AWhat changed
Generated and filtered language-model evaluation datasets with language models and human relevance checks, using them to identify scaling and training trends in behaviors such as sycophancy and stated preferences.
Scientific scope
Generated and filtered language-model evaluation datasets with language models and human relevance checks, using them to identify scaling and training trends in behaviors such as sycophancy and stated preferences. The assessed contribution is the evaluation resource and its design, not a model capability score. Answers to constructed questions are measured outputs, not proof of a model's private intentions or consciousness. Label agreement and dataset relevance do not guarantee coverage of all behaviors or validity outside the evaluated models.
Evidence
The linked original works support the scoped statement and contribution attribution. WRS has not independently replayed experiments or reconstructed a full mathematical proof. The assessed contribution is the evaluation resource and its design, not a model capability score. Answers to constructed questions are measured outputs, not proof of a model's private intentions or consciousness. Label agreement and dataset relevance do not guarantee coverage of all behaviors or validity outside the evaluated models.
Contributors
- The named authors in the research record · SCIENCE: Source-listed authors for the exact contribution; experimental subjects are not autonomous scientific authors. Source support and predecessor work retain separate roles. Individual effort shares and unresolved identities are not inferred.
Provisional WRS assessment, edition 2026-10-23.4. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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