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  <title id="title">GLiNER StreamingSpan architecture</title>
  <desc id="desc">Minimal architecture diagram showing a cached entity-type prompt on the left, an incrementally appended input stream on the right, a causal transformer with a reusable key-value cache, cached label and word states, rolling span representations, similarity scores, and a revised entity snapshot.</desc>

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  <text x="900" y="52" class="title" text-anchor="middle">GLiNER StreamingSpan Architecture</text>
  <text x="900" y="75" class="subtitle" text-anchor="middle">Causal cache reuse · rolling span revision · complete entity snapshots</text>
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  <text x="82" y="151" class="tiny" font-weight="700">SERIALIZED ON THE COLD PASS</text>
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    <text x="233" y="193.5" class="chip-text">&lt;&lt;LABEL&gt;&gt;</text>
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  <text x="435" y="249" class="small" text-anchor="middle">label marker states → compact label encoder → cached label embeddings</text>

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  <text x="950" y="151" class="tiny" font-weight="700">CACHED TEXT · t−1</text>
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  <text x="1335" y="249" class="small" text-anchor="middle">the session may receive one word, sentence, or arbitrary text chunk per call</text>

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  <text x="900" y="344" class="section" text-anchor="middle">1 · Causal Transformer Language Model</text>
  <text x="900" y="368" class="body" text-anchor="middle">SmolLM2, Qwen, or another compatible decoder backbone</text>
  <text x="900" y="388" class="small" text-anchor="middle">Cold pass: prompt + first chunk · warm pass: current tokens attend to cached history</text>

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  <text x="900" y="413.5" class="chip-text">new token IDs + absolute positions</text>
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  <text x="1266.5" y="413.5" class="chip-text">updated KV cache · t</text>

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  <text x="1567.5" y="541" class="body" text-anchor="middle" font-weight="700">Session cache</text>
  <text x="1567.5" y="561" class="small mono" text-anchor="middle">KV · words · labels · span scores</text>

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  <text x="450" y="666" class="heading" text-anchor="middle">2A · Label representation layer</text>
  <text x="450" y="691" class="body" text-anchor="middle">prompt slice through &lt;&lt;SEP&gt;&gt; → compact context encoder → one vector per &lt;&lt;LABEL&gt;&gt;</text>
  <text x="450" y="710" class="tiny" text-anchor="middle">computed on the cold pass and reused for every warm append</text>

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  <text x="1315" y="666" class="heading" text-anchor="middle">2B · Rolling span representation layer</text>
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    <text x="1134" y="850">(1,3)</text>
    <text x="1266" y="850">(3,4)</text>
    <text x="1398" y="850">(4,4)</text>
    <text x="1530" y="850">(4,6)</text>
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  <text x="610" y="913" class="heading" text-anchor="middle">3 · Entity type × span similarity</text>
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  <text x="1440" y="917" class="heading" text-anchor="middle">4 · Revise history and decode</text>
  <text x="1180" y="946" class="body">• matching (start, end): replace the recent score</text>
  <text x="1180" y="970" class="body">• new boundary: append → threshold → flat / nested NER</text>
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  <text x="1180" y="1002" class="tiny" font-weight="700">COMPLETE SNAPSHOT · t</text>
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  <text x="900" y="1082" class="body" text-anchor="middle" font-weight="650">Every append reuses KV, word, and label caches · only rolling candidates are rescored · the result is the full current snapshot</text>
  <text x="900" y="1106" class="small mono" text-anchor="middle">StreamingBatch  ·  AsyncStreamingEngine  ·  inference(session_id=…)</text>
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