Frontier AI development has reached an inflection point: as models rapidly advance in capabilities, the need for sophisticated evaluation has become a decisive factor in competitive success. That&rsquo;s why today we're announcing updates to Scale Evaluation, our platform that helps teams identify model weaknesses and validate improvements.
Our updated platform introduces four key capabilities: instant model comparison across thousands of tests, multi-dimensional performance visualization, automated error discovery, and targeted improvement guidance&mdash;all designed to help teams identify weaknesses faster and make more confident release decisions. These updates build on Scale Evaluation&rsquo;s foundation introduced <a href="https://scale.com/blog/leaderboard">last year</a>, broadening access to frontier evaluation capabilities.
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&ldquo;Scale Evaluation made it easier to identify and solve our performance gaps. We started with a focused set of human evaluations - 10,000+ test cases per week - to assess our model's multilingual capabilities. The insights were so valuable that we doubled our evaluation volume and expanded to more languages. It has given us a clearer picture of our model's performance.&rdquo; &mdash; Research Lead at a top-five frontier AI lab&nbsp;
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<h3 dir="ltr">Why Evaluations Matter&nbsp;</h3>
<a href="https://arxiv.org/html/2502.10709v1#:~:text=2019%20%3B%20Hendrycks%20et%C2%A0al,endless.%20However">Human evaluation remains the gold standard of progress</a> in frontier LLMs but is difficult to scale. Evaluation bottlenecks slow releases, and teams need assessments quickly to inform release decisions. As much promise as frontier models show for self-evaluation in some verifiable domains, in many others human judgment has no substitute.
Scale's evaluation framework draws on years of experience with leading AI labs and research initiatives, including through our SEAL team, which has developed novel benchmarks and leaderboards for frontier models.&nbsp;
<h2 dir="ltr">The Evaluation Challenge</h2>
As models approach human-level performance on standard benchmarks, traditional evaluation methods no longer provide meaningful differentiation. Benchmark saturation creates a critical need for more sophisticated evaluation that can guide targeted improvement efforts.
Timely, trustworthy measurement of release candidates is crucial for finding model failure modes before users do. In development, post-training data and compute are limited, and teams need to prioritize resources to address failure modes that real users notice. Concrete evidence of progress on important failure modes is needed to measure which training strategies work.
Automated metrics and LLM self-evaluation, while powerful, have many limitations. Model preferences for their own output styles are an obstacle to frontier comparisons, and even the most careful rubrics will fail to assess the creativity, nuance, and subjective qualities easily seen by humans.
As an evaluation partner for frontier AI labs, Scale brings established expertise to this challenge. The Biden White House selected Scale to power DEF CON 31's public red-teaming, and our SEAL research team created groundbreaking benchmarks including <a href="https://scale.com/leaderboard/enigma_eval">EnigmaEval</a>, <a href="https://scale.com/leaderboard/multichallenge">MultiChallenge</a>, <a href="https://scale.com/research/mask">MASK</a>, and <a href="https://scale.com/leaderboard/humanitys_last_exam">Humanity's Last Exam</a>. Due to our expertise, the National Institute of Standards and Technology partnered with Scale to do joint evaluation benchmark development and through DoD partnerships, we've also developed specialized evaluation standards for defense applications.
<h2 dir="ltr">Scale's Evaluation Framework</h2>
Today we&rsquo;re introducing new analytics capabilities that transform how teams visualize, analyze, and act on evaluation results. These enhancements represent a new era in frontier model evaluation - moving from simple performance benchmarks to comprehensive, actionable insights.
<img src="https://img.plasmic.app/img-optimizer/v1/img?src=947a6109d7b13958674d4d1f304c7e86.png&amp;f=webp&amp;q=75" width="534" height="356">
Since our initial launch, Scale Evaluation has evolved to provide deeper insights through:
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Model Comparisons: Our enhanced platform allows model builders to instantly compare performance across thousands of tasks. Teams can directly benchmark their models against previous versions or industry competitors, with clear visualizations where improvements or regressions occur.&nbsp;
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Results Analysis: With our updated visualizations features, labs can break down performance by language, topic, task type and other dimensions. Our console produces cross-sectional reports that group responses by criteria like error modes, task domain, or language.
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Automated Error Discovery: Our system now automatically identifies recurring patterns in model failures by analyzing evaluator feedback. We highlight recurring issues&mdash;contradictions, ignored instructions, misleading citations&mdash;and provide a high-level view of labeler feedback for each.
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Targeted Improvement Guidance: We help teams fix issues, whether that means retraining on targeted data or delaying a release until problems are resolved.
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Let&rsquo;s walk through how this works in practice. When human contributors identify poor responses or improvement opportunities, they provide text justifications explaining the model&rsquo;s mistake. Our Scale evaluation system clusters similar justifications into legible, descriptive labels we call &ldquo;error modes,&rdquo; each describing a specific type of failure.
For example, a contributor might say, &ldquo;The model ignored that I said to write it for ESL speakers.&rdquo; This, along with many similar justifications, might be clustered into a single error mode with a model-generated label &ldquo;Failure to follow prose style instructions.&rdquo;
<img src="https://img.plasmic.app/img-optimizer/v1/img?src=d8317811c973756c4b6c96382b7ed417.png&amp;f=webp&amp;q=75" width="587" height="391">
The Scale Evaluation console presents error modes such as these as categories for cross-sectional analysis alongside other performance metrics, helping teams select among release candidates or prioritize further post-training mitigations.
By analyzing error modes across categories, languages, and prompt types, teams can see exactly where models are struggling and focus efforts where improvement is needed most. This targeted approach transforms the model improvement process from educated guesswork to data-driven development.
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<h2 dir="ltr">Looking Ahead</h2>
As models continue to evolve, so will our evaluation capabilities. We want to continue helping labs build frontier models by providing increasingly sophisticated evaluation frameworks. We&rsquo;re advancing our multimodal, agentic, and nuanced reasoning evaluation methodologies to keep pace with rapidly developing capabilities. By combining rigorous human evaluation with powerful analytics tools, Scale aims to accelerate the development of more capable, reliable, and beneficial AI systems. We believe that systematic, transparent evaluation is fundamental to AI advancement.
To learn more about Scale Evaluation and how it can enhance your model development process, reach out to your Scale representative or contact us at <a href="mailto:evaluation@scale.com" target="_blank" rel="noopener">evaluation@scale.com</a>.
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Scale has enhanced its Evaluation platform, introducing advanced analytics and visualization capabilities.