Caching for the Future: Scrub Jay Episodic Memory Principles for Agent Memory Systems
- URL: http://arxiv.org/abs/2608.04746v1
- Date: Wed, 05 Aug 2026 12:12:44 GMT
- Title: Caching for the Future: Scrub Jay Episodic Memory Principles for Agent Memory Systems
- Abstract summary: Existing memory architectures treat all memories as equally persistent and systematically contaminate retrieved context with outdated facts.<n>We show that per-memory, type-conditioned temporal decay, can be operationalized as an auto-classified coefficient $_i$ in an external LLM-agent memory store.<n>We introduce the Temporal Generalization Test (TGT), a benchmark with held-out retention intervals and a Generalization Gap (GenGap) metric.
- Score: 5.321970297768025
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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memory architectures treat all memories as equally persistent and systematically contaminate retrieved context with outdated facts.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. We introduce the Temporal Generalization Test (TGT), a benchmark with held-out retention intervals and a Generalization Gap (GenGap) metric. On TGT, ScrubJay-MEM is the only retrieval-based system with substantially positive GenGap ($+0.108$); on MemoryAgentBench EventQA-64k it improves F1 by $+2.66$ over Mem0 and $+3.09$ over Qwen3-Embedding-4B under a llm backbone. A decay ablation collapses GenGap by $5.7\times$, establishing type-conditioned decay as necessary for the result. Gains narrow under stronger backbones and reverse on fact-consolidation tasks, scoping the contribution to temporal reasoning over perishable facts.
Related papers
- Dual-Grained Agent Memory and Shapley Context Attribution for Multimodal Agentic Learner [55.80248281761638]
multimodal large language models (MLLMs) deliver impressive perception yet still falter on scientific and mathematical reasoning.<n>We propose DG-Mem, a dual-grained agentic memory framework that augments a frozen MLLM with a non- externally stored memory built once from training-time rollouts and consulted read-only at test time.
arXiv Detail & Related papers (2026-08-24T13:56:01Z) - Structured Memory for Edge Language Models: Persistent Context and Corpus Retrieval via O(1) SSM State Injection [0.0]
State-Space Models (SSMs) avoid prefill costs by construction.<n>Pre-encodes document corpora offline as SSM hidden states.<n>Prefill latency is reduced from $sim$27 s to $$6 ms on edge hardware.
arXiv Detail & Related papers (2026-08-03T17:43:36Z) - Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents [72.3752981234916]
Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.<n>We present Memory as a Controlled Process (MemCon), a framework that models memory operations as a Markov Decision Process.<n>MemCon consistently outperforms multiple memory baselines by up to 15.2 points in task success while reducing token consumption by 5-20%.
arXiv Detail & Related papers (2026-07-15T08:32:40Z) - Enhancing Software Engineering Through Closed-Loop Memory Optimization [50.418699819003486]
We introduce ours, a closed-loop framework for memory augmentation in software engineering (SE) agents.<n>ours grounds memory utility in textitvalidated downstream impact, establishing utility as a task-agnostic textbf benchmark and an annotation-free textbfoptimization signal.<n>Results demonstrate that ours consistently improves SE agents across settings, achieving absolute gains of up to $uparrow5.25%$ in success rate and $uparrow4.63%$ in resolve efficiency.
arXiv Detail & Related papers (2026-06-04T03:17:21Z) - ElasticMem: Latent Memory as a Learnable Resource for LLM Agents [25.70415765824153]
ElasticMem is a memory-augmented LLM framework that learns to use memory as an elastic latent resource.<n>We evaluate ElasticMem on MemorySuite, covering memory-intensive QA and embodied agent control.
arXiv Detail & Related papers (2026-05-29T00:34:40Z) - Mem-$π$: Adaptive Memory through Learning When and What to Generate [65.31077328444077]
We present a framework for adaptive memory in large language model (LLM) agents, where useful guidance is generated on demand.<n>Mem-$$ consistently outperforms retrieval-based and prior RL-optimized memory baselines.
arXiv Detail & Related papers (2026-05-20T17:51:05Z) - MemQ: Integrating Q-Learning into Self-Evolving Memory Agents over Provenance DAGs [47.66730296440261]
We introduce MemQ, which propagates credit backward through a provenance DAG that records which memories were retrieved when each new memory was created.<n>Across six benchmarks, MemQ achieves the highest success rate on all six in generalization evaluation and runtime learning.<n>We further study how $$ and $$ interact with the EC-MDP structure, providing principled guidance for parameter selection and future research.
arXiv Detail & Related papers (2026-05-08T18:30:24Z) - Persistent Memory Through Triple-Loop Consolidation in a Non-Gradient Dissipative Cognitive Architecture [0.0]
We introduce Deep Memory, a non-gradient persistent memory mechanism operating through a triple-loop consolidation cycle.<n>We demonstrate that discrete expert routing via Mixture-of-Experts (MoE) gating is a causal prerequisite for DM.<n>These results establish DM as a falsifiable mechanism for persistent memory in non-gradient cognitive systems, with functional parallels to hippocampal consolidation.
arXiv Detail & Related papers (2026-03-28T08:29:48Z) - Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution [52.76038908826961]
We propose $textbfReMe$ ($textitRemember Me, Refine Me$) to bridge the gap between static storage and dynamic reasoning.<n>ReMe innovates across the memory lifecycle via three mechanisms: $textitmulti-faceted distillation$, which extracts fine-grained experiences.<n>Experiments on BFCL-V3 and AppWorld demonstrate that ReMe establishes a new state-of-the-art in agent memory system.
arXiv Detail & Related papers (2025-12-11T14:40:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.