{ "metadata": { "language": "zh-CN", "updated_at": "2026-11-11", "version": "1.2.0", "record_count": 13, "canonical_url": "https://programasweights.com/zh/reading-list/", "english_url": "https://programasweights.com/reading-list/" }, "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", "summary_zh": "学习由共享循环解释器执行的神经程序，并把程序表示存入程序记忆。", "translations": { "zh-CN": { "summary": "学习由共享循环解释器执行的神经程序，并把程序表示存入程序记忆。", "relationship": "PAW 引用这项关于学习程序表示和共享解释器的研究，并将编译器与解释器接口用于自然语言定义的模糊函数。", "program_form": "存在键值记忆中的程序嵌入。", "conditioning": "程序标识、参数和环境观测。", "execution": "循环解释器调用子程序，并在环境中执行动作。" } } }, { "id": "1609.09106", "title": "HyperNetworks", "authors": "David Ha, Andrew Dai, Quoc V. Le", "first_public": "2016-09-27", "first_public_source": "https://arxiv.org/abs/1609.09106", "arxiv_first_submission": "2016-09-27", "date_label": "arXiv v1", "arxiv": "1609.09106", "paper_url": "https://arxiv.org/abs/1609.09106", "venue": "ICLR 2017", "lens": [ "generated-adapters" ], "related_terms": [ "hypernetwork", "weight generation", "fast weights", "conditional parameters" ], "summary": "Uses one neural network to generate the weights of another.", "contribution": "Applies weight generation to convolutional and recurrent networks, relaxing weight sharing across layers and generating dynamic weights for sequence models.", "limitations": "The hypernetwork is trained end to end for the target architecture.", "program_form": "Weights for the target network.", "conditioning": "Layer embeddings or the current recurrent state.", "execution": "The target network runs with the generated weights.", "relationship": "Provides the weight-generation mechanism used by later description-to-adapter methods and PAW.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "David Ha", "Andrew Dai", "Quoc V. Le" ], "record_url": "https://programasweights.com/reading-list/papers/1609.09106/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-1609-09106", "citation_date": "2016-09-27", "json_url": "https://programasweights.com/reading-list/papers/1609.09106/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/1609.09106/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/1609.09106/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/1609.09106/citation.csl.json", "publication": { "title": "HyperNetworks", "author_names": [ "David Ha", "Andrew M. Dai", "Quoc V. Le" ], "year": 2017, "type": "paper-conference", "container_title": "International Conference on Learning Representations", "url": "https://openreview.net/forum?id=rkpACe1lx", "source_url": "https://openreview.net/pdf?id=rkpACe1lx", "verified_at": "2026-10-19" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/1609.09106/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/1609.09106/publication.csl.json", "summary_zh": "用一个神经网络生成另一个神经网络的权重。", "translations": { "zh-CN": { "summary": "用一个神经网络生成另一个神经网络的权重。", "relationship": "超网络提供权重生成机制。PAW 用这一机制从函数描述生成供小模型执行的适配器。", "program_form": "目标网络的权重。", "conditioning": "层嵌入或当前循环状态。", "execution": "目标网络使用生成的权重完成计算。" } } }, { "id": "2101.00190", "title": "Prefix-Tuning: Optimizing Continuous Prompts for Generation", "authors": "Xiang Lisa Li, Percy Liang", "first_public": "2021-01-01", "first_public_source": "https://arxiv.org/abs/2101.00190", "arxiv_first_submission": "2021-01-01", "date_label": "arXiv v1", "arxiv": "2101.00190", "paper_url": "https://arxiv.org/abs/2101.00190", "venue": "ACL-IJCNLP 2021", "lens": [ "peft" ], "related_terms": [ "prefix-tuning", "continuous prompts", "virtual tokens", "parameter-efficient fine-tuning" ], "summary": "Adapts a frozen language model by learning a short sequence of continuous prefix vectors.", "contribution": "Trains prefixes for table-to-text generation and summarization while updating far fewer parameters than full fine-tuning.", "limitations": "A separate prefix is optimized for each task.", "program_form": "Continuous prefix activations.", "conditioning": "Training examples for the target task.", "execution": "The frozen language model attends to the learned prefix.", "relationship": "PAW evaluates compiler-generated prefixes alongside LoRA adapters.