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Jon Allen

Publications and source records attributed to Jon Allen.

3 recordsLinked to original sources

Cosmos 3: Omnimodal World Models for Physical AI

We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-transformers architecture. By supporting highly flexible input-output configurations, Cosmos 3 seamlessly unifies critical modalities for Physical AI -- effectively subsuming vision-language models, video generators, world simulators, and world-action models into a single framework. Our evaluation demonstrates that Cosmos 3 establishes a new state-of-the-art across a diverse suite of understanding and generation tasks, demonstrating omnimodal world models as scalable, general-purpose backbones for embodied agents. Our post-trained Cosmos 3 models were ranked as the best open-source Text-to-Image and Image-to-Video models by Artificial Analysis, and the best policy model by RoboArena at the time the technical report was written. To accelerate open research and deployment in Physical AI, we make our code, model checkpoints, curated synthetic datasets, and evaluation benchmark available under the Linux Foundation's OpenMDW-1.1 License at https://github.com/nvidia/cosmos and https://huggingface.co/collections/nvidia/cosmos3. The project website is available at https://research.nvidia.com/labs/cosmos-lab/cosmos3.

cs.CV

Cannabis Impairment Monitoring Using Objective Eye Tracking Analytics

The continuing growth in cannabis legalization necessitates the development of rapid, objective methods for assessing impairment to ensure public and occupational safety. Traditional measurement techniques are subjective, time-consuming, and do not directly measure physical impairment. This study introduces objective metrics derived from eye-tracking analytics to address these limitations. We employed a head-mounted display to present 20 subjects with smooth pursuit performance, horizontal saccade, and simple reaction time tasks. Individual and group performance was compared before and after cannabis use. Results demonstrated significant changes in oculomotor control post-cannabis consumption, with smooth pursuit performance showing the most substantial signal. The objective eye-tracking data was used to develop supervised learning models, achieving a classification accuracy of 89% for distinguishing between sober and impaired states when normalized against baseline measures. Eye-tracking is the optimal candidate for a portable, rapid, and objective tool for assessing cannabis impairment, offering significant improvements over current subjective and indirect methods.

cs.HC

Gauge Fixing and Constrained Dynamics

We review the Dirac formalism for dealing with constraints in a canonical Hamiltonian formulation and discuss gauge freedom and display constraints for gauge theories in a general context. We introduce the Dirac bracket and show that it provides a consistent method to remove any gauge freedom present. We discuss stability in evolution of gauge theories and show that fixing all gauge freedom is sufficient to ensure well-posedness for a large class of gauge theories. Electrodynamics provides examples of the methods outlined for general gauge theories. Future work will apply the formalism, and results derived here, to General Relativity.

math-ph