluating the Long-Term Memory of Large Language Models (2025.findings-acl)
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| Challenge: | Recent studies have not thoroughly investigated the memory performance of large language models in long-term tasks. |
| Approach: | They propose a dataset to evaluate the long-term memory capabilities of large language models. |
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