TBNAI
Back Up Before You Regret: Measuring Backup Awareness and Automating Backup in Coding Agents
Publication: cs.AI updates on arXiv.org
Author: Xu Li, Simon Yu, Minzhou Pan, Yiyou Sun, Zhaorun Chen, Bo Li, Weiyan Shi
Source published: 22/10/2026, 04:00:00 UTC
Briefing created: 22/10/2026, 04:59:45 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 22/10/2026, 04:03:25 UTC.
Briefings
A new benchmark RegretBench identifies 205 executable tasks where users regretted state changes, revealing coding agents complete 86.1% of tasks but only 15.2% back up before modifications. The proposed BackUpper tool restores 92.2% of regret moments at $0.014 per task with 7x fewer tokens than self-backup, while respecting user preferences via an 84.5% F1 score.
Key points
- Agents complete 86.1% of tasks on RegretBench but only 15.2% back up before state changes.
- BackUpper restores 92.2% of regret moments at $0.014 per task with 7x fewer tokens than self-backup.
Context
Research on coding agents shows they modify real-world state autonomously but lack backup awareness. The RegretBench benchmark uses 205 tasks from X, GitHub, and Stack Overflow to measure this gap.
Limitations
RegretBench relies on user-reported regret moments from public platforms; coverage is limited to these sources. BackUpper's rule table expansion depends on subagent decisions, and token savings are relative to self-backup baseline.
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