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papersTODAY 04:00 UTC

Recoverability Proposed as a System Primitive for Long-Horizon AI Agents

A new arXiv paper argues that long-running AI agents interrupted mid-task need a built-in notion of recoverability, since restarting wastes effort while resuming from unverified state can propagate earlier mistakes. The authors frame saved state alone as insufficient and propose treating recovery as a core system-level capability rather than an afterthought. The work is a preprint and has not yet been peer reviewed.

arXiv long-horizon-ai-agents peer review preprints recoverability

COVERAGE · 1 REPORT · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AIRecoverability as a System Primitive for Long-Horizon AI Agents ↗TODAY 04:00 UTC