Returning to Development States: When Full Sandbox Snapshots Help Coding Agents
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Coding agents often make useful progress before they fail, but retry-from-scratch discards the machine state created along the way. This makes failure recovery an interesting search problem: if intermediate environment states preserve meaningful progress, restoring and branching from them could outperform repeatedly restarting from the initial state. We implement Go-Explore-style search over full Daytona snapshots and find that higher and more efficient solve rates depend mainly on progress that sits outside the repository, such as databases, services, and other runtime state. Our results suggest that full-snapshot search is most effective when long-horizon coding work spans multiple services whose state cannot be reconstructed from a single diff.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 7 of its 9 references that are in the map. First posted on arXiv 9 Oct 2026.
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7 of 9 references.
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Same direction, newest first
- 2024SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement26 Oct
- 2024Agentless: Demystifying LLM-based Software Engineering Agents1 Jul
- 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repair25 Mar
Other references · 4
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
- 2020First return, then explorecited by 266
- 2025CodeMonkeys: Scaling Test-Time Compute for Software Engineeringcited by 51
- 2024Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Modelscited by 33
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- 2026VibeMemBench: Evaluating Memory Systems for Coding Agents on Real Repository Coding Tasks
- 2026SWE-Explore: Benchmarking How Coding Agents Explore Repositories
- 2025NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents
- 2026SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks
- 2026RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades
- 2026Beyond Isolated Tasks: A Framework for Evaluating Coding Agents on Sequential Software Evolution
- 2026SWE Refactor Bench: Can Coding Agents Complete a Long-Horizon, Whole-Repository Stack Migration?