Research Methodology
Collaboration
and Quality
We believe AI agents should augment human capability, not replace it. We design for collaborative orchestration where humans and agents work together across strategy and execution.
Difficult, realistic, reliably verifiable, and hard to hack tasks are essential to progress RL research. We obsess over these criteria and develop multi-modal pixel-perfect environments as a conduit for them.
Designed for validity
Integrity
Temporal Integrity
Frame-accurate state control captures state-action pairs with millisecond precision, preserving causal relationships across multi-step trajectories. Models learn correct temporal dependencies rather than spurious correlations from drift.
Grounding
Deterministic Grounding
Information leakage prevention through careful segmentation of state information eliminates training data poisoning. Deterministic execution ensures identical outcomes for identical inputs, enabling reproducible experiments.
Realism
Deep Realism
Pixel-perfect clones extend beyond surface appearance with multiple layers of fidelity replicating functional behavior, state management, and interaction patterns. Multi-layer realism enables transfer learning from training to deployment without degradation.
Data Distribution
Realistic Data Distribution
Task sets reflect in-distribution usage patterns rather than edge cases or synthetic scenarios. Training on realistic distributions improves model generalization to actual deployment conditions.
Bespoke Generation
Bespoke Task Generation
Custom task specification to evaluation-ready deployment in under 24 hours. Automated generation enables rapid iteration on difficulty calibration, verification mechanisms, and task diversity.