LightWrk provides an evaluation framework that measures spatial fidelity in robotics and game AI world models, detecting issues like clipping, physics violations, and unrealistic interactions. Clients can opt for a one‑time audit with a detailed failure‑mode report or subscribe to a continuous evaluation service with custom ontologies, scoring protocols, and automated reporting to guide model improvements.
Funding
Funding not disclosed
Founders
Product
Problem
Robotics and game AI teams struggle to detect spatial errors in their world models—such as object clipping, incorrect gravity, or unrealistic interactions—because existing testing tools lack systematic, scalable evaluation of spatial fidelity.
Solution
LightWrk offers a dedicated evaluation framework that quantifies spatial fidelity of world model outputs. Clients can choose a one‑time audit where a fixed set of model sequences is analyzed and a structured failure‑mode report is delivered. For ongoing needs, LightWrk provides a subscription‑based infrastructure that includes custom ontologies, failure taxonomies, scoring protocols, and trained evaluators to run continuous evaluation pipelines. The service integrates with existing training loops, delivering clear metrics and actionable insights that help teams iteratively improve model performance and reduce costly simulation errors.
Target Audience
Primary customers are robotics research labs, game AI development teams, and organizations building foundational 3D scene reconstruction or physics simulation models that require rigorous spatial validation.
Features
- Fixed‑fee audit that processes a defined batch of model outputs and returns a detailed failure‑mode report
- Monthly retainer infrastructure with customizable ontologies and failure taxonomies tailored to specific domains (robotics, game AI, physics simulation)
- Scoring protocols that quantify clipping, physics violations, causal reasoning errors, and interaction rule breaches
- Automated evaluator training and continuous evaluation runs to maintain consistent assessment over time
- Structured reporting format that highlights error patterns and provides actionable recommendations for model retraining