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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>The Road to Convergence — CVPR 2026 Tutorial</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link href="https://fonts.googleapis.com/css2?family=DM+Serif+Display:ital@0;1&family=IBM+Plex+Sans:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
<style>
:root {
--bg: #0a0a0f;
--surface: #12121a;
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--text: #e8e8f0;
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--accent: #6c5ce7;
--accent2: #a29bfe;
--accent3: #fd79a8;
--accent4: #00cec9;
--gradient1: linear-gradient(135deg, #6c5ce7, #a29bfe);
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--mono: 'JetBrains Mono', monospace;
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html { scroll-behavior: smooth; }
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color: var(--text);
font-family: var(--sans);
line-height: 1.7;
font-weight: 400;
overflow-x: hidden;
}
/* ---- NAV ---- */
nav {
position: fixed; top: 0; left: 0; right: 0; z-index: 100;
background: rgba(10,10,15,0.85);
backdrop-filter: blur(20px);
border-bottom: 1px solid var(--border);
padding: 0 2rem;
transition: transform 0.3s;
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nav .inner {
max-width: 1200px; margin: 0 auto;
display: flex; align-items: center; justify-content: space-between;
height: 60px;
}
nav .logo {
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text-decoration: none; letter-spacing: -0.02em;
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nav ul { list-style: none; display: flex; gap: 1.8rem; }
nav ul li a {
color: var(--text-dim); text-decoration: none; font-size: 0.82rem;
font-weight: 500; letter-spacing: 0.04em; text-transform: uppercase;
transition: color 0.2s;
}
nav ul li a:hover { color: var(--text); }
/* ---- HERO ---- */
.hero {
min-height: 100vh;
display: flex; align-items: center; justify-content: center;
position: relative; overflow: hidden;
padding: 6rem 2rem 4rem;
}
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content: '';
position: absolute; top: -50%; left: -50%;
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background:
radial-gradient(ellipse 600px 600px at 30% 40%, rgba(108,92,231,0.12), transparent),
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radial-gradient(ellipse 400px 400px at 50% 80%, rgba(0,206,201,0.06), transparent);
animation: heroBg 20s ease infinite alternate;
}
@keyframes heroBg {
0% { transform: translate(0,0) rotate(0deg); }
100% { transform: translate(-3%,2%) rotate(3deg); }
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position: absolute; inset: 0;
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linear-gradient(rgba(255,255,255,0.015) 1px, transparent 1px),
linear-gradient(90deg, rgba(255,255,255,0.015) 1px, transparent 1px);
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mask-image: radial-gradient(ellipse 70% 60% at 50% 50%, black 40%, transparent 100%);
}
.hero-content {
position: relative; z-index: 2; text-align: center;
max-width: 900px;
}
.hero-badge {
display: inline-flex; align-items: center; gap: 0.6rem;
background: var(--surface2); border: 1px solid var(--border);
border-radius: 100px; padding: 0.4rem 1.2rem 0.4rem 0.6rem;
font-size: 0.78rem; font-weight: 500; color: var(--text-dim);
margin-bottom: 2rem;
animation: fadeUp 0.8s ease both;
}
.hero-badge .dot {
width: 8px; height: 8px; border-radius: 50%;
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}
@keyframes pulse {
0%,100% { opacity: 1; } 50% { opacity: 0.4; }
}
.hero h1 {
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animation: fadeUp 0.8s 0.15s ease both;
}
.hero h1 em {
font-style: italic;
background: var(--gradient1); -webkit-background-clip: text;
-webkit-text-fill-color: transparent;
}
.hero p.subtitle {
font-size: 1.1rem; color: var(--text-dim); max-width: 640px;
margin: 0 auto 2.5rem; line-height: 1.8; font-weight: 300;
animation: fadeUp 0.8s 0.3s ease both;
}
.hero-meta {
display: flex; gap: 2.5rem; justify-content: center; flex-wrap: wrap;
animation: fadeUp 0.8s 0.45s ease both;
}
.hero-meta .item {
display: flex; flex-direction: column; align-items: center; gap: 0.2rem;
}
.hero-meta .label { font-size: 0.7rem; text-transform: uppercase; letter-spacing: 0.1em; color: var(--text-dim); }
