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20242026
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cs.SE2026

The Best Programming Language for Tokenmaxxing: An Investigation of Coding Agent Behavior Across Programming Languages

Zixuan Wu, Carolyn Jane Anderson, Arjun Guha

Although coding agents are now very effective in a variety of programming languages, this paper first shows that the cost (in tokens) can very significantly by programming language…

cs.SE2026

Learning Reasoning World Models for Parallel Code

Gautam Singh, Arjun Guha, Bhavya Kailkhura +1

Large language models have shown remarkable ability in serial code generation, but they still struggle with parallel code for which training data is comparatively scarce. A common…

cs.SE2026

AgentPack: A Dataset of Code Changes, Co-Authored by Agents and Humans

Yangtian Zi, Zixuan Wu, Aleksander Boruch-Gruszecki +2

Fine-tuning large language models for code editing has typically relied on mining commits and pull requests. The working hypothesis has been that commit messages describe human int…

cs.SE2025

"I Would Have Written My Code Differently'': Beginners Struggle to Understand LLM-Generated Code

Yangtian Zi, Luisa Li, Arjun Guha +2

Large language models (LLMs) are being increasingly adopted for programming work. Prior work shows that while LLMs accelerate task completion for professional programmers, beginnin…

cs.SE2024

Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions

Federico Cassano, Luisa Li, Akul Sethi +8

A significant amount of research is focused on developing and evaluating large language models for a variety of code synthesis tasks. These include synthesizing code from natural l…