ClawVM: Harness-Managed Virtual Memory for Stateful Tool-Using LLM Agents
Summary: arXiv:2604.10352v1 Announce Type: new
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 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, ClawVM eliminates all policy-controllable faults whenever the minimum-fidelity set fits within the token budget, confirmed by an offline oracle, and adds median.
Introduction to ClawVM
In the rapidly evolving landscape of artificial intelligence, the integration of stateful tool-using large language model (LLM) agents has become increasingly significant. These agents rely on context windows to function as working memory, yet existing frameworks often fall short in effectively managing state persistence and durability. This shortcoming can lead to various operational failures, including lost states during compaction, ineffective flushes on resets, and problematic writebacks. The introduction of ClawVM marks a transformative step in addressing these issues.
Key Features of ClawVM
ClawVM offers a host of innovative features designed to enhance the reliability and efficiency of state management in LLM agents:
- Typed Pages: State is organized into typed pages, which simplifies the management of different data types and enhances the overall system’s robustness.
- Minimum-Fidelity Invariants: The framework establishes invariants that maintain a minimum level of fidelity, ensuring consistent performance under varying loads.
- Multi-Resolution Representations: ClawVM supports multi-resolution representations, allowing for efficient resource allocation within a defined token budget.
- Validated Writeback: Every lifecycle boundary is accompanied by validated writeback, ensuring that state changes are tracked and recorded accurately.
The Role of the Harness
The harness plays a critical role in the operation of ClawVM. By assembling prompts, mediating tool interactions, and monitoring lifecycle events, it serves as a natural enforcement point for state management contracts. This integration allows for deterministic and auditable residency and durability, significantly mitigating the risk of state loss and other related failures.
Performance and Testing
Extensive testing of ClawVM across synthetic workloads, real-session traces, and adversarial stress tests has demonstrated its effectiveness. The framework successfully eliminates all policy-controllable faults, provided that the minimum-fidelity set remains within the token budget. This claim is further substantiated through confirmation by an offline oracle, showcasing the system’s reliability in practical applications.
Conclusion
As the demand for stateful tool-using LLM agents continues to grow, innovations like ClawVM are essential for overcoming existing limitations in state management. With its advanced features and robust testing, ClawVM not only enhances the performance of LLM agents but also provides a solid foundation for future developments in the field of AI. The advent of this virtual memory layer represents a significant advancement in ensuring that LLM agents can operate effectively and reliably in increasingly complex environments.