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "Xiang Lisa Li", "Percy Liang" ], "record_url": "https://programasweights.com/reading-list/papers/2101.00190/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2101-00190", "citation_date": "2021-01-01", "json_url": "https://programasweights.com/reading-list/papers/2101.00190/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2101.00190/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2101.00190/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2101.00190/citation.csl.json", "publication": { "title": "Prefix-Tuning: Optimizing Continuous Prompts for Generation", "author_names": [ "Xiang Lisa Li", "Percy Liang" ], "year": 2021, "type": "paper-conference", "container_title": "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", "url": "https://aclanthology.org/2021.acl-long.353/", "source_url": "https://aclanthology.org/2021.acl-long.353/", "verified_at": "2026-10-19", "doi": "10.18653/v1/2021.acl-long.353", "publisher": "Association for Computational Linguistics", "pages": "4582-4597" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2101.00190/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2101.00190/publication.csl.json", "method_comparisons": [ { "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" } ], "summary_zh": "训练一段连续前缀向量，让冻结的语言模型适应目标任务。", "translations": { "zh-CN": { "summary": "训练一段连续前缀向量，让冻结的语言模型适应目标任务。", "relationship": "PAW 把编译器生成的前缀作为神经程序的一种表示，并与生成的 LoRA 适配器进行比较。", "program_form": "连续前缀激活值。", "conditioning": "目标任务的训练样本。", "execution": "冻结的语言模型在生成时读取学到的前缀。" } } }, { "id": "2101.00420", "title": "Learning to Generate Task-Specific Adapters from Task Description", "authors": "Qinyuan Ye, Xiang Ren", "first_public": "2021-01-02", "first_public_source": "https://arxiv.org/abs/2101.00420", "arxiv_first_submission": "2021-01-02", "date_label": "arXiv v1", "arxiv": "2101.00420", "paper_url": "https://arxiv.org/abs/2101.00420", "venue": "ACL-IJCNLP 2021", "lens": [ "generated-adapters" ], "related_terms": [ "Hypter", "task descriptions", "generated adapters", "zero-shot learning" ], "summary": "Introduces Hypter, which generates adapters from natural-language task descriptions.", "contribution": "Combines a description encoder and adapter generator with BART. Tests generalization to unseen tasks on ZEST and synthetic SQuAD.", "limitations": "Training requires a collection of tasks with descriptions and labeled examples.", "program_form": "Bottleneck adapters inserted into a text-to-text model.", "conditioning": "A natural-language task description.", "execution": "BART performs the task with the generated adapters.", "relationship": "An early description-to-adapter method cited by PAW. PAW develops the programming interface for saving and calling the resulting neural functions.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "Qinyuan Ye", "Xiang Ren" ], "record_url": "https://programasweights.com/reading-list/papers/2101.00420/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2101-00420", "citation_date": "2021-01-02", "json_url": "https://programasweights.com/reading-list/papers/2101.00420/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2101.00420/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2101.00420/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2101.00420/citation.csl.json", "publication": { "title": "Learning to Generate Task-Specific Adapters from Task Description", "author_names": [ "Qinyuan Ye", "Xiang Ren" ], "year": 2021, "type": "paper-conference", "container_title": "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)", "url": "https://aclanthology.org/2021.acl-short.82/", "source_url": "https://aclanthology.org/2021.acl-short.82/", "verified_at": "2026-10-19", "doi": "10.18653/v1/2021.acl-short.82", "publisher": "Association for Computational Linguistics", "pages": "646-653" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2101.00420/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2101.00420/publication.