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<div class="page">

  <header>
    <div class="wordmark">Grounded <span>Systems</span></div>
    <div class="tagline">Independent ML Research &amp; Engineering</div>
    <div class="rule"></div>
    <p class="framing">
      Independent research and engineering work in retrieval systems, structured prediction,
      and language-grounded generation. Projects focus on controlled-vocabulary mapping,
      embedding-based retrieval, and constrained LLM output, with a focus on domains where
      precision and reliability matter more than open-ended generation.
    </p>
  </header>

  <div class="section-label">Projects</div>

  <div class="projects">

    <a class="project" href="https://huggingface.co/FoodDesert/Boring_Embeddings" target="_blank" rel="noopener">
      <div class="project-header">
        <div class="project-name">Boring Embeddings</div>
        <div class="project-arrow">↗</div>
      </div>
      <p class="project-desc">
        <strong>Negative textual inversion embeddings for generative image models.</strong>
        Trained on community-engagement signals rather than manually curated defect lists,
        capturing unnamed visual patterns associated with low-quality outputs that tag-based
        approaches cannot express. Used as a default negative embedding in several popular
        Stable Diffusion workflows.
      </p>
      <div class="project-tags">
        <span class="stat">10M+ generations</span>
        <span class="tag">Textual Inversion</span>
        <span class="tag">Representation Learning</span>
        <span class="tag">Generative Models</span>
      </div>
    </a>

    <a class="project" href="https://huggingface.co/spaces/FoodDesert/Prompt_Squirrel_RAG" target="_blank" rel="noopener">
      <div class="project-header">
        <div class="project-name">Prompt Squirrel — Tag Retrieval System</div>
        <div class="project-arrow">↗</div>
      </div>
      <p class="project-desc">
        <strong>Retrieval-augmented system mapping natural language to a controlled vocabulary.</strong>
        Three-stage pipeline: LLM query reformulation into tag-like search phrases,
        HNSW approximate nearest-neighbor retrieval over fine-tuned FastText embeddings,
        and constrained LLM selection restricted to valid vocabulary items,
        eliminating hallucination of out-of-vocabulary terms entirely.
        Embeddings fine-tuned via alias-augmentation: domain alias metadata is injected
        as controlled noise during training, pulling misspellings and paraphrases toward
        their canonical forms in embedding space. Context rescoring uses late fusion of
        dense embedding similarity with a co-occurrence signal derived from
        tag co-occurrence pseudo-documents reduced via SVD. Modular per-category LLM query strategies
        run in parallel; listwise reranking chosen over pointwise specifically to enforce
        consistency across the selected tag set. Generalizes to any domain requiring
        unstructured-to-taxonomy mapping.
      </p>
      <div class="project-tags">
        <span class="tag">Retrieval Pipeline</span>
        <span class="tag">Constrained Generation</span>
        <span class="tag">FastText · FAISS · HNSW</span>
        <span class="tag">Late Fusion</span>
        <span class="tag">Listwise LLM Reranking</span>
        <span class="tag">Alias Augmentation</span>
        <span class="tag">Co-occurrence Modeling</span>
      </div>
    </a>

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    All work independently developed and deployed. 
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