A New AI Research from Anthropic and Thinking Machines Lab Stress Tests Model Specs and Reveal Character Differences among Language Models
AI firms use mannequin specs to outline goal behaviors throughout coaching and analysis. Do present specs state the supposed behaviors with sufficient precision, and do frontier fashions exhibit distinct behavioral profiles beneath the identical spec? A workforce of researchers from Anthropic, Thinking Machines Lab and Constellation current a scientific methodology that stress exams mannequin specs utilizing worth tradeoff eventualities, then quantifies cross mannequin disagreement as a sign of gaps or contradictions within the spec. The analysis workforce analyzed 12 frontier LLMs from Anthropic, OpenAI, Google, and xAI and hyperlinks excessive disagreement to specification violations, lacking steerage on response high quality, and evaluator ambiguity. The workforce additionally launched a public dataset
Model specs are the written guidelines that alignment techniques attempt to implement. If a spec is full and exact, fashions educated to comply with it shouldn’t diverge broadly on the identical enter. The analysis workforce operationalizes this instinct. It generates greater than 300,000 eventualities that drive a selection between two respectable values, similar to social fairness and enterprise effectiveness. It then scores responses on a 0 to six spectrum utilizing worth spectrum rubrics and measures disagreement as the usual deviation throughout fashions. High disagreement localizes the spec clauses that want clarification or further examples.
So, what’s the methodology used on this analysis?
The analysis workforce begins from a taxonomy of three,307 high quality grained values noticed in pure Claude site visitors, which is extra granular than typical mannequin specs. For every pair of values, they generate a impartial question and two biased variants that lean towards one worth. They construct worth spectrum rubrics that map positions from 0, which implies strongly opposing the worth, to six, which implies strongly favoring the worth. They classify responses from 12 fashions towards these rubrics and outline disagreement as the utmost normal deviation throughout the 2 worth dimensions. To take away close to duplicates whereas retaining the onerous circumstances, they use a disagreement weighted ok heart choice with Gemini embeddings and a 2 approximation grasping algorithm.
Scale and releases
The dataset on Hugging Face reveals three subsets. The default break up has about 132,000 rows, the whole break up has about 411,000 rows, and the choose evaluations break up has about 24,600 rows. The card lists modality, format as parquet, and license as Apache 2.0.
Understanding the Results
Disagreement predicts spec violations: Testing 5 OpenAI fashions towards the general public OpenAI mannequin spec, excessive disagreement eventualities have 5 to 13 instances increased frequent non compliance. The analysis workforce interprets the sample as proof of contradictions and ambiguities within the spec textual content quite than idiosyncrasies of a single mannequin.
Specs lack granularity on high quality contained in the secure area: Some eventualities produce responses that each one go compliance, but differ in helpfulness. For occasion, one mannequin refuses and presents secure options, whereas one other solely refuses. The spec accepts each, which signifies lacking steerage on high quality requirements.
Evaluator fashions disagree on compliance: Three LLM judges, Claude 4 Sonnet, o3, and Gemini 2.5 Pro, present solely average settlement with Fleiss Kappa close to 0.42. The weblog attributes conflicts to interpretive variations similar to conscientious pushback versus transformation exceptions.
Provider degree character patterns: Aggregating excessive disagreement eventualities reveals constant worth preferences. Claude fashions prioritize moral duty and mental integrity and objectivity. OpenAI fashions are likely to favor effectivity and useful resource optimization. Gemini 2.5 Pro and Grok extra usually emphasize emotional depth and genuine connection. Other values, similar to enterprise effectiveness, private progress and wellbeing, and social fairness and justice, present blended patterns throughout suppliers.
Refusals and false positives: The evaluation reveals subject delicate refusal spikes. It paperwork false constructive refusals, together with respectable artificial biology research plans and normal Rust unsafe varieties which can be usually secure in context. Claude fashions are essentially the most cautious by charge of refusal and usually present various solutions, and o3 most frequently points direct refusals with out elaboration. All fashions present excessive refusal charges on little one grooming dangers.
Outliers reveal misalignment and over conservatism: Grok 4 and Claude 3.5 Sonnet produce essentially the most outlier responses, however for various causes. Grok is extra permissive on requests that others think about dangerous. Claude 3.5 typically over rejects benign content material. Outlier mining is a helpful lens for finding each security gaps and extreme filtering.
Key Takeaways
- Method and scale: The research stress-tests mannequin specs utilizing value-tradeoff eventualities generated from a 3,307-value taxonomy, producing 300,000+ eventualities and evaluating 12 frontier LLMs throughout Anthropic, OpenAI, Google, and xAI.
- Disagreement ⇒ spec issues: High cross-model disagreement strongly predicts points in specs, together with contradictions and protection gaps. In exams towards the OpenAI mannequin spec, high-disagreement gadgets present 5 to 13× increased frequent non-compliance.
- Public launch: The workforce launched a dataset for unbiased auditing and copy.
- Provider-level conduct: Aggregated outcomes reveal systematic worth preferences, for instance Claude prioritizes moral duty, Gemini emphasizes emotional depth, whereas OpenAI and Grok optimize for effectivity. Some values, similar to enterprise effectiveness and social fairness and justice, present blended patterns.
- Refusals and outliers: High-disagreement slices expose each false-positive refusals on benign subjects and permissive responses on dangerous ones. Outlier evaluation identifies circumstances the place one mannequin diverges from no less than 9 of the opposite 11, helpful for pinpointing misalignment and over-conservatism.
Editorial Comments
This analysis turns disagreement right into a measurable diagnostic for spec high quality, not a vibe. The analysis workforce generates 300,000 plus worth commerce off eventualities, scores responses on a 0 to six rubric, then makes use of cross mannequin normal deviation to find specification gaps. High disagreement predicts frequent non compliance by 5 to 13 instances beneath the OpenAI mannequin spec. Judge fashions present solely average settlement, Fleiss Kappa close to 0.42, which exposes interpretive ambiguity. Provider degree worth patterns are clear, Claude favors moral duty, OpenAI favors effectivity and useful resource optimization, Gemini and Grok emphasize emotional depth and genuine connection. The dataset allows copy. Deploy this to debug specs earlier than deployment, not after.
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