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" } ], "summary_zh": "提出 Hypter，根据自然语言任务描述生成适配器，并检验对未见任务的泛化。", "translations": { "zh-CN": { "summary": "提出 Hypter，根据自然语言任务描述生成适配器，并检验对未见任务的泛化。", "relationship": "PAW 引用这项从描述生成适配器的研究，并进一步建立保存、加载和调用神经函数的编程接口。", "program_form": "插入文本到文本模型的瓶颈适配器。", "conditioning": "自然语言任务描述。", "execution": "BART 加载生成的适配器后执行任务。" } } }, { "id": "2106.09685", "title": "LoRA: Low-Rank Adaptation of Large Language Models", "authors": "Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen", "first_public": "2021-06-17", "first_public_source": "https://arxiv.org/abs/2106.09685", "arxiv_first_submission": "2021-06-17", "date_label": "arXiv v1", "arxiv": "2106.09685", "paper_url": "https://arxiv.org/abs/2106.09685", "venue": "ICLR 2022", "lens": [ "peft" ], "related_terms": [ "LoRA", "low-rank adaptation", "parameter-efficient fine-tuning", "adapters" ], "summary": "Adapts a pretrained model with low-rank weight updates while keeping the original weights fixed.", "contribution": "Reduces the parameters trained and stored for each task. Evaluates adaptation across several language-model families.", "limitations": "A separate adapter is trained for each task.", "program_form": "Low-rank matrices that update selected model weights.", "conditioning": "Training examples for the target task.", "execution": "The language model applies the low-rank updates during inference.", "relationship": "PAW uses LoRA as a program representation and generates the adapter with a trained compiler.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "Edward J. Hu", "Yelong Shen", "Phillip Wallis", "Zeyuan Allen-Zhu", "Yuanzhi Li", "Shean Wang", "Lu Wang", "Weizhu Chen" ], "record_url": "https://programasweights.com/reading-list/papers/2106.09685/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2106-09685", "citation_date": "2021-06-17", "json_url": "https://programasweights.com/reading-list/papers/2106.09685/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2106.09685/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2106.09685/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2106.09685/citation.csl.json", "publication": { "title": "LoRA: Low-Rank Adaptation of Large Language Models", "author_names": [ "Edward Hu", "Yelong Shen", "Phillip Wallis", "Zeyuan Allen-Zhu", "Yuanzhi Li", "Shean Wang", "Lu Wang", "Weizhu Chen" ], "year": 2022, "type": "paper-conference", "container_title": "International Conference on Learning Representations", "url": "https://openreview.net/forum?id=nZeVKeeFYf9", "source_url": "https://openreview.net/pdf?id=nZeVKeeFYf9", "verified_at": "2026-10-19" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2106.09685/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2106.09685/publication.csl.json", "method_comparisons": [ { "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" } ], "summary_zh": "冻结预训练模型的原始权重，训练低秩权重更新来适应任务。", "translations": { "zh-CN": { "summary": "冻结预训练模型的原始权重，训练低秩权重更新来适应任务。", "relationship": "PAW 用 LoRA 表示程序中的参数部分，由训练好的编译器生成适配器权重。", "program_form": "用于更新指定模型权重的低秩矩阵。", "conditioning": "目标任务的训练样本。", "execution": "语言模型在推理时使用低秩权重更新。" } } }, { "id": "2211.12485", "title": "HyperTuning: Toward Adapting Large Language Models without Back-propagation", "authors": "Jason Phang, Yi Mao, Pengcheng He, Weizhu Chen", "first_public": "2022-11-22", "first_public_source": "https://arxiv.org/abs/2211.12485", "arxiv_first_submission": "2022-11-22", "date_label": "arXiv v1", "arxiv": "2211.12485", "paper_url": "https://arxiv.org/abs/2211.12485", "venue": "ICML 2023", "lens": [ "generated-adapters" ], "related_terms": [ "HyperTuning", "HyperT5", "few-shot adaptation", "generated LoRA" ], "summary": "Produces