arXiv AI June 30, 2026 By Mellow Baixuan Chen, Xiangguo Sun

When Does Overlap Help? OSU-Mem and a Cell-Conditional Analysis of Trajectory Memory for LLM Agents

arXiv:2606. 28376v1 Announce Type: cross Abstract: Long-horizon large language model (LLM) agents accumulate interaction trajectories that quickly exceed any practical prompt budget, and existing memory methods either truncate aggressively and lose non-local evidence or retain boilerplate that degrades decision quality.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 21

Retain or Consolidate? Budget-Dependent Operator Selection for Language Agent Memory

arXiv:2607. 17545v1 Announce Type: new Abstract: Language agents depend on memory across interactions.

By Qingcan Kang, Mingyang Liu, Shixiong Kai, Kaichao Liang, Zhentao Tang, Yuqi Cui, Tao Zhong, Mingxuan Yuan

llms agents benchmarks safety

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.

By YuFei Luo, Xiucheng Xu, Zhen Yang

llms agents

arXiv AI

5d ago

Beyond Retrieval: Query-Conditioned Reuse of Long-Horizon Agent Trajectories

arXiv:2608. 12847v1 Announce Type: new Abstract: Retrieval can identify a past trajectory that may matter, yet it does not specify how an acting agent should use that trajectory after users, entities, constraints, or environment state have changed.

By Yifei Li, Heng Wang, Lingling Zhang, Muye Huang, Xinyu Zhang, Jiashuai Liu, Hang Yan, Rongman Xu

rag agents

arXiv AI

Jun 16

T-Mem: Memory That Anticipates, Not Archives

arXiv:2606. 15405v1 Announce Type: cross Abstract: Long-term memory is essential for conversational agents to remain coherent across extended dialogues, follow through on commitments made many sessions earlier, and adapt their behaviour to each user.

By Weidong Guo, Dakai Wang, Zixuan Wang, Hui Liu, Yu Xu

llms agents benchmarks

arXiv Machine Learning

Jun 30

MemDelta: Controlled Baselines and Hidden Confounds in Agent Memory Evaluation

arXiv:2606. 29914v1 Announce Type: cross Abstract: Agent memory systems are increasingly evaluated against RAG and full-context baselines, but reported gains often mix changes in the memory method with changes in the language model, embedding model, or retrieval pipeline, making it unclear what is actually being measured.

By Kuan Wang

llms rag agents

arXiv AI

Jul 31

IFCMemoryBench: Evaluating Long-Term Memory of LLM-Based Agents in BIM Information Retrieval

arXiv:2607. 26072v1 Announce Type: cross Abstract: Long-term memory is becoming a core capability of LLM-based agents, but existing evaluations largely test conversational recall in open-domain or persona-grounded settings.

By Changyu Du, Alexander Vosseler, Filippo Mazza, Andr\'e Borrmann

llms agents benchmarks

Related stories

Retain or Consolidate? Budget-Dependent Operator Selection for Language Agent Memory

MemSIF: From Structured Interactions to Dual-Track Fact Memory for LLM Agents

Beyond Retrieval: Query-Conditioned Reuse of Long-Horizon Agent Trajectories

T-Mem: Memory That Anticipates, Not Archives

MemDelta: Controlled Baselines and Hidden Confounds in Agent Memory Evaluation

IFCMemoryBench: Evaluating Long-Term Memory of LLM-Based Agents in BIM Information Retrieval