What Twelve LLM Agent Benchmark Papers Disclose About Themselves: A Pilot Audit and an Open Scoring Schema
arXiv:2605.21404v1 Announce Type: new Abstract: We read twelve well-known LLM agent benchmark papers and recorded, dimension by dimension, what each paper actually says about how its evaluation was run. The motivation came from a familiar frustration: two papers will report results on the same benchmark with the same model name and disagree, and you cannot tell why -- the scaffold, the sampling settings, the subset, or the evaluator version. In many cases the published artifact does not let you answer. This paper is an implementation report on the attempt. We designed a small audit schema (fiv
The proliferation of LLM agent research has led to inconsistencies in reporting and evaluation, necessitating a structured approach to understand benchmark results.
Reliable benchmarking and transparent reporting are critical for advancing AI agent development and ensuring trust in reported capabilities, guiding future investment and research.
This audit attempts to standardize evaluation reporting for LLM agents, potentially improving the comparability and reproducibility of research outcomes.
- · AI Researchers
- · AI Developers
- · Companies investing in LLM agents
- · Misleading benchmark reports
- · Undisciplined AI research practices
Improved clarity and comparability of LLM agent benchmark results.
Faster, more reliable progress in AI agent development due to better understanding of model performance.
Increased investor confidence in agentic AI technologies as performance metrics become more robust and verifiable.
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Read at arXiv cs.LG