task-specific parameters for a frozen language model from a few demonstrations.", "contribution": "Introduces HyperT5 models that generate prefixes or LoRA parameters. Hyperpretraining and multitask training prepare the generator for new tasks.", "limitations": "The hypermodel accepts a limited number of demonstrations. The paper also studies further task-specific tuning.", "program_form": "Generated prefix or LoRA parameters.", "conditioning": "A few examples of the task.", "execution": "A frozen T5 model runs with the generated parameters.", "relationship": "PAW cites HyperTuning as prior work on generating adaptation parameters from examples.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "Jason Phang", "Yi Mao", "Pengcheng He", "Weizhu Chen" ], "record_url": "https://programasweights.com/reading-list/papers/2211.12485/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2211-12485", "citation_date": "2022-11-22", "json_url": "https://programasweights.com/reading-list/papers/2211.12485/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2211.12485/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2211.12485/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2211.12485/citation.csl.json", "publication": { "title": "HyperTuning: Toward Adapting Large Language Models without Back-propagation", "author_names": [ "Jason Phang", "Yi Mao", "Pengcheng He", "Weizhu Chen" ], "year": 2023, "type": "paper-conference", "container_title": "Proceedings of the 40th International Conference on Machine Learning", "url": "https://proceedings.mlr.press/v202/phang23a.html", "source_url": "https://proceedings.mlr.press/v202/phang23a.html", "verified_at": "2026-10-19", "volume": "202", "publisher": "PMLR", "pages": "27854-27875" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2211.12485/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2211.12485/publication.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" } ], "summary_zh": "根据几个任务示例，为冻结的语言模型生成前缀或 LoRA 参数。", "translations": { "zh-CN": { "summary": "根据几个任务示例，为冻结的语言模型生成前缀或 LoRA 参数。", "relationship": "PAW 引用 HyperTuning，讨论从示例生成模型适配参数的方法。", "program_form": "生成的前缀或 LoRA 参数。", "conditioning": "少量任务示例。", "execution": "冻结的 T5 模型使用生成的参数执行任务。" } } }, { "id": "2212.10315", "title": "HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation", "authors": "Hamish Ivison, Akshita Bhagia, Yizhong Wang, Hannaneh Hajishirzi, Matthew Peters", "first_public": "2022-12-20", "first_public_source": "https://arxiv.org/abs/2212.10315", "arxiv_first_submission": "2022-12-20", "date_label": "arXiv v1", "arxiv": "2212.10315", "paper_url": "https://arxiv.org/abs/2212.10315", "venue": "ACL 2023", "lens": [ "generated-adapters" ], "related_terms": [ "HINT", "instruction tuning", "hypernetwork", "instruction fusion" ], "summary": "Turns instructions and examples into reusable prefixes, adapters, and encoded instruction representations.", "contribution": "Combines a hypernetwork with instruction fusion so repeated calls can reuse the instruction computation. Evaluates zero-shot and few-shot generalization.", "limitations": "The generated modules and cached instruction representations are tied to the trained encoder-decoder model.", "program_form": "Prefixes, adapters, and encoded instruction representations.", "conditioning": "Task instructions and optional examples.", "execution": "An encoder-decoder model uses the generated modules and attends to cached instruction representations.", "relationship": "HINT reuses generated modules and an encoded instruction. PAW reuses a program containing text and adaptation parameters.