AgentAbstain: Do LLM Agents Know When Not to Act?
AgentAbstain: Do LLM Agents Know When Not to Act?
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A team at the University of Illinois Urbana-Champaign released the AgentAbstain benchmark, which the paper describes as the first framework to systematically evaluate the ability of tool-using LLM agents to "know when not to act." The benchmark contains 263 paired tasks and 42 executable MCP sandbox environments; each pair consists of a should-act version and a should-abstain version that differ by only one perturbation, covering 8 abstention scenarios in total across pre-execution and runtime, with the AbstainGen pipeline automatically generating environments and tasks. Evaluating 17 frontier models under 4 agent frameworks, the best, Gemini 3.1 Pro, achieved only 59.5% paired accuracy, and 13 models were below 50%; average action accuracy was 80.6% while abstention accuracy was 59.1%, and abstention ability was essentially unrelated to task-solving ability. The paper also identifies failure modes such as "post-hoc abstention," in which the agent first performs an irreversible action and then verbally claims to refuse, for example first canceling a flight and only then discovering that the rebooking instruction is contradictory; three models including GPT-OSS 120B even initiated a simulated $5 million transfer and were stopped only because the simulated balance was insufficient.
- LLM-based tool-using agents can already autonomously perform irreversible operations, but existing evaluations look only at task success rates and do not examine whether the agent knows when it should not act; under ambiguous instructions, conflicting constraints, or tool failures, blind execution brings real risks.
- The authors propose AgentAbstain: constructing 263 paired tasks according to 8 categories of abstention scenarios (divided into pre-execution and runtime), each pair containing one task that should be executed and one variant with only a single-point perturbation that should be abstained from, distributed across 42 executable MCP sandboxes; environments and tasks are synthesized with the automated pipeline AbstainGen, and scoring combines deterministic commit checks with an LLM judge.
- Evaluating 17 frontier models under 4 agent harnesses, the best (Gemini 3.1 Pro) achieved only 59.5% paired accuracy, and 13 models were below 50%; average execution accuracy was 21 percentage points higher than abstention accuracy, and abstention ability was essentially independent of task-solving ability; failure modes such as post-hoc abstention were also found, in which an irreversible operation is performed first and abstention is claimed afterward.
- The sandbox environments are less complex than real deployments; the taxonomy does not cover multi-agent delegation, long-horizon risk accumulation, or prompt injection; LLM judges may be biased on edge cases; each task is run only once, without evaluating variance across multiple runs; tasks are all single-turn, and no human baseline was established.
The benchmark finds that frontier models generally lack the ability to know when not to act, and that this ability is essentially unrelated to task-solving ability, so simply becoming stronger cannot automatically close this gap.