Upload index.html

index.html

@@ -11,13 +11,20 @@
       --bg: #ffffff;
       --text: #111111;
       --muted: #777777;
+      --muted-2: #a0a0a0;
       --line: #e8e8e8;
       --soft: #f7f7f7;
-      --max: 920px;
+      --soft-2: #fbfbfb;
+      --max: 980px;
+      --side: 182px;
+      --gap: 38px;
     }
 
-    * {
-      box-sizing: border-box;
+    * { box-sizing: border-box; }
+
+    html {
+      scroll-behavior: smooth;
+      scroll-padding-top: 24px;
     }
 
     body {
@@ -33,13 +40,19 @@
       text-decoration: none;
     }
 
-    a:hover {
-      text-decoration: underline;
+    a:hover { text-decoration: underline; }
+
+    .shell {
+      width: min(calc(100% - 36px), calc(var(--max) + var(--side) + var(--gap)));
+      margin: auto;
+      display: grid;
+      grid-template-columns: var(--side) minmax(0, var(--max));
+      gap: var(--gap);
+      align-items: start;
     }
 
     .wrap {
-      width: min(calc(100% - 36px), var(--max));
-      margin: auto;
+      width: min(100%, var(--max));
     }
 
     header {
@@ -74,24 +87,37 @@
       letter-spacing: -.055em;
     }
 
+    h2 {
+      margin: 0 0 18px;
+      font-size: 21px;
+      letter-spacing: -.02em;
+    }
+
+    h3 {
+      margin: 0 0 12px;
+      font-size: 15px;
+      letter-spacing: -.01em;
+    }
+
+    p { margin: 0 0 14px; }
+
     .lead {
       margin: 0;
-      max-width: 650px;
+      max-width: 700px;
       color: #333;
       font-size: 17px;
     }
 
+    .lead.small {
+      font-size: 15px;
+      max-width: 800px;
+    }
+
     section {
       padding: 38px 0;
       border-top: 1px solid var(--line);
     }
 
-    h2 {
-      margin: 0 0 18px;
-      font-size: 21px;
-      letter-spacing: -.02em;
-    }
-
     .note-list {
       border-top: 1px solid var(--line);
     }
@@ -150,9 +176,7 @@
       font-weight: 400;
     }
 
-    .current {
-      background: var(--soft);
-    }
+    .current { background: var(--soft); }
 
     .status {
       margin-top: 18px;
@@ -160,6 +184,147 @@
       font-size: 12px;
     }
 
