# Rebooting Microreboot: Architectural Support for Safe, Parallel Recovery in Microservice Systems

arXiv: https://arxiv.org/abs/2604.09963 · HTML: https://mlsanity.com/paper/2604.09963/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 11 Apr 2026.

## Builds on (most cited first, 7 of 7)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Identifying the Risks of LM Agents with an LM-Emulated Sandbox](https://mlsanity.com/paper/2309.15817/) (2023)
- [ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages](https://mlsanity.com/paper/2402.10753/) (2024)
- [VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation](https://mlsanity.com/paper/2510.05156/) (2025)
- [STRATUS: A Multi-agent System for Autonomous Reliability Engineering of Modern Clouds](https://mlsanity.com/paper/2506.02009/) (2025)
- [SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction](https://mlsanity.com/paper/2509.00581/) (2025)
- [The Benefit of Hindsight: Tracing Edge-Cases in Distributed Systems](https://mlsanity.com/paper/2202.05769/) (2022)

## Abstract (arXiv)

Microreboot enables fast recovery by restarting only the failing component, but in modern microservices naive restarts are unsafe: dense dependencies mean rebooting one service can disrupt many callers. Autonomous remediation agents compound this by actuating raw infrastructure commands without safety guarantees. We make microreboot practical by separating planning from actuation: a three-agent architecture (diagnosis, planning, verification) proposes typed remediation plans over a seven-action ISA with explicit side-effect semantics, and a small microkernel validates and executes each plan transactionally. Agents are explicitly untrusted; safety derives from the ISA and microkernel. To determine where restart is safe, we infer recovery boundaries online from distributed traces, computing minimal restart groups and ordering constraints. On industrial traces (Alibaba, Meta) and DeathStarBench with fault injection, recovery-group inference runs in 21 ms at P99; typed actuation reduces agent-caused harm by 95% in simulation and achieves 0% harm online. The primary value is safety, not speed: LLM inference overhead increases TTR for services with fast auto-restart.