.hero-meta .value { font-size: 0.95rem; font-weight: 600; }
.hero-meta .value.highlight { color: var(--accent2); }
@keyframes fadeUp {
from { opacity: 0; transform: translateY(24px); }
to { opacity: 1; transform: translateY(0); }
}
/* ---- SECTIONS ---- */
section { padding: 5rem 2rem; }
.container { max-width: 1100px; margin: 0 auto; }
.section-label {
font-family: var(--mono); font-size: 0.72rem; font-weight: 500;
text-transform: uppercase; letter-spacing: 0.15em;
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}
.section-title {
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}
.section-desc {
color: var(--text-dim); font-size: 1rem; max-width: 640px;
line-height: 1.8; font-weight: 300; margin-bottom: 3rem;
}
/* ---- ABOUT ---- */
#about { border-top: 1px solid var(--border); }
.about-grid {
display: grid; grid-template-columns: 1fr 1fr 1fr; gap: 1.5rem;
}
.about-card {
background: var(--surface);
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border-radius: 16px; padding: 2rem;
transition: border-color 0.3s, transform 0.3s;
}
.about-card:hover { border-color: var(--accent); transform: translateY(-4px); }
.about-card .icon {
width: 44px; height: 44px; border-radius: 12px;
display: flex; align-items: center; justify-content: center;
font-size: 1.2rem; margin-bottom: 1.2rem;
}
.about-card:nth-child(1) .icon { background: rgba(108,92,231,0.15); color: var(--accent2); }
.about-card:nth-child(2) .icon { background: rgba(253,121,168,0.15); color: var(--accent3); }
.about-card:nth-child(3) .icon { background: rgba(0,206,201,0.15); color: var(--accent4); }
.about-card h3 {
font-family: var(--serif); font-size: 1.15rem; margin-bottom: 0.6rem;
}
.about-card p { font-size: 0.88rem; color: var(--text-dim); line-height: 1.7; }
/* ---- SCHEDULE ---- */
#schedule { background: var(--surface); border-top: 1px solid var(--border); border-bottom: 1px solid var(--border); }
.timeline { position: relative; }
.timeline::before {
content: ''; position: absolute; left: 28px; top: 0; bottom: 0;
width: 2px; background: var(--border);
}
.timeline-item {
display: flex; gap: 2rem; padding: 1.8rem 0;
position: relative;
}
.timeline-dot {
width: 16px; height: 16px; border-radius: 50%;
border: 2px solid var(--accent); background: var(--bg);
flex-shrink: 0; margin-top: 0.3rem;
position: relative; left: 21px; z-index: 2;
}
.timeline-item:nth-child(2) .timeline-dot { border-color: var(--accent3); }
.timeline-item:nth-child(3) .timeline-dot { border-color: var(--accent4); }
.timeline-item:nth-child(4) .timeline-dot { border-color: #fdcb6e; }
.timeline-item:nth-child(5) .timeline-dot { border-color: #55efc4; }
.timeline-item:nth-child(6) .timeline-dot { border-color: var(--accent2); }
.timeline-body { padding-left: 1rem; }
.timeline-time {
font-family: var(--mono); font-size: 0.75rem; color: var(--accent2);
letter-spacing: 0.05em; margin-bottom: 0.3rem;
}
.timeline-body h3 {
font-family: var(--serif); font-size: 1.2rem; margin-bottom: 0.5rem;
}
.timeline-body p { font-size: 0.88rem; color: var(--text-dim); line-height: 1.7; max-width: 640px; }
.timeline-speaker {
display: inline-block; margin-top: 0.6rem;
font-size: 0.75rem; font-weight: 500; color: var(--accent3);
background: rgba(253,121,168,0.1);
padding: 0.15rem 0.7rem; border-radius: 100px;
}
/* ---- SPEAKERS ---- */
#speakers { }
.speakers-grid {
display: grid; grid-template-columns: repeat(auto-fill, minmax(300px, 1fr)); gap: 1.5rem;
}
.speaker-card {
background: var(--surface); border: 1px solid var(--border);
border-radius: 16px; padding: 2rem;
display: flex; flex-direction: column; gap: 1rem;
transition: border-color 0.3s;
}
.speaker-card:hover { border-color: var(--accent); }
.speaker-header { display: flex; align-items: center; gap: 1rem; }
.speaker-avatar {
width: 56px; height: 56px; border-radius: 14px;
display: flex; align-items: center; justify-content: center;
font-family: var(--serif); font-size: 1.3rem; font-weight: 700;
flex-shrink: 0;
}
.speaker-card:nth-child(1) .speaker-avatar { background: rgba(108,92,231,0.2); color: var(--accent2); }
.speaker-card:nth-child(2) .speaker-avatar { background: rgba(253,121,168,0.2); color: var(--accent3); }
.speaker-card:nth-child(3) .speaker-avatar { background: rgba(0,206,201,0.2); color: var(--accent4); }