+    .side {
+      position: sticky;
+      top: 18px;
+      padding-top: 24px;
+      min-height: 100vh;
+    }
+
+    .side-label {
+      color: var(--muted-2);
+      font-size: 10px;
+      text-transform: uppercase;
+      letter-spacing: .12em;
+      margin-bottom: 10px;
+    }
+
+    .model-link {
+      display: block;
+      border: 1px solid var(--line);
+      border-radius: 10px;
+      padding: 11px 12px;
+      background: var(--soft-2);
+      transition: background .15s ease, border-color .15s ease, transform .15s ease;
+    }
+
+    .model-link:hover {
+      text-decoration: none;
+      background: var(--soft);
+      border-color: #dcdcdc;
+      transform: translateY(-1px);
+    }
+
+    .model-link strong {
+      display: block;
+      font-size: 12px;
+      line-height: 1.25;
+    }
+
+    .model-link span {
+      display: block;
+      margin-top: 3px;
+      color: var(--muted);
+      font-size: 10px;
+      line-height: 1.35;
+    }
+
+    .side-note {
+      margin-top: 12px;
+      color: var(--muted-2);
+      font-size: 10px;
+      line-height: 1.4;
+    }
+
+    .metric-grid {
+      margin-top: 22px;
+      display: grid;
+      grid-template-columns: repeat(3, minmax(0, 1fr));
+      border-top: 1px solid var(--line);
+      border-left: 1px solid var(--line);
+    }
+
+    .metric {
+      padding: 16px;
+      border-right: 1px solid var(--line);
+      border-bottom: 1px solid var(--line);
+      min-height: 96px;
+    }
+
+    .metric .k {
+      color: var(--muted);
+      font-size: 10px;
+      text-transform: uppercase;
+      letter-spacing: .08em;
+    }
+
+    .metric .v {
+      margin-top: 6px;
+      font-size: 21px;
+      letter-spacing: -.03em;
+    }
+
+    .metric .s {
+      margin-top: 3px;
+      color: var(--muted);
+      font-size: 11px;
+    }
+
+    .callout {
+      margin: 20px 0 0;
+      padding: 16px 18px;
+      border-left: 2px solid #cfcfcf;
+      background: var(--soft-2);
+      font-size: 13px;
+      color: #333;
+    }
+
+    .example-grid {
+      display: grid;
+      grid-template-columns: 1fr 1fr;
+      gap: 14px;
+      margin-top: 20px;
+    }
+
+    .example {
+      border: 1px solid var(--line);
+      background: #fff;
+      padding: 16px;
+      min-width: 0;
+    }
+
+    .example .eyebrow {
+      font-size: 10px;
+      color: var(--muted);
+      text-transform: uppercase;
+      letter-spacing: .08em;
+      margin-bottom: 8px;
+    }
+
+    .example pre {
+      margin: 0;
+      white-space: pre-wrap;
+      word-break: break-word;
+      font: inherit;
+      font-size: 12px;
+      line-height: 1.55;
+    }
+
+    .conclusion-list {
+      margin: 0;
+      padding-left: 18px;
+      color: #222;
+      font-size: 13px;
+    }
+
+    .conclusion-list li + li { margin-top: 8px; }
+
+    .tiny {
+      color: var(--muted);
+      font-size: 11px;
+      line-height: 1.5;
+    }
+
     footer {
       padding: 24px 0 40px;
       border-top: 1px solid var(--line);
@@ -167,11 +332,39 @@
       font-size: 11px;
     }
 
-    @media (max-width: 700px) {
-      .hero {
-        padding-top: 54px;
+    @media (max-width: 880px) {
+      .shell {
+        grid-template-columns: 1fr;
+        gap: 0;
       }
 
+      .side {
+        position: static;
+        min-height: auto;
+        padding: 18px 0 0;
+      }
+
+      .side-label { margin-bottom: 8px; }
+
+      .model-link {
+        display: inline-block;
+        min-width: 170px;
+      }
+
+      .side-note { display: none; }
+
+      .hero { padding-top: 50px; }
+
+      .metric-grid {
+        grid-template-columns: 1fr;
+      }
+
+      .example-grid {
+        grid-template-columns: 1fr;
+      }
+    }
+
+    @media (max-width: 700px) {
       .note-item {
         grid-template-columns: 1fr auto;
       }
@@ -186,340 +379,341 @@
 <body>
 
 <header>
-  <div class="wrap">
-    <nav>
-      <a class="brand" href="#">Oris Notes</a>
-      <span>OrisTeam · 2026</span>
-    </nav>
+  <div class="shell">
+    <div></div>
+    <div class="wrap">
+      <nav>
+        <a class="brand" href="#">Oris Notes</a>
+        <span>OrisTeam · 2026</span>
+      </nav>
+    </div>
   </div>
 </header>
 
 <main>
+  <div class="shell">
+
+    <aside class="side" aria-label="Model index">
+      <div class="side-label">Models</div>
+      <a class="model-link" href="#vyuhu-1">
+        <strong>Vyuhu 1.0</strong>
+        <span>~493M · training run</span>
+      </a>
+      <div class="side-note">
+        Low-key model index. More model notes can be added here later.
+      </div>
+    </aside>
 
-  <div class="hero">
-    <div class="wrap">
-      <h1>Oris Notes</h1>
-      <p class="lead">
-        Short technical notes on Oris models, architectures and training runs.
-      </p>
-    </div>
-  </div>
-
-  <section>
     <div class="wrap">
-      <h2>Notes</h2>
-
-      <div class="note-list">
-        <a class="note-item" href="#vyuhu-1">
-          <div class="date">August 2026</div>
 
