arXiv AI
Recoverability as a System Primitive for Long-Horizon AI Agents
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arXiv AI
Oct 21
Lost in Recovery: Demystifying Recovery Behavior of Computer-Use Agents via Goal-Oriented Action Replay
arXiv:2610.27954v1 Announce Type: new Abstract: Recent advances in large (vision) language models have enabled computer-use agents that automate digital workflows via fine-grained keyboard and mouse...
arXiv AI
Sep 1
Safe to Resume? Breaking Execution Continuity of Agent Execution via Rollback
arXiv:2608.29381v1 Announce Type: cross Abstract: AI agents are moving toward persistent, stateful execution across various applications, accumulating execution state and external effects that are co...
arXiv AI
Oct 26
Permission to Retry: A Study of Authorization and Recovery in AI Agent Workflows
The paper investigates how AI agents can reliably recover from changes in authorization while executing tasks that involve external tools. It defines distinct obligations—checkpointing, permission to dispatch, evidence of external effects, and permission to reconcile—and evaluates them through a finite conformance study on 336 histories across multiple hosts, schedules, and provider contracts. The study demonstrates that all paired host cells produce identical outcomes and that various retry strategies (cached, revalidation, stable-key) successfully pass the majority of histories, while a component study of InfinityOS further isolates the impact of authority, contracts, and observations on recovery.
Hugging Face Trending Papers
Jun 29
Always-OnAgents:A Survey of Persistent Memory, State, and Governance in LLMAgents
Always-on agents are systems whose future behavior depends on durable state accumulated across earlier interactions. We treat them as persistent-state systems: the operative system includes retrievable memories, but also task ledgers, permissions, credentials, commitments, provenance and audit records, shared state, trigger conditions, and externally committed effects linked to those records.
arXiv AI
Aug 5
Designing for Doubt: The Case for Informed Abstention in Autonomous Agents
arXiv:2606. 02965v2 Announce Type: replace Abstract: As large language models gain tool access and are deployed as autonomous agents capable of editing records, executing transactions, and modifying infrastructure, we still evaluate them based on the sole metric of task completion.
arXiv AI
Jun 30
Always-OnAgents:A Survey of Persistent Memory, State, and Governance in LLMAgents
arXiv:2606. 30306v1 Announce Type: cross Abstract: Always-on agents are systems whose future behavior depends on durable state accumulated across earlier interactions.