.speaker-card:nth-child(4) .speaker-avatar { background: rgba(253,203,110,0.2); color: #fdcb6e; }
.speaker-card:nth-child(5) .speaker-avatar { background: rgba(85,239,196,0.2); color: #55efc4; }
.speaker-info h3 { font-family: var(--serif); font-size: 1.1rem; }
.speaker-info .role { font-size: 0.8rem; color: var(--text-dim); margin-top: 0.15rem; }
.speaker-tag {
display: inline-block; font-size: 0.68rem; font-weight: 600;
text-transform: uppercase; letter-spacing: 0.08em;
padding: 0.15rem 0.6rem; border-radius: 100px;
background: rgba(108,92,231,0.15); color: var(--accent2);
margin-top: -0.3rem; align-self: flex-start;
}
.speaker-card:nth-child(4) .speaker-tag { background: rgba(253,203,110,0.15); color: #fdcb6e; }
.speaker-card:nth-child(5) .speaker-tag { background: rgba(85,239,196,0.15); color: #55efc4; }
.speaker-bio { font-size: 0.85rem; color: var(--text-dim); line-height: 1.75; }
.speaker-link {
font-size: 0.82rem; color: var(--accent2); text-decoration: none;
font-weight: 500; transition: color 0.2s;
}
.speaker-link:hover { color: var(--text); }
/* ---- TOPICS ---- */
#topics { background: var(--surface); border-top: 1px solid var(--border); border-bottom: 1px solid var(--border); }
.topics-list { display: flex; flex-direction: column; gap: 1rem; }
.topic-item {
display: flex; align-items: flex-start; gap: 1.5rem;
background: var(--bg); border: 1px solid var(--border);
border-radius: 14px; padding: 1.5rem 2rem;
transition: border-color 0.3s;
}
.topic-item:hover { border-color: var(--accent); }
.topic-num {
font-family: var(--serif); font-size: 1.8rem; color: var(--accent);
line-height: 1; flex-shrink: 0; width: 36px; opacity: 0.6;
}
.topic-content h3 { font-size: 1rem; font-weight: 600; margin-bottom: 0.35rem; }
.topic-content p { font-size: 0.85rem; color: var(--text-dim); line-height: 1.7; }
/* ---- PUBLICATIONS ---- */
#publications {}
.pub-list { display: flex; flex-direction: column; gap: 0.8rem; }
.pub-item {
background: var(--surface); border: 1px solid var(--border);
border-radius: 12px; padding: 1.2rem 1.6rem;
font-size: 0.87rem; line-height: 1.7; color: var(--text-dim);
transition: border-color 0.3s;
}
.pub-item:hover { border-color: var(--accent); }
.pub-item strong { color: var(--text); font-weight: 500; }
.pub-venue {
display: inline-block; font-family: var(--mono); font-size: 0.7rem;
font-weight: 500; color: var(--accent4);
background: rgba(0,206,201,0.1); padding: 0.1rem 0.5rem;
border-radius: 4px; margin-left: 0.3rem;
}
/* ---- RESOURCES ---- */
#resources { background: var(--surface); border-top: 1px solid var(--border); }
.resources-grid { display: grid; grid-template-columns: 1fr 1fr 1fr; gap: 1.5rem; }
.resource-card {
background: var(--bg); border: 1px solid var(--border);
border-radius: 16px; padding: 2rem; text-align: center;
transition: border-color 0.3s, transform 0.3s;
}
.resource-card:hover { border-color: var(--accent); transform: translateY(-4px); }
.resource-card .r-icon { font-size: 2rem; margin-bottom: 1rem; }
.resource-card h3 { font-family: var(--serif); font-size: 1.1rem; margin-bottom: 0.5rem; }
.resource-card p { font-size: 0.85rem; color: var(--text-dim); line-height: 1.6; }
.resource-card .badge {
display: inline-block; margin-top: 1rem;
font-size: 0.72rem; font-weight: 600; text-transform: uppercase;
letter-spacing: 0.08em; padding: 0.3rem 0.9rem;
border-radius: 100px; border: 1px solid var(--border);
color: var(--text-dim);
}
/* ---- FOOTER ---- */
footer {
border-top: 1px solid var(--border);
padding: 3rem 2rem;
text-align: center;
}
footer p { font-size: 0.82rem; color: var(--text-dim); }
footer a { color: var(--accent2); text-decoration: none; }
/* ---- MOBILE ---- */
@media (max-width: 768px) {
nav ul { display: none; }
.about-grid, .resources-grid { grid-template-columns: 1fr; }
.speakers-grid { grid-template-columns: 1fr; }
.hero-meta { gap: 1.5rem; }
.topic-item { flex-direction: column; gap: 0.8rem; }
.timeline::before { left: 18px; }
.timeline-dot { left: 11px; }
section { padding: 3.5rem 1.2rem; }
}
/* ---- SCROLL ANIMATIONS ---- */
.reveal {
opacity: 0; transform: translateY(30px);
transition: opacity 0.7s ease, transform 0.7s ease;
}
.reveal.visible { opacity: 1; transform: translateY(0); }
</style>
</head>
<body>
<!-- NAV -->
<nav>
<div class="inner">