-          <div>
-            <div class="note-title">Vyuhu 1.0</div>
-            <div class="note-desc">
-              Architecture finalized · full base-model training in progress
-            </div>
-          </div>
-
-          <div class="arrow">→</div>
-        </a>
+      <div class="hero">
+        <h1>Oris Notes</h1>
+        <p class="lead">
+          Short technical notes on Oris models, architectures and training runs.
+        </p>
       </div>
-    </div>
-  </section>
 
-  <section id="vyuhu-1">
-    <div class="wrap">
-      <h2>Vyuhu 1.0</h2>
-
-      <p class="lead" style="font-size:15px;max-width:760px">
-        The Vyuhu architecture has completed its initial architecture-validation stage.
-        The current design is stable enough to move from architecture experiments to a full training run.
-      </p>
-
-      <p class="status">
-        Development now focuses on Vyuhu 1.0, a larger generation of the architecture built from the lessons learned with
-        <a href="https://huggingface.co/OrisTeam/Vyuhu-280M-Base-1704m" target="_blank" rel="noopener">
-          OrisTeam/Vyuhu-280M-Base-1704m ↗
-        </a>.
-      </p>
-    </div>
-  </section>
+      <section>
+        <h2>Notes</h2>
 
-  <section>
-    <div class="wrap">
-      <h2>Architecture</h2>
-
-      <div class="table-wrap">
-        <table>
-          <thead>
-            <tr>
-              <th>Property</th>
-              <th>Vyuhu 280M</th>
-              <th>Vyuhu 1.0</th>
-            </tr>
-          </thead>
-
-          <tbody>
-            <tr>
-              <td>Parameters</td>
-              <td>282.68M</td>
-              <td><strong>~493M</strong></td>
-            </tr>
-            <tr>
-              <td>Hidden size</td>
-              <td>1152</td>
-              <td><strong>1280</strong></td>
-            </tr>
-            <tr>
-              <td>Context</td>
-              <td>1024</td>
-              <td><strong>1536</strong></td>
-            </tr>
-            <tr>
-              <td>Q / KV heads</td>
-              <td>18 / 6</td>
-              <td><strong>20 / 4</strong></td>
-            </tr>
-            <tr>
-              <td>Head dimension</td>
-              <td>64</td>
-              <td><strong>64</strong></td>
-            </tr>
-            <tr>
-              <td>FFN</td>
-              <td>3584</td>
-              <td><strong>3840</strong></td>
-            </tr>
-            <tr>
-              <td>GQA anchors</td>
-              <td>4</td>
-              <td><strong>6</strong></td>
-            </tr>
-            <tr>
-              <td>Elastic stages</td>
-              <td>3</td>
-              <td><strong>5</strong></td>
-            </tr>
-            <tr>
-              <td>Elastic blocks</td>
-              <td>12</td>
-              <td><strong>17</strong></td>
-            </tr>
-            <tr>
-              <td>Compute paths</td>
-              <td>4</td>
-              <td><strong>3</strong></td>
-            </tr>
-            <tr>
-              <td>Engram memory</td>
-              <td>—</td>
-              <td><strong>2 / 3-gram</strong></td>
-            </tr>
-            <tr>
-              <td>MTP</td>
-              <td>—</td>
-              <td><strong>t+2 training objective</strong></td>
-            </tr>
-          </tbody>
-        </table>
-      </div>
-    </div>
-  </section>
+        <div class="note-list">
+          <a class="note-item" href="#vyuhu-1">
+            <div class="date">August 2026</div>
 
