Contextual Memory Virtualisation: DAG-Based State Management and Structurally Lossless Trimming for LLM Agents

• URL: http://arxiv.org/abs/2602.22402v1
• Date: Wed, 25 Feb 2026 20:52:52 GMT
• Title: Contextual Memory Virtualisation: DAG-Based State Management and Structurally Lossless Trimming for LLM Agents
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As large language models engage in extended reasoning tasks, they accumulate significant state -- architectural mappings, trade-off decisions, codebase conventions -- within the context window. This understanding is lost when sessions reach context limits and undergo lossy compaction. We propose Contextual Memory Virtualisation (CMV), a system that treats accumulated LLM understanding as version-controlled state. Borrowing from operating system virtual memory, CMV models session history as a Directed Acyclic Graph (DAG) with formally defined snapshot, branch, and trim primitives that enable context reuse across independent parallel sessions. We introduce a three-pass structurally lossless trimming algorithm that preserves every user message and assistant response verbatim while reducing token counts by a mean of 20% and up to 86% for sessions with significant overhead by stripping mechanical bloat such as raw tool outputs, base64 images, and metadata. A single-user case-study evaluation across 76 real-world coding sessions demonstrates that trimming remains economically viable under prompt caching, with the strongest gains in mixed tool-use sessions, which average 39% reduction and reach break-even within 10 turns. A reference implementation is available at https://github.com/CosmoNaught/claude-code-cmv.

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