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<main class="wrap">

  <header class="head">
    <div class="head-top">
      <div class="head-brand">
        <div class="head-icon">🔁</div>
        <div>
          <div class="head-title">Second Loop</div>
          <div class="head-subtitle">Honesty &amp; self-correction in language models</div>
        </div>
      </div>
      <div class="head-right">
        <span class="submitted-label">Submitted by</span>
        <div class="submitted-name">Serghei Brinza</div>
        <div class="status-pill"><span class="status-dot"></span>Static · project hub</div>
      </div>
    </div>
    <div class="head-tag">★&nbsp; Three experiments · one arc &nbsp;★</div>
    <div class="head-meta">
      <div><div class="k">Subject model</div><div class="v">Qwen2.5-3B-Instruct (frozen)</div></div>
      <div><div class="k">Independent judge</div><div class="v">Qwen2.5-7B-Instruct</div></div>
      <div><div class="k">License</div><div class="v">MIT</div></div>
    </div>
  </header>

  <section class="arc">
    <div class="t">The arc</div>
    <div class="body">
      Three demo Spaces, one through-line. <b>(1)</b> Can a confidently memorized error in a frozen
      LLM be <b>durably corrected</b>? <b>(2)</b> Can that correction <b>survive a noisy notebook</b>
      whose external entries are partly unreliable? <b>(3)</b> How <b>rarely can external truth
      arrive</b> before calibration collapses back to the raw model? Every linked Space is fully
      static — no model is loaded, and every number is a verbatim output of the live experimental run.
    </div>
  </section>

  <section class="cards">
    <div class="card">
      <div class="num">Part 1</div>
      <div class="name">Scar-Survival</div>
      <div class="hook">A memorized LLM error, corrected — how durable is the fix? Turn the mechanism
        on, reload the frozen model, then stress it with a counterfeit fact.</div>
      <div class="key">0/12 → 12/12 · holds 10/10 reloads · 6/12 survive</div>
      <div class="links">
        <a class="space" href="https://huggingface.co/spaces/Laborator/scar-survival" target="_blank" rel="noopener">Open Space ↗</a>
        <a class="gh" href="https://github.com/SergheiBrinza/scar-survival" target="_blank" rel="noopener">GitHub ↗</a>
      </div>
    </div>

    <div class="card">
      <div class="num">Part 2</div>
      <div class="name">External Grounding</div>
      <div class="hook">Lifting self-correction from 50% to 100% under a noisy notebook. Drag the
        guardian through six versions and watch which traps get fixed — and which regress.</div>
      <div class="key">50% → 100% · 66.7% plateau · +fixed / −broken</div>
      <div class="links">
        <a class="space" href="https://huggingface.co/spaces/Laborator/external-grounding" target="_blank" rel="noopener">Open Space ↗</a>
        <a class="gh" href="https://github.com/SergheiBrinza/external-grounding" target="_blank" rel="noopener">GitHub ↗</a>
      </div>
    </div>

    <div class="card solo">
      <div class="num">Part 3</div>
      <div class="name">Thin Channel</div>
      <div class="hook">How rarely external truth can arrive before calibration collapses. Move the
        lever from “every day” to “never” and watch the curve hold — until the cliff.</div>
      <div class="key">finite schedule holds · zero contact collapses to raw 3B</div>
      <div class="links">
        <a class="space" href="https://huggingface.co/spaces/Laborator/thin-channel" target="_blank" rel="noopener">Open Space ↗</a>
        <a class="gh" href="https://github.com/SergheiBrinza/thin-channel" target="_blank" rel="noopener">GitHub ↗</a>
      </div>
    </div>
  </section>

  <footer class="foot">
    <div class="ftitle">About</div>
    <div class="b">
      Second Loop is independent research on whether a language model can be made to correct its own
      confident mistakes and stay honest under pressure. The three Spaces above are interactive,
      static visualizations of the experiments — no live model runs in any of them, and every value
      shown is a verbatim output of the original run, bundled as data alongside each page. Source
      code, raw per-run results and methodology live in the GitHub repository linked on every card.
    </div>
    <div class="attrib">
      Subject model Qwen2.5-3B-Instruct · independent judge Qwen2.5-7B-Instruct (both Apache-2.0,
      Alibaba Cloud). Run on a single RTX 3090. No model weights are redistributed here. Demo code: MIT.
    </div>
  </footer>

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