Fortunate Recall (FR) introduces an ontology-driven policy layer that categorizes personal facts into over ten behavioral types and applies tailored lifecycle rules—such as differential decay, supersession, and event-time validity—to manage memory persistence in large language models. The FR-Bank implementation, independent of underlying infrastructure, achieves a 76.9% pass rate on the new LifecycleBench benchmark and improves LongMemEval-S performance, while significantly reducing confabulation rates compared to prior systems. Ablation studies show that the generic lifecycle metadata drives correctness, whereas the behavioral ontology enhances calibration and reduces downstream hallucinations.
By Ansuman Mullick, Eray T\"uz\"un
arXiv:2606. 26511v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) gives agents access to accumulated knowledge, but has no model of time.
By Neeraj Yadav
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.
By Liuyin Wang
arXiv:2607. 04281v1 Announce Type: cross Abstract: Semantic caching reduces the latency and cost of retrieval-augmented generation (RAG) by serving cached answers to semantically similar queries, but most existing methods do not model the time-varying freshness of open-web evidence.
By Muhammad Mansoor, Tahir Ahmad, Yeo-Chan Yoon
arXiv:2606. 01138v2 Announce Type: replace-cross Abstract: Agent-memory frameworks -- mem0, Letta/MemGPT, Cognee, Zep/Graphiti, MemoryOS, MemTensor -- each ship their own SDK, storage layout, and operational vocabulary.
By Thamilvendhan Munirathinam
arXiv:2608.21867v1 Announce Type: new
Abstract: LLM agents are moving from single-prompt use to long task streams in which reusable memory becomes a core capability for terminal, software-engineering...
By Haoyu Wang, Guangyuan Dong, He Liang, Zijing Zhang, Jiachen Luo, Chuang Liu, Chao Xue, Hao Tang
Agent Zero Memory is a provenance‑aware long‑term memory system for large language model agents that distills user interactions into three parallel memory structures: an episodic timeline, an associative entity‑event knowledge graph, and a semantic, citation‑locked hierarchical documentary memory. Retrieval is performed via an intent gate, source router, and concurrent searches across the three systems, producing integrated, cited answers that exclude fabrication and require evidence the reader has opened. The system achieves state‑of‑the‑art performance on LongMemEval (95.60%) and LoCoMo (93.60%) while offering a favorable accuracy‑cost‑latency trade‑off across multiple backbone LLMs.
By Ming Wu, Pengyuan Zhu
arXiv:2606. 15903v1 Announce Type: cross Abstract: Where an LLM sits in an agent memory pipeline -- between the recall plane that retrieves stored facts (extensively benchmarked) and the control plane that mutates them via supersede, release, purge (largely untested) -- shapes which forgetting failure modes the system recovers.
By Dongxu Yang
arXiv:2608.22141v1 Announce Type: new
Abstract: Enterprise repositories are large, heteroge- neous, and continuously updated, making re- trieval difficult when efficient access, source- faithful evid...
By Xinyuan Song, Bowen Zhu, Hasibul Haque, Liang Zhao
arXiv:2608. 12365v1 Announce Type: cross Abstract: For fifty years, data systems have answered two questions.
By Ganesh S
The paper introduces MERIT, a benchmark that evaluates the marginal benefit of long‑term memory for tool‑using large language model agents while explicitly accounting for cost. MERIT provides episodic tool‑use tasks across three domains, verifies dependence on earlier‑episode facts, and measures memory operations in tokens and dollars. Experiments on GPT‑4.1‑mini, Claude Haiku 4.5, and Claude Sonnet 5 show that memory can significantly improve task success, but its utility varies widely across models and memory implementations, and full replay is rarely cost‑effective.
By Shweta Mishra, Shashank Mishra
LLM agents that persist across sessions accumulate stored memories whose validity varies enormously by content type, yet existing memory architectures treat all memories as equally persistent and systematically contaminate retrieved context with outdated facts. We show that per-memory, type-conditioned temporal decay, a property of western scrub jay episodic memory, can be operationalized as an auto-classified coefficient $π_i$ in an external LLM-agent memory store, yielding ScrubJay-MEM: each memory is encoded as a jointly-bound What--Where--When tuple with an estimated perishability $π_i$ and utility horizon $τ_i$, retrieved by query-adaptive scoring, and revised retroactively at $O(1)$ LLM calls per update.