arXiv AI
PolyMemDB: A Polyglot Database System for AI Memory Management
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arXiv Computation and Language
3d ago
AtomMem: Building Simple and Effective Memory System for LLM Agents via Atomic Facts
AtomMem is a long‑term memory system for large language models that stores high‑value atomic facts extracted from long‑form interactions. It organizes these facts into hierarchical event structures and temporal profiles, enabling coherent episodic context and dynamic tracking of user attributes. During retrieval, an associative memory graph connects fragmented memories, and experiments on the LoCoMo benchmark show state‑of‑the‑art performance across reasoning tasks.
arXiv AI
Jun 15
VikingMem: A Memory Base Management System for Stateful LLM-based Applications
arXiv:2605. 29640v3 Announce Type: replace Abstract: Large Language Models have revolutionized interactive applications; however, their finite context windows pose a critical data management challenge for maintaining stateful, long-term interactions.
arXiv AI
Jul 21
Accurate and Efficient Long-Term Memory for LLM Agents
arXiv:2607. 16211v1 Announce Type: new Abstract: LLM agents augmented with persistent memory can recall past interactions, but existing systems suffer from two limitations: flat, unstructured storage loses relational context needed for multi-hop and temporal reasoning, and reliance on expensive LLM-based classification makes them impractical for latency-sensitive deployment.
arXiv AI
Jul 7
MRMS: A Multi-Resolution Memory Substrate for Long-Lived AI Agents
arXiv:2607. 04617v1 Announce Type: new Abstract: Long-lived AI agents require continuity across interactions, but continuity cannot be obtained by simply extending the prompt window.
Hugging Face Trending Papers
Jul 6
MRMS: A Multi-Resolution Memory Substrate for Long-Lived AI Agents
Long-lived AI agents require continuity across interactions, but continuity cannot be obtained by simply extending the prompt window. An agent must preserve useful prior experience, retrieve it selectively, distinguish personal context from external evidence, and revise memory when the underlying situation changes.
arXiv AI
Sep 2
Beyond Static Summarization: Proactive Memory Extraction for LLM Agents
The paper "Beyond Static Summarization: Proactive Memory Extraction for LLM Agents" identifies two shortcomings in current memory extraction for large language model agents: (1) extraction occurs ahead of time and mixes multiple types of information, leading to loss of useful details, and (2) extraction is typically one‑off, allowing errors and hallucinations to persist. To address these issues, the authors propose ProMem, a proactive framework that separates details, events, and relations, applies distinct extraction strategies for each, checks for completeness, and verifies facts at an atomic level. Experiments demonstrate that ProMem enhances memory completeness and question‑answering accuracy while maintaining a favorable balance between quality and token cost.