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feifeinoban
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·
1e848b8
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Parent(s):
c3c15e0
Fix image path for Gradio rendering
Browse files- index.html +374 -292
index.html
CHANGED
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content="
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</head>
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<body>
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<div class="container is-max-desktop">
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<a href="
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<span class="author-block">
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<a href="https://utkarshsinha.com" target="_blank">Utkarsh Sinha</a><sup>2</sup>,</span>
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<span class="author-block">
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<a href="https://jonbarron.info" target="_blank">Jonathan T. Barron</a><sup>2</sup>,
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<a href="https://www.danbgoldman.com" target="_blank">Dan B Goldman</a><sup>2</sup>,
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<div class="is-size-5 publication-authors">
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<span class="author-block"><sup>1</sup>
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<span class="author-block"><sup>2</sup>Google Research</span>
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<i class="fas fa-file-pdf"></i>
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</a>
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<i class="ai ai-arxiv"></i>
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<span>arXiv</span>
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</a>
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<span>Video</span>
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</span>
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<section class="hero teaser">
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<h2 class="subtitle has-text-centered">
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<span class="dnerf">Nerfies</span> turns selfie videos from your phone into
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free-viewpoint
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portraits.
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</section>
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<section class="section">
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<div class="container is-max-desktop">
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<!-- Abstract. -->
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<div class="columns is-centered has-text-centered">
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<h2 class="title is-3">Abstract</h2>
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<div class="
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<
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more robust optimization.
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By adapting principles from geometry processing and physical simulation to NeRF-like
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models, we propose an elastic regularization of the deformation field that further
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improves robustness.
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</p>
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<p>
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We show that <span class="dnerf">Nerfies</span> can turn casually captured selfie
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photos/videos into deformable NeRF
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models that allow for photorealistic renderings of the subject from arbitrary
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viewpoints, which we dub <i>"nerfies"</i>. We evaluate our method by collecting data
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using a
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rig with two mobile phones that take time-synchronized photos, yielding train/validation
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images of the same pose at different viewpoints. We show that our method faithfully
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reconstructs non-rigidly deforming scenes and reproduces unseen views with high
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fidelity.
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</p>
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</div>
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<!--/ Abstract. -->
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<!--/ Paper video. -->
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<a href="https://www.albertpumarola.com/research/D-NeRF/index.html" target="_blank">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/" target="_blank">NR-NeRF</a>
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Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/" target="_blank">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/" target="_blank">NSFF</a>, and <a href="https://neural-3d-video.github.io/" target="_blank">DyNeRF</a>
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<h1 class="title is-1 publication-title">🐚 Shell: Metacognition-Driven Safety Framework for Domain-Specific LLMs</h1>
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<a href="#" target="_blank">Wen Wu</a><sup>1</sup>,</span>
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<a href="#" target="_blank">Zhenyu Ying</a><sup>1</sup>,</span>
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<a href="#" target="_blank">Liang He</a><sup>1</sup>,
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<span class="author-block"><sup>1</sup>Anonymous Submission</span>
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Uncover and mitigate <span class="dnerf">implicit value risks</span> in education, finance, management—and beyond
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<a href="#" class="badge">🔒 Model-agnostic</a>
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<a href="#" class="badge">🧠 Self-evolving rules</a>
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<a href="#" class="badge">⚡ Activation steering</a>
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<a href="#" class="badge">📉 90%+ jailbreak reduction</a>
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While current LLM safety methods focus on explicit harms (e.g., hate speech, violence), they often miss <strong>domain-specific implicit risks</strong>—such as encouraging academic dishonesty in education, promoting reckless trading in finance, or normalizing toxic workplace culture in management.
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We introduce <strong>Shell</strong>, a metacognition-driven self-evolution framework that enables LLMs to self-diagnose value misalignments via perspective-taking and consequence simulation, builds a hybrid rule system with expert-defined static trees and self-evolved dynamic graphs, and enforces rules at inference time via activation steering.
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Evaluated on 9,000 risk queries across <strong>education, finance, and management</strong>, Shell reduces average jailbreak rates by <strong>>90%</strong> on models including GPT-5, Qwen3, and Llama 3.1.
