PELV-VCR Paper

This repository contains the LaTeX source, metadata, and automated submission pipeline for the paper titled "PELV-VCR: A Conceptual Framework for Enhancing Self-Supervised Video Representation Learning via Neurocognitively Inspired Mechanisms" by William Kyle Million.

About

The paper introduces the Perceptual Emergence Layer for VJ-VCR (PELV-VCR), a novel conceptual framework that enhances Meta AI's Video Joint Embedding Predictive Architecture with Variance-Covariance Regularization (VJ-VCR) by integrating computational modules inspired by human cognitive processes. These include a Saliency Masker, Meta-Contextual Intent Vector, Pattern Entangler, and Counterfactual Synthesizer.

Repository Structure

• draft/ - Contains the LaTeX source files
• scripts/ - Contains tools for metadata processing and validation
• .github/workflows/ - Contains CI/CD pipeline for automated arXiv submission
• metadata.yaml - Structured metadata for the paper
• references.bib - Bibliography in BibTeX format
• Makefile - Build automation for PDF generation and validation

Quickstart

1. Edit Metadata: Update metadata.yaml with your actual ORCID URI.
2. Write Paper Content: Populate the sections in draft/main.tex with your paper's content.
3. Add References: Fill references.bib with the BibTeX entries for all citations.
4. Local Build:
   • Run make pdf to compile draft/main.pdf.
   • Run make check to verify PDF/A compliance.
5. GitHub Workflow:
   • Configure the necessary secrets in the repository settings.
   • Tag a release (e.g., v0.1.0) to trigger the automatic submission workflow.

License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.