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "Hamish Ivison", "Akshita Bhagia", "Yizhong Wang", "Hannaneh Hajishirzi", "Matthew Peters" ], "record_url": "https://programasweights.com/reading-list/papers/2212.10315/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2212-10315", "citation_date": "2022-12-20", "json_url": "https://programasweights.com/reading-list/papers/2212.10315/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2212.10315/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2212.10315/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2212.10315/citation.csl.json", "publication": { "title": "HINT: Hypernetwork Instruction Tuning for Efficient Zero- and Few-Shot Generalisation", "author_names": [ "Hamish Ivison", "Akshita Bhagia", "Yizhong Wang", "Hannaneh Hajishirzi", "Matthew E. Peters" ], "year": 2023, "type": "paper-conference", "container_title": "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", "url": "https://aclanthology.org/2023.acl-long.631/", "source_url": "https://aclanthology.org/2023.acl-long.631/", "verified_at": "2026-10-19", "doi": "10.18653/v1/2023.acl-long.631", "publisher": "Association for Computational Linguistics", "pages": "11272-11288" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2212.10315/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2212.10315/publication.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" } ], "summary_zh": "把指令和示例转化为前缀、适配器及编码后的指令表示，供多次调用复用。", "translations": { "zh-CN": { "summary": "把指令和示例转化为前缀、适配器及编码后的指令表示，供多次调用复用。", "relationship": "HINT 复用生成的模块和编码后的指令；PAW 保存并复用包含文本与适配参数的程序。", "program_form": "前缀、适配器和编码后的指令表示。", "conditioning": "任务指令和提供的示例。", "execution": "编码器—解码器模型使用生成的模块，并读取缓存的指令表示。" } } }, { "id": "2304.08467", "title": "Learning to Compress Prompts with Gist Tokens", "authors": "Jesse Mu, Xiang Lisa Li, Noah Goodman", "first_public": "2023-04-17", "first_public_source": "https://arxiv.org/abs/2304.08467", "arxiv_first_submission": "2023-04-17", "date_label": "arXiv v1", "arxiv": "2304.08467", "paper_url": "https://arxiv.org/abs/2304.08467", "venue": "NeurIPS 2023", "lens": [ "context-compression" ], "related_terms": [ "gist tokens", "prompt compression", "activation caching", "amortized inference" ], "summary": "Compresses an instruction into a few reusable token activations.", "contribution": "Trains with an attention mask that forces later tokens to use gist activations in place of the original prompt.", "limitations": "Compression can reduce output quality. The cached activations are tied to the trained model.", "program_form": "Cached activations at a few gist-token positions.", "conditioning": "The instruction or prompt.", "execution": "The language model generates outputs while attending to the cached gist.", "relationship": "PAW cites gisting as another way to reuse instruction computation. Gisting stores activations; PAW stores a program with text and adaptation parameters.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "Jesse Mu", "Xiang Lisa Li", "Noah Goodman" ], "record_url": "https://programasweights.com/reading-list/papers/2304.08467/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2304-08467", "citation_date": "2023-04-17", "json_url": "https://programasweights.com/reading-list/papers/2304.08467/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2304.08467/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2304.08467/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2304.08467/citation.csl.json", "publication": { "title": "Learning to Compress Prompts with Gist Tokens", "author_names": [ "Jesse Mu", "Xiang Li", "Noah Goodman" ], "year": 2023, "type": "paper-conference", "container_title": "Advances in Neural Information Processing Systems", "url": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/3d77c6dcc7f143aa2154e7f4d5e22d68-Abstract-Conference.html", "source_url": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/3d77c6dcc7f143aa2154e7f4d5e22d68-Abstract-Conference.html", "verified_at": "2026-10-19", "volume": "36", "doi": "10.52202/075280-0848", "pages": "19327-19352" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2304.08467/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2304.08467/publication.csl.json", "method_comparisons": [ { "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" } ], "summary_zh": "将一段指令压缩为几个可复用的 gist token 激活值。", "translations": { "zh-CN": { "summary": "将一段指令压缩为几个可复用的 gist token 激活值。", "relationship": "PAW 引用 gisting，讨论如何复用指令计算。