MemMux for runtime verification of parallel coding agents
A local runtime for managing and verifying resource usage of fleets of parallel coding agents.
MemMux is a local runtime that provides runtime verification and honest resource attribution for fleets of parallel coding agents. It emits observable signals for operators or auditors to check while agents run, covering per-agent attribution, complete reclamation, escaped-process visibility, bounded footprint under overcommit, and monitoring overhead.
Managing and verifying the resource usage of multiple AI coding agents running concurrently on a single workstation.
Not specified
http://arxiv.org/abs/2610.07257v1
MemMux is a new framework for managing fleets of parallel coding agents. It provides runtime verification and resource attribution, ensuring agents operate reliably and account for their computational usage, promoting transparency and efficiency in AI development.
It treats resource governance as a runtime verification problem, emitting signals that can be checked while agents are running. This allows for better control and understanding of agent behavior and resource consumption.
- MemMux: Runtime Verification and Honest Resource Attribution for Fleets of Parallel Coding Agents ↗arXiv · arXiv cs.AI · Oct 5, 2026 · paraphrased
First seen on Subvolts Oct 5, 2026. Updated Oct 8, 2026. Spotted a problem? Tell us.
- MemMux: Runtime Verification and Honest Resource Attribution for Fleets of Parallel Coding AgentsAnthropic · arXiv cs.AI · Oct 5, 2026
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
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Questions people ask
- Which AI tools does this work with?
- Any current model. It was seen with in the sources.
- Where did this come from?
- arXiv (arXiv cs.AI). Subvolts collected, normalized and credited it; the link above opens the original.