Shared Selective Persistent Memory for Agentic LLM Systems

• URL: http://arxiv.org/abs/2607.09493v1
• Date: Fri, 10 Jul 2026 15:07:00 GMT
• Title: Shared Selective Persistent Memory for Agentic LLM Systems
• Abstract summary: LLM systems generate code through multi-turn tool use.<n>Each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive.<n>We introduce shared selective persistent memory, an architecture that identifies and retains four categories of reusable context.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive. Naively persisting entire conversation histories is token-inefficient and counterproductive: irrelevant context degrades generation quality. We introduce shared selective persistent memory, an architecture that identifies and retains four categories of reusable context (task specifications, data schemas, tool configurations, and output constraints) while discarding session-specific reasoning traces. Crucially, this memory is shared: workspaces encapsulating selective memory can be transferred across users with role-based access control, enabling collaborative reuse without redundant specification. We implement it in a deployed collaborative workspace platform where LLM agents produce, edit, and maintain git-versioned artifacts (dashboards, reports, and data-driven documents) from heterogeneous sources (CSV, SQL, REST APIs, and MCP servers). A complementary zero-token data refresh mechanism decouples generated programs from runtime data, enabling artifact reuse without re-invocation. Across three enterprise scenarios, shared selective persistent memory achieves 96% task completion (vs. 79% without memory and 71% with full history). Zero-token refresh eliminates LLM re-invocation for recurring updates (14x task-time reduction), while summary-driven generation cuts per-invocation token cost by 97x versus raw data injection. A replication on four public datasets confirms generalizability, with zero-token refresh succeeding in 12/12 trials. Notably, naive full-history persistence actively degrades completion by biasing the agent with stale traces, while selective memory outperforms both extremes.

