Papers with LTM
Long Time No See! Open-Domain Conversation with Long-Term Persona Memory (2022.findings-acl)
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| Challenge: | Existing persona dialogue datasets and models can build long-term relationships with humans . however, current open-domain dialogue systems cannot build long relationships with users . |
| Approach: | They propose a long-term memory conversation dataset and a dialogue generation framework with long-Term memory mechanism to extract and continuously update long-time persona memory. |
| Outcome: | The proposed system outperforms baselines in terms of long-term dialogue consistency . the proposed system can build long-lasting relationships between humans and bots . |
Lightweight LLM Agent Memory with Small Language Models (2026.acl-long)
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Jiaquan Zhang, Chaoning Zhang, Shuxu Chen, Zhenzhen Huang, Pengcheng Zheng, Zhicheng Wang, Ping Guo, Fan Mo, Sung-Ho Bae, Jie Zou, Jiwei Wei, Yang Yang
| Challenge: | Existing external memory systems for LLMs have low online overhead but are unstable in accumulating latency over long interactions. |
| Approach: | They propose a lightweight memory system for better agent memory driven by Small Language Models . lightmem modularizes memory retrieval, writing, and long-term consolidation . they show consistent gains across model scales and high efficiency . |
| Outcome: | The proposed system improves agent memory but has low latency and low online overhead . it separates online processing from offline consolidation to enable efficient memory invocation . the proposed system achieves an average F1 improvement of 2.5 over A-MEM on LoCoMo . |
Towards Demonstration-Aware Large Language Models for Machine Translation (2024.findings-acl)
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| Challenge: | Large language models for machine translation often face difficulties in leveraging demonstrations to further improve their performance. |
| Approach: | They propose a novel approach that integrates demonstration-aware training and inference strategies within the framework of tuning-based LTMs. |
| Outcome: | The proposed model integrates demonstration-aware training and inference strategies within tuning-based LTMs. |
Visual Inception: Compromising Long-term Planning in Agentic Recommenders via Multimodal Memory Poisoning (2026.acl-long)
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| Challenge: | Existing research focuses on prompt injection or immediate adversarial misclassification of user-uploaded images. |
| Approach: | They propose a dual-process defense framework inspired by human cognition to mitigate this vulnerability by injecting triggers into user-uploaded images that act as "sleeper agents" |
| Outcome: | The proposed framework achieves about 85% Goal-Hit Rate (GHR) while reducing the risk to 10% with configurable latency trade-offs. |
Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents (2026.acl-long)
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| Challenge: | Existing methods handle long-term memory (LTM) and short-term (STM) as separate components, relying on heuristics or auxiliary controllers, which limits adaptability and end-to-end optimization. |
| Approach: | They propose a framework that integrates LTM and STM management directly into the agent's policy and propose 'agentic memory' to train such unified behaviors. |
| Outcome: | The proposed framework outperforms strong memory-augmented baselines on five long-horizon benchmarks and achieves higher-quality long-term memory and more efficient context usage. |