-  <section>
-    <div class="wrap">
-      <h2>Compute paths</h2>
-
-      <div class="table-wrap">
-        <table>
-          <thead>
-            <tr>
-              <th>Profile</th>
-              <th>Schedule</th>
-              <th>Active blocks</th>
-            </tr>
-          </thead>
-
-          <tbody>
-            <tr class="current">
-              <td><strong>HIGH</strong></td>
-              <td>[2, 4, 5, 4, 2]</td>
-              <td>23</td>
-            </tr>
-            <tr>
-              <td><strong>MEDIUM</strong></td>
-              <td>[1, 2, 3, 2, 1]</td>
-              <td>15</td>
-            </tr>
-            <tr>
-              <td><strong>LOW</strong></td>
-              <td>[0, 1, 1, 1, 0]</td>
-              <td>9</td>
-            </tr>
-          </tbody>
-        </table>
-      </div>
+            <div>
+              <div class="note-title">Vyuhu 1.0</div>
+              <div class="note-desc">
+                Architecture finalized · full base-model training in progress
+              </div>
+            </div>
 
-      <p class="status">
-        The new generation keeps deterministic compute selection while increasing global attention depth
-        and adding lightweight n-gram memory and multi-token prediction during training.
-      </p>
+            <div class="arrow">→</div>
+          </a>
+        </div>
+      </section>
+
+      <section id="vyuhu-1">
+        <h2>Vyuhu 1.0</h2>
+
+        <p class="lead small">
+          The Vyuhu architecture has completed its initial architecture-validation stage.
+          The current design is stable enough to move from architecture experiments to a full training run.
+        </p>
+
+        <p class="status">
+          Development now focuses on Vyuhu 1.0, a larger generation of the architecture built from the lessons learned with
+          <a href="https://huggingface.co/OrisTeam/Vyuhu-280M-Base-1704m" target="_blank" rel="noopener">
+            OrisTeam/Vyuhu-280M-Base-1704m ↗
+          </a>.
+        </p>
+
+        <div class="metric-grid">
+          <div class="metric">
+            <div class="k">Current checkpoint</div>
+            <div class="v">1.041B</div>
+            <div class="s">training tokens seen</div>
+          </div>
+          <div class="metric">
+            <div class="k">Old reference</div>
+            <div class="v">1.704B</div>
+            <div class="s">Vyuhu 280M training tokens</div>
+          </div>
+          <div class="metric">
+            <div class="k">Current eval</div>
+            <div class="v">3.22 / 3.23 / 3.31</div>
+            <div class="s">HIGH / MEDIUM / LOW around step 10.5k</div>
+          </div>
+        </div>
+      </section>
+
+      <section>
+        <h2>Architecture</h2>
+
+        <div class="table-wrap">
+          <table>
+            <thead>
+              <tr>
+                <th>Property</th>
+                <th>Vyuhu 280M</th>
+                <th>Vyuhu 1.0</th>
+              </tr>
+            </thead>
+
+            <tbody>
+              <tr><td>Parameters</td><td>282.68M</td><td><strong>~493M</strong></td></tr>
+              <tr><td>Hidden size</td><td>1152</td><td><strong>1280</strong></td></tr>
+              <tr><td>Context</td><td>1024</td><td><strong>1536</strong></td></tr>
+              <tr><td>Q / KV heads</td><td>18 / 6</td><td><strong>20 / 4</strong></td></tr>
+              <tr><td>Head dimension</td><td>64</td><td><strong>64</strong></td></tr>
+              <tr><td>FFN</td><td>3584</td><td><strong>3840</strong></td></tr>
+              <tr><td>GQA anchors</td><td>4</td><td><strong>6</strong></td></tr>
+              <tr><td>Elastic stages</td><td>3</td><td><strong>5</strong></td></tr>
