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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>PaperMap — Interactive Research Paper Library</title>
<meta name="description" content="PaperMap turns landmark AI research papers into interactive learning products. Explore visual explainers for Transformers, GPT-3, InstructGPT & RLHF.">
<meta name="keywords" content="PaperMap, interactive papers, Transformer, GPT-3, InstructGPT, RLHF, deep learning, machine learning, AI education">
<meta name="author" content="Adil Shamim">
<meta name="robots" content="index, follow">
<meta name="theme-color" content="#f59e0b">
<meta name="referrer" content="strict-origin-when-cross-origin">
<link rel="canonical" href="https://papermap.vercel.app/">
<link rel="icon" type="image/svg+xml" href="./assets/favicon.svg">
<link rel="shortcut icon" href="./assets/favicon.svg">
<meta property="og:title" content="PaperMap — Interactive Research Paper Library">
<meta property="og:description" content="Visual, interactive explainers for landmark AI research papers.">
<meta property="og:type" content="website">
<meta property="og:url" content="https://papermap.vercel.app/">
<meta property="og:site_name" content="PaperMap">
<meta property="og:image" content="https://papermap.vercel.app/assets/favicon.svg">
<meta name="twitter:card" content="summary_large_image">
<meta name="twitter:title" content="PaperMap — Interactive Paper Library">
<meta name="twitter:description" content="Explore AI research papers as interactive learning products.">
<meta name="twitter:image" content="https://papermap.vercel.app/assets/favicon.svg">
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;500;600&family=Instrument+Serif:ital@0;1&family=DM+Sans:wght@300;400;500;600&display=swap" rel="stylesheet">
<style>
:root {
--bg: #ffffff;
--bg2: #f8fafc;
--bg3: #f1f5f9;
--bg4: #e2e8f0;
--border: rgba(15,23,42,0.10);
--border2: rgba(15,23,42,0.18);
--text: #0f172a;
--text2: #475569;
--text3: #64748b;
--amber: #f59e0b;
--amber-dim: rgba(245,158,11,0.12);
--blue: #3b82f6;
--blue-dim: rgba(59,130,246,0.12);
--teal: #14b8a6;
--teal-dim: rgba(20,184,166,0.12);
--red: #ef4444;
--green: #22c55e;
--green-dim: rgba(34,197,94,0.12);
--purple: #a855f7;
--purple-dim: rgba(168,85,247,0.12);
--mono: 'IBM Plex Mono', monospace;
--serif: 'Instrument Serif', serif;
--sans: 'DM Sans', sans-serif;
--radius: 10px;
--radius-lg: 16px;
}
*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
html { scroll-behavior: smooth; }
body {
background:
radial-gradient(circle at 6% 0%, rgba(245,158,11,0.10), transparent 34%),
radial-gradient(circle at 93% 18%, rgba(20,184,166,0.08), transparent 36%),
radial-gradient(circle at 50% 90%, rgba(59,130,246,0.06), transparent 40%),
linear-gradient(180deg, #fffefb 0%, #ffffff 42%, #fcfdff 100%),
var(--bg);
color: var(--text);
font-family: var(--sans);
font-size: 15px;
line-height: 1.7;
overflow-x: hidden;
}
::selection { background: var(--amber); color: #000; }
::-webkit-scrollbar { width: 6px; }
::-webkit-scrollbar-track { background: #f8fafc; }
::-webkit-scrollbar-thumb { background: #cbd5e1; border-radius: 3px; }
.skip-link {
position: absolute; left: 12px; top: -44px;
background: #0f172a; color: #fff;
font-family: var(--mono); font-size: 12px;
text-decoration: none; padding: 8px 12px;
border-radius: 8px; z-index: 300;
transition: top .2s;
}
.skip-link:focus { top: 10px; }
/* NAV */
nav {
position: fixed; top: 0; left: 0; right: 0; z-index: 100;
background: rgba(255,255,255,0.86);
backdrop-filter: blur(12px);
border-bottom: 1px solid var(--border);
box-shadow: 0 6px 28px rgba(15,23,42,0.06);
padding: 0 2rem;
display: flex; align-items: center; justify-content: space-between;
height: 54px;
}
.nav-logo {
font-family: var(--mono); font-size: 13px;
color: var(--amber); letter-spacing: -0.02em;
text-decoration: none; display: inline-flex;
align-items: center;
}
.nav-links { display: flex; gap: 4px; }
.nav-actions { display: flex; gap: 10px; align-items: center; }
.nav-link {
