What Benchmarks Don't Measure: The Case for Evaluating Abstention Competence in Autonomous Agents
arXiv:2606.02965v1 Announce Type: new Abstract: Benchmarks for autonomous agents measure whether agents complete tasks, yet this framing is systematically blind to whether an agent should have proceeded at all. Agents trained under human-feedback objectives develop a structural tendency to proceed even when they lack the inputs, evidence, or authorization to act safely, a disposition we term compliance bias, because both the reward signal and the benchmark scoring regime treat proceeding as the correct default regardless of whether the preconditions for safe action are present. We make three c
The paper identifies a critical systemic flaw in current autonomous agent development, 'compliance bias,' as AI systems become more autonomous and are deployed in real-world scenarios.
This highlights a fundamental safety and reliability issue that impacts the trustworthiness and widespread adoption of autonomous AI, demanding a re-evaluation of current benchmarking and training paradigms.
The focus shifts from merely task completion to assessing an agent's ability to 'abstain' when conditions for safe action are absent, fundamentally altering how AI competence is evaluated and developed.
- · AI safety researchers
- · AI ethics organizations
- · Companies developing robust AI governance frameworks
- · Developers of 'abstention competence' metrics
- · Developers prioritizing speed over safety in AI deployment
- · Benchmarks solely focused on task completion
- · Agents with 'compliance bias'
Refined benchmarking criteria for autonomous agents will emerge, incorporating measures of 'abstention competence'.
AI development pipelines will integrate new training methodologies to reduce 'compliance bias' and enhance agents' ability to identify and appropriately abstain from unsafe actions.
This could lead to a two-tiered system for AI certification, with 'abstention-competent' agents gaining greater regulatory approval and market trust, shifting competitive advantages.
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Read at arXiv cs.AI