The client needed a reliable way to benchmark frontier models on difficult STEM problems that go beyond saturated academic datasets. In addition, the client imposed strict requirements on question form and answer structure. Problems had to be unambiguous, solvable, and mathematically or scientifically correct, with a single final answer and controlled numeric precision where applicable. 

The challenge was to deliver a large set of hard, evaluation-safe STEM problems, maintaining domain diversity and consistent frontier-level performance characteristics.

Turing applied a rigorous, expert-driven data generation and validation workflow designed for frontier model benchmarking. The process emphasized domain depth, correctness, originality, and calibrated difficulty, with all problems mapped to clearly defined subdomains across core STEM fields.

1. Expert-authored problem sourcing

Candidate problems were selected to meet the client’s strict requirements around structure, answer uniqueness, and evaluation safety.

Each problem was required to:

• Be single-part with exactly one final answer
• Avoid proof-based, explanatory, or demonstration-style formats
• Exclude yes/no, fill-in-the-blank, or multi-select questions
• Enforce controlled numeric precision where applicable
• Remain original and resistant to search-based lookup

Only problems that satisfied these criteria advanced to domain calibration.

2. Domain and subdomain calibration

Problems were distributed across four primary STEM domains, including physics, chemistry, biology, and mathematics, and mapped exclusively to 40+ subdomains defined in the project taxonomy.

Subdomains were defined using established academic classification systems and research standards. Taxonomy design was grounded in:

• Prestigious journals and formal frameworks, including Physical Review journals, Nature Physics, Journal of the American Chemical Society, Angewandte Chemie, Cell, PNAS, Lancet, JAMA, Mathematics Subject Classification (MSC) 2020
• Research areas represented in academic departments, ensuring alignment with graduate-level curricula
• Key research papers spanning foundational theory, applied methods, and modern model development
• Subject matter expert judgment, balancing historical foundations, conceptual breadth, and current research directions

3. Difficulty calibration and model validation

All problems were rated at graduate to PhD level, comparable to advanced coursework, qualifying exams, or research-level reasoning challenges. Difficulty was calibrated using frontier-model performance signals to ensure problems consistently stressed deep reasoning rather than surface pattern matching.

Problems that were solved too easily or showed instability were revised or removed to maintain a consistent hard-HLE difficulty band.

4. Multi-stage review and validation

Each problem underwent a structured, multi-stage validation process:

• First-pass review: Structural correctness, taxonomy alignment, clarity, and originality
• Second-pass validation: Independent subject matter experts verified scientific accuracy, answer uniqueness, and reasoning soundness
• Consensus enforcement: Disagreements were resolved through expert discussion, with final decisions recorded
• External originality checks: Google-proof and Perplexity-proof validation ensured novelty

Only problems that passed all stages were included in the final delivery.

• Delivered more than 5,000 evaluation-ready HLE-grade STEM problems aligned to frontier benchmarking needs
• Achieved 100% acceptance from the client, confirming correctness and difficulty requirements
• Maintained consistent hard-HLE difficulty, producing low pass@k signals across state-of-the-art models
• Ensured clean evaluation safety, with no ambiguous answers, format violations, or trivial problem types
• Established a scalable pipeline for continued expansion as frontier models evolve

The client received a large-scale, evaluation-safe dataset, enabling precise benchmarking of frontier models on advanced STEM reasoning. With strict correctness guarantees, calibrated difficulty, and domain diversity, the dataset supports reliable comparison across model versions without introducing evaluation artifacts.

This foundation allows the client to continue stress-testing new frontier systems as reasoning capabilities advance.