{ "metadata": { "version": "1.2.0", "updated_at": "2026-10-19", "search_refreshed_at": "2026-10-19", "topic_id": "neural-programs", "name": "Programs and reusable artifacts", "definition": "Learn programs that can be saved and run on new inputs.", "record_count": 4, "topic_url": "https://programasweights.com/reading-list/guides/fuzzy-functions/", "source_url": "https://programasweights.com/reading-list/data/papers.json" }, "comparisons": [ { "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" ] } ], "demos": [ ], "papers": [ { "id": "1511.06279", "title": "Neural Programmer-Interpreters", "authors": "Scott Reed, Nando de Freitas", "first_public": "2015-11-19", "first_public_source": "https://arxiv.org/abs/1511.06279", "arxiv_first_submission": "2015-11-19", "date_label": "arXiv v1", "arxiv": "1511.06279", "paper_url": "https://arxiv.org/abs/1511.06279", "venue": "ICLR 2016", "lens": [ "neural-programs" ], "related_terms": [ "neural programmer-interpreter", "neural programs", "program memory", "compositional execution" ], "summary": "Learns neural programs that share a recurrent interpreter and a program memory.", "contribution": "Learns from execution traces and composes subprograms for addition, sorting, and 3D-object canonicalization.", "limitations": "Training requires execution traces and task-specific interfaces.", "program_form": "Program embeddings stored in a key-value memory.", "conditioning": "A program identifier, arguments, and observations of the environment.", "execution": "A recurrent interpreter calls subprograms and takes actions in the environment.", "relationship": "PAW cites this work on learned program representations and shared interpreters.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "Scott Reed", "Nando de Freitas" ], "record_url": "https://programasweights.com/reading-list/papers/1511.06279/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-1511-06279", "citation_date": "2015-11-19", "json_url": "https://programasweights.com/reading-list/papers/1511.06279/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/1511.06279/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/1511.06279/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/1511.06279/citation.csl.json", "publication": { "title": "Neural Programmer-Interpreters", "author_names": [ "Scott Reed", "Nando de Freitas" ], "year": 2016, "type": "paper-conference", "container_title": "International Conference on Learning Representations", "url": "https://arxiv.org/abs/1511.06279", "source_url": "https://arxiv.org/pdf/1511.06279", "verified_at": "2026-10-19", "source_note": "The paper PDF identifies ICLR 2016; the conference program confirms the presentation." }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/1511.06279/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/1511.06279/publication.csl.json" }, { "id": "2407.11004", "title": "The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data Annotators", "authors": "Tzu-Heng Huang, Catherine Cao, Vaishnavi Bhargava, Frederic Sala", "first_public": "2024-06-25", "first_public_source": "https://arxiv.org/abs/2407.11004", "arxiv_first_submission": "2024-06-25", "date_label": "arXiv v1", "arxiv": "2407.11004", "paper_url": "https://arxiv.org/abs/2407.11004", "venue": "NeurIPS 2024", "lens": [ "neural-programs" ], "related_terms": [ "ALCHEmist", "program synthesis", "automated labeling", "reusable tools" ], "summary": "Asks a language model to write labeling programs that can be saved and run locally.", "contribution": "Reuses generated code across many labeling decisions and combines noisy program outputs with weak supervision.", "limitations": "Label quality depends on the model’s ability to write useful programs. Complex tasks can produce code that is harder to inspect.", "program_form": "Symbolic labeling programs.", "conditioning": "A task description, labeling instructions, and a function signature.", "execution": "Generated programs label examples locally; their outputs can be combined to train a downstream model.", "relationship": "PAW evaluates an ALCHEmist-style approach. Both build reusable tools: ALCHEmist produces symbolic code, while PAW produces programs for a neural interpreter.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "Tzu-Heng Huang", "Catherine Cao", "Vaishnavi Bhargava", "Frederic Sala" ], "record_url": "https://programasweights.com/reading-list/papers/2407.11004/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2407-11004", "citation_date": "2024-06-25", "json_url": "https://programasweights.com/reading-list/papers/2407.11004/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2407.11004/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2407.11004/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2407.11004/citation.csl.json", "publication": { "title": "The ALCHEmist: Automated Labeling 500x CHEaper than LLM Data Annotators", "author_names": [ "Tzu-Heng Huang", "Catherine Cao", "Vaishnavi Bhargava", "Frederic Sala" ], "year": 2024, "type": "paper-conference", "container_title": "Advances in Neural Information Processing Systems", "url": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/72802bef5cf1a3449e909b20c2ae18d5-Abstract-Conference.html", "source_url": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/72802bef5cf1a3449e909b20c2ae18d5-Abstract-Conference.html", "verified_at": "2026-10-19", "volume": "37", "doi": "10.52202/079017-2003", "pages": "62648-62672" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2407.11004/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2407.11004/publication.csl.json", "method_comparisons": [ { "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/" } ] }, { "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" } ] }, { "id": "2609.04199", "title": "Compile by Training: Turning Natural-Language Specifications into Local Neural Functions", "authors": "Yuntian Deng, Pengyu Nie, Stuart Shieber", "first_public": "2026-09-03", "first_public_source": "https://arxiv.org/abs/2609.04199", "arxiv_first_submission": "2026-09-03", "date_label": "arXiv v1", "arxiv": "2609.04199", "paper_url": "https://arxiv.org/abs/2609.04199", "original_url": "https://arxiv.org/html/2609.04199v1", "venue": "EMNLP 2026 System Demonstrations", "venue_source": "https://arxiv.org/abs/2609.04199", "lens": [ "neural-programs", "training-compilation" ], "related_terms": [ "compile by training", "training-based compilation", "specification-specific optimization", "synthetic supervision", "local neural function", "FuzzyBench-Hard", "ProgramAsWeights" ], "summary": "Compiles a function description into a reusable local program by generating training examples and optimizing an adapter.", "contribution": "Makes task-specific training a software build step. Raises FuzzyBench-Hard semantic accuracy (LEM) from 22.4% to 83.6% and demonstrates composition in a website helper, avatar control, and English–Claudish translation.", "limitations": "Synthetic examples can inherit teacher errors. Cold compilation takes 50.9 seconds for one specification on a B300. FuzzyBench-Hard selects fast-compiler failures under exact match; the reported accuracy uses an LLM judge.", "program_form": "A LoRA adapter, runtime prompt, specification, and interpreter metadata in a PAW program.", "conditioning": "The specification guides teacher synthesis. PAW’s fast compiler supplies the initial adapter and runtime prompt.", "execution": "A frozen Qwen3-0.6B interpreter runs the saved function locally without further teacher calls.", "relationship": "Extends PAW with training at compile time while keeping its program format and runtime interface.", "relationship_source": "https://arxiv.org/html/2609.04199v1#S1", "project_url": "https://programasweights.com/playground?compiler=paw-ft-bs48", "demo_url": "https://programasweights.com/playground?compiler=paw-ft-bs48", "code_url": "https://github.com/programasweights/compile-by-training", "provenance": "September 2026 arXiv paper; its record lists EMNLP 2026 System Demonstrations.", "first_public_note": "The paper reports public-service measurements from May 2026.", "author_names": [ "Yuntian Deng", "Pengyu Nie", "Stuart Shieber" ], "record_url": "https://programasweights.com/reading-list/papers/2609.04199/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2609-04199", "citation_date": "2026-09-03", "json_url": "https://programasweights.com/reading-list/papers/2609.04199/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2609.04199/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2609.04199/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2609.04199/citation.csl.json", "method_comparisons": [ { "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" } ] } ] }