+              <tr><td>Elastic blocks</td><td>12</td><td><strong>17</strong></td></tr>
+              <tr><td>Compute paths</td><td>4</td><td><strong>3</strong></td></tr>
+              <tr><td>Engram memory</td><td>—</td><td><strong>2 / 3-gram</strong></td></tr>
+              <tr><td>MTP</td><td>—</td><td><strong>t+2 training objective</strong></td></tr>
+            </tbody>
+          </table>
+        </div>
+      </section>
+
+      <section>
+        <h2>Compute paths</h2>
+
+        <div class="table-wrap">
+          <table>
+            <thead>
+              <tr>
+                <th>Profile</th>
+                <th>Schedule</th>
+                <th>Active blocks</th>
+              </tr>
+            </thead>
+
+            <tbody>
+              <tr class="current"><td><strong>HIGH</strong></td><td>[2, 4, 5, 4, 2]</td><td>23</td></tr>
+              <tr><td><strong>MEDIUM</strong></td><td>[1, 2, 3, 2, 1]</td><td>15</td></tr>
+              <tr><td><strong>LOW</strong></td><td>[0, 1, 1, 1, 0]</td><td>9</td></tr>
+            </tbody>
+          </table>
+        </div>
+
+        <p class="status">
+          The new generation keeps deterministic compute selection while increasing global attention depth
+          and adding lightweight n-gram memory and multi-token prediction during training.
+        </p>
+      </section>
+
+      <section id="frozen-eval">
+        <h2>Frozen eval progression</h2>
+
+        <p class="lead small">
+          A fixed 10M-token evaluation sample is reused across checkpoints. The important part is not only that all paths improve,
+          but that after the early catch-up phase they continue to fall in a remarkably similar way.
+        </p>
+
+        <div class="table-wrap" style="margin-top:22px">
+          <table>
+            <thead>
+              <tr>
+                <th>Step</th>
+                <th>HIGH</th>
+                <th>MEDIUM</th>
+                <th>LOW</th>
+              </tr>
+            </thead>
+
+            <tbody>
+              <tr><td>1000</td><td>4.6923</td><td>4.9207</td><td>5.6081</td></tr>
+              <tr><td>1500</td><td>4.1547</td><td>4.2283</td><td>4.4372</td></tr>
+              <tr><td>2000</td><td>3.9331</td><td>3.9800</td><td>4.0886</td></tr>
+              <tr><td>2500</td><td>3.8115</td><td>3.8422</td><td>3.9300</td></tr>
+              <tr><td>3000</td><td>3.7198</td><td>3.7458</td><td>3.8220</td></tr>
+              <tr><td>3500</td><td>3.6415</td><td>3.6600</td><td>3.7384</td></tr>
+              <tr><td>4000</td><td>3.5803</td><td>3.6013</td><td>3.6675</td></tr>
+              <tr><td>4500</td><td>3.5255</td><td>3.5462</td><td>3.6127</td></tr>
+              <tr><td>5000</td><td>3.4785</td><td>3.4931</td><td>3.5710</td></tr>
+              <tr><td>5500</td><td>3.4675</td><td>3.4783</td><td>3.5403</td></tr>
+              <tr><td>6000</td><td>3.4199</td><td>3.4340</td><td>3.5122</td></tr>
+              <tr><td>6500</td><td>3.3807</td><td>3.3915</td><td>3.4657</td></tr>
+              <tr><td>7000</td><td>3.3535</td><td>3.3629</td><td>3.4334</td></tr>
+              <tr><td>8000</td><td>3.3217</td><td>3.3321</td><td>3.4044</td></tr>
+              <tr><td>9000</td><td>3.2654</td><td>3.2770</td><td>3.3469</td></tr>
+              <tr><td>10000</td><td>3.2351</td><td>3.2461</td><td>3.3190</td></tr>
+              <tr class="current"><td><strong>10500</strong></td><td><strong>3.2215</strong></td><td><strong>3.2325</strong></td><td><strong>3.3090</strong></td></tr>
+            </tbody>
+          </table>
+        </div>
+
+        <div class="callout">
+          From step 6500 to ~10500 the three paths improve by almost the same absolute amount.
+          That is different from the early phase, where LOW has to catch up rapidly.
