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<title>Model Tiers</title>
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body { background: #0f1117; font-family: 'Segoe UI', system-ui, sans-serif; display: flex; align-items: center; justify-content: center; min-height: 100vh; }
.slide { width: 960px; padding: 56px 72px; }
h2 { color: #e2e8f0; font-size: 1.5rem; font-weight: 400; letter-spacing: 0.05em; text-transform: uppercase; margin-bottom: 8px; text-align: center; }
.subtitle { text-align: center; color: #6b7280; font-size: 0.88rem; margin-bottom: 48px; }
.tiers { display: flex; align-items: flex-end; gap: 20px; margin-bottom: 40px; }
.tier { flex: 1; border-radius: 10px 10px 0 0; display: flex; flex-direction: column; justify-content: flex-end; position: relative; }
.tier-inner { padding: 20px 16px 24px; border-radius: 10px; border: 1.5px solid; }
.tier.det .tier-inner { background: #0d1f16; border-color: #22c55e; }
.tier.tiny .tier-inner { background: #0c1a30; border-color: #3b82f6; }
.tier.med .tier-inner { background: #12122a; border-color: #8b5cf6; }
.tier.large .tier-inner { background: #2a1100; border-color: #f59e0b; }
.tier.llm .tier-inner { background: #2a0f0f; border-color: #ef4444; }
.tier-badge { font-size: 0.62rem; letter-spacing: 0.14em; text-transform: uppercase; font-weight: 700; margin-bottom: 8px; }
.det .tier-badge { color: #4ade80; }
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.large .tier-badge { color: #fbbf24; }
.llm .tier-badge { color: #f87171; }
.tier-name { font-size: 0.95rem; font-weight: 600; color: #e2e8f0; margin-bottom: 10px; line-height: 1.3; }
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.large .tier-params { color: #fbbf24; }
.llm .tier-params { color: #f87171; }
.tier-detail { font-size: 0.76rem; color: #6b7280; line-height: 1.5; }
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.axis-labels span { font-size: 0.7rem; color: #4a5568; }
.hypothesis { background: #1a1f2e; border: 1px solid #2d3748; border-radius: 10px; padding: 24px 28px; }
.hyp-title { font-size: 0.65rem; letter-spacing: 0.12em; text-transform: uppercase; color: #6b7280; margin-bottom: 12px; }
.hyp-rule { font-size: 0.88rem; color: #94a3b8; line-height: 1.8; font-family: 'Courier New', monospace; }
.hyp-rule .dim { color: #4a5568; }
.hyp-rule .hi { color: #22d3ee; font-weight: 600; }
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<div class="slide">
<h2>Entropy → Model Tier</h2>
<div class="subtitle">The smallest model that clears the quality target for a given gate's entropy budget</div>
<div class="tiers">
<div class="tier det" style="position:relative;">
<div class="marker">← already valid</div>
<div class="tier-inner">
<div class="tier-badge">Tier 0</div>
<div class="tier-name">Deterministic Rules</div>
<div class="tier-params">0 params</div>
<div class="tier-detail">Perfect when inputoutput mapping is explicit and stable. No training cost. Fully auditable.</div>
</div>
</div>
<div class="tier tiny" style="position:relative;">
<div class="marker current">← trained baseline</div>
<div class="tier-inner">
<div class="tier-badge">Tier 1 · current</div>
<div class="tier-name">Tiny Specialist (Fabricate)</div>
<div class="tier-params">~213K params · ~800KB</div>
<div class="tier-detail">Binary/multiclass classifiers. Local, fast, inspectable. Target for bounded gates with sufficient entropy.</div>
</div>
</div>
<div class="tier med">
<div class="tier-inner">
<div class="tier-badge">Tier 2 · planned</div>
<div class="tier-name">Medium Specialist</div>
<div class="tier-params">~10× tier 1 · ~23M params</div>
<div class="tier-detail">For gates where tiny tier misses due to higher label boundary fuzziness or longer context dependence.</div>
</div>
</div>
<div class="tier large">
<div class="tier-inner">
<div class="tier-badge">Tier 3 · planned</div>
<div class="tier-name">Large Specialist</div>
<div class="tier-params">~16× tier 1 · ~34M params</div>
<div class="tier-detail">For gates with high lexical variation, world-knowledge dependence, or complex slot interdependence.</div>
</div>
</div>
<div class="tier llm">
<div class="tier-inner">
<div class="tier-badge">Tier 4 · escalation</div>
<div class="tier-name">SLM / LLM</div>
<div class="tier-params">billions of params</div>
<div class="tier-detail">Only when the gate is genuinely open-ended. Not a default. Escalation when specialists abstain.</div>
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<div class="axis">
<span class="axis-label-left">low entropy</span>
<div class="axis-line"></div>
<span class="axis-label-right">high entropy</span>
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<div class="axis-labels">
<span>simple lookup</span>
<span>binary classification</span>
<span>slot filling</span>
<span>complex routing</span>
<span>open reasoning</span>
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<div class="hypothesis">
<div class="hyp-title">Current sizing hypothesis</div>
<div class="hyp-rule">
<span class="dim">if</span> entropy <span class="hi">&lt;= A</span> <span class="dim"></span> deterministic<br>
<span class="dim">if</span> entropy <span class="hi">&lt;= B</span> <span class="dim"></span> tiny specialist (~213K params)<br>
<span class="dim">if</span> entropy <span class="hi">&lt;= C</span> <span class="dim"></span> medium specialist (~23M params)<br>
<span class="dim">if</span> entropy <span class="hi">&gt; C</span> <span class="dim"></span> large specialist or escalate to SLM/LLM
<span class="note">Before moving up a tier: check schema, label set, missing context, and factorization options first.</span>
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