High-quality RL task packages for scientific reasoning and computation. Real research problems with Docker environments, independent verifiers and complete agent trajectories.
Estimated production capacity, measured in complete research task packages. Scope and delivery schedules are agreed with your team.
Develop scientific agents on estimation, causal decisions, robust learning and probabilistic prediction. Choose the domains and difficulty mix that fit your training program.
Defined research questions, scientific inputs and expected outputs.
Build contexts, dependency specifications and task resource limits.
Scoring logic with recorded task-level and component evidence.
Observable model actions, tool results and saved artifacts.
These examples show the kinds of scientific work in this collection. Contact Sales for an invitation to inspect the complete packages and evaluate the data.
Allocate teacher information and adversarial exposure across a shared student. Improve attacked risk while respecting clean-performance constraints.
Model audiovisual synergy and individual rating differences. Estimate parameters, uncertainty intervals and scientific conclusions from ordinal observations.
Select an intervention and evaluate its effect on independent observations. Inspect point estimates, interval quality and policy regret.
Use teacher information available at training time to improve student predictions. Deliver models evaluated across eight fixed scientific tasks.
Allocate a limited observation budget across strata. Combine predictions and observed responses to estimate targets with calibrated uncertainty.
Predict responses to a newly calibrated questionnaire from historical answers. Evaluate probability tensors and information curves under a fixed scientific contract.
Discuss available tasks, a tailored collection or ongoing production. We’ll help you choose samples to evaluate first.
