[2606.25244] Reading AI Model Compilation in MLIR Through the Lens of Formal Theories

-->

Computer Science > Programming Languages

arXiv:2606.25244 (cs)

[Submitted on 24 Jun 2026]

Title:Reading AI Model Compilation in MLIR Through the Lens of Formal Theories

Authors:Javed Absar<br>View a PDF of the paper titled Reading AI Model Compilation in MLIR Through the Lens of Formal Theories, by Javed Absar

View PDF<br>HTML (experimental)

Abstract:Compiler infrastructures such as MLIR rest on a set of design principles: IR abstractions, interfaces, match-and-rewrite, flow analysis, type conversion, staged lowering, and so on. These concepts have proven themselves in practice. Good designs typically arrive through engineering knowledge, intuition and experience. Many of them, however, have correspondences in formal theory. MLIR's match-and-rewrite engine has correspondence to a \emph{term-rewriting-system}~\cite{baadernipkow1998}; staged lowering has the structure of \emph{refinement calculus}~\cite{back1998}; and range analysis is grounded in \emph{abstract interpretation}~\cite{cousot1977,cousot1979}. Highlighting these correspondences is useful because each theory supplies vocabulary precise enough to discuss structural questions. Moreover, as coding agents lower the cost of implementation, good design and abstractions become the main concern~\cite{Lattner2026ClaudeCCompiler}. A coding agent can generate a pass, but it can only reason over the semantics the representation exposes. When essential structure is missing, the limitation is one of abstraction, not of implementation. The natural next question is how to design that substrate well. Well-chosen abstractions emerge from experience and intuition, but they often mirror concepts given a more precise treatment in formal theory. We argue that knowledge of these formal concepts clarifies what completeness means for a given abstraction, what the ideal design would be, and where practical trade-offs depart from it.

Subjects:

Programming Languages (cs.PL)

Cite as:<br>arXiv:2606.25244 [cs.PL]

(or<br>arXiv:2606.25244v1 [cs.PL] for this version)

https://doi.org/10.48550/arXiv.2606.25244

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Javed Absar [view email]<br>[v1]<br>Wed, 24 Jun 2026 00:02:38 UTC (25 KB)

Full-text links:<br>Access Paper:

View a PDF of the paper titled Reading AI Model Compilation in MLIR Through the Lens of Formal Theories, by Javed Absar<br>View PDF<br>HTML (experimental)<br>TeX Source

view license

Current browse context:

cs.PL

next >

new<br>recent<br>| 2026-06

Change to browse by:

cs

References & Citations

NASA ADS<br>Google Scholar

Semantic Scholar

export BibTeX citation<br>Loading...

BibTeX formatted citation

&times;

loading...

Data provided by:

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? |<br>Disable MathJax (What is MathJax?)