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Physical-invariant rollout evaluation is relevant in principle, but the stored text does not identify a robot task.

Source: published project information · editorial review 9 Oct 2026. This status does not indicate a failed link check.

Models / Research

worldproof · BuceaGeorgia

WorldProof evaluates world-model rollout predictions against reference data and physical invariants.

deep-learningevaluationevaluation-metricsjepalerobot
Publisher repository ↗
worldproof · BuceaGeorgia resource page screenshot
Resource screenshot · captured 2026-10-08 · visually reviewed

Overview

WorldProof evaluates world-model rollout predictions against reference data and physical invariants.

Publisher summary: “A reality check for world models: evaluate rollout predictions against ground truth and physical invariants, and get a report card showing where and why…”

Use its repository to inspect the implementation, documentation and supported workflows. This directory record identifies the maintainer, code license and related resources; it does not claim that the project has been installed or tested on your robot.

Project information

Maintainer
BuceaGeorgia
Primary language
Python
Repository code license
Apache-2.0
Model-weight license
Not recorded
Dataset license
Not recorded
Hardware design license
Not recorded
Maintenance
Not archived in the last recorded metadata
Last observed repository push
2026-08-06

Code licenses do not automatically cover model weights, datasets or hardware designs. Unrecorded license details require confirmation with the publisher.

Documentation sections

  • See a report card
  • Install
  • Quickstart (under a minute, no download)
  • Two commands
  • run a model and a sim oracle to produce a folder of rollouts
  • score them (runs the metrics your machine supports, reports what it skipped)

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