{
  "id": "2607.02512",
  "title": "Program-as-Weights: A Programming Paradigm for Fuzzy Functions",
  "authors": "Wentao Zhang, Liliana Hotsko, Woojeong Kim, Pengyu Nie, Stuart Shieber, Yuntian Deng",
  "first_public": "2026-07-02",
  "first_public_source": "https://arxiv.org/abs/2607.02512",
  "arxiv_first_submission": "2026-07-02",
  "date_label": "arXiv v1",
  "arxiv": "2607.02512",
  "paper_url": "https://arxiv.org/abs/2607.02512",
  "venue": "NeurIPS 2026 (accepted)",
  "lens": [
    "neural-programs",
    "generated-adapters"
  ],
  "related_terms": [
    "Program-as-Weights",
    "ProgramAsWeights",
    "PAW",
    "fuzzy-function programming",
    "neural compiler",
    "neural interpreter",
    "FuzzyBench"
  ],
  "summary": "Proposes fuzzy-function programming: compile a natural-language function specification into a neural program, then reuse it locally.",
  "contribution": "Develops the compiler–interpreter abstraction and programs that combine text with learned parameters. Introduces the 10M-example FuzzyBench dataset, local execution interfaces, and image-conditioned compilation. Prefix tuning and LoRA are two implementations.",
  "limitations": "Each compiler produces programs for a specific interpreter. Compiled parameters are opaque, and FuzzyBench uses synthetic data.",
  "program_form": "A discrete pseudo-program and a compiler-generated parameter-efficient module.",
  "conditioning": "A function specification, optionally with examples. The image extension also accepts visual information.",
  "execution": "A frozen lightweight interpreter loads the program and runs new inputs locally.",
  "relationship": "Compile by Training builds on this programming abstraction. It keeps the same interface and refines the fast compiler’s prediction using synthesized examples.",
  "relationship_source": "https://arxiv.org/html/2609.04199v1#S1",
  "project_url": "https://programasweights.com/",
  "code_url": "https://github.com/programasweights",
  "equal_contribution": [
    "Wentao Zhang",
    "Liliana Hotsko",
    "Woojeong Kim"
  ],
  "venue_source": "https://yuntiandeng.com/",
  "provenance": "July 2026 arXiv paper. NeurIPS 2026 acceptance is listed on Yuntian Deng’s homepage.",
  "author_names": [
    "Wentao Zhang",
    "Liliana Hotsko",
    "Woojeong Kim",
    "Pengyu Nie",
    "Stuart Shieber",
    "Yuntian Deng"
  ],
  "record_url": "https://programasweights.com/reading-list/papers/2607.02512/",
  "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2607-02512",
  "citation_date": "2026-07-02",
  "json_url": "https://programasweights.com/reading-list/papers/2607.02512/record.json",
  "markdown_url": "https://programasweights.com/reading-list/papers/2607.02512/index.md",
  "bibtex_url": "https://programasweights.com/reading-list/papers/2607.02512/citation.bib",
  "csl_url": "https://programasweights.com/reading-list/papers/2607.02512/citation.csl.json",
  "method_comparisons": [
    {
      "title": "From generated adapters to reusable functions",
      "text": "Hypter, HINT, HyperTuning, and Text-to-LoRA generate adaptations from descriptions or examples. PAW uses generated parameters as part of a program that can be saved, called from ordinary code, and reused by a fixed interpreter.",
      "source_ids": [
        "2101.00420",
        "2212.10315",
        "2211.12485",
        "2506.06105",
        "2607.02512"
      ],
      "source_urls": [
        "https://arxiv.org/html/2607.02512v1#S10",
        "https://proceedings.mlr.press/v267/charakorn25a.html"
      ],
      "source_labels": [
        "PAW related work",
        "Text-to-LoRA paper"
      ],
      "topic_id": "generated-adapters",
      "topic_label": "Generated weights and adapters",
      "topic_url": "https://programasweights.com/reading-list/guides/compilation/#generated-adapters"
    },
    {
      "title": "Code and neural programs",
      "text": "ALCHEmist generates symbolic labeling programs. PAW compiles fuzzy functions into text and learned parameters. Both build an artifact once and reuse it; one runs as code, the other on a neural interpreter.",
      "source_ids": [
        "2407.11004",
        "2607.02512"
      ],
      "source_urls": [
        "https://proceedings.neurips.cc/paper_files/paper/2024/hash/72802bef5cf1a3449e909b20c2ae18d5-Abstract-Conference.html",
        "https://arxiv.org/html/2607.02512v1#S6"
      ],
      "source_labels": [
        "ALCHEmist paper",
        "PAW evaluation"
      ],
      "topic_id": "neural-programs",
      "topic_label": "Programs and reusable artifacts",
      "topic_url": "https://programasweights.com/reading-list/guides/fuzzy-functions/"
    },
    {
      "title": "What gets reused?",
      "text": "Gisting caches a few instruction activations. Generative Adapter stores context in parameter updates. PAW saves a function as a program containing text and a neural module.",
      "source_ids": [
        "2304.08467",
        "2411.05877",
        "2607.02512"
      ],
      "source_urls": [
        "https://arxiv.org/abs/2304.08467",
        "https://arxiv.org/abs/2411.05877",
        "https://arxiv.org/html/2607.02512v1#S2"
      ],
      "source_labels": [
        "Gist paper",
        "Generative Adapter paper",
        "PAW program definition"
      ],
      "topic_id": "context-compression",
      "topic_label": "Context compression",
      "topic_url": "https://programasweights.com/reading-list/guides/compilation/#context-compression"
    },
    {
      "title": "Program form and compiler",
      "text": "LoRA represents adaptation as low-rank weight updates; prefix tuning uses continuous activations. Their original methods learn these parameters from examples. PAW generates both forms from specifications.",
      "source_ids": [
        "2106.09685",
        "2101.00190",
        "2607.02512"
      ],
      "source_urls": [
        "https://arxiv.org/abs/2106.09685",
        "https://aclanthology.org/2021.acl-long.353/",
        "https://arxiv.org/html/2607.02512v1#S3"
      ],
      "source_labels": [
        "LoRA paper",
        "Prefix-tuning paper",
        "PAW implementations"
      ],
      "topic_id": "peft",
      "topic_label": "Parameter-efficient building blocks",
      "topic_url": "https://programasweights.com/reading-list/guides/compilation/#peft"
    },
    {
      "title": "Fast prediction and training",
      "text": "PAW’s fast compiler predicts a program. Compile by Training starts from that prediction, generates examples, and trains an adapter. Both use the same runtime interface; the extra training improves semantic accuracy on FuzzyBench-Hard at a higher compilation cost.",
      "source_ids": [
        "2607.02512",
        "2609.04199"
      ],
      "source_urls": [
        "https://arxiv.org/html/2607.02512v1#S3",
        "https://arxiv.org/html/2609.04199v1#S3",
        "https://arxiv.org/html/2609.04199v1#S5"
      ],
      "source_labels": [
        "PAW compiler implementations",
        "Compile by Training method",
        "Compile by Training evaluation"
      ],
      "topic_id": "training-compilation",
      "topic_label": "Compilation through training",
      "topic_url": "https://programasweights.com/reading-list/guides/compilation/#training-compilation"
    }
  ]
}