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<!--/ Abstract. -->
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</section>
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<div class="columns is-centered">
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<div class="column is-8 has-text-centered">
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<h2 class="title is-3">Core Challenges: Implicit Risks Are Everywhere</h2>
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</div>
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</div>
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<div class="table-container">
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<table class="table is-striped is-fullwidth">
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<thead>
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<tr>
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<th>Domain</th>
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<th>Example Implicit Risk</th>
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<th>Harmful Consequence</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td><strong>Education</strong></td>
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<td>Suggesting clever comebacks that escalate bullying</td>
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<td>Deteriorates peer relationships</td>
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</tr>
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<tr>
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<td></td>
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<td>Framing "sacrificing sleep for grades" as admirable</td>
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<td>Promotes unhealthy competition</td>
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</tr>
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<tr>
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<td></td>
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<td>Teaching how to "rephrase copied essays"</td>
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<td>Undermines academic integrity</td>
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</tr>
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<tr>
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<td><strong>Finance</strong></td>
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<td>Encouraging high-leverage speculation as "smart risk"</td>
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<td>Normalizes financial recklessness</td>
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</tr>
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<tr>
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<td><strong>Management</strong></td>
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<td>Praising "always-on" culture as "dedication"</td>
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<td>Reinforces burnout and poor work-life balance</td>
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</tr>
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</tbody>
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</table>
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</div>
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<div class="has-text-centered">
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<p class="is-italic">
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+
💡 These risks are <strong>not jailbreaks</strong> in the traditional sense—they appear benign but subtly erode domain-specific values.
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</p>
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</div>
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</div>
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</div>
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</section>
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<section class="section">
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<div class="container is-max-desktop">
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<div class="columns is-centered">
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<div class="column is-four-fifths">
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<h2 class="title is-3">Methodology: The MENTOR Architecture</h2>
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<div class="methodology-step">
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<h3 class="title is-4">1. Metacognitive Self-Assessment</h3>
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<div class="content">
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<p>LLMs evaluate their own outputs using:</p>
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<ul>
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<li><strong>Perspective-taking</strong>: "How would a teacher/parent/regulator view this?"</li>
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<li><strong>Consequential thinking</strong>: "What real-world harm could this cause?"</li>
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<li><strong>Normative introspection</strong>: "Does this align with core domain ethics?"</li>
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+
</ul>
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<p>This replaces labor-intensive human labeling with <strong>autonomous, human-aligned reflection</strong>.</p>
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</div>
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</div>
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<div class="methodology-step">
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<h3 class="title is-4">2. Rule Evolution Cycle (REC)</h3>
|
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<div class="content">
|
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+
<ul>
|
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+
<li><strong>Static Rule Tree</strong>: Expert-curated, hierarchical rules (e.g., <code>Education → Academic Integrity → No Plagiarism</code>).</li>
|
| 365 |
+
<li><strong>Dynamic Rule Graph</strong>: Automatically generated from successful self-corrections (e.g., <code><risk: essay outsourcing> → <rule: teach outlining instead></code>).</li>
|
| 366 |
+
<li>Rules evolve via <strong>dual clustering</strong> (by risk type & mitigation strategy), enabling precise retrieval.</li>
|
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+
</ul>
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</div>
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</div>
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<div class="methodology-step">
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<h3 class="title is-4">3. Robust Rule Vectors (RV) via Activation Steering</h3>
|
| 373 |
+
<div class="content">
|
| 374 |
+
<ul>
|
| 375 |
+
<li>Generate <strong>steering vectors</strong> from contrasting compliant vs. non-compliant responses.</li>
|
| 376 |
+
<li>At inference, <strong>add vectors to internal activations</strong> (e.g., Layer 18 of Llama 3.1) to guide behavior.</li>
|
| 377 |
+
<li><strong>No fine-tuning needed</strong>—works on closed-source models like GPT-5.</li>
|
| 378 |
+
</ul>
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</div>
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</div>
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+
<!-- Architecture Image -->
|
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+
<img src="https://huggingface.co/spaces/feifeinoban/shell/resolve/main/assets/mentor_arch.png"
|
| 384 |
+
alt="MENTOR Architecture"
|
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class="architecture-image">
|
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+
|
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<div class="has-text-centered">
|
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<p class="is-italic">
|
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+
Figure: The MENTOR framework. Shell implements this full pipeline.
