arXiv AI September 7, 2026 By Zhenyuan Zhang, Xianzhang Jia, Zhiqin Yang, Zhenbo Song, Wei Xue, Sirui Han, Yike Guo

MemCoRe: Recovering Evidence from Progressively Compressed Factual Knowledge for Agent Memory

MemCoRe is a memory system for large language model agents that organizes factual knowledge into a compression hierarchy, progressively reducing redundancy while preserving retrieval structure. The hierarchy compresses detailed records into keywords and then into topic groups, allowing evidence to be located by searching across levels. Experiments show that MemCoRe outperforms current state‑of‑the‑art baselines in retrieving relevant evidence for downstream reasoning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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MemRefine: LLM-Guided Compression for Long-Term Agent Memory

arXiv:2606. 13177v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly expected to operate over long-term interactions, where information from past dialogues must be preserved and recalled to support future tasks.

By Minjae Kim, Jinheon Baek, Soyeong Jeong, Sung Ju Hwang

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Large language model (LLM) agents are increasingly expected to operate over long-term interactions, where information from past dialogues must be preserved and recalled to support future tasks. However, as interactions accumulate, the memory store grows without bound and fills with redundant entries that inflate storage cost and degrade retrieval by crowding out the most useful evidence.

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arXiv AI

Oct 15

Recall What Is Missing: Composing Evidence from Long-Term Agent Memory

arXiv:2610.15642v1 Announce Type: new Abstract: Under a limited context budget, a long-term agent must recall not merely memories that are individually relevant, but evidence that is collectively suf...

By Qi Feng, Jicong Fan

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arXiv AI

Jun 10

Infini Memory: Maintainable Topic Documents for Long-Term LLM Agent Memory

arXiv:2606. 10677v1 Announce Type: new Abstract: Long-term LLM agents need persistent memory that can track changing facts and provide relevant evidence across sessions.

By Suozhao Ji, Baodong Wu, Zehao Wang, Lei Xia, Qingping Li, Ruisong Wang, Wenbo Ding, Zhenhua Zhu, Boxun Li, Guohao Dai, Yu Wang

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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.

By Aijun Yang, Qianxue Guo, Ziyi Huang, Yuxuan Chen, Shiyou Qian, Jian Cao

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Hugging Face Trending Papers

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Self-Correcting Long-Horizon Search Agents via Tree-Structured Memory

Large language model (LLM)-based search agents answer questions through multi-step interactions with external environments. However, providing complete execution trajectories to the LLM causes unbounded context growth and introduces noise.

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MemRefine: LLM-Guided Compression for Long-Term Agent Memory

MemRefine: LLM-Guided Compression for Long-Term Agent Memory

Recall What Is Missing: Composing Evidence from Long-Term Agent Memory

Infini Memory: Maintainable Topic Documents for Long-Term LLM Agent Memory

Self-Correcting Long-Horizon Search Agents via Tree-Structured Memory

Self-Correcting Long-Horizon Search Agents via Tree-Structured Memory