mlsanityData to 7 Oct 2026

Crab: A Semantics-Aware Checkpoint/Restore Runtime for Agent Sandboxes

arXiv 2604.28138 ↗First posted 30 Apr 2026cs.OS cs.AIcited by 11

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Autonomous agents act through sandboxed containers and microVMs whose state spans filesystems, processes, and runtime artifacts. Checkpoint and restore (C/R) of this state is needed for fault tolerance, spot execution, RL rollout branching, and safe rollback-yet existing approaches fall into two extremes: application-level recovery preserves chat history but misses OS-side effects, while full per-turn checkpointing is correct but too expensive under dense co-location. The root cause is an agent-OS semantic gap: agent frameworks see tool calls but not their OS effects; the OS sees state changes but lacks turn-level context to judge recovery relevance. This gap hides massive sparsity: over 75% of agent turns produce no recovery-relevant state, so most checkpoints are unnecessary. Crab (Checkpoint-and-Restore for Agent SandBoxes) is a transparent host-side runtime that bridges this gap without modifying agents or C/R backends. An eBPF-based inspector classifies each turn's OS-visible effects to decide checkpoint granularity; a coordinator aligns checkpoints with turn boundaries and overlaps C/R with LLM wait time; and a host-scoped engine schedules checkpoint traffic across co-located sandboxes. On shell-intensive and code-repair workloads, Crab raises recovery correctness from 8% (chat-only) to 100%, cuts checkpoint traffic by up to 87%, and stays within 1.9% of fault-free execution time.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 13 of its 49 references that are in the map. First posted on arXiv 30 Apr 2026.

Builds on

13 of 49 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026AgentCgroup: Understanding and Controlling OS Resources of AI Agents10 Feb
  2. 2025Speculative Actions: A Lossless Framework for Faster Agentic Systems5 Oct
  3. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May

Other references · 10

  1. 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 515
  2. 2023SpotServe: Serving Generative Large Language Models on Preemptible Instancescited by 83
  3. 2025Tree Search for LLM Agent Reinforcement Learningcited by 73
  4. 2025Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystemcited by 28
  5. 2025RollArt: Disaggregated Multi-Task Agentic RL Training at Scalecited by 11
  6. 2025AnchorTP: Resilient LLM Inference with State-Preserving Elastic Tensor Parallelismcited by 3
  7. 2025FailSafe: High-performance Resilient Servingcited by 3
  8. 2026Making MoE-based LLM Inference Resilient with Tarragoncited by 2
  9. 2021VELOC: VEry Low Overhead Checkpointing in the Age of Exascalecited by 1
  10. 2025FailLite: Failure-Resilient Model Serving for Resource-Constrained Edge Environmentscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 11.

11 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026DART: Semantic Recoverability for Structured Tool Agents
  2. 2026ACRFence: Preventing Semantic Rollback Attacks in Agent Checkpoint-Restore
  3. 2026ToolMisuseBench: An Offline Deterministic Benchmark for Tool Misuse and Recovery in Agentic Systems
  4. 2026Planarian: Managing Agent State with Statepoints
  5. 2026AgentRewind: Recoverable Execution for Long-Horizon LLM Agents
  6. 2026EvoUndo: Recoverability-Constrained Self-Evolution for LLM Agent Harnesses
  7. 2026The Hidden Footprint: Making Storage a First-Class Metric for LLM Agent Evaluation
  8. 2026From Faulty Memories to Corrected Actions: Dependency-Guided Rollback Repair for Memory-Augmented Agents