arXiv Machine Learning By Daniel Thi Graviet, Tanmay Shah

Returning to Development States: When Full Sandbox Snapshots Help Coding Agents

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The paper investigates whether restoring full sandbox snapshots can aid coding agents in recovering from failures. By implementing a Go‑Explore‑style search over complete Daytona snapshots, the authors find that higher and more efficient solve rates arise when progress is stored outside the repository—such as in databases, services, and other runtime state. The study suggests that full‑snapshot search is most beneficial for long‑horizon coding tasks that span multiple services whose state cannot be reconstructed from a simple diff.

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arXiv AI
Oct 26

SWE-Shift: Evaluating Coding Agents in Evolving Workspaces

SWE-Shift is a new benchmark that tests coding agents in environments where tasks and workspaces can change mid-execution. It presents 120 scenarios across 12 change families, each evaluated at nine intervention points, comparing a Clean continuation (no change) with a Shifted continuation (task-relevant update). The benchmark shows that all evaluated agents perform better on Clean than on Shifted, with significant drops in success rates and zero paired success for certain change types.

By Victor Shea-Jay Huang, Rongqing Li, Zhangyue Yin, Kelong Mao, Yaqi Zhao, Xinyuan Wang, Yao Yin, Lirui Luo, Renrui Zhang, Hongsheng Li