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[2602.18456] Beyond single-channel agentic benchmarking
Summary
This paper critiques the current single-channel benchmarking of AI safety, advocating for a more holistic approach that considers the interplay between AI agents and human operators.
Why It Matters
As AI systems become increasingly integrated into safety-critical environments, understanding their operational reliability in conjunction with human performance is crucial. This paper proposes a shift in evaluation methods that could enhance safety assessments and reduce risks associated with AI deployment.
Key Takeaways
- Current AI safety benchmarks often evaluate agents in isolation, neglecting their interaction with human operators.
- Redundancy and diverse error modes are essential for effective risk mitigation in AI systems.
- Evaluating the reliability of human-AI collaborations can lead to more accurate safety assessments.
- The paper provides a case study demonstrating the utility of imperfect AI systems in enhancing safety.
- A paradigm shift in AI benchmarking could align practices with established safety-critical engineering principles.
Computer Science > Computers and Society arXiv:2602.18456 (cs) [Submitted on 5 Feb 2026] Title:Beyond single-channel agentic benchmarking Authors:Nelu D. Radpour View a PDF of the paper titled Beyond single-channel agentic benchmarking, by Nelu D. Radpour View PDF HTML (experimental) Abstract:Contemporary benchmarks for agentic artificial intelligence (AI) frequently evaluate safety through isolated task-level accuracy thresholds, implicitly treating autonomous systems as single points of failure. This single-channel paradigm diverges from established principles in safety-critical engineering, where risk mitigation is achieved through redundancy, diversity of error modes, and joint system reliability. This paper argues that evaluating AI agents in isolation systematically mischaracterizes their operational safety when deployed within human-in-the-loop environments. Using a recent laboratory safety benchmark as a case study demonstrates that even imperfect AI systems can nonetheless provide substantial safety utility by functioning as redundant audit layers against well-documented sources of human failure, including vigilance decrement, inattentional blindness, and normalization of deviance. This perspective reframes agentic safety evaluation around the reliability of the human-AI dyad rather than absolute agent accuracy, with a particular emphasis on uncorrelated error modes as the primary determinant of risk reduction. Such a shift aligns AI benchmarking with established ...