<a href="#" class="logo">UMM <span>Tutorial</span></a>
<ul>
<li><a href="#about">About</a></li>
<li><a href="#schedule">Schedule</a></li>
<li><a href="#speakers">Speakers</a></li>
<li><a href="#topics">Topics</a></li>
<li><a href="#publications">Papers</a></li>
<li><a href="#resources">Resources</a></li>
</ul>
</div>
</nav>
<!-- HERO -->
<section class="hero">
<div class="grid-overlay"></div>
<div class="hero-content">
<div class="hero-badge">
<span class="dot"></span>
CVPR 2026 Tutorial
</div>
<h1>The Road to <em>Convergence</em>:<br>Evolution of Unified Multimodal Models</h1>
<p class="subtitle">A comprehensive tutorial on the architecture design, representation learning, training dynamics, and evaluation of unified multimodal models that integrate understanding and generation within a single framework.</p>
<div class="hero-meta">
<div class="item">
<span class="label">Conference</span>
<span class="value highlight">CVPR 2026</span>
</div>
<div class="item">
<span class="label">Duration</span>
<span class="value">Half-Day (~3.5 hrs)</span>
</div>
<div class="item">
<span class="label">Format</span>
<span class="value">Hybrid (In-person + Zoom)</span>
</div>
<div class="item">
<span class="label">Audience</span>
<span class="value">100–300 Attendees</span>
</div>
</div>
</div>
</section>
<!-- ABOUT -->
<section id="about">
<div class="container">
<div class="section-label reveal">Overview</div>
<div class="section-title reveal">Three Central Questions</div>
<div class="section-desc reveal">We structure this tutorial around three fundamental questions that define the design space of unified multimodal models.</div>
<div class="about-grid">
<div class="about-card reveal">
<div class="icon">🏗</div>
<h3>How to Model?</h3>
<p>A systematic taxonomy of UMM architectures — External Expert Integration, Modular Joint Modeling, and End-to-End Unified Modeling — with trade-off analysis between autoregressive, diffusion, and hybrid approaches.</p>
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<h3>How to Represent?</h3>
<p>The "Unified Tokenizer" debate: continuous representations (e.g., CLIP) vs. discrete tokens (e.g., VQ-VAE), and hybrid encoding strategies balancing semantic understanding with generative fidelity.</p>
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<h3>How to Train?</h3>
<p>The full training lifecycle — from constructing interleaved image-text data to unified pre-training objectives and advanced post-training alignment methods such as DPO and GRPO.</p>
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<div class="section-title reveal">Tutorial Outline</div>
<div class="section-desc reveal">A structured half-day journey from foundational motivations to advanced architectures and practical training recipes.</div>
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<div class="timeline-time">Session 1 · 30 min</div>
<h3>Introduction & Motivation</h3>
<p>Tracing the evolution of multimodal AI from isolated expertise to Unified Multimodal Models. We introduce the core motivations driving unification — particularly the mutual reinforcement between understanding and generation — and provide a rigorous definition of UMMs.</p>
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<div class="timeline-time">Session 2 · 45 min</div>
<h3>Modeling Architectures</h3>
<p>A systematic taxonomy including External Expert Integration, Modular Joint Modeling, and End-to-End Unified Modeling. Deep dive into trade-offs between autoregressive, diffusion, and emerging AR-Diffusion hybrid approaches.</p>
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<div class="timeline-time">Session 3 · 45 min</div>
<h3>The Unified Tokenizer Challenge</h3>
<p>Comparing continuous representations versus discrete tokenization schemes. Review of encoding/decoding strategies and state-of-the-art hybrid approaches — cascade and dual-branch designs — bridging semantic richness with generative fidelity.</p>
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<div class="timeline-time">Session 4 · 45 min</div>
<h3>Training Recipes & Data</h3>
<p>Constructing high-quality modality-interleaved datasets, unified pre-training objectives, and advanced post-training alignment methods including preference-based approaches such as DPO and GRPO.</p>
