Checklist
For all authors…

Do the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope? [Yes]

Did you describe the limitations of your work? [Yes] See Sec. 5 .

Did you discuss any potential negative societal impacts of your work? [No] Although we feel our work demonstrates the potential of NCAs as viable alternatives to common recurrent network architectures (ViTCA being our evidential contribution), our experiments intentionally tend towards the direction of optimizing model efficiency (and single-GPU training accessibility) rather than towards the increasingly popular direction of scaling upwards. However, as much as our work demonstrates the downward-scaling capabilities of NCAs, we also acknowledge that this similarly applies going upward, and as such, can be abused ( e.g . , creating a “deepfake”-capable ViTCA).

Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes]

If you are including theoretical results…

Did you state the full set of assumptions of all theoretical results [N/A]

Did you include complete proofs of all theoretical results? [N/A]

If you ran experiments…

Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] Code and instructions to reproduce results are included in the supplemental material.

Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes]

Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? [No] Given the combination of time and computational restrictions and our exhaustive list of experiments, we opted to prioritize experiment variety and dataset coverage as an implicit substitute for re-running experiments under different random seeds. For all experiments, we kept a fixed random seed, even pointing out (deterministic) differences caused by gradient checkpointing when used (see Appendix A ).

Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes]

If you are using existing assets (e.g., code, data, models) or curating/releasing new assets…

If your work uses existing assets, did you cite the creators? [Yes]

Did you mention the license of the assets? [N/A] Licensed frameworks used such as PyTorch (BSD-style) and Hydra (MIT) will be mentioned in acknowledgements.

Did you include any new assets either in the supplemental material or as a URL? [No] No new assets—aside from code and training our models—were created for the purposes of this work.

Did you discuss whether and how consent was obtained from people whose data you’re using/curating? [No] We used publicly available datasets.

Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content? [No] Although not discussed in the manuscript, we would like to point that the datasets we used that could potentially contain personally identifiable information (CelebA, CIFAR10, Tiny ImageNet) each have restrictions and/or acknowledgements of such potential issues. Also, our work is not focused on classifying persons and ViTCA is not a generative model, e.g . , it can not generate new faces.

If you used crowdsourcing or conducted research with human subjects…

Did you include the full text of instructions given to participants and screenshots, if applicable? [N/A]

Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable? [N/A]

Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation? [N/A]