font-size: 12px; color: var(--text2);
padding: 5px 10px; border-radius: 6px;
text-decoration: none; cursor: pointer;
transition: all .2s; font-family: var(--mono);
}
.nav-link:hover { background: var(--bg3); color: var(--text); }
.nav-link.active { background: var(--amber-dim); color: var(--amber); }
.nav-badge {
background: var(--amber-dim); color: var(--amber);
font-family: var(--mono); font-size: 11px;
padding: 3px 8px; border-radius: 20px;
border: 1px solid rgba(245,158,11,0.2);
}
.nav-toggle {
display: none; width: 34px; height: 34px;
border-radius: 8px; border: 1px solid var(--border2);
background: #fff; color: var(--text);
font-family: var(--mono); font-size: 14px; cursor: pointer;
}
.mobile-menu {
position: fixed; top: 54px; left: 0; right: 0; z-index: 99;
display: none; padding: 10px;
background: rgba(255,255,255,0.95);
border-bottom: 1px solid var(--border);
backdrop-filter: blur(10px);
}
.mobile-menu.open { display: grid; grid-template-columns: 1fr 1fr; gap: 8px; }
.mobile-menu a {
text-decoration: none; font-family: var(--mono); font-size: 12px;
color: var(--text2); border: 1px solid var(--border);
border-radius: 8px; background: #fff; padding: 8px 10px;
}
.mobile-menu a:hover { border-color: rgba(245,158,11,0.35); color: var(--amber); }
/* HERO */
.hero {
min-height: 100vh;
display: flex; flex-direction: column;
align-items: center; justify-content: center;
text-align: center;
padding: 6rem 2rem 4rem;
position: relative; overflow: hidden;
}
.hero > * { position: relative; z-index: 1; }
.hero-grid {
position: absolute; inset: 0;
background-image:
linear-gradient(rgba(15,23,42,0.04) 1px, transparent 1px),
linear-gradient(90deg, rgba(15,23,42,0.04) 1px, transparent 1px);
background-size: 60px 60px;
mask-image: radial-gradient(ellipse 80% 60% at 50% 50%, black, transparent);
pointer-events: none; z-index: 0;
}
.hero-glow {
position: absolute; top: 40%; left: 50%;
transform: translate(-50%,-50%);
width: 700px; height: 500px;
background: radial-gradient(ellipse, rgba(245,158,11,0.12) 0%, transparent 70%);
pointer-events: none; z-index: 0;
animation: driftGlow 10s ease-in-out infinite;
}
.hero-eyebrow {
font-family: var(--mono); font-size: 11px;
color: var(--amber); letter-spacing: 0.12em;
text-transform: uppercase;
padding: 5px 14px; border: 1px solid rgba(245,158,11,0.25);
border-radius: 20px; margin-bottom: 2rem;
background: var(--amber-dim);
}
.hero-title {
font-family: var(--serif);
font-size: clamp(2.4rem, 6vw, 4.5rem);
line-height: 1.1; letter-spacing: -0.02em;
margin-bottom: 1.5rem;
}
.hero-title em { color: var(--amber); font-style: italic; }
.hero-subtitle {
max-width: 620px; color: var(--text2);
font-size: 16px; margin-bottom: 2.5rem; line-height: 1.8;
}
.hero-actions { display: flex; gap: 12px; flex-wrap: wrap; justify-content: center; }
.btn-primary {
background: var(--amber); color: #000;
font-weight: 600; font-size: 14px;
padding: 10px 24px; border-radius: 8px;
border: none; cursor: pointer; text-decoration: none;
transition: all .2s; display: inline-block;
}
.btn-primary:hover { background: #fbbf24; transform: translateY(-1px); box-shadow: 0 8px 24px rgba(245,158,11,0.35); }
.btn-outline {
background: transparent; color: var(--text);
font-size: 14px; padding: 10px 24px; border-radius: 8px;
border: 1px solid var(--border2); cursor: pointer;
text-decoration: none; display: inline-block;
transition: all .2s;
}
.btn-outline:hover { background: #ffffff; border-color: rgba(245,158,11,0.35); }
.hero-stats {
display: flex; gap: 3rem; margin-top: 4rem;
padding-top: 3rem; border-top: 1px solid var(--border);
}
.stat { text-align: center; }
.stat-num { font-family: var(--mono); font-size: 1.6rem; font-weight: 600; color: var(--amber); }
.stat-label { font-size: 12px; color: var(--text3); margin-top: 2px; }
/* SECTIONS */
.page-section {
max-width: 1100px; margin: 0 auto;
padding: 5rem 2rem;
}
.section-tag {
font-family: var(--mono); font-size: 11px;
color: var(--amber); letter-spacing: 0.1em;
text-transform: uppercase; margin-bottom: 0.75rem;