+          At this point there is still no obvious capacity wall: the paths are separated, but they are still moving downward together.
+        </div>
+
+        <p class="status">
+          Protocol: frozen 10M-token sample · identical tokenized data at every checkpoint · selected checkpoint evaluation.
+        </p>
+      </section>
+
+      <section id="training-exposure">
+        <h2>Training exposure</h2>
+
+        <p class="lead small">
+          The comparison is deliberately not flattering to Vyuhu 1.0. The old 280M reference had already seen about 1.704B tokens,
+          while this 1.0 snapshot is at about 1.041B.
+        </p>
+
+        <div class="metric-grid">
+          <div class="metric">
+            <div class="k">Old 280M</div>
+            <div class="v">~6.0</div>
+            <div class="s">tokens per stored parameter, using 1.704B / 282.68M</div>
+          </div>
+          <div class="metric">
+            <div class="k">Vyuhu 1.0</div>
+            <div class="v">~2.1</div>
+            <div class="s">tokens per stored parameter, using 1.041B / ~493M</div>
+          </div>
+          <div class="metric">
+            <div class="k">Important caveat</div>
+            <div class="v">shared paths</div>
+            <div class="s">active-path parameter exposure is not identical to dense-model token/parameter accounting</div>
+          </div>
+        </div>
+
+        <div class="callout">
+          This makes the current comparison conservative for 1.0. The old run had more total training exposure relative to its stored parameter count.
+          Vyuhu 1.0 also distributes learning across shared paths, so a simple dense-model token/parameter ratio is only a rough reference, not a compute-equivalent metric.
+        </div>
+      </section>
+
+      <section id="dataset-note">
+        <h2>Dataset note</h2>
+
+        <p class="lead small">
+          Vyuhu 1.0 is not a continuation of the old 280M dataset recipe. The training mix was changed as part of the new run.
+        </p>
+
+        <p class="tiny">
+          The newer corpus is more deliberately shaped around language formation and robust continuation:
+          more maximally clean Polish text, more ambiguity and varied contexts so representations are reinforced across different formulations,
+          and a broader knowledge signal instead of relying on a single encyclopedic source. In the current trainer the main mixture is
+          50% <strong>clean</strong>, 25% <strong>keep</strong>, 15% <strong>Wikipedia</strong>, 7% <strong>split</strong>,
+          with smaller law/judicial, balanced NKJP and OpenSubtitles components. Wikipedia is therefore useful, but intentionally not the whole knowledge story.
+        </p>
+
+        <div class="callout">
+          This matters when reading the generations below. Better language structure cannot be attributed to architecture alone:
+          architecture, training objective, Engram, MTP and the changed data mixture all moved at once.
+        </div>
+      </section>
+
+      <section id="generation-comparison">
+        <h2>Generation comparison</h2>
+
+        <p class="lead small">
+          Same sampling setup, same prompt family, multiple seeds. The examples below are not a benchmark score;
+          they are qualitative snapshots showing how the failure mode changed.
+        </p>
+
+        <div class="example-grid">
+          <div class="example">
+            <div class="eyebrow">Old Vyuhu 280M · Vasudeva · 1.704B tokens</div>
+            <pre>Polska jest, jak każdy inny, nie mniej niebezpieczny.
+– Nie możemy tego nazwać świętem i świętem chrześcijańskim [...]
+Zaznaczył przy tym, że w tym roku przypadają one po raz pierwszy od trzech dekad.</pre>
+          </div>
 
