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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width,initial-scale=1" />
<title>NULLs — Natively Unlearnable Large Language Models</title>
<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=EB+Garamond:ital,wght@0,400;0,500;0,600;0,700;1,400;1,500&family=IBM+Plex+Sans:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet" />
<style>
:root {
--bg: #f6f3ec;
--paper: #fbf9f3;
--ink: #1a1612;
--ink-soft: #4a423a;
--muted: #7d7468;
--rule: #d9d1bf;
--rule-soft: #e8e1cf;
--accent: oklch(0.42 0.13 24); /* deep burgundy */
--accent-soft: oklch(0.94 0.04 24);
--accent-2: oklch(0.55 0.10 200); /* teal slate for sink-on */
--accent-3: oklch(0.55 0.13 350); /* pink for sink-off */
--accent-4: oklch(0.60 0.11 60); /* amber for retrain */
--serif: "EB Garamond", "Iowan Old Style", Georgia, serif;
--sans: "IBM Plex Sans", -apple-system, BlinkMacSystemFont, sans-serif;
--mono: "JetBrains Mono", ui-monospace, "SF Mono", monospace;
}
* { box-sizing: border-box; }
html, body { margin: 0; padding: 0; }
body {
background: var(--bg);
color: var(--ink);
font-family: var(--sans);
font-weight: 400;
-webkit-font-smoothing: antialiased;
line-height: 1.55;
font-size: 17px;
}
/* ===== Top thin rule ===== */
.topbar {
height: 6px;
background: var(--accent);
}
.topbar::after {
content: "";
display: block;
height: 1px;
background: var(--ink);
margin-top: 1px;
}
/* ===== Page shell ===== */
.shell { max-width: 1120px; margin: 0 auto; padding: 0 40px; }
@media (max-width: 720px) { .shell { padding: 0 22px; } }
/* ===== Hero ===== */
.hero { padding: 60px 0 32px; }
.eyebrow {
font-family: var(--mono);
font-size: 11.5px;
letter-spacing: 0.14em;
text-transform: uppercase;
color: var(--accent);
display: flex;
align-items: center;
gap: 14px;
}
.eyebrow .dot {
width: 6px; height: 6px; border-radius: 50%;
background: var(--accent);
display: inline-block;
}
h1.title {
font-family: var(--serif);
font-weight: 500;
font-size: clamp(40px, 6.2vw, 76px);
line-height: 1.02;
letter-spacing: -0.01em;
margin: 22px 0 18px;
color: var(--ink);
}
h1.title em {
font-style: italic;
color: var(--accent);
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.tagline {
font-family: var(--serif);
font-style: italic;
font-size: clamp(20px, 2.2vw, 26px);
color: var(--ink-soft);
max-width: 760px;
margin: 0 0 36px;
line-height: 1.35;
}
/* ===== Authors ===== */
.authors {
display: flex;
flex-wrap: wrap;
gap: 4px 22px;
margin-bottom: 12px;
font-size: 18px;
}
.authors .author { color: var(--ink); }
.authors sup {
color: var(--accent);
font-size: 0.65em;
font-weight: 600;
margin-left: 2px;
top: -0.6em;
position: relative;
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.affils {
display: flex;
flex-wrap: wrap;
gap: 4px 22px;
color: var(--muted);
font-size: 14px;
font-family: var(--mono);
margin-bottom: 30px;
}
.affils sup { color: var(--accent); font-weight: 600; margin-right: 3px; }
/* ===== Link buttons ===== */
.links {
display: flex;
flex-wrap: wrap;
gap: 10px;
margin: 6px 0 18px;
}
.link-btn {
display: inline-flex;
align-items: center;
gap: 9px;
padding: 10px 16px;
border: 1px solid var(--ink);
border-radius: 999px;
color: var(--ink);
text-decoration: none;
font-size: 14px;
font-weight: 500;
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transition: transform 0.15s ease, background 0.15s ease, color 0.15s ease;
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color: var(--paper);
transform: translateY(-1px);
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background: var(--ink);
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}
.link-btn.primary:hover {
background: var(--accent);
border-color: var(--accent);
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.link-btn svg { width: 15px; height: 15px; }
/* ===== Section structure ===== */
section.block { padding: 56px 0; border-top: 1px solid var(--rule); }
section.block:first-of-type { border-top: 1px solid var(--ink); }
.section-head {
display: grid;
grid-template-columns: 180px 1fr;
gap: 40px;
