arXiv：cs.AI· Sumanyu Muku·· 6 小时前AI 评分42
MemMux：面向并行编程智能体集群的运行时验证与资源归属
MemMux: Runtime Verification and Honest Resource Attribution for Fleets of Parallel Coding Agents
AI 导读
MemMux 是一个本地运行时，将并行编程智能体的资源治理转化为可核验信号，包括按智能体归属内存、完整回收进程子树、发现逃逸子进程以及在超配下控制内存占用。
正文
Abstract:Developers increasingly run a fleet of coding agents side by side on one workstation. The tools they reach for, terminal multiplexers like tmux and a new generation of agent managers, were built to arrange windows, not to govern memory. When ten agents each spawn language servers, test runners, and browsers, no standard tool can say how much memory belongs to which agent, confirm that a terminated agent's descendants are gone, notice a child that has escaped its agent, or keep the machine off the swap cliff when an OOM kill would silently discard uncommitted work. We treat these as runtime-verification problems: an agent-hosting substrate should continuously emit observable signals an operator or auditor can check while agents run. We present MemMux, a local runtime that turns resource governance into checkable signals (per-agent attribution, complete reclamation, escaped-process visibility, bounded footprint under overcommit, and monitoring overhead), with a claims-disciplined benchmark against tmux, a purpose-built agent multiplexer, and a raw-process baseline on identical workloads. Under a binding memory budget on a Linux host, MemMux keeps the fleet under budget (7.5 GiB) with zero swap by admitting a subset and reclaiming under pressure, while the ungoverned tools run every agent, pin the machine at its RAM ceiling (2x over budget), and spill about 2 GiB into swap. MemMux reclaims 100% of a terminated agent's process subtree where the raw baseline strands half of it, and it alone surfaces escaped children (10 of 10 detected). We report the cost: the 1 Hz attribution scan runs near 0.6% CPU at one agent but 2.7% at ten, above our 2% target. Running the harness on real Claude Code sessions shows 100% attribution and low overhead carry over to live agent trees. We release the engine, benchmark, and a one-command reproducer.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.07257 [cs.AI] |
| (or arXiv:2610.07257v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07257 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sumanyu Muku [view email]
[v1]
Mon, 5 Oct 2026 18:56:34 UTC (23 KB)
来源：arXiv：cs.AI · arxiv.org