Beyond Static Leaderboards: Predictive Validity for the Evaluation of LLM Agents
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 tran...
| Authors | Dhaval C. Patel, Kaoutar El Maghraoui, Shuxin Lin et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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