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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.07575 |
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| _version_ | 1866914093447774208 |
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| author | Cheng, Zerui Wohnig, Stella Gupta, Ruchika Alam, Samiul Abdullahi, Tassallah Ribeiro, João Alves Nielsen-Garcia, Christian Mir, Saif Li, Siran Orender, Jason Bahrainian, Seyed Ali Kirste, Daniel Gokaslan, Aaron Glinka, Mikołaj Eickhoff, Carsten Wolff, Ruben |
| author_facet | Cheng, Zerui Wohnig, Stella Gupta, Ruchika Alam, Samiul Abdullahi, Tassallah Ribeiro, João Alves Nielsen-Garcia, Christian Mir, Saif Li, Siran Orender, Jason Bahrainian, Seyed Ali Kirste, Daniel Gokaslan, Aaron Glinka, Mikołaj Eickhoff, Carsten Wolff, Ruben |
| contents | The meteoric rise of AI, with its rapidly expanding market capitalization, presents both transformative opportunities and critical challenges. Chief among these is the urgent need for a new, unified paradigm for trustworthy evaluation, as current benchmarks increasingly reveal critical vulnerabilities. Issues like data contamination and selective reporting by model developers fuel hype, while inadequate data quality control can lead to biased evaluations that, even if unintentionally, may favor specific approaches. As a flood of participants enters the AI space, this "Wild West" of assessment makes distinguishing genuine progress from exaggerated claims exceptionally difficult. Such ambiguity blurs scientific signals and erodes public confidence, much as unchecked claims would destabilize financial markets reliant on credible oversight from agencies like Moody's. In high-stakes human examinations (e.g., SAT, GRE), substantial effort is devoted to ensuring fairness and credibility; why settle for less in evaluating AI, especially given its profound societal impact? This position paper argues that the current laissez-faire approach is unsustainable. We contend that true, sustainable AI advancement demands a paradigm shift: a unified, live, and quality-controlled benchmarking framework robust by construction, not by mere courtesy and goodwill. To this end, we dissect the systemic flaws undermining today's AI evaluation, distill the essential requirements for a new generation of assessments, and introduce PeerBench (with its prototype implementation at https://www.peerbench.ai/), a community-governed, proctored evaluation blueprint that embodies this paradigm through sealed execution, item banking with rolling renewal, and delayed transparency. Our goal is to pave the way for evaluations that can restore integrity and deliver genuinely trustworthy measures of AI progress. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07575 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Benchmarking is Broken -- Don't Let AI be its Own Judge Cheng, Zerui Wohnig, Stella Gupta, Ruchika Alam, Samiul Abdullahi, Tassallah Ribeiro, João Alves Nielsen-Garcia, Christian Mir, Saif Li, Siran Orender, Jason Bahrainian, Seyed Ali Kirste, Daniel Gokaslan, Aaron Glinka, Mikołaj Eickhoff, Carsten Wolff, Ruben Artificial Intelligence Machine Learning The meteoric rise of AI, with its rapidly expanding market capitalization, presents both transformative opportunities and critical challenges. Chief among these is the urgent need for a new, unified paradigm for trustworthy evaluation, as current benchmarks increasingly reveal critical vulnerabilities. Issues like data contamination and selective reporting by model developers fuel hype, while inadequate data quality control can lead to biased evaluations that, even if unintentionally, may favor specific approaches. As a flood of participants enters the AI space, this "Wild West" of assessment makes distinguishing genuine progress from exaggerated claims exceptionally difficult. Such ambiguity blurs scientific signals and erodes public confidence, much as unchecked claims would destabilize financial markets reliant on credible oversight from agencies like Moody's. In high-stakes human examinations (e.g., SAT, GRE), substantial effort is devoted to ensuring fairness and credibility; why settle for less in evaluating AI, especially given its profound societal impact? This position paper argues that the current laissez-faire approach is unsustainable. We contend that true, sustainable AI advancement demands a paradigm shift: a unified, live, and quality-controlled benchmarking framework robust by construction, not by mere courtesy and goodwill. To this end, we dissect the systemic flaws undermining today's AI evaluation, distill the essential requirements for a new generation of assessments, and introduce PeerBench (with its prototype implementation at https://www.peerbench.ai/), a community-governed, proctored evaluation blueprint that embodies this paradigm through sealed execution, item banking with rolling renewal, and delayed transparency. Our goal is to pave the way for evaluations that can restore integrity and deliver genuinely trustworthy measures of AI progress. |
| title | Benchmarking is Broken -- Don't Let AI be its Own Judge |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.07575 |