Related papers

• EdgeMem: LLM-Free Agent Memory Construction and Retrieval via Evidence-Preserving Multi-Anchor Hypergraph [37.27140929522461]
  Existing methods compress interaction histories into summaries or other LLM-generated representations.<n>We propose EdgeMem, an agent-memory method built around a simple principle: preserve original interaction turns and organize them through complementary content.
  arXiv  Detail & Related papers  (2026-09-03T11:17:03Z)
• Zero-Mem: Zero-Token Memory Operations for LLM Agents [17.228305588778]
  Zero-Mem preserves original interaction traces as its source of record.<n>Zero-Mem achieves competitive performance across long-memory and long-context question-answering benchmarks.
  arXiv  Detail & Related papers  (2026-07-31T13:01:06Z)
• 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)
• PACMS: Submodular Context Selection as a Pluggable Engine for LLM Agents [0.9134244356393664]
  LLM agents operate over a context window that fills from several directions simultaneously.<n>The prevailing mechanism is recency truncation, sometimes paired with periodic summarization.<n>Retrieval augmented generation fetches external documents into the prompt but does not arbitrate the agent's emphalready-present pooled context.
  arXiv  Detail & Related papers  (2026-06-18T10:22:57Z)
• TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management [0.0]
  We present TokenMizer, an open-source proxy system that models large language model sessions as a typed knowledge graph.<n>It produces resume blocks averaging 78 tokens (range: 42-124) - 2x smaller than evaluated baselines.<n>Across all sessions, TokenMizer mean task recall 51.0%, decision recall 46.6%, and file recall 58.7%.
  arXiv  Detail & Related papers  (2026-06-04T16:12:28Z)
• Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents [51.30250860677378]
  We propose MRAgent, a framework that combines an associative memory graph with an active reconstruction mechanism.<n>Running on a Cue-Tag-Content graph, our active reconstruction mechanism integrates reasoning directly into memory access.<n>Experiments on the LoCoMo benchmark and LongMemEval benchmark demonstrate significant improvements over strong baselines.
  arXiv  Detail & Related papers  (2026-06-04T11:29:46Z)
• CALMem : Application-Layer Dual Memory for Conversational AI [0.0]
  CALMem is an application-layer dual memory architecture that gives conversational assistants virtually effective context without any modification to the underlying model.<n>A token-budget-adaptive injection mechanism, called the MOIM, automatically retrieves and injects relevant past context each turn.<n>We describe the architecture, design decisions, and performance characteristics, and analyse the trade-offs that guided each implementation choice.
  arXiv  Detail & Related papers  (2026-05-20T05:23:57Z)
• Rethinking How to Remember: Beyond Atomic Facts in Lifelong LLM Agent Memory [88.81430082035617]
  LLM agents require a memory system that can faithfully store, efficiently retrieve, and deeply reason over accumulated dialogue history.<n>We propose TriMem, which maintains three coexisting representation granularities, including raw dialogue segments anchored by source identifiers for storage fidelity.
  arXiv  Detail & Related papers  (2026-05-19T15:05:06Z)
• Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data [52.80567461420316]
  We propose a Memory-Augmented LLM-based Multi-Agent System (textbfMALMAS) for automated feature generation.<n>MALMAS decomposes the generation process into agents with distinct responsibilities, and a Router Agent activates an appropriate subset of agents per iteration.<n>We further integrate a memory module comprising procedural memory, feedback memory, and conceptual memory, enabling iterative refinement that adaptively guides subsequent feature generation.
  arXiv  Detail & Related papers  (2026-04-22T07:09:30Z)
• GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0) [56.81743709880371]
  Long-horizon large language model (LLM) agents are fundamentally limited by context.<n>We argue that long-horizon performance is determined not by context length, but by how much decision-relevant information is maintained within a finite context budget.<n>We present GenericAgent (GA), a general-purpose, self-evolving LLM agent system built around a single principle: information density and context.
  arXiv  Detail & Related papers  (2026-04-18T17:59:15Z)
• MemCollab: Cross-Agent Memory Collaboration via Contrastive Trajectory Distillation [16.57908722474221]
  Large language model (LLM)-based agents rely on memory mechanisms to reuse knowledge from past problem-solving experiences.<n>Existing approaches typically construct memory in a per-agent manner, tightly coupling stored knowledge to a single model's reasoning style.<n>We found that naively transferring memory between agents often degrades performance, as such memory entangles task-relevant knowledge with agent-specific biases.<n>We propose MemCollab, a collaborative memory framework that constructs agent-agnostic memory by contrasting reasoning trajectories generated by different agents on the same task.
  arXiv  Detail & Related papers  (2026-03-24T14:05:47Z)
• Contextual Memory Virtualisation: DAG-Based State Management and Structurally Lossless Trimming for LLM Agents [0.0]
  We propose Contextual Memory Virtualisation (CMV), a system that treats accumulated LLM understanding as version-controlled state.<n> CMV models session history as a Directed Acyclic Graph (DAG) with formally defined snapshot, branch, and trim primitives.<n>A single-user case-study evaluation shows that trimming remains economically viable under prompt caching.
  arXiv  Detail & Related papers  (2026-02-25T20:52:52Z)
• Learning to Share: Selective Memory for Efficient Parallel Agentic Systems [49.78267008828593]
  Agentic systems solve complex tasks by coordinating multiple agents that iteratively reason, invoke tools, and exchange intermediate results.<n>Recent approaches deploy multiple agent teams running in parallel to explore diverse reasoning trajectories.<n>We propose Learning to Share (LTS), a learned shared-memory mechanism for parallel agentic frameworks.
  arXiv  Detail & Related papers  (2026-02-05T18:20:21Z)
• Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory [89.65731902036669]
  Evo-Memory is a streaming benchmark and framework for evaluating self-evolving memory in large language model (LLM) agents.<n>We evaluate over ten representative memory modules and evaluate them across 10 diverse multi-turn goal-oriented and single-turn reasoning and QA datasets.
  arXiv  Detail & Related papers  (2025-11-25T21:08:07Z)

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.