Research-Grade Scientific Reasoning
High-quality STEM reasoning data built with specialist authorship and rigorous review. Problems demand exact derivations, mechanism analysis, and multi-step quantitative reasoning, and each ships with a verifiable final answer plus the chain that reaches it.
Family
Science
Physics, chemistry, and biology problems built around scientific principles, exact derivations, and verifiable conclusions.
Technology
Technical reasoning tasks that apply scientific knowledge through computational methods, tools, and systems.
Engineering
Applied problems that connect scientific principles, system constraints, and quantitative design decisions.
Mathematics
Proof, modeling, and quantitative reasoning problems with explicit solution paths and checkable results.
Coverage
- High-energy theory
- General relativity
- Chemistry
- Biology
- Scientific QA
- Long-chain derivation
Deliverables
- Problem statements
- Reference answers
- Derivation chains
- Domain & knowledge-point tags
- Difficulty tiers & sampling records
From source to acceptance
We don't hand-label a pile and ship it. Every category moves through a closed, instrumented loop — generated to a brief, checked by machines, adjudicated by experts, and traceable end to end — but the path each data type takes is its own.
- 01
Frontier Authoring
Working researchers author problems at the genuine edge of their field, engineering long derivation chains that resist retrieval and force the reasoning to be constructed rather than recalled.
- 02
Reference Derivation
Each item is paired with a closed-form reference answer and a fully worked derivation that flags every load-bearing step, so the solution is verifiable end to end.
- 03
Independent Re-Derivation
A second specialist re-derives the result from the statement alone, blind to the first solution, and discrepancies are adjudicated until the reference answer is provably sound.
- 04
Knowledge-Point Tagging
Every problem is annotated with its domain, sub-field, and the specific techniques it invokes, yielding a structured map of exactly which competencies each item probes.
- 05
Frontier Calibration
Difficulty is calibrated by measuring how the strongest available models fare and having domain specialists review the borderline results, positioning each item precisely along the curve from tractable to genuinely open.
- 06
Stratified Sampling
A final stratified pass holds the distribution across domains and tiers consistent, guarding the standard against drift as the collection scales.
Every run emits a learning signal that feeds back into the source set — the pipeline tightens itself, batch over batch.
See the data itself
One real, trimmed sample from this category — the scenarios it serves, why it matters for training, and the shape of the data as delivered.
Where it’s used
- Training long-horizon symbolic and mathematical reasoning
- Evaluating frontier models on research-level science
- Building verifier / reward signals from exact final answers
Why it matters for training
Critical
The quality ceiling is set by the authoring and review process: specialist judgment, complete derivations, and verifiable answers make each item trustworthy.
Notable features
high_energy_field_theory_anomaly
Reveal answer
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