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ml: the frontier leaderboard + client ladder; catalog to 23 models
P1 currency: client-tier card moves to ara-diac-small-2.1 (DER 4.57; 2.0 was 4.82, 1.0 was 8.26) with fp16 listed; ara-diac-layerdrop-1.0 added (DER 5.78 at 63% of parameters, int4 95 MiB browser tier); ara-diac-2.0 status released; section deck states the 23-model catalog. P2 the leaderboard section: protocol-matched SadeedDiac-25 table (every cell from the ledger; the Claude-3.7 row labeled vendor-published, protocol undisclosed, not reproduced by us), the 300M client ladder (8.26 - 5.29 - 4.82 - 4.57) with the five registered negatives named, and the Hebrew same-test line. Astro build clean; the built page emits dist/client/ml/index.html with the section.
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src/pages/ml.astro

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@@ -8,7 +8,7 @@ const serverModels = [
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pair: "Arabic → haraqat",
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metric: "DER 2.29 · OOD WER 17.38",
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artifact: "fp32 · 2.7 GiB · parts",
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status: "releasing",
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status: "released",
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},
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{
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id: "khm-latn-1.0",
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const clientModels = [
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{
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id: "ara-diac-small-2.0",
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id: "ara-diac-small-2.1",
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task: "diacritization",
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pair: "Arabic → haraqat",
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metric: "DER 4.82 (1.0 was 8.26)",
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artifact: "int8 · ~0.5 GiB",
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status: "releasing",
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metric: "DER 4.57 (2.0 was 4.82 · 1.0 was 8.26)",
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artifact: "int8 · 264 MiB · fp16 · 674 MiB",
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status: "released",
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},
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{
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id: "ara-diac-layerdrop-1.0",
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task: "diacritization",
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pair: "Arabic → haraqat",
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metric: "DER 5.78 · 63% of the parameters",
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artifact: "int4 · 95 MiB · browser-native",
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status: "released",
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},
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{
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id: "tha-g2p-small-1.0",
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<p class="eyebrow">The catalogue</p>
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<h2>Every model, with its number.</h2>
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<p class="section-deck">
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No model is published without a measured metric next to its teacher's, on the same
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harness, in the open. Students are distilled from frozen teachers and gated at a
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pre-agreed error budget. Each entry resolves from the models.yaml index; artifacts above
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GitHub's 2&nbsp;GiB cap ship as sha256-verified parts that the runtimes reassemble
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transparently.
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The index resolves 23 models across five languages. No model is published without a
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measured metric next to its teacher's, on the same harness, in the open. Students are
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distilled from frozen teachers and gated at a pre-agreed error budget. Each entry
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resolves from the models.yaml index; artifacts above GitHub's 2&nbsp;GiB cap ship as
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sha256-verified parts that the runtimes reassemble transparently.
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</p>
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</header>
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@@ -197,6 +205,153 @@ const clientModels = [
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</div>
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</section>
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<!-- ════════════════════════════════════════════════════════════════
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THE LEADERBOARD — one instrument, every system
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════════════════════════════════════════════════════════════════ -->
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<section class="section surface-deep" id="leaderboard">
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<div class="container">
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<header class="section-head">
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<p class="eyebrow">The frontier, measured</p>
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<h2>580M parameters against the frontier.</h2>
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<p class="section-deck">
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SadeedDiac-25 — all 1,200 paragraphs, Misraj's own evaluator, windowed zero-skip
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protocol, one instrument for every row. Our dedicated 580M teacher is the best
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non-vendor system measured on it, 3.2× better than the 1.5B model the benchmark was
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built around. And the frontier is moving the wrong way: every GLM successor to 5.2 is
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3–5× worse, losing classical haraqat (wrong vowels on 10.05% of marked positions for
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5.3-Flash, vs 5.2's 2.64% — which matches our 580M teacher's 2.62% to 0.02pp).
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</p>
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</header>
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<div class="lb-wrap">
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<table class="lb-table">
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<thead>
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<tr>
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<th>System</th>
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<th class="tnum">Params</th>
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<th class="tnum">DER (CE)</th>
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<th class="tnum">DER (w/o CE)</th>
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<th>Protocol note</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>Claude-3.7-Sonnet</td>
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<td class="tnum">—</td>
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<td class="tnum">1.3941</td>
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<td class="tnum">0.7693</td>
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<td class="lb-dim">vendor-published · protocol undisclosed · not reproduced by us</td>
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</tr>
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<tr class="lb-ours">
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<td>Ours · ara-diac-2.0 (r7)</td>
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<td class="tnum">580M</td>
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<td class="tnum"><strong>2.2864</strong></td>
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<td class="tnum"><strong>1.3343</strong></td>
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<td>best dedicated model measured</td>
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</tr>
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<tr>
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<td>GLM-5.2</td>
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<td class="tnum">—</td>
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<td class="tnum">2.5060</td>
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<td class="tnum">1.5537</td>
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<td class="lb-dim">our reproduction · raw</td>
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</tr>
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<tr>
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<td>Gemini-Flash-2.0</td>
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<td class="tnum">—</td>
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<td class="tnum">3.1926</td>
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<td class="tnum">2.3783</td>
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<td class="lb-dim">same protocol</td>
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</tr>
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<tr>
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<td>GPT-4</td>
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<td class="tnum">—</td>
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<td class="tnum">3.8645</td>
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<td class="tnum">3.8645</td>
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<td class="lb-dim">same protocol</td>
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</tr>
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<tr>
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<td>Sadeed</td>
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<td class="tnum">1.5B</td>
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<td class="tnum">7.2915</td>
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<td class="tnum">5.2625</td>
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<td class="lb-dim">prior dedicated state of the art</td>
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</tr>
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<tr>
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<td>GLM-5.3-Flash</td>
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<td class="tnum">—</td>
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<td class="tnum">8.7978</td>
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<td class="tnum">6.6368</td>
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<td class="lb-dim">zero-skip · reasoning_effort=low</td>
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</tr>
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<tr>
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<td>GLM-5.3</td>
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<td class="tnum">—</td>
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<td class="tnum">9.8971</td>
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<td class="tnum">7.8219</td>
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<td class="lb-dim">zero-skip · reasoning_effort=low</td>
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</tr>
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<tr>
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<td>glm-4.7-flash</td>
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<td class="tnum">—</td>
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<td class="tnum">13.2256</td>
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<td class="tnum">10.3206</td>
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<td class="lb-dim">zero-skip · thinking-disabled</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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<p class="lb-note">
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WER columns, decode protocols, and paired bootstrap CIs for every row:
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<a href="https://github.com/interscript/rababa/blob/main/docs/RESULTS.md"
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>rababa/docs/RESULTS.md</a
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>. The GLM-5.3 family cannot disable thinking; low effort is its plainest protocol.
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</p>
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<h3 class="tier-label">
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The client ladder <span>300M student, identical architecture and artifact size</span>
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</h3>
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<ul class="ledger ladder">
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<li class="ledger-row">
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<span class="ledger-task">rung 1.0</span>
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<span class="ledger-body"><code class="ledger-id">AdamW · r6 labels · 3 ep</code></span>
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<span class="ledger-metric tnum">8.26</span>
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<span class="ledger-artifact">the shipped baseline</span>
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</li>
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<li class="ledger-row">
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<span class="ledger-task">+ Muon</span>
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<span class="ledger-body"><code class="ledger-id">optimizer swap</code></span>
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<span class="ledger-metric tnum">5.29</span>
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<span class="ledger-artifact">−2.96pp</span>
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</li>
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<li class="ledger-row">
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<span class="ledger-task">+ r7 labels</span>
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<span class="ledger-body"><code class="ledger-id">fresher teacher labels</code></span>
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<span class="ledger-metric tnum">4.82</span>
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<span class="ledger-artifact">−0.47pp</span>
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</li>
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<li class="ledger-row">
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<span class="ledger-task">+ 6 epochs</span>
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<span class="ledger-body"><code class="ledger-id">ara-diac-small-2.1</code></span>
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<span class="ledger-metric tnum">4.57</span>
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<span class="ledger-artifact">−0.25pp · released</span>
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</li>
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</ul>
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<p class="lb-note">
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What didn't move it — every rung pre-registered, measured, and kept in the log:
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multi-token-prediction auxiliary 5.09 (+0.26pp); classical-register swap at constant
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budget 5.81 (+0.98pp); register add at matched epochs 4.82 (flat — it cancelled the epoch
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gain); on-policy GKD 6.00 (+1.18pp, the worst rung); depth-halved 5.78 (63% of the
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parameters — shipped anyway as the browser tier). At SFT convergence, supervision quality
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dominates. The ladder is closed.
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</p>
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<p class="lb-note">
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Hebrew, same discipline: 16.43% DER on Biblical Hebrew, where the modern-Hebrew state of
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the art degrades to 35.6% on the same test.
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</p>
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</div>
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</section>
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<!-- ════════════════════════════════════════════════════════════════
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THE CONTRACT — IMF v1
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════════════════════════════════════════════════════════════════ -->
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max-width: 42rem;
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line-height: 1.55;
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}
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/* ── The leaderboard ──────────────────────────────────────────── */
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.lb-wrap {
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overflow-x: auto;
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margin: 0 -1.5rem;
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padding: 0 1.5rem;
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}
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.lb-table {
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width: 100%;
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border-collapse: collapse;
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font-size: 0.9375rem;
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}
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.lb-table th {
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font-family: var(--font-mono);
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font-size: var(--text-micro);
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letter-spacing: 0.12em;
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text-transform: uppercase;
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text-align: left;
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color: var(--color-stone);
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border-bottom: 2px solid var(--color-ink);
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padding: 0.6rem 1rem 0.6rem 0;
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}
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.lb-table td {
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padding: 0.7rem 1rem 0.7rem 0;
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border-bottom: 1px dotted var(--color-rule);
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vertical-align: baseline;
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}
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.lb-table th.tnum,
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.lb-table td.tnum {
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text-align: right;
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font-variant-numeric: tabular-nums;
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white-space: nowrap;
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}
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.lb-dim {
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color: var(--color-stone);
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font-size: 0.8125rem;
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}
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.lb-ours td {
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background: color-mix(in srgb, var(--color-highlight) 12%, transparent);
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}
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.lb-note {
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font-size: 0.875rem;
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color: var(--color-stone);
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margin-top: 1.5rem;
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max-width: 48rem;
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line-height: 1.55;
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}
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.lb-note a {
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color: var(--color-highlight);
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}
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.ladder .ledger-artifact {
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font-size: 0.8125rem;
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}
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.ledger-note a {
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color: var(--color-highlight);
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}

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