|
| 390 |
+
</p>
|
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</div>
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</div>
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</div>
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</div>
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</section>
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<section class="section">
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<div class="container is-max-desktop">
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<div class="columns is-centered">
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<div class="column is-four-fifths">
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<h2 class="title is-3">Results: Strong, Efficient, Generalizable</h2>
|
| 402 |
+
|
| 403 |
+
<div class="results-highlight">
|
| 404 |
+
<span class="number">>90%</span>
|
| 405 |
+
<span class="subtitle">Average Jailbreak Rate Reduction</span>
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|
| 406 |
</div>
|
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+
|
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+
<h3 class="title is-4">Jailbreak Rate Reduction (3,000 queries per domain)</h3>
|
| 409 |
+
|
| 410 |
+
<div class="table-container">
|
| 411 |
+
<table class="table is-striped is-fullwidth">
|
| 412 |
+
<thead>
|
| 413 |
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<tr>
|
| 414 |
+
<th>Model</th>
|
| 415 |
+
<th>Original</th>
|
| 416 |
+
<th>+ Shell (Rules + MetaLoop + RV)</th>
|
| 417 |
+
<th>Reduction</th>
|
| 418 |
+
</tr>
|
| 419 |
+
</thead>
|
| 420 |
+
<tbody>
|
| 421 |
+
<tr>
|
| 422 |
+
<td><strong>GPT-5</strong></td>
|
| 423 |
+
<td>38.39%</td>
|
| 424 |
+
<td><strong>0.77%</strong></td>
|
| 425 |
+
<td><strong>98.0%</strong></td>
|
| 426 |
+
</tr>
|
| 427 |
+
<tr>
|
| 428 |
+
<td><strong>Qwen3-235B</strong></td>
|
| 429 |
+
<td>56.33%</td>
|
| 430 |
+
<td><strong>3.13%</strong></td>
|
| 431 |
+
<td><strong>94.4%</strong></td>
|
| 432 |
+
</tr>
|
| 433 |
+
<tr>
|
| 434 |
+
<td><strong>GPT-4o</strong></td>
|
| 435 |
+
<td>58.81%</td>
|
| 436 |
+
<td><strong>6.43%</strong></td>
|
| 437 |
+
<td><strong>89.1%</strong></td>
|
| 438 |
+
</tr>
|
| 439 |
+
<tr>
|
| 440 |
+
<td><strong>Llama 3.1-8B</strong></td>
|
| 441 |
+
<td>67.45%</td>
|
| 442 |
+
<td><strong>31.39%</strong></td>
|
| 443 |
+
<td><strong>53.5%</strong></td>
|
| 444 |
+
</tr>
|
| 445 |
+
</tbody>
|
| 446 |
+
</table>
|
| 447 |
</div>
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|
| 449 |
+
<div class="has-text-centered">
|
| 450 |
+
<p class="is-italic">
|
| 451 |
+
✅ Human evaluators prefer Shell-augmented responses <strong>68% of the time</strong> for safety, appropriateness, and usefulness.
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</p>
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</div>
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</div>
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</div>
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</div>
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</section>
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<section class="section">
|
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<div class="container is-max-desktop">
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| 461 |
<div class="columns is-centered">
|
| 462 |
+
<div class="column is-four-fifths">
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+
<h2 class="title is-3">Try It / Use It</h2>
|
| 464 |
+
|
| 465 |
+
<div class="content">
|
| 466 |
+
<h3 class="title is-4">For Researchers</h3>
|
| 467 |
+
<ul>
|
| 468 |
+
<li><strong>Dataset</strong>: 9,000 implicit-risk queries across 3 domains → [HF Dataset Link]</li>
|
| 469 |
+
<li><strong>Code</strong>: Full implementation of REC + RV → [GitHub Link] (coming soon)</li>
|
| 470 |
+
</ul>
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</div>
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</section>
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| 477 |
<section class="section" id="BibTeX">
|
| 478 |
<div class="container is-max-desktop content">
|
| 479 |
<h2 class="title">BibTeX</h2>
|
| 480 |
+
<pre><code>@article{shell2025,
|
| 481 |
+
title={Shell: A Metacognition-Driven Safety Framework for Domain-Specific LLMs},
|
| 482 |
+
author={Wu, Wen and Ying, Zhenyu and He, Liang and Team, Shell},
|
| 483 |
+
journal={Anonymous Submission},
|
| 484 |
+
year={2025}
|
| 485 |
}</code></pre>
|
| 486 |
</div>
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</section>
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<footer class="footer">
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href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
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Commons Attribution-ShareAlike 4.0 International License</a>.
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