margin-bottom: 36px;
align-items: baseline;
}
@media (max-width: 720px) { .section-head { grid-template-columns: 1fr; gap: 8px; } }
.section-no {
font-family: var(--mono);
font-size: 12px;
letter-spacing: 0.14em;
color: var(--accent);
text-transform: uppercase;
padding-top: 8px;
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h2.section-title {
font-family: var(--serif);
font-weight: 500;
font-size: clamp(28px, 3.4vw, 42px);
margin: 0;
letter-spacing: -0.01em;
line-height: 1.1;
}
/* ===== Abstract / prose ===== */
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display: grid;
grid-template-columns: 180px 1fr;
gap: 40px;
}
@media (max-width: 720px) { .prose-grid { grid-template-columns: 1fr; gap: 16px; } }
.prose-grid .side {
font-family: var(--mono);
font-size: 11.5px;
letter-spacing: 0.12em;
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text-transform: uppercase;
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.prose p {
font-family: var(--serif);
font-size: 20px;
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text-wrap: pretty;
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font-family: var(--serif);
font-size: 4.2em;
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float: left;
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color: var(--accent);
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.prose strong {
font-weight: 600;
color: var(--ink);
}
/* ===== TL;DR card ===== */
.tldr {
background: var(--paper);
border: 1px solid var(--ink);
border-radius: 4px;
padding: 28px 30px;
margin-top: 28px;
position: relative;
}
.tldr-label {
font-family: var(--mono);
font-size: 11px;
letter-spacing: 0.18em;
color: var(--accent);
text-transform: uppercase;
margin-bottom: 12px;
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align-items: center;
gap: 10px;
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content: "";
width: 22px; height: 1px;
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display: inline-block;
}
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font-family: var(--serif);
font-size: 22px;
line-height: 1.45;
margin: 0;
color: var(--ink);
}
/* ===== Key stats ===== */
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display: grid;
grid-template-columns: repeat(4, 1fr);
gap: 0;
margin-top: 36px;
border-top: 1px solid var(--ink);
border-bottom: 1px solid var(--ink);
}
@media (max-width: 720px) { .stats { grid-template-columns: repeat(2, 1fr); } }
.stat {
padding: 22px 22px 22px 0;
border-right: 1px solid var(--rule);
padding-left: 22px;
}
.stat:first-child { padding-left: 0; }
.stat:last-child { border-right: none; }
@media (max-width: 720px) {
.stat:nth-child(2) { border-right: none; }
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font-size: 44px;
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letter-spacing: -0.02em;
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font-size: 11px;
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text-transform: uppercase;
color: var(--muted);
margin-top: 8px;
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}
/* ===== Schematic ===== */
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margin-top: 8px;
}
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margin-bottom: 10px;
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font-family: var(--serif);
font-style: italic;
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position: relative;
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top: 50%;
width: 28%;
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display: grid;
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gap: 16px;
align-items: stretch;
}
@media (max-width: 880px) {
.schem-headers { display: none; }
.schem-grid { grid-template-columns: 1fr; }
}
.schem-card {
background: var(--paper);
border: 1px solid var(--rule);
border-radius: 10px;
padding: 18px 14px 12px;
display: flex;
flex-direction: column;
}
.schem-card.tinted {
background: #ECE7F0;
border-color: #d6cee0;
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.schem-title {
