Research
Publications
Our contributions to AI evaluation methodology and post-training infrastructure.
Digital Twin Environments for Frontier Model Evaluation
Kulta Lab Research Team
We present a methodology for constructing high-fidelity digital twin environments that capture the full complexity of real-world deployment contexts for AI model evaluation. Our approach models dynamic state changes, information asymmetry, and error recovery requirements.
Preprint
Mar 2026
Evaluation
Digital Twin
RL
Failure-to-Insight: Systematic Capability Mapping for Post-Training
Kulta Lab Research Team
We introduce a framework for translating model failure patterns into structured training environment specifications. By rigorously mapping where SOTA models fail, we generate precise capability maps that drive targeted environment design.
Preprint
Feb 2026
Post-Training
RL Environments
Capability Mapping