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<div class="timeline-time">Session 5 · 30 min</div>
<h3>Evaluation, Applications & Future Directions</h3>
<p>Reviewing existing benchmarks for standardized evaluation, discussing real-world applications in robotics and autonomous driving, and highlighting open challenges including scalable unified tokenizers and unified world models.</p>
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<div class="timeline-time">Session 6 · 15 min</div>
<h3>Unified Codebase & Integration</h3>
<p>A practical walkthrough of our unified multimodal codebase, explaining how core components — tokenizers, multimodal encoders, and generative backbones — are organized and connected in practice.</p>
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<div class="section-title reveal">Meet the Team</div>
<div class="section-desc reveal">Our tutorial is led by researchers from academia and industry with extensive experience in multimodal foundation models.</div>
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<h3>Jindong Wang</h3>
<div class="role">Assistant Professor, William & Mary</div>
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<span class="speaker-tag">Presenter</span>
<div class="speaker-bio">Faculty member of the Future of Life Institute. Former Senior Researcher at Microsoft Research Asia. 60+ papers with 23,000+ citations (h-index 54). World's Top 2% Highly Cited Scientists. Extensive tutorial experience at IJCAI, WSDM, KDD, AAAI, and CVPR.</div>
<a href="https://jd92.wang/" target="_blank" class="speaker-link">jd92.wang →</a>
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<h3>Hao Chen</h3>
<div class="role">Research Scientist, Google DeepMind</div>
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<span class="speaker-tag">Presenter</span>
<div class="speaker-bio">Ph.D. from Carnegie Mellon University (advised by Prof. Bhiksha Raj). Research on data-centric learning for reliable foundation models, including pre-training data imperfections, catastrophic inheritance, and multimodal generalization. Published at NeurIPS, ICML, and ICLR.</div>
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<h3>Jiakui Hu</h3>
<div class="role">Ph.D. Student, Peking University</div>
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<span class="speaker-tag">Presenter</span>
<div class="speaker-bio">Research on unified models, computational imaging, and inductive biases in visual foundation models. First-author papers at ICCV, CVPR, ICLR, ICML, and AAAI. Reviewer for major conferences and journals.</div>
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<h3>Zhaolong Su</h3>
<div class="role">Ph.D. Student, William & Mary</div>
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<span class="speaker-tag">Code Preparation</span>
<div class="speaker-bio">Conducts research on unified multimodal training and foundation models in the Department of Data Science.</div>
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<h3>Sharon Li</h3>
<div class="role">Associate Professor, UW–Madison</div>
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<span class="speaker-tag">Advisor</span>
<div class="speaker-bio">Research on reliable and safe AI systems. Alfred P. Sloan Fellowship and MIT Technology Review Innovators Under 35 recipient. Ph.D. from Cornell University, postdoc at Stanford University.</div>
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<div class="section-label reveal">Coverage</div>
<div class="section-title reveal">Key Topics</div>
<div class="section-desc reveal">From architectural paradigms to real-world deployment, the tutorial covers the full spectrum of unified multimodal model research.</div>
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<div class="topic-num">01</div>
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<h3>Evolution of Multimodal Models</h3>
<p>From isolated multimodal understanding or generation systems to unified multimodal foundation models capable of handling both tasks simultaneously.</p>
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<h3>Modeling Paradigms for UMMs</h3>
<p>A taxonomy of architectures including External Expert Integration, Modular Joint Modeling, and End-to-End Unified Modeling, with comparisons between autoregressive, diffusion, and hybrid approaches.</p>
