mirror of
https://github.com/hanzoai/papers.git
synced 2026-11-03 02:27:30 +00:00
zeekay
0d1d121451
Fix shared/ include paths to ../shared/ for 55 subdirectory papers so
that hanzocover.sty and lstlang.tex resolve when building from each
paper's own directory (the build pattern used by latexmk per paper).
Build fixes:
- hanzo-operative/sections/03-screen-understanding-pipeline.tex: move
\COMMENT from after \FOR{...} onto the \STATE line so the algorithmic
env doesn't mis-parse.
- hanzo-network-architecture (abstract + sections 04, 12, 13): escape
unicode mu (U+03BC) as $\mu$ for pdflatex.
- ntt-transform/sections/08-benchmark-results.tex: same mu escape.
All 55 Tier-2/3 papers now build clean with latexmk -pdf.
64 lines
2.5 KiB
TeX
64 lines
2.5 KiB
TeX
\documentclass[11pt,twocolumn]{article}
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\usepackage{../shared/hanzocover}
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\usepackage[utf8]{inputenc}
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\usepackage{CJK}
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\usepackage{amsmath,amssymb,amsthm}
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\usepackage{graphicx}
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\usepackage{booktabs}
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\usepackage{algorithm}
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\usepackage{algorithmic}
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\usepackage{hyperref}
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\usepackage{xcolor}
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\usepackage{listings}
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\input{../shared/lstlang}
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\usepackage{tikz}
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\usetikzlibrary{shapes,arrows,positioning,fit,backgrounds}
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\definecolor{codegreen}{rgb}{0,0.6,0}
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\definecolor{codegray}{rgb}{0.5,0.5,0.5}
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\definecolor{codepurple}{rgb}{0.58,0,0.82}
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\definecolor{backcolour}{rgb}{0.95,0.95,0.92}
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\lstdefinestyle{mystyle}{
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backgroundcolor=\color{backcolour},
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commentstyle=\color{codegreen},
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keywordstyle=\color{codepurple},
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numberstyle=\tiny\color{codegray},
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stringstyle=\color{codegreen},
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basicstyle=\ttfamily\footnotesize,
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breakatwhitespace=false,
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breaklines=true,
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captionpos=b,
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keepspaces=true,
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numbers=left,
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numbersep=5pt,
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showspaces=false,
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showstringspaces=false,
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showtabs=false,
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tabsize=2
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}
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\lstset{style=mystyle}
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\title{Jin: Unified Multimodal AI Architecture for Vision-Language, Audio, and Cross-Modal Understanding}
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\author{Antje Worring, Zach Kelling\thanks{[email protected]} \\ \textit{Hanzo Industries \quad Lux Industries \quad Zoo Labs Foundation} \\ \texttt{[email protected]}}
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\date{April 2023}
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\begin{document}
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\hanzocoverpage
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\begin{abstract}
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We present Jin, a hypermodal AI architecture that unifies text, vision, audio, and extensible custom modalities through a joint embedding predictive framework. Jin combines three key innovations: (1) modality-specific encoders projecting heterogeneous inputs into a shared latent space with learnable modality embeddings; (2) a diffusion transformer backbone with Mixture of Experts (MoE) routing for efficient cross-modal reasoning; (3) hierarchical zooming JEPA (z-JEPA) for multi-scale visual understanding. Our experiments demonstrate that Jin achieves 82.1\% accuracy on ImageNet with zero-shot transfer, outperforms single-modality baselines by 15\% on AudioCaps retrieval, and enables novel cross-modal generation tasks. The architecture supports runtime modality extension without retraining the core model, enabling deployment flexibility for diverse applications.
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\end{abstract}
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\input{sections/01-introduction}
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\input{sections/02-background}
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\input{sections/03-jin-architecture}
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\input{sections/04-training}
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\input{sections/05-experiments}
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\input{sections/06-applications}
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\input{sections/07-discussion}
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\input{sections/99-conclusion}
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\end{document}
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