arXiv AI
Aug 6
MemSIF: From Structured Interactions to Dual-Track Fact Memory for LLM Agents
arXiv:2608. 01742v2 Announce Type: replace Abstract: Long-term memory is critical for LLM agents operating over long-horizon interactions.
MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing
arXiv:2605. 23986v2 Announce Type: replace-cross Abstract: Memory is a fundamental component for long-context LLM agents, supporting persistent state across interactions through a continuous serve-and-update lifecycle.
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arXiv AI
Sep 10
MemForest: Efficient Agent Memory Management via EventTree Partitioning and Progressive Merging
arXiv:2609.08273v1 Announce Type: new Abstract: Agent memory systems have demonstrated significant potential in long-term dialogue, personalized assistants, and video understanding. However, continuo...
arXiv AI
Jun 10
Less Context, More Accuracy: A Bi-Temporal Memory Engine for LLM Agents Where a Lean Retrieved Context Beats the Full History
arXiv:2606. 09900v1 Announce Type: cross Abstract: Long-term memory is the missing layer for LLM agents: across sessions they forget, and the common workaround -- replaying the whole history into the prompt -- is expensive, slow, and, as distractors accumulate, less accurate.
arXiv AI
2d ago
Mem++: Non-Destructive Memory for Long-Term Organizational LLM Agents
arXiv:2610.02002v1 Announce Type: cross Abstract: Large Language Model (LLM) agents now take part in organizational work, where many authors record decisions across documents over months. Because a r...
arXiv AI
Jul 8
Akashic: A Low-Overhead LLM Inference Service with MemAttention
arXiv:2607. 05708v1 Announce Type: new Abstract: Recent LLM-based agent systems continuously accumulate context across multi-turn interactions, tool invocations, and cross-session workflows.
arXiv AI
2d ago
MemFit: Efficient Long-Term Agentic Memory
MemFit is a long‑term memory system designed for conversational agents that stores each dialogue turn verbatim in an append‑only store, enabling near‑instantaneous, LLM‑free insertion. It indexes turns using segment summaries and employs an LLM‑free, multi‑path retrieval strategy that blends lexical and semantic signals with cross‑encoder reranking over caption‑augmented episodes. Experiments on LoCoMo, MemGallery, and LongMemEval‑S demonstrate state‑of‑the‑art performance while drastically reducing memory construction time and cost.
Hugging Face Trending Papers
Jul 7
Akashic: A Low-Overhead LLM Inference Service with MemAttention
Recent LLM-based agent systems continuously accumulate context across multi-turn interactions, tool invocations, and cross-session workflows. Replaying the full history for every request quickly becomes impractical: long contexts increase prefill cost, may exceed context limits, and often bury task-relevant evidence in irrelevant content, degrading both serving efficiency and output quality.
arXiv AI
Jul 16
Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents
arXiv:2607. 13591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.
arXiv AI
Jul 28
ConsistencyGate: Preventing Memory Contamination in LLM Agents via Self-Consistency Admission Control
arXiv:2607. 22962v1 Announce Type: new Abstract: LLM agents that operate over many turns accumulate facts in an external memory store and reuse them as premises for downstream reasoning.
arXiv Computation and Language
3d ago
MemCodex: Self-Programming Hierarchical Memory for Language Agents
arXiv:2609.39765v1 Announce Type: new Abstract: Agent memory faces heterogeneous access needs: a single-hop question may require one piece of evidence, whereas a multi-hop question must combine evide...
arXiv AI
4d ago
Mnemon: Raw Records, Fast Judgments, Slow Thoughts
Mnemon is a memory agent that stores conversations as raw, dated records and uses a fast System 1 decision model (Jev) to quickly judge the relevance of records, while a slow System 2 LLM plans searches and composes answers. The agent consolidates records into topic timelines and value histories in the background, enabling efficient retrieval without rewriting conversations into structured formats. Experiments show Mnemon achieving high scores on LoCoMo and LongMemEval‑S with low context length and cost, and Jev outperforming LLMs in evidence separation and speed.
arXiv AI
Aug 12
Self-Correcting Long-Horizon Search Agents via Tree-Structured Memory
arXiv:2608. 10676v1 Announce Type: new Abstract: Large language model (LLM)-based search agents answer questions through multi-step interactions with external environments.