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<h3>Unified Tokenizer & Representation Design</h3>
<p>Continuous versus discrete representations, their advantages and limitations, and emerging hybrid encoding strategies that balance semantic understanding and generative fidelity.</p>
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<div class="topic-num">04</div>
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<h3>Training Lifecycle & Alignment</h3>
<p>Construction of modality-interleaved datasets, unified pre-training objectives, and post-training alignment methods such as DPO and GRPO.</p>
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<h3>Benchmarks, Applications & Open Challenges</h3>
<p>Evaluation protocols, real-world applications in robotics and autonomous driving, and future directions such as scalable unified tokenizers and unified world models.</p>
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<div class="section-label reveal">Related Work</div>
<div class="section-title reveal">Selected Publications</div>
<div class="section-desc reveal">Representative publications by the organizers and foundational research in unified multimodal models.</div>
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<strong>Jiakui Hu, et al.</strong> Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities. <span class="pub-venue">Survey</span>
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<strong>Jindong Wang, Hao Chen, et al.</strong> On Fairness of Unified Multimodal Large Language Models for Image Generation. <span class="pub-venue">NeurIPS 2025</span>
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<strong>Jindong Wang, Hao Chen, et al.</strong> Is Your (Reasoning) Multimodal Language Model Vulnerable toward Distractions? <span class="pub-venue">AAAI 2026</span>
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<strong>Hao Chen, et al.</strong> ImageFolder: Autoregressive Image Generation with Folded Tokens. <span class="pub-venue">ICLR 2025</span>
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<strong>Hao Chen, et al.</strong> Masked Autoencoders Are Effective Tokenizers for Diffusion Models. <span class="pub-venue">ICML 2025</span>
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<strong>Sharon Li, et al.</strong> Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach. <span class="pub-venue">ICML 2025 Oral</span>
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<strong>Jindong Wang, et al.</strong> Open-Vocabulary Calibration for Vision–Language Models. <span class="pub-venue">ICML 2024</span>
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<strong>Zhaolong Su, Hao Chen, Jindong Wang, et al.</strong> UniGame: Turning a Unified Multimodal Model Into Its Own Adversary. <span class="pub-venue">Preprint</span>
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<!-- RESOURCES -->
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<div class="section-label reveal">Open Science</div>
<div class="section-title reveal">Materials & Resources</div>
<div class="section-desc reveal">We are committed to open science and ensuring reproducibility. All materials will be publicly available.</div>
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<h3>Slides</h3>
<p>All presentation slides will be made publicly available on this website following the event.</p>
<span class="badge">Coming Soon</span>
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<div class="r-icon">📚</div>
<h3>Bibliography</h3>
<p>An annotated compilation of all references discussed in the tutorial as a comprehensive reading list.</p>
<span class="badge">Coming Soon</span>
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<div class="r-icon">💻</div>
<h3>Codebase</h3>
<p>Open-source unified multimodal codebase with annotated pointers to models (e.g., Emu, Janus) and datasets.</p>
<span class="badge">Coming Soon</span>
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</section>
<!-- FOOTER -->
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<p>CVPR 2026 Tutorial — The Road to Convergence · <a href="https://umm-tutorial.github.io" target="_blank">umm-tutorial.github.io</a></p>
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