font-family: var(--serif);
font-weight: 600;
font-size: 18px;
text-align: center;
color: var(--ink);
margin-bottom: 6px;
letter-spacing: -0.005em;
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.schem-card svg {
width: 100%;
height: auto;
display: block;
margin-top: auto;
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.schem-caption {
margin-top: 18px;
padding-top: 14px;
border-top: 1px dashed var(--rule);
font-size: 14px;
color: var(--ink-soft);
line-height: 1.55;
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.schem-caption .legend-row {
display: flex;
flex-wrap: wrap;
gap: 18px;
margin-top: 10px;
font-size: 13px;
font-family: var(--mono);
color: var(--muted);
letter-spacing: 0.04em;
}
.schem-caption .legend-row .lg {
display: inline-flex;
align-items: center;
gap: 7px;
}
.schem-caption .legend-row .swatch-pie {
width: 14px; height: 14px;
border-radius: 50%;
background: conic-gradient(#E89B85 0 12.5%, #E5B96B 0 25%, #9CC598 0 37.5%, #88A8CC 0 50%, #A99BC4 0 62.5%, #DDA0BC 0 75%, #82B6B0 0 87.5%, #B5A878 0);
border: 1px solid rgba(0,0,0,0.15);
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width: 12px; height: 12px; border-radius: 2px;
background: #88A8CC;
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width: 12px; height: 12px; border-radius: 2px;
border: 1.5px dashed #c44;
color: #c44;
font-size: 10px;
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text-align: center;
}
/* ===== Three-step ===== */
.steps {
display: grid;
grid-template-columns: repeat(3, 1fr);
gap: 22px;
margin-top: 36px;
}
@media (max-width: 720px) { .steps { grid-template-columns: 1fr; } }
.step {
background: var(--paper);
border: 1px solid var(--rule);
padding: 24px 22px;
border-radius: 4px;
position: relative;
}
.step-no {
font-family: var(--serif);
font-style: italic;
font-size: 56px;
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line-height: 0.8;
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}
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font-family: var(--serif);
font-size: 22px;
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font-weight: 500;
}
.step p {
font-size: 14.5px;
color: var(--ink-soft);
margin: 0;
line-height: 1.55;
}
/* ===== Figure / chart blocks ===== */
.figure {
background: var(--paper);
border: 1px solid var(--rule);
border-radius: 4px;
padding: 26px 26px 22px;
margin-top: 8px;
}
.fig-row {
display: grid;
grid-template-columns: 1fr 1fr 1fr;
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@media (max-width: 900px) { .fig-row { grid-template-columns: 1fr; } }
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line-height: 1.55;
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font-size: 11px;
letter-spacing: 0.12em;
text-transform: uppercase;
color: var(--accent);
margin-right: 10px;
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svg.chart { width: 100%; height: auto; display: block; }
/* ===== Interactive demo ===== */
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background: var(--ink);
color: var(--paper);
border-radius: 4px;
padding: 36px 36px 30px;
margin-top: 8px;
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display: flex;
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<body>
<div class="topbar"></div>
<div class="shell">
<!-- ===== HERO ===== -->
<header class="hero">
<div class="eyebrow"><span class="dot"></span> Preprint · Under Review · Nov 2025</div>
<h1 class="title">Natively <em>Unlearnable</em><br>Large Language Models</h1>
<p class="tagline">An architecture in which source-level data removal reduces to a deployment-time masking operation — no gradient updates, no access to the training corpus.</p>
<div class="authors">
<span class="author">Gaurav Ghosal<sup>1</sup></span>
<span class="author">Pratyush Maini<sup>2</sup></span>
<span class="author">Aditi Raghunathan<sup>1</sup></span>
</div>
<div class="affils">
<span><sup>1</sup>Carnegie Mellon University</span>
<span><sup>2</sup>DatologyAI</span>
</div>
<div class="links">
