Beyond Static Leaderboards: Predictive Validity for the Evaluation of LLM Agents

Hugging Face Daily Papers Papers
agent-evaluation leaderboards predictive-validity out-of-distribution benchmarks llm-agents research

Summary

This paper argues that aggregate-score leaderboards for LLM agent benchmarks fail to capture deployment-relevant dimensions and show rank instability. It proposes ranking configurations by predictive validity—the correlation between in-sample and out-of-sample rank—and introduces a twelve-tier measurement apparatus along with falsifiable out-of-distribution criteria.

Agent benchmarks are growing fast, but no single benchmark touches more than four or five of the dimensions that deployment exposes. This paper aggregates the largest coordinated deep-dive of one MCP-based industrial-agent benchmark to date: fourteen parallel implementation studies covering new asset classes (including a multi-modal visual extension), alternative orchestrations, retrieval strategies, reasoning modes, infrastructure optimizations, and evaluation-methodology probes. Consolidating those studies with seven prior agent benchmarks, we argue that aggregate-score leaderboards systematically underspecify deployed-agent evaluation. Rankings derived from aggregate scores do not transfer to out-of-distribution settings; recent public-to-hidden competition retrospectives provide direct empirical evidence of this rank instability. We propose ranking configurations by predictive validity, the correlation between in-sample and out-of-sample rank, rather than in-sample mean, and report a twelve-tier measurement apparatus that exposes the deployment-relevant dimensions HELM and its agent-era successors collapse. The position is operationalized through three falsifiable out-of-distribution criteria with explicit thresholds; existing evidence partly supports it but is too thin to confirm. We close with a pre-registered pilot design and a field-level vision for what the next generation of agentic benchmarks should report.
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Abstract

Aggregate-score leaderboards in agent benchmarks fail to capture deployment-relevant dimensions and show rank instability, necessitating new evaluation frameworks based on predictive validity and out-of-distribution criteria.

Agent benchmarksare growing fast, but no single benchmark touches more than four or five of the dimensions that deployment exposes. This paper aggregates the largest coordinated deep-dive of one MCP-based industrial-agent benchmark to date: fourteen parallel implementation studies covering new asset classes (including a multi-modal visual extension), alternative orchestrations, retrieval strategies, reasoning modes, infrastructure optimizations, and evaluation-methodology probes. Consolidating those studies with seven prioragent benchmarks, we argue thataggregate-score leaderboardssystematically underspecify deployed-agent evaluation. Rankings derived from aggregate scores do not transfer toout-of-distribution settings; recent public-to-hidden competition retrospectives provide direct empirical evidence of thisrank instability. We propose ranking configurations bypredictive validity, the correlation between in-sample and out-of-sample rank, rather than in-sample mean, and report a twelve-tier measurement apparatus that exposes thedeployment-relevant dimensionsHELMand itsagent-era successorscollapse. The position is operationalized through threefalsifiable out-of-distribution criteriawith explicit thresholds; existing evidence partly supports it but is too thin to confirm. We close with a pre-registered pilot design and a field-level vision for what the next generation of agentic benchmarks should report.

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