Gisting 保存激活值，PAW 保存包含文本与适配参数的程序。", "program_form": "少量 gist token 位置上的缓存激活值。", "conditioning": "指令或提示词。", "execution": "语言模型读取缓存的 gist 表示来生成输出。" } } }, { "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/" } ], "summary_zh": "让语言模型编写可保存、可本地执行的标注程序，并用弱监督组合多个程序的输出。", "translations": { "zh-CN": { "summary": "让语言模型编写可保存、可本地执行的标注程序，并用弱监督组合多个程序的输出。", "relationship": "PAW 评测了 ALCHEmist 风格的方法。ALCHEmist 生成符号代码，PAW 生成由神经解释器执行的程序，两者都构建可复用的工具。", "program_form": "符号标注程序。", "conditioning": "任务描述、标注要求和函数签名。", "execution": "生成的程序在本地标注数据，其输出可进一步用于训练下游模型。" } } }, { "id": "2411.05877", "title": "Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass", "authors": "Tong Chen, Hao Fang, Patrick Xia, Xiaodong Liu, Benjamin Van Durme, Luke Zettlemoyer, Jianfeng Gao, Hao Cheng", "first_public": "2024-11-08", "first_public_source": "https://arxiv.org/abs/2411.05877", "arxiv_first_submission": "2024-11-08", "date_label": "arXiv v1", "arxiv": "2411.05877", "paper_url": "https://arxiv.org/abs/2411.05877", "venue": "ICLR 2025", "lens": [ "generated-adapters", "context-compression" ], "related_terms": [ "Generative Adapter", "contextualization", "fast weights", "streaming context" ], "summary": "Converts new context into low-rank model updates in one forward pass.", "contribution": "Trains an adapter generator with self-supervision. Tests learning from documents, demonstrations, and conversation history.", "limitations": "The adapter generator is tied to its frozen base model.", "program_form": "Additive low-rank adapters.", "conditioning": "Documents, demonstrations, or conversation history, including successive chunks of context.", "execution": "The adapted model answers new inputs using context stored in its parameter updates.", "relationship": "PAW cites this context-to-adapter method. Generative Adapter stores information from context; PAW compiles the behavior of a function.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "first_public_note": "An AFM 2024 OpenReview version appeared before the first arXiv submission.", "author_names": [ "Tong Chen", "Hao Fang", "Patrick Xia", "Xiaodong Liu", "Benjamin Van Durme", "Luke Zettlemoyer", "Jianfeng Gao", "Hao Cheng" ], "record_url": "https://programasweights.com/reading-list/papers/2411.05877/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2411-05877", "citation_date": "2024-11-08", "json_url": "https://programasweights.com/reading-list/papers/2411.05877/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2411.05877/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2411.05877/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2411.05877/citation.csl.json", "publication": { "title": "Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass", "author_names": [ "Tong Chen", "Hao Fang", "Patrick Xia", "Xiaodong Liu", "Benjamin Van Durme", "Luke Zettlemoyer", "Jianfeng Gao", "Hao Cheng" ], "year": 2025, "type": "paper-conference", "container_title": "International Conference on Learning Representations", "url": "https://openreview.net/forum?id=bc3sUsS6ck", "source_url": "https://openreview.net/pdf?id=bc3sUsS6ck", "verified_at": "2026-10-19" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2411.05877/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2411.05877/publication.csl.json", "method_comparisons": [ { "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" } ], "summary_zh": "通过一次前向传播，将新上下文转化为低秩参数更新。", "translations": { "zh-CN": { "summary": "通过一次前向传播，将新上下文转化为低秩参数更新。", "relationship": "Generative Adapter 将上下文信息写入适配器；PAW 把函数的行为编译成程序。", "program_form": "可叠加的低秩适配器。", "conditioning": "文档、示例或对话历史，也支持连续到来的上下文片段。", "execution": "模型通过参数更新中保存的上下文信息回答新输入。" } } }, { "id": "2506.06105", "title": "Text-to-LoRA: Instant Transformer Adaption", "authors": "Rujikorn