display: flex; align-items: center; gap: 8px;
}
.section-tag::before {
content: ''; display: block;
width: 20px; height: 1px;
background: var(--amber);
}
.section-title {
font-family: var(--serif);
font-size: clamp(1.8rem, 3vw, 2.4rem);
line-height: 1.2; margin-bottom: 1rem;
}
.section-title em { color: var(--amber); font-style: italic; }
.section-desc { color: var(--text2); max-width: 580px; font-size: 15px; margin-bottom: 2.5rem; }
/* CARDS */
.card {
background: var(--bg2);
border: 1px solid var(--border);
border-radius: var(--radius-lg);
padding: 1.5rem;
box-shadow: 0 10px 25px rgba(15,23,42,0.04);
transition: transform .25s ease, box-shadow .25s ease, border-color .25s ease;
}
.card:hover {
transform: translateY(-2px);
box-shadow: 0 16px 32px rgba(15,23,42,0.08);
border-color: rgba(245,158,11,0.22);
}
.card-label {
font-family: var(--mono); font-size: 10px;
text-transform: uppercase; letter-spacing: 0.08em;
color: var(--text3); margin-bottom: 6px;
}
.card-sm {
background: var(--bg3);
border: 1px solid var(--border);
border-radius: var(--radius);
padding: 1rem 1.2rem;
}
/* PAPER CARDS */
.paper-grid {
display: grid;
grid-template-columns: repeat(3, 1fr);
gap: 16px;
}
.paper-card {
background: var(--bg2);
border: 1px solid var(--border);
border-radius: var(--radius-lg);
padding: 1.5rem;
box-shadow: 0 10px 25px rgba(15,23,42,0.04);
transition: transform .3s ease, box-shadow .3s ease, border-color .3s ease;
display: flex;
flex-direction: column;
position: relative;
overflow: hidden;
}
.paper-card::before {
content: '';
position: absolute;
top: 0; left: 0; right: 0;
height: 3px;
border-radius: var(--radius-lg) var(--radius-lg) 0 0;
}
.paper-card.amber::before { background: var(--amber); }
.paper-card.blue::before { background: var(--blue); }
.paper-card.teal::before { background: var(--teal); }
.paper-card.purple::before { background: var(--purple); }
.paper-card.green::before { background: var(--green); }
.paper-card:hover {
transform: translateY(-4px);
box-shadow: 0 20px 40px rgba(15,23,42,0.10);
border-color: rgba(245,158,11,0.25);
}
.paper-tag {
display: inline-flex; align-items: center; gap: 5px;
border-radius: 20px;
font-family: var(--mono); font-size: 10px;
letter-spacing: 0.05em; text-transform: uppercase;
padding: 4px 10px; margin-bottom: 12px;
width: fit-content;
}
.paper-tag.live {
border: 1px solid rgba(34,197,94,0.3);
color: #0b7a58;
background: rgba(34,197,94,0.10);
}
.paper-tag.live::before {
content: '';
width: 6px; height: 6px;
border-radius: 50%;
background: var(--green);
animation: pulse 2s infinite;
}
.paper-year {
font-family: var(--mono);
font-size: 11px;
color: var(--text3);
margin-bottom: 6px;
}
.paper-card h3 {
font-family: var(--serif);
font-size: 1.15rem;
line-height: 1.3;
margin-bottom: 8px;
color: var(--text);
}
.paper-card p {
color: var(--text2);
font-size: 13px;
line-height: 1.65;
flex: 1;
}
.paper-meta {
display: flex;
flex-wrap: wrap;
gap: 6px;
margin-top: 12px;
padding-top: 12px;
border-top: 1px solid var(--border);
}
.paper-meta-tag {
font-family: var(--mono);
font-size: 10px;
padding: 3px 7px;
border-radius: 4px;
background: var(--bg3);
color: var(--text3);
border: 1px solid var(--border);
}
.paper-link {
margin-top: 14px;
display: inline-flex; align-items: center; gap: 6px;
text-decoration: none;
color: var(--amber); font-weight: 600;
font-size: 13px; font-family: var(--mono);
transition: all .2s;
padding: 8px 16px;
border-radius: 8px;
border: 1px solid rgba(245,158,11,0.25);
background: var(--amber-dim);
}
.paper-link:hover {
background: var(--amber);
color: #000;
border-color: var(--amber);
transform: translateY(-1px);
}
/* FEATURES */
.features-grid {
display: grid;
grid-template-columns: repeat(3, 1fr);
gap: 16px;
margin-top: 2rem;
}
.feature-card {
background: var(--bg2);
border: 1px solid var(--border);
border-radius: var(--radius-lg);
padding: 1.5rem;
transition: transform .25s ease, box-shadow .25s ease;
}
.feature-card:hover {
transform: translateY(-2px);
box-shadow: 0 12px 28px rgba(15,23,42,0.08);
}
.feature-icon {
