We study the work models need to do next.
Useful data exposes a real capability boundary. AxisX connects expert judgment, task environments and executable verification, grounding research in concrete training and evaluation problems.
Longitudinal state & memory
In changing environments, a model must retain who knows what, which version applies, and why a conclusion changes.
Time-scoped questions · State revision · Evidence lineageCollaboration between agents
Move from individual execution to agents exchanging constraints, handing off evidence, reviewing artifacts and recovering from failure.
Delegation · Access boundaries · RecoveryContinuously renewed evaluation
Turn model failures into reproducible tasks. Use new references, environments and verifiers to check whether the next iteration resolves them.
Failure analysis · Task variants · RegressionTurn research into an evolving capability.
From delivery records today to maintained challenge sets and model feedback.
Operationalize expert and agent work
Connect qualification, task versions, source evidence, trajectories, QA and batch acceptance in one operating record.
- Domain and engineering qualification
- Versioned rubrics and edge cases
- Evidence-backed review records
Build a challenge-set foundry
Turn original STEM problems, regulated-domain tasks, environments, verifiers, golden runs and failure patterns into maintained evaluation assets.
- Expert-authored STEM challenge sets
- Regulated-domain golden sets
- Executable ML and software verifiers
Close the model-improvement loop
Connect benchmark deltas and trajectory failures to targeted post-training data, new adversarial tasks and the next evaluation release.
- Failure-driven task generation
- Corrected agent trajectories
- Continuous private benchmark versioning
From scientific reasoning to complex execution.
STEM challenge-set foundry
Original expert-authored problems, reference derivations, verifiers, difficulty calibration and contamination-risk screening.
Regulated-domain sandboxes
Controlled context, tools and access boundaries for finance, legal and medical expert work.
Agent trajectory intelligence
Structured action, observation, recovery and failure evidence for training and evaluation.
MLE / MLS evaluation operations
Reproducible engineering and research tasks with artifact, metric and scientific-validity review.
Executable software intelligence
Behavioral reconstruction and long-horizon repository work with isolated environments and programmatic verifiers.
Benchmark-native data partner
One accountable system connecting task authors, experts, runtime evidence, adjudication and model feedback.
The next dataset starts with your capability goal.
Share the gaps your existing evaluations miss. Define the next task worth building with us.