AgentAbstain: Do LLM Agents Know When Not to Act?
Computer Science > Artificial Intelligence arXiv:2607.10059 (cs) [Submitted on 11 Jul 2026] Title:AgentAbstain: Do LLM Agents Know When Not to Act? This gap poses real risks: under ambiguity, conflicting constraints, or tool failures, agents may execute unintended and irreversible actions. To close this gap, we present the first systematic evaluation framework for agentic abstention: the calibrated ability of tool-using LLM agents to recognize when not to act.
- ▪Computer Science > Artificial Intelligence arXiv:2607.10059 (cs) [Submitted on 11 Jul 2026] Title:AgentAbstain: Do LLM Agents Know When Not to Act?
- ▪This gap poses real risks: under ambiguity, conflicting constraints, or tool failures, agents may execute unintended and irreversible actions.
- ▪To close this gap, we present the first systematic evaluation framework for agentic abstention: the calibrated ability of tool-using LLM agents to recognize when not to act.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2607.10059 (cs) [Submitted on 11 Jul 2026] Title:AgentAbstain: Do LLM Agents Know When Not to Act? Authors:Xun Liu, Yi Evie Zhang, Vira Kasprova, Parisa Rabbani, Pardis Sadat Zahraei, Tianyu Zhang, Ali Ebrahimpour-Boroojeny, Varun Chandrasekaran View a PDF of the paper titled AgentAbstain: Do LLM Agents Know When Not to Act?, by Xun Liu and 7 other authors View PDF HTML (experimental) Abstract:Agent systems based on large language models (LLMs) are increasingly deployed for autonomous tasks, yet existing evaluations mostly focus on task success rather than whether agents know when to abstain. This gap poses real risks: under ambiguity, conflicting constraints, or tool failures, agents may execute unintended and irreversible actions.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
Discussion
0 commentsMore from arXiv.org
