6 citations · 6 across the 11 of their papers we have counts for
5 papers · 1 filter
Better, Faster, Stronger: Programmatic Skill Learning Best Reduces Agent Cost
Zixi Huang, Xiