Quasar: A Programming Language Specialized for LLM Code Actions

[2506.12202] Quasar: A Programming Language Specialized for LLM Code Actions Skip to main content Search Submit Donate Log in Computer Science > Programming Languages arXiv:2506.12202 (cs) [Submitted on 13 Jun 2025 ( v1 ), last revised 25 Aug 2026 (this version, v2)] Title: Quasar: A Programming Language Specialized for LLM Code Actions Authors: Stephen Mell , Botong Zhang , David Mell , Shuo Li , Ramya Ramalingam , Nathan Yu , Stephan Zdancewic , Osbert Bastani View PDF HTML (experimental) Abstract: Large language models (LLMs) often call external tools to solve tasks. One effective strategy is for LLMs to write code, enabling them to use complex control flow such as conditionals and loops. Such code actions are typically represented as Python code, since LLMs are proficient at writing it. However, many programming language features that would support more effective code actions are difficult to implement for Python. We propose separating internal code that captures program logic from external calls to tools that interact with the world. New features can then easily be implemented by (1) annotating external calls with the effects relevant to that feature, and (2) modifying the execution of the internal code to track this information. We develop a novel programming language, Quasar, that implements this idea. To illustrate its utility, we implement several useful features on top of Quasar to enhance code actions: access control with batched user queries to improve security, autoparallelization of external calls to reduce latency, and conformal prediction for uncertainty quantification to mitigate hallucinations. Subjects: Programming Languages (cs.PL) ; Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG) Cite as: arXiv:2506.12202 [cs.PL] (or arXiv:2506.12202v2 [cs.PL] for this version) https://doi.org/10.48550/arXiv.2506.12202 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Third Conference on Language Modeling (COLM 2026) Submission history From: Stephen Mell [ view email ] [v1] Fri, 13 Jun 2025 20:11:22 UTC (3,809 KB) [v2] Tue, 25 Aug 2026 01:54:00 UTC (4,600 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: cs.PL < prev | next > new | recent | 2025-06 Change to browse by: cs cs.AI cs.CR cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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