Charakorn, Edoardo Cetin, Yujin Tang, Robert Tjarko Lange", "first_public": "2025-06-06", "first_public_source": "https://arxiv.org/abs/2506.06105", "arxiv_first_submission": "2025-06-06", "date_label": "arXiv v1", "arxiv": "2506.06105", "paper_url": "https://arxiv.org/abs/2506.06105", "venue": "ICML 2025", "lens": [ "generated-adapters" ], "related_terms": [ "Text-to-LoRA", "T2L", "task description", "adapter generation" ], "summary": "Uses a hypernetwork to generate LoRA adapters from task descriptions.", "contribution": "Trains hypernetworks to reconstruct existing adapters or learn from supervised task data. Tests adapter compression and generation for unseen tasks.", "limitations": "Reconstruction-trained T2L underperforms the supervised variant on unseen tasks.", "program_form": "Generated LoRA matrices.", "conditioning": "A natural-language task description.", "execution": "A base transformer performs the task with the generated LoRA.", "relationship": "PAW cites Text-to-LoRA as prior work on generating adapters. PAW develops fuzzy-function programming, FuzzyBench, and an interface for reusable neural programs.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "Rujikorn Charakorn", "Edoardo Cetin", "Yujin Tang", "Robert Tjarko Lange" ], "record_url": "https://programasweights.com/reading-list/papers/2506.06105/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2506-06105", "citation_date": "2025-06-06", "json_url": "https://programasweights.com/reading-list/papers/2506.06105/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2506.06105/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2506.06105/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2506.06105/citation.csl.json", "publication": { "title": "Text-to-LoRA: Instant Transformer Adaption", "author_names": [ "Rujikorn Charakorn", "Edoardo Cetin", "Yujin Tang", "Robert Tjarko Lange" ], "year": 2025, "type": "paper-conference", "container_title": "Proceedings of the 42nd International Conference on Machine Learning", "url": "https://proceedings.mlr.press/v267/charakorn25a.html", "source_url": "https://proceedings.mlr.press/v267/charakorn25a.html", "verified_at": "2026-10-19", "volume": "267", "publisher": "PMLR", "pages": "7485-7514" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2506.06105/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2506.06105/publication.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" } ], "summary_zh": "用超网络根据任务描述生成 LoRA 适配器，研究适配器压缩和未见任务的适配。", "translations": { "zh-CN": { "summary": "用超网络根据任务描述生成 LoRA 适配器，研究适配器压缩和未见任务的适配。", "relationship": "PAW 引用 Text-to-LoRA 的适配器生成方法，并发展模糊函数编程、FuzzyBench 和可复用神经程序的接口。", "program_form": "生成的 LoRA 矩阵。", "conditioning": "自然语言任务描述。", "execution": "基础 Transformer 使用生成的 LoRA 执行任务。" } } }, { "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://arxiv.org/abs/2607.02512", "provenance": "July 2026 arXiv paper; NeurIPS 2026.", "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", "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", "publication": { "title": "Program-as-Weights: A Programming Paradigm for Fuzzy Functions", "author_names": [ "Wentao Zhang", "Liliana Hotsko", "Woojeong Kim", "Pengyu Nie", "Stuart Shieber", "Yuntian Deng" ], "year": 2026, "type": "paper-conference", "container_title": "The Fortieth Annual Conference on Neural Information Processing Systems", "url": "https://arxiv.org/abs/2607.02512", "source_url": "https://arxiv.org/abs/2607.02512", "verified_at": "2026-10-24", "preferred_citation": true, "citation_key": "zhang2026programasweights" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2607.02512/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2607.02512/publication.csl.json", "preferred_citation": { "title": "Program-as-Weights: A Programming Paradigm for Fuzzy Functions", "author_names": [ "Wentao Zhang", "Liliana Hotsko", "Woojeong Kim", "Pengyu Nie", "Stuart Shieber", "Yuntian Deng" ], "year": 