font-size: 1.5rem;
margin-bottom: 10px;
}
.feature-card h3 {
font-size: 14px;
font-weight: 600;
margin-bottom: 6px;
}
.feature-card p {
color: var(--text2);
font-size: 13px;
line-height: 1.65;
}
/* HIGHLIGHT STRIP */
.highlight-strip {
color: var(--amber);
font-weight: 500;
}
/* DIVIDER */
.divider { height: 1px; background: var(--border); max-width: 1100px; margin: 0 auto; }
/* PILL */
.pill {
display: inline-block; padding: 2px 8px; border-radius: 20px;
font-size: 11px; font-family: var(--mono);
}
.pill-amber { background: var(--amber-dim); color: var(--amber); border: 1px solid rgba(245,158,11,0.2); }
.pill-blue { background: var(--blue-dim); color: var(--blue); border: 1px solid rgba(59,130,246,0.2); }
.pill-teal { background: var(--teal-dim); color: var(--teal); border: 1px solid rgba(20,184,166,0.2); }
/* FOOTER */
footer {
max-width: 1100px; margin: 0 auto;
padding: 3rem 2rem;
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From research papers<br>to <em>interactive products</em>
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PaperMap turns landmark AI research papers into visual, interactive learning experiences.
Explore the Transformer, GPT-3, InstructGPT, RAG, and LoRA — rebuilt as production-grade explainers.
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<h2 class="section-title">Published <em>Explainers</em></h2>
<p class="section-desc">Each paper is a self-contained interactive guide with visual demos, architecture breakdowns, quizzes, and research-accurate detail from the original publications.</p>
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<!-- Paper 1: Attention Is All You Need -->
<article class="paper-card amber">
<span class="paper-tag live">Live</span>
<div class="paper-year">Vaswani et al. · 2017</div>
<h3>Attention Is All You Need</h3>
<p>The paper that started it all. Interactive walkthrough of the Transformer architecture with tokenization, embeddings, self-attention, multi-head attention, positional encoding, and full encoder-decoder visualization.</p>
<div class="paper-meta">
<span class="paper-meta-tag">Transformer</span>
<span class="paper-meta-tag">Self-Attention</span>
<span class="paper-meta-tag">NLP</span>
<span class="paper-meta-tag">Architecture</span>
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<a class="paper-link" href="./paper/Attention_Is_All_You_Need.html">Open Explainer →</a>
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<span class="paper-tag live">Live</span>
<div class="paper-year">Brown et al. · 2020</div>
<h3>Language Models are Few-Shot Learners</h3>
<p>How 175 billion parameters unlocked in-context learning. Interactive few-shot demos, scaling law visualizations, benchmark tables, data contamination analysis, and the full GPT-3 model family.</p>
<div class="paper-meta">
<span class="paper-meta-tag">GPT-3</span>
<span class="paper-meta-tag">Few-Shot</span>
<span class="paper-meta-tag">Scaling Laws</span>
<span class="paper-meta-tag">175B</span>
</div>
<a class="paper-link" href="./paper/Language_Models_are_Few_Shot_Learners.html">Open Explainer →</a>
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<!-- Paper 3: InstructGPT -->
<article class="paper-card teal">
<span class="paper-tag live">Live</span>
<div class="paper-year">Ouyang et al. · 2022</div>
<h3>Training Language Models to Follow Instructions with Human Feedback</h3>
<p>The RLHF paper that paved the way for ChatGPT. Interactive 3-step pipeline (SFT → Reward Model → PPO), human evaluation results, bias analysis, and qualitative examples.</p>
<div class="paper-meta">
<span class="paper-meta-tag">InstructGPT</span>
<span class="paper-meta-tag">RLHF</span>
<span class="paper-meta-tag">PPO</span>
<span class="paper-meta-tag">Alignment</span>
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<a class="paper-link" href="./paper/Training_Language_Models_to_Follow_Instructions_with_Human_Feedback.html">Open Explainer →</a>
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<!-- Paper 4: RAG -->
