A Literate Programming Environment for Human and Machine Agents

• URL: http://arxiv.org/abs/2608.24644v1
• Date: Tue, 25 Aug 2026 14:54:24 GMT
• Title: A Literate Programming Environment for Human and Machine Agents
• Abstract summary: This paper introduces an environment for constructing literate programs in concert with language-aware machine agents.<n>It includes a grammar for executable program essays, a name-graph which relates prose, names and executable artifacts, and a binding mechanism for existing languages and testing toolsets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces an environment for constructing literate programs in concert with language-aware machine agents. This environment includes a grammar for executable program essays, a parser that treats names as first-class objects, an internal name-graph which relates prose, names and executable artifacts, and a binding mechanism for existing languages and testing toolsets. This supports co-location of code with its most relevant natural language and structured data context, making better use of Large Language Model (LLM) context windows. It also provides LLM coding agents with a toolset more analogous to the symbol-aware search and usage information available in human programmer-facing Integrated Development Environments (IDEs). We describe a working implementation with bindings to three established programming languages, and several example programs.

Related papers

• debug-gym: A Text-Based Environment for Interactive Debugging [55.11603087371956]
  Large Language Models (LLMs) are increasingly relied upon for coding tasks.<n>We posit that LLMs can benefit from the ability to interactively explore a to gather the information relevant to their task.<n>We present a textual environment, namely debug-gym, for developing LLM-based agents in an interactive coding setting.
  arXiv  Detail & Related papers  (2025-03-27T14:43:28Z)
• Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal Languages [21.18996339478024]
  We introduce emphsynthetic programming elicitation and compilation (SPEAC) SPEAC produces syntactically correct programs more frequently and without sacrificing semantic correctness. We empirically evaluate the performance of SPEAC in a case study for the UCLID5 formal verification language.
  arXiv  Detail & Related papers  (2024-06-05T22:16:19Z)
• IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators [49.903001442804594]
  This work investigates the prospect of leveraging compiler intermediate representations (IR) to improve the multilingual capabilities of Code-LMs. We first compile SLTrans, a parallel dataset consisting of nearly 4M self-contained source code files. Next, we carry out continued causal language modelling training on SLTrans, forcing the Code-LMs to learn the IR language. Our resulting models, dubbed IRCoder, display sizeable and consistent gains across a wide variety of code generation tasks and metrics.
  arXiv  Detail & Related papers  (2024-03-06T17:52:08Z)
• LILO: Learning Interpretable Libraries by Compressing and Documenting Code [71.55208585024198]
  We introduce LILO, a neurosymbolic framework that iteratively synthesizes, compresses, and documents code. LILO combines LLM-guided program synthesis with recent algorithmic advances in automated from Stitch. We find that AutoDoc boosts performance by helping LILO's synthesizer to interpret and deploy learned abstractions.
  arXiv  Detail & Related papers  (2023-10-30T17:55:02Z)
• COMEX: A Tool for Generating Customized Source Code Representations [7.151800146054561]
  COMEX is a framework that allows researchers and developers to create and combine multiple code-views. It can analyze both method-level snippets and program-level snippets by using both intra-procedural and inter-procedural snippets. It is built on tree-sitter - a widely used incremental analysis tool that supports over 40 languages.
  arXiv  Detail & Related papers  (2023-07-10T16:46:34Z)
• Natural Language Commanding via Program Synthesis [0.29360071145551064]
  We present Semantic Interpreter, a natural language-friendly AI system for productivity software such as Microsoft Office. LLMs are excellent at understanding user intent expressed as natural language, but not sufficient for fulfilling application-specific user intent. We introduce the Office Domain Specific Language (O), a concise, high-level language specialized for performing actions in and interacting with entities in Office applications.
  arXiv  Detail & Related papers  (2023-06-06T07:28:49Z)
• Identifying concept libraries from language about object structure [56.83719358616503]
  We leverage natural language descriptions for a diverse set of 2K procedurally generated objects to identify the parts people use. We formalize our problem as search over a space of program libraries that contain different part concepts. By combining naturalistic language at scale with structured program representations, we discover a fundamental information-theoretic tradeoff governing the part concepts people name.
  arXiv  Detail & Related papers  (2022-05-11T17:49:25Z)
• Using Document Similarity Methods to create Parallel Datasets for Code Translation [60.36392618065203]
  Translating source code from one programming language to another is a critical, time-consuming task. We propose to use document similarity methods to create noisy parallel datasets of code. We show that these models perform comparably to models trained on ground truth for reasonable levels of noise.
  arXiv  Detail & Related papers  (2021-10-11T17:07:58Z)
• Leveraging Language to Learn Program Abstractions and Search Heuristics [66.28391181268645]
  We introduce LAPS (Language for Abstraction and Program Search), a technique for using natural language annotations to guide joint learning of libraries and neurally-guided search models for synthesis. When integrated into a state-of-the-art library learning system (DreamCoder), LAPS produces higher-quality libraries and improves search efficiency and generalization.
  arXiv  Detail & Related papers  (2021-06-18T15:08:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.