English

ClawVM: Harness-Managed Virtual Memory for Stateful Tool-Using LLM Agents

Artificial Intelligence 2026-04-14 v1 Operating Systems Software Engineering

Abstract

Stateful tool-using LLM agents treat the context window as working memory, yet today's agent harnesses manage residency and durability as best-effort, causing recurring failures: lost state after compaction, bypassed flushes on reset, and destructive writeback. We present \textsc{ClawVM}, a virtual memory layer that manages state as typed pages with minimum-fidelity invariants, multi-resolution representations under a token budget, and validated writeback at every lifecycle boundary. Because the harness already assembles prompts, mediates tools, and observes lifecycle events, it is the natural enforcement point; placing the contract there makes residency and durability deterministic and auditable. Across synthetic workloads, 12 real-session traces, and adversarial stress tests, \textsc{ClawVM} eliminates all policy-controllable faults whenever the minimum-fidelity set fits within the token budget, confirmed by an offline oracle, and adds median <50 microseconds of policy-engine overhead per turn.

Keywords

Cite

@article{arxiv.2604.10352,
  title  = {ClawVM: Harness-Managed Virtual Memory for Stateful Tool-Using LLM Agents},
  author = {Mofasshara Rafique and Laurent Bindschaedler},
  journal= {arXiv preprint arXiv:2604.10352},
  year   = {2026}
}

Comments

8 pages, 1 figure, 10 tables; accepted at EuroMLSys '26 (6th Workshop on Machine Learning and Systems, co-located with EuroSys 2026)

R2 v1 2026-07-01T12:04:35.350Z