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<span class="paper-tag live">Live</span>
<div class="paper-year">Lewis et al. · 2020</div>
<h3>Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks</h3>
<p>An interactive deep dive into the original RAG paper: dense retrieval with DPR, BART generation, non-parametric memory with Wikipedia, and benchmark gains on knowledge-intensive QA.</p>
<div class="paper-meta">
<span class="paper-meta-tag">RAG</span>
<span class="paper-meta-tag">Dense Retrieval</span>
<span class="paper-meta-tag">BART</span>
<span class="paper-meta-tag">Knowledge QA</span>
</div>
<a class="paper-link" href="./paper/Retrieval-Augmented%20Generation%20for%20Knowledge-Intensive%20NLP%20Tasks.html">Open Explainer →</a>
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<!-- Paper 5: LoRA -->
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<span class="paper-tag live">Live</span>
<div class="paper-year">Hu et al. · 2021</div>
<h3>LoRA: Low-Rank Adaptation of Large Language Models</h3>
<p>An interactive walkthrough of LoRA covering low-rank updates, parameter-efficient fine-tuning, practical deployment tradeoffs, and why LoRA matches full fine-tuning at a fraction of the cost.</p>
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<span class="paper-meta-tag">LoRA</span>
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<span class="paper-meta-tag">Low-Rank</span>
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<a class="paper-link" href="./paper/LoRA%20Low-Rank%20Adaptation%20of%20Large%20Language%20Models.html">Open Explainer →</a>
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<!-- ABOUT -->
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<div class="section-tag">About</div>
<h2 class="section-title">What is <em>PaperMap</em>?</h2>
<p class="section-desc">A scalable, static-first platform that transforms dense research papers into interactive learning products — no frameworks, no build tools, no dependencies.</p>
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<div class="feature-card">
<div class="feature-icon">🎯</div>
<h3>Research-Accurate</h3>
<p>Every number, formula, and architectural detail is sourced directly from the original paper. Section references included throughout.</p>
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<div class="feature-icon">⚡</div>
<h3>Interactive Demos</h3>
<p>Tokenizers, attention maps, few-shot playgrounds, scaling charts, and quiz blocks — learn by building intuition, not just reading.</p>
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<h3>Consistent Design System</h3>
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<p class="section-desc">PaperMap grows one paper at a time. Each explainer is handcrafted with the same production-quality standards.</p>
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<div class="roadmap-item done">
<h4>✓ Attention Is All You Need</h4>
<p>Transformer architecture, tokenization, embeddings, self-attention, multi-head attention, positional encoding, training.</p>
</div>
<div class="roadmap-item done">
<h4>✓ GPT-3: Few-Shot Learners</h4>
<p>In-context learning, scaling laws, 175B architecture, benchmark results, data contamination, societal impact.</p>
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<div class="roadmap-item done">
<h4>✓ InstructGPT & RLHF</h4>
<p>3-step RLHF pipeline, reward modeling, PPO, human evaluations, alignment tax, bias analysis.</p>
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<div class="roadmap-item done">
<h4>✓ RAG: Retrieval-Augmented Generation</h4>
<p>Dense retrieval with DPR, BART generation, non-parametric memory, and SOTA gains on knowledge-intensive QA.</p>
</div>
<div class="roadmap-item done">
<h4>✓ LoRA: Low-Rank Adaptation</h4>
<p>Low-rank updates for efficient LLM fine-tuning with massive parameter savings and zero extra inference latency.</p>
</div>
<div class="roadmap-item next">
<h4>→ Next: More Papers</h4>
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