Carolyn Jane Anderson — accepted AI-conference papers

2026

Agnostics: Learning to Synthesize Code in Any Programming Language with a Universal Reinforcement Learning Environment

Aleksander Boruch-Gruszecki, Yangtian Zi, Zixuan Wu, Tejas Oberoi, Carolyn Jane Anderson, Joydeep Biswas +1

ICLR 2026poster

Large language models (LLMs) already excel at writing code in high-resource languages such as Python and JavaScript, yet stumble on low-resource languages that remain essential to science and engineering. Besides the obvious shortage of pre-training data, post-training itself is a bottleneck: every…

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2025

GlyphPattern: An Abstract Pattern Recognition for Vision-Language Models

Zixuan Wu, Yoolim Kim, Carolyn Jane Anderson

ACL 2025finding

Vision-Language Models (VLMs) have made rapid progress in reasoning across visual and textual data. While VLMs perform well on vision tasks that they are trained on, our results highlight key challenges in abstract pattern recognition. We present GlyphPattern, a 954 item dataset that pairs 318 human…

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2025

Substance Beats Style: Why Beginning Students Fail to Code with LLMs

Francesca Lucchetti, Zixuan Wu, Arjun Guha, Molly Q Feldman, Carolyn Jane Anderson

NAACL 2025long

Although LLMs are increasing the productivity of professional programmers, existing work shows that beginners struggle to prompt LLMs to solve text-to-code tasks (Nguyen et al., 2024; Prather et al., 2024b; Mordechai et al., 2024). Why is this the case? This paper explores two competing hypotheses a…

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2023

Exploring Social Biases of Large Language Models in a College Artificial Intelligence Course

Skylar Kolisko, Carolyn Jane Anderson

AAAI 2023technical

Large neural network-based language models play an increasingly important role in contemporary AI. Although these models demonstrate sophisticated text generation capabilities, they have also been shown to reproduce harmful social biases contained in their training data. This paper presents a projec…

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