-      <p class="status">
-        <strong>Status:</strong> full base-model training in progress.
-      </p>
-    </div>
-  </section>
+          <div class="example">
+            <div class="eyebrow">Vyuhu 1.0 · HIGH · 1.041B tokens</div>
+            <pre>Polska jest na fali. "Jesteśmy w szoku"
+Wiadomości24 » Wydarzenia » Polityka » Polska jest na fali.
+"Nasze porażki są mniejsze, a nasz cel jest inny. Będziemy walczyć o to, żeby utrzymać się na szczycie".</pre>
+          </div>
 
+          <div class="example">
+            <div class="eyebrow">Old Vyuhu 280M · Aniruddha</div>
+            <pre>Polska jest, że jego "oznaczenie" i "animację moralna".
+- A jednak to nie tylko jest to, jak by było. A może o tym powiedzieć nam wszyscy [...]</pre>
+          </div>
 
-  <section id="frozen-eval">
-    <div class="wrap">
-      <h2>Frozen eval sample</h2>
-
-      <p class="lead" style="font-size:15px;max-width:760px">
-        A fixed 10M-token evaluation sample is reused across checkpoints to make progress directly comparable.
-        The table below evaluates the same frozen sample at selected checkpoints from step 1000 through 6500.
-      </p>
-
-      <div class="table-wrap" style="margin-top:22px">
-        <table>
-          <thead>
-            <tr>
-              <th>Step</th>
-              <th>HIGH</th>
-              <th>HIGH MTP</th>
-              <th>MEDIUM</th>
-              <th>MEDIUM MTP</th>
-              <th>LOW</th>
-              <th>LOW MTP</th>
-            </tr>
-          </thead>
-
-          <tbody>
-            <tr>
-              <td><strong>1000</strong></td>
-              <td>4.6923</td>
-              <td>6.9068</td>
-              <td>4.9207</td>
-              <td>7.0909</td>
-              <td>5.6081</td>
-              <td>7.4272</td>
-            </tr>
-            <tr>
-              <td><strong>1500</strong></td>
-              <td>4.1547</td>
-              <td>6.4989</td>
-              <td>4.2283</td>
-              <td>6.5649</td>
-              <td>4.4372</td>
-              <td>6.7248</td>
-            </tr>
-            <tr>
-              <td><strong>2000</strong></td>
-              <td>3.9331</td>
-              <td>6.2152</td>
-              <td>3.9800</td>
-              <td>6.2631</td>
-              <td>4.0886</td>
-              <td>6.3697</td>
-            </tr>
-            <tr>
-              <td><strong>2500</strong></td>
-              <td>3.8115</td>
-              <td>6.0240</td>
-              <td>3.8422</td>
-              <td>6.0578</td>
-              <td>3.9300</td>
-              <td>6.1465</td>
-            </tr>
-            <tr>
-              <td><strong>3000</strong></td>
-              <td>3.7198</td>
-              <td>5.9009</td>
-              <td>3.7458</td>
-              <td>5.9354</td>
-              <td>3.8220</td>
-              <td>6.0111</td>
-            </tr>
-            <tr>
-              <td><strong>3500</strong></td>
-              <td>3.6415</td>
-              <td>5.7945</td>
-              <td>3.6600</td>
-              <td>5.8154</td>
-              <td>3.7384</td>
-              <td>5.9015</td>
-            </tr>
-            <tr>
-              <td><strong>4000</strong></td>
-              <td>3.5803</td>
-              <td>5.7165</td>
-              <td>3.6013</td>
-              <td>5.7459</td>
-              <td>3.6675</td>
-              <td>5.8220</td>
-            </tr>
-            <tr>
-              <td><strong>4500</strong></td>
-              <td>3.5255</td>
-              <td>5.6505</td>
-              <td>3.5462</td>
-              <td>5.6737</td>
-              <td>3.6127</td>
-              <td>5.7479</td>
-            </tr>
-            <tr>
-              <td><strong>5000</strong></td>
-              <td>3.4785</td>
-              <td>5.5925</td>
-              <td>3.4931</td>
-              <td>5.6102</td>
-              <td>3.5710</td>
-              <td>5.6935</td>
-            </tr>
-            <tr>
-              <td><strong>5500</strong></td>
-              <td>3.4675</td>
-              <td>5.5608</td>
-              <td>3.4783</td>
-              <td>5.5748</td>
-              <td>3.5403</td>
-              <td>5.6529</td>
-            </tr>
-            <tr>
-              <td><strong>6000</strong></td>
-              <td>3.4199</td>
-              <td>5.5091</td>
-              <td>3.4340</td>
-              <td>5.5263</td>
-              <td>3.5122</td>
-              <td>5.6150</td>
-            </tr>
-            <tr class="current">
-              <td><strong>6500</strong></td>
-              <td><strong>3.3807</strong></td>
-              <td>5.4734</td>
-              <td><strong>3.3915</strong></td>
-              <td>5.4866</td>
-              <td><strong>3.4657</strong></td>
-              <td>5.5773</td>
-            </tr>
-          </tbody>
-        </table>
-      </div>
+          <div class="example">
+            <div class="eyebrow">Vyuhu 1.0 · LOW</div>
+            <pre>Rząd poinformował, że na terenie gminy Czerniaków w dniach od 6 do 14 czerwca nie będzie możliwości parkowania.
+- W związku z tym, że w najbliższym czasie planujemy kolejne inwestycje [...]</pre>
+          </div>
 