2026, "type": "paper-conference", "container_title": "The Fortieth Annual Conference on Neural Information Processing Systems", "url": "https://arxiv.org/abs/2607.02512", "source_url": "https://arxiv.org/abs/2607.02512", "verified_at": "2026-10-24", "preferred_citation": true, "citation_key": "zhang2026programasweights" }, "citation_url": "https://arxiv.org/abs/2607.02512", "preprint_citation_date": "2026-07-02", "preprint_bibtex_url": "https://programasweights.com/reading-list/papers/2607.02512/preprint.bib", "preprint_csl_url": "https://programasweights.com/reading-list/papers/2607.02512/preprint.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" } ], "summary_zh": "提出模糊函数编程：将自然语言函数说明编译成神经程序，保存后由小模型在本地反复执行。", "translations": { "zh-CN": { "summary": "提出模糊函数编程：将自然语言函数说明编译成神经程序，保存后由小模型在本地反复执行。", "relationship": "PAW 建立编译器与解释器接口，程序同时包含文本和生成的参数，并提供 FuzzyBench、本地运行接口及图像条件编译。Compile by Training 沿用这一程序格式和运行时。", "program_form": "文本伪程序与编译器生成的参数高效模块，如 LoRA 适配器或前缀向量。", "conditioning": "函数说明，也支持输入输出示例；图像扩展还接收视觉信息。", "execution": "冻结的轻量解释器加载保存的程序，在本地处理新输入。" } } }, { "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://aclanthology.org/2026.emnlp-demos.78/", "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; published in EMNLP 2026 System Demonstrations, pages 830–839.", "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", "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", "publication": { "title": "Compile by Training: Turning Natural-Language Specifications into Local Neural Functions", "author_names": [ "Yuntian Deng", "Pengyu Nie", "Stuart Shieber" ], "year": 2026, "type": "paper-conference", "container_title": "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", "editor_names": [ "Sweta Agrawal", "Alberto Abad" ], "month": 10, "month_name": "October", "address": "Budapest, Hungary", "publisher": "Association for Computational Linguistics", "pages": "830-839", "bibtex_pages": "830--839", "doi": "10.18653/2026.emnlp-demos.78", "isbn": "979-8-89176-461-3", "url": "https://aclanthology.org/2026.emnlp-demos.78/", "source_url": "https://aclanthology.org/2026.emnlp-demos.78/", "verified_at": "2026-11-06", "preferred_citation": true, "citation_key": "deng-etal-2026-compile" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2609.04199/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2609.04199/publication.csl.json", "preferred_citation": { "title": "Compile by Training: Turning Natural-Language Specifications into Local Neural Functions", "author_names": [ "Yuntian Deng", "Pengyu Nie", "Stuart Shieber" ], "year": 2026, "type": "paper-conference", "container_title": "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", "editor_names": [ "Sweta Agrawal", "Alberto Abad" ], "month": 10, "month_name": "October", "address": "Budapest, Hungary", "publisher": "Association for Computational Linguistics", "pages": "830-839", "bibtex_pages": "830--839", "doi": "10.18653/2026.emnlp-demos.78", "isbn": "979-8-89176-461-3", "url": "https://aclanthology.org/2026.emnlp-demos.78/", "source_url": "https://aclanthology.org/2026.emnlp-demos.78/", "verified_at": "2026-11-06", "preferred_citation": true, "citation_key": "deng-etal-2026-compile" }, "citation_url": "https://aclanthology.org/2026.emnlp-demos.78/", "preprint_citation_date": "2026-09-03", "preprint_bibtex_url": "https://programasweights.com/reading-list/papers/2609.04199/preprint.bib", "preprint_csl_url": "https://programasweights.com/reading-list/papers/2609.04199/preprint.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" } ], "summary_zh": "根据函数说明生成训练样本，再训练 LoRA 适配器，将一次函数定义构建成可复用的本地神经程序。", "translations": { "zh-CN": { "summary": "根据函数说明生成训练样本，再训练 LoRA 适配器，将一次函数定义构建成可复用的本地神经程序。", "relationship": "在 PAW 的快速编译结果上增加针对当前函数的训练，保留 PAW 的程序格式和解释器接口。", "program_form": "包含 LoRA 适配器、运行提示词、函数说明和解释器元数据的 PAW 程序。", "conditioning": "函数说明指导教师模型生成样本，PAW 快速编译器提供初始适配器与运行提示词。", "execution": "冻结的 Qwen3-0.6B 解释器执行保存的函数，不再调用教师模型。" } } } ] }