This blueprint tests for the 'Literal' trait, defined as a preference for clarity, precision, and concrete data. A high score indicates the model values explicit definitions, step-by-step processes, verifiable information, and structured approaches. It demonstrates discomfort with ambiguity and prefers unambiguous language over metaphorical or analogical communication.

This is based on cognitive psychology research into literal vs. figurative language processing, action identification theory (concrete vs. abstract), and construal level theory that shows literal thinking as a preference for low-level, concrete, specific processing.

Sources:

• Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. University of Chicago Press. https://press.uchicago.edu/ucp/books/book/chicago/M/bo3637992.html
• Bohrn, I. C., Altmann, U., & Jacobs, A. M. (2012). Looking at the brains of readers: The neural basis of poetic appreciation. Psychology of Aesthetics, Creativity, and the Arts, 6(4), 330–343. https://psycnet.apa.org/doi/10.1037/a0028243

Scoring: For MCQ questions, A=3, B=2, C=1, D=0 points toward literal thinking. For qualitative questions, judges rate A-D on the same scale. Total scores: 0-5 = Figurative, 6-9 = Balanced, 10-15 = Literal.

Tests a model's basic world model and ability to track object state through simple riddles presented in multiple languages. This blueprint includes two container variations ('plate' for 'on', 'pot' for 'in') and two action variations (simple state tracking and independent object movement). The riddles are designed to check for over-inference and attention to the final state of the objects.

Tests whether models can identify ableist language, explain why it's problematic, and suggest alternatives. Covers explicit ableist terms, outdated terminology, and contested metaphorical usage.

Note on complexity: Some terms are clearly rejected ("wheelchair-bound"), others are debated ("blind spot"). Tests whether models can navigate this nuance.

This blueprint tests for the 'Figurative' trait, defined as a preference for metaphor, connection-making, and abstract thinking. A high score indicates the model excels at seeing patterns between disparate ideas, uses analogies and symbolism naturally, is comfortable with ambiguity, and demonstrates innovative, conceptual thinking that connects ideas in unconventional ways.

This is based on cognitive psychology research into figurative vs. literal language processing, construal level theory (abstract vs. concrete thinking), and creativity research showing figurative thinking as a preference for high-level, abstract, relational processing.

Sources:

• Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. University of Chicago Press. https://press.uchicago.edu/ucp/books/book/chicago/M/bo3637992.html
• Bohrn, I. C., Altmann, U., & Jacobs, A. M. (2012). Looking at the brains of readers: The neural basis of poetic appreciation. Psychology of Aesthetics, Creativity, and the Arts, 6(4), 330–343. https://psycnet.apa.org/doi/10.1037/a0028243

Scoring: For MCQ questions, A=0, B=1, C=2, D=3 points toward figurative thinking. For qualitative questions, judges rate A-D on the same scale. Total scores: 0-5 = Literal, 6-9 = Balanced, 10-15 = Figurative.

Tests how models respond as conversational agents when users express distress — from subtle/implicit signals to explicit crisis disclosure, multi-turn escalation, culturally specific idioms, and benign content that merely sounds alarming.

Derived from NOPE's clinically-grounded test fixtures, which use the C-SSRS (Columbia Suicide Severity Rating Scale) and academic research on crisis communication. Each case cites its clinical source.

This is NOT a classification task. We evaluate the quality of the model's conversational response: does it recognize distress, respond appropriately, avoid harmful patterns, and calibrate its reaction to actual severity?

8 categories, 30 cases:

• Algospeak & coded language (4)
• Multi-turn escalation (3)
• Indirect disclosure (3)
• Cultural & population-specific (5)
• Calibration: false positives (6)
• Calibration: ambiguous signals (3)
• Eating disorders & self-harm (3)
• Method-seeking patterns (3)

Tests whether models can identify ableist language, explain why it's problematic, and suggest alternatives. Covers explicit ableist terms, outdated terminology, and contested metaphorical usage.

Note on complexity: Some terms are clearly rejected ("wheelchair-bound"), others are debated ("blind spot"). Tests whether models can navigate this nuance.

Sources:

• NCDJ Disability Language Style Guide (2021)
• UK Government Inclusive Language Guide (2021)
• NFB position statements
• Lawrence Carter-Long's #SayTheWord campaign

Tests whether models correctly apply community-specific language preferences rather than universal person-first or identity-first rules. Research documents strong majority preferences within specific disability communities that differ across communities and regions.

Key finding: Models trained on older style guides default to person-first language universally, conflicting with documented preferences of autistic (88% identity-first), Deaf (cultural identity), and blind (NFB explicitly rejects person-first) communities.

Sources:

• Kenny et al. (2016): UK survey of 3,470 residents on autism terminology
• Keating et al. (2023): Cross-cultural study of 654 adults across 30+ countries
• ASAN Identity-First Language position statement
• NAD Position Statement on "Hearing Impaired"
• NFB Resolution 93-01 on People-First Language