<a class="link-btn primary" href="https://ar-forum.github.io/assets/nulls_preprint.pdf">
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M14 3H6a2 2 0 0 0-2 2v14a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V9z"/><polyline points="14 3 14 9 20 9"/></svg>
Read the Paper
</a>
<a class="link-btn" href="https://arxiv.org/" >
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><rect x="3" y="4" width="18" height="16" rx="2"/><path d="M3 10h18"/></svg>
arXiv
</a>
<a class="link-btn" href="https://github.com/AR-FORUM/NULLS">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M12 .5C5.7.5.5 5.7.5 12c0 5.1 3.3 9.4 7.9 10.9.6.1.8-.2.8-.6v-2c-3.2.7-3.9-1.5-3.9-1.5-.5-1.3-1.3-1.7-1.3-1.7-1.1-.7.1-.7.1-.7 1.2.1 1.8 1.2 1.8 1.2 1.1 1.8 2.8 1.3 3.5 1 .1-.8.4-1.3.8-1.6-2.6-.3-5.3-1.3-5.3-5.8 0-1.3.5-2.3 1.2-3.1-.1-.3-.5-1.5.1-3.2 0 0 1-.3 3.3 1.2 1-.3 2-.4 3-.4s2 .1 3 .4c2.3-1.6 3.3-1.2 3.3-1.2.7 1.7.2 2.9.1 3.2.8.8 1.2 1.9 1.2 3.1 0 4.5-2.7 5.5-5.3 5.8.4.4.8 1.1.8 2.2v3.3c0 .3.2.7.8.6 4.6-1.5 7.9-5.9 7.9-10.9C23.5 5.7 18.3.5 12 .5z"/></svg>
Code
</a>
<a class="link-btn" href="/cdn-cgi/l/email-protection#7a1d1d1215091b163a1b141e081f0d5419170f541f1e0f">
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><rect x="3" y="5" width="18" height="14" rx="2"/><path d="m3 7 9 6 9-6"/></svg>
Contact
</a>
</div>
</header>
<!-- ===== ABSTRACT ===== -->
<section class="block">
<div class="prose-grid">
<div class="side">§ Abstract</div>
<div class="prose">
<p>A growing line of work proposes <strong>training-time interventions</strong> for unlearning — modifying the pre-training objective, data pipeline, or routing so that information can later be removed without revisiting the corpus. Existing approaches, however, either fail to fully isolate per-source information or scale poorly: parameter cost grows with the number of removable units, and shared knowledge degrades alongside the target.</p>
<p>We propose <strong>NULLs (Natively Unlearnable LLMs)</strong>, which impose an architectural separation between sources during training. Each layer's neurons are partitioned into a <strong>shared backbone</strong> that learns transferable structure across all sources, and a pool of <strong>memorization sink neurons</strong> over which each source is assigned a known sparse mask. At deployment, unlearning reduces to disabling a source's mask.</p>
<p>Because masks are combinatorial, a fixed pool of <span class="hl">8,000 neurons supports millions of independently controllable sources</span> without growing the model. On Wikipedia (∼6M articles), disabling an article's sink sharply suppresses article-specific knowledge while preserving semantically related facts — closely matching the gold standard of retraining from scratch.</p>
<div class="tldr">
<div class="tldr-label">Summary</div>
<p>In standard pre-training, knowledge from distinct sources is jointly encoded across shared parameters, leaving post-hoc unlearning ill-posed. NULLs imposes an architectural separation at training time: each source is assigned a deterministic sparse mask over a shared pool of memorization sink neurons. Unlearning is then implemented by excluding the source's mask from the admissible set at inference, without modifying any model weight or revisiting the training corpus.</p>
</div>
<div class="stats">
<div class="stat">
<div class="stat-num">~6M</div>
<div class="stat-label">Wikipedia articles assigned independent sink masks</div>
</div>
<div class="stat">
<div class="stat-num">8,000</div>
<div class="stat-label">Sink neurons; admissible mask count is combinatorial in the pool size</div>
</div>
<div class="stat">
<div class="stat-num">1B</div>
<div class="stat-label">Parameter model; no parameter overhead over the baseline transformer</div>
</div>
<div class="stat">
<div class="stat-num">0</div>
<div class="stat-label">Model weights modified during unlearning</div>
</div>
</div>
</div>
</div>
</section>
<!-- ===== THE PROBLEM ===== -->
<section class="block">
<div class="section-head">
<div class="section-no">§ 01 — The problem</div>
