GLOSSARY
The physical-AI proof vocabulary
Plain-English definitions of the terms behind WorldFlux — physical AI, world models, evidence packs, and the standards they map to.
- Physical AI
- AI that perceives and acts in the physical world — robots, humanoids, and autonomous machines — as opposed to purely digital AI such as chatbots. Reliability matters because failures happen in the real world, not just on a screen.
- World model
- A model that learns the dynamics of an environment so it can predict the outcome of actions. World models are a foundation for robot planning and control.
- Vision-language-action (VLA) model
- A robot-control model that maps camera images and a natural-language instruction directly to actions. OpenVLA and Physical Intelligence π are examples.
- Proof layer
- The independent layer that turns an AI evaluation into signed, verifiable evidence others can trust. It sits between self-reported metrics and handing over your model — the category WorldFlux defines.
- Evidence pack
- A tamper-evident bundle of a claim, the test protocol, the evidence (metrics, logs, artifacts), and the provenance, cryptographically signed and shareable as an expiring, revocable link anyone can re-verify.
- Bring-your-own-compute (BYO-compute) evaluation
- Running an evaluation on your own hardware so your model weights, keys, and data never leave it. The evaluation tool ingests only the outputs, never the model itself.
- Chain of custody
- A verifiable record of how a result was produced and by whom — from the run on your hardware to the signed evidence pack — so a reviewer can trust the result without trusting the vendor.
- Sigstore
- A public standard for cryptographically signing software and artifacts so that anyone can verify their origin and integrity. WorldFlux signs every evidence pack with Sigstore.
- ML bill-of-materials (ML-BOM, CycloneDX)
- A machine-readable inventory of the models, datasets, and dependencies that make up an AI system, expressed in the CycloneDX standard. It ships inside each WorldFlux evidence pack.
- LIBERO
- A standard robot-manipulation benchmark suite used to evaluate vision-language-action policies across a set of tasks.
- Deployment gap
- The difference between benchmark or demo performance and real-world reliability. WorldFlux makes it measurable: OpenVLA scored 74.4% on the standard LIBERO suite but 24.4% once the scene was changed.
- EU AI Act, Article 11
- The provision of the EU AI Act that requires makers of high-risk AI to produce technical documentation and conformity evidence before the system can be sold. WorldFlux evidence packs are designed to map to it.