Grounded in expertise.
Problems that depend on genuine domain knowledge, connected derivations and defensible assumptions.
INDEPENDENT EXPERT DATA STUDIO / 001
Expert data for models that reason and act.
Original expert work. Real source material. Verifiable outcomes.
We create mathematically rigorous training data, demanding scientific benchmarks, and realistic agent tasks for frontier AI systems—designed to test what a model can actually do.
BUILT FOR THE DIFFICULT CASES
SCROLL TO EXPLORE ↓
01 / PERSPECTIVEWHY THIS WORK MATTERS
Difficulty is easy to claim.
Rigor is harder to build.
Axiom Zero creates original expert training data and benchmarks for frontier AI, with a focus on mathematics, physics and demanding scientific reasoning.
We turn technical literature and complex professional material into tasks that require sustained reasoning and domain judgment. The challenge should come from the substance of the work—not from vague instructions or hidden assumptions.
Traceable to evidence.
Sources, definitions and reference material that support a clear path from question to answer.
Verifiable by design.
Checked solutions, explicit scoring criteria and reference artifacts that make evaluation meaningful.
02 / CAPABILITIESTHREE COMPLEMENTARY FORMATS
Training data, benchmarks
and agent task packs.
Each format is designed around a specific capability—with the artifacts needed to evaluate it.
Original expert
STEM data.
Professionally authored mathematics and physics problems where progress depends on getting the previous step right. Tasks require derivation, modeling, calculation and domain judgment.
Each item pairs an original prompt with a worked reference solution, explicit assumptions and scoring criteria. Datasets can be scoped for post-training or held-out evaluation.
MathematicsPhysicsExpert solutions
αβALGEBRAIC TOPOLOGY
For an oriented surface Σ2, describe a basis of H1(Σ2; ℤ) and its intersection pairing.
SOLUTION OUTLINE
01
Identify four independent cycle classes.
02
Use a canonical symplectic basis.
H1(Σ2; ℤ) ≅ ℤ4
03
Evaluate oriented intersections.
ai · bj = δij
Illustrative excerpt · not a full proofEXAMPLE ASSESSMENT
Correct homology rank25%
Valid basis construction25%
Pairing signs justified30%
Orientation convention20%
Illustrative score50 / 100
Illustration of grading, not a real submission.
Research-grounded
text-only & multimodal tasks.
Original questions grounded in scientific papers, technical reports, experimental methods and demanding quantitative material. Inputs can range from dense text and tables to multi-panel figures and cross-document evidence.
Solving a task may require reconciling observations, interpreting an experimental method or combining visual measurements with a physical model. Explicit sources, units and uncertainty criteria keep the reasoning checkable.
Research literatureEvidence synthesisMultimodal
scientific_figure.png
SYNTHETIC DATA
EXAMPLE TASK
Compare the model fits with the measurements in panels B and C, then identify residual features in panel D that merit closer investigation. Explain what the evidence supports.
Evidence → inference → verificationAgent tasks &
reference artifacts.
Multi-step task packs that require an agent to investigate, calculate, use tools and produce a substantive deliverable. Assignments combine source files, realistic dependencies and clear constraints.
Tasks may culminate in a technical memo, reproducible notebook or revised analytical artifact. Expert reference deliverables and outcome-based rubrics define what successful work looks like.
Multi-file inputsTool useDeliverables
ONE TASK. MULTIPLE INPUT FORMATS.BRIEF / PAPER / DATA / FIGURE / CODE
Demonstration visuals are illustrative. Mathematical excerpts and scientific plots are not presented as completed client work or published research.
03 / METHODOLOGYFROM SPECIFICATION TO EVIDENCE
THE STANDARD WE DESIGN FOR
Expert work, with evidence you can inspect.
A strong dataset makes the task, its source material and its success criteria fit together. We design the whole package, not just the prompt.
A clear problem to solve.
Define the target capability, expected deliverable and constraints. Set the intended use: post-training data, benchmark or agent evaluation.
Context worth preserving.
Keep the definitions, methods, units, figure context and source references needed to reason correctly. Complexity should not depend on missing information.
An answer that stands up.
Supply worked solutions or completed reference artifacts. Independently check derivations, calculations, units and the evidence behind each conclusion.
Scoring that measures work.
Use explicit tolerances, executable checks and expert rubrics as appropriate. Assess substantive correctness while allowing legitimate alternative approaches.
THE IDEA BEHIND THE NAME
Start with explicit premises.
Follow the reasoning.
Check the result.
For us, Axiom Zero is a mathematical starting point: an insistence on clarity before complexity and evidence behind every answer.
04 / CONTACTSTART WITH THE QUESTION
LET'S TALK
Have a difficult problem worth getting right?
Tell us the domain, the source material and what the model or agent should learn to do. We can scope original STEM problems, research-grounded tasks or full agent briefs with reference artifacts.
contact@axiomzero.it