<h2 class="section-title">The limits of post-hoc unlearning are structural.</h2>
</div>
<table class="compare">
<thead>
<tr>
<th>Approach</th>
<th>What it does</th>
<th>Why it fails</th>
</tr>
</thead>
<tbody>
<tr>
<td class="label">Gradient-based<br><span style="font-family:var(--mono);font-size:11px;letter-spacing:.1em;color:var(--muted);text-transform:uppercase;">NPO, gradient ascent</span></td>
<td>Fine-tunes the trained model to reduce likelihood on the forget set.</td>
<td class="bad">Shared and target knowledge degrade at comparable rates (Fig. 1c).</td>
</tr>
<tr>
<td class="label">Parameter editing<br><span style="font-family:var(--mono);font-size:11px;letter-spacing:.1em;color:var(--muted);text-transform:uppercase;">localized edits</span></td>
<td>Identifies and overwrites weights associated with the target fact.</td>
<td class="bad">Target information is distributed across shared parameters; precise localization is unavailable.</td>
</tr>
<tr>
<td class="label">One model per source<br><span style="font-family:var(--mono);font-size:11px;letter-spacing:.1em;color:var(--muted);text-transform:uppercase;">expert modules</span></td>
<td>Allocates disjoint parameter sets to each data partition.</td>
<td class="bad">Parameter count scales linearly with the number of sources; sparse sources underfit.</td>
</tr>
<tr>
<td class="label">Retraining from scratch<br><span style="font-family:var(--mono);font-size:11px;letter-spacing:.1em;color:var(--muted);text-transform:uppercase;">gold standard</span></td>
<td>Removes the source and retrains the full model from scratch.</td>
<td class="bad">Computationally infeasible at web scale; serves only as a reference baseline.</td>
</tr>
<tr style="background: var(--paper);">
<td class="label" style="color:var(--accent);">NULLs <em>(ours)</em></td>
<td>Assigns each source a sparse mask over a shared sink pool, imposed during training.</td>
<td class="good">Mask deactivation approximates retraining from scratch without weight modification.</td>
</tr>
</tbody>
</table>
</section>
<!-- ===== METHOD ===== -->
<section class="block">
<div class="section-head">
<div class="section-no">§ 02 — Method</div>
<h2 class="section-title">An architectural separation between shared and source-specific parameters.</h2>
</div>
<div class="schematic">
<div class="schem-headers">
<div></div>
<div class="schem-header-label">Training</div>
<div class="schem-header-label">Inference</div>
</div>
<div class="schem-grid">
<!-- ============ STANDARD ============ -->
<div class="schem-card">
<div class="schem-title">Standard pre-training</div>
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</linearGradient>
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<circle cx="0" cy="0" r="8" fill="none" stroke="#1a1612" stroke-width="0.6" stroke-opacity="0.55"/>
</symbol>
</defs>
<!-- 8 source tokens at top -->
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<!-- Funnel from sources to shared neurons -->
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<!-- 13 shared pie neurons -->
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</g>
<!-- Shared underline + label -->
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<text x="162" y="235" text-anchor="middle" font-family="EB Garamond, serif" font-style="italic" font-size="15" fill="#4a423a">Shared</text>
</svg>
</div>
<!-- ============ NULLs ============ -->
<div class="schem-card tinted">
<div class="schem-title">NULLs</div>
<svg viewBox="0 0 540 260" xmlns="http://www.w3.org/2000/svg" aria-label="NULLs training: sources route to sinks and shared backbone">
<defs>
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<!-- 8 sources -->
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<!-- Funnel to shared (left half only) -->
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<!-- Curves from each source to its matching sink -->
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<!-- 7 shared pie neurons -->
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</svg>
</div>
<!-- ============ UNLEARNING ============ -->