-      <p class="status">
-        <strong>Protocol:</strong> frozen 10M-token sample · identical tokenized data at every checkpoint · evaluation at selected checkpoints.
-        This isolates model progress from sampling noise in the eval set.
-      </p>
-    </div>
-  </section>
+          <div class="example">
+            <div class="eyebrow">Old Vyuhu 280M · Sankarshana</div>
+            <pre>Historia Polski od początku XX wieku do końca XIX wieku, a Polska od początku XXI wieku.
+Należałoby zatem wyróżnić się z tłumu. Każdy zainteresowany może zostać zaproszony na konferencję historyczną [...]</pre>
+          </div>
 
+          <div class="example">
+            <div class="eyebrow">Vyuhu 1.0 · HIGH</div>
+            <pre>Historia Polski 1935–1945. Do 1939 roku w składzie niepodległego państwa polskiego.
+Urodził się 26 czerwca 1868, jako syn Ludwika i Adelajdy z domu Gwidoko [...]</pre>
+          </div>
+        </div>
+      </section>
+
+      <section id="interpretation">
+        <h2>What the generations suggest</h2>
+
+        <ul class="conclusion-list">
+          <li><strong>Vyuhu 1.0 is already more structurally stable.</strong> Even LOW usually keeps sentence shape, document form and local continuity instead of collapsing into syntax noise.</li>
+          <li><strong>That does not mean the model already “knows” the facts.</strong> It often selects the right kind of document or topic frame, then fills it with wrong dates, invented institutions or mixed historical events.</li>
+          <li><strong>The interesting change is that it is starting to try to use knowledge.</strong> The model increasingly reaches for dates, institutions, named entities, article structure, citations, lists and historical framing. The retrieval-like shape is there; the internal factual representation is not yet fully consolidated.</li>
+          <li><strong>This is a better failure mode for continued pretraining.</strong> The model is less likely to lose Polish itself; the remaining problem is increasingly one of content fidelity and stored knowledge.</li>
+          <li><strong>LOW is not behaving like the old smallest path.</strong> The smallest 1.0 route often looks like a compact version of the same model family rather than a partially collapsed network.</li>
+          <li><strong>HIGH and MEDIUM are still unusually close.</strong> Their loss curves and generations suggest that the shared representation is doing most of the work at the current stage. A later divergence would be a sign that MEDIUM is approaching its capacity limit while HIGH still has room.</li>
+        </ul>
+
+        <div class="callout">
+          The current evidence should not be read as “Vyuhu 1.0 has no knowledge”.
+          A more accurate reading is: it is already learning to organize and invoke knowledge-like structure, but at ~1.04B tokens it has not yet consolidated enough of the underlying facts to make those structures reliably correct.
+        </div>
+      </section>
+
+      <section id="status">
+        <h2>Status</h2>
+        <p class="status">
+          Full base-model training in progress. Current qualitative comparison uses Vyuhu 1.0 around 1.041B training tokens
+          against the archived Vyuhu 280M reference at about 1.704B.
+        </p>
+      </section>
 
+    </div>
+  </div>
 </main>
 
 <footer>
-  <div class="wrap">
-    Oris Notes · OrisTeam · 2026
+  <div class="shell">
+    <div></div>
+    <div class="wrap">
+      Oris Notes · OrisTeam · 2026
+    </div>
   </div>
 </footer>