Frontier benchmarks
Original evaluations that separate fluent output from genuine competence and expose where advanced models still break.
- Capability-specific test suites
- Expert rubrics and reference answers
- Adversarial and contamination checks
AxiotaraIntelligenceFoundational truth that guides intelligence
The scientific data layer for frontier intelligence.
Axiotara creates evaluation-grade benchmarks and high-signal post-training data for frontier labs and advanced language models.
What we build
Better questions create better intelligence.
At the frontier, generic datasets stop revealing what matters. We build around the precise capability your model needs to acquire—or prove.
Post-training data
High-signal demonstrations, critiques, and preference data that improve scientific reasoning, precision, and reliability.
- Expert demonstrations and critiques
- Preference pairs and error corrections
- Reasoning-rich scientific tasks
Scientific coverage
Depth where surface-level data fails.
We assemble specialists around each program, then design to the standards, language, and edge cases of the field.
01Advanced STEM reasoning
Mathematics, physics, chemistry, biology
02Code & systems
Repository logic, kernels, distributed systems
03Agentic tool use
Multi-step trajectories, browsers, environments
04Multimodal science
Figures, diagrams, spatial and visual reasoning
05Domain RL environments
Verifiable tasks, simulators, reward signals
06Evaluation & verifiers
Held-out tests, rubrics, deterministic checks
07Technical professions
Medicine, engineering, law, quantitative fields
08Multilingual technical data
Specialized knowledge across languages
The Axiotara standard
Quality is designed into every layer.
Define the capability
Start with the behavior, boundary conditions, and failure modes that matter—not a generic task taxonomy.
Build with experts
Domain specialists construct original tasks, reference material, grading logic, and difficult counterexamples.
Stress the data
Every delivery is checked for ambiguity, leakage, grading consistency, and unintended shortcuts.
Ship for the loop
Clean schemas, complete metadata, and transparent documentation make the data immediately usable.
Start a conversation
What should your next model be able to do?
Tell us the capability, scientific domain, or evaluation gap you're working on.