activity
20152026
most citedProGraML: Graph-based Deep Learning for Program Optimization and Analysis

24 citations · 59 across the 20 of their papers we have counts for

collaborators

21 papers

math.NT2026

ABC implies that Ramanujan's tau function misses almost all primes

David Kurniadi Angdinata, Evan Chen, Chris Cummins +21

Lehmer conjectured that Ramanujan's tau-function never vanishes. In a related direction, a folklore conjecture asserts that infinitely many primes arise as absolute values of Raman…

math.CO2026

Dead ends in square-free digit walks

Evan Chen, Chris Cummins, Ben Eltschig +18

We study "dead ends" in square-free digit walks: square-free integers such that, in base , every one-digit extension is non-square-free. In base , the stochastic…

math.CO2026

Fel's Conjecture on Syzygies of Numerical Semigroups

Evan Chen, Chris Cummins, GSM +18

Let be a numerical semigroup and its semigroup ring. The Hilbert numerator of determines normalized alternating syzygy power sums $K_…

cs.LG2024

Don't Transform the Code, Code the Transforms: Towards Precise Code Rewriting using LLMs

Chris Cummins, Volker Seeker, Jordi Armengol-Estapé +3

Tools for rewriting, refactoring and optimizing code should be fast and correct. Large language models (LLMs), by their nature, possess neither of these qualities. Yet, there remai…

cs.PL2024★ 2 cited

Meta Large Language Model Compiler: Foundation Models of Compiler Optimization

Chris Cummins, Volker Seeker, Dejan Grubisic +4

Large Language Models (LLMs) have demonstrated remarkable capabilities across a variety of software engineering and coding tasks. However, their application in the domain of code a…

cs.PL2024

Compiler generated feedback for Large Language Models

Dejan Grubisic, Chris Cummins, Volker Seeker +1

We introduce a novel paradigm in compiler optimization powered by Large Language Models with compiler feedback to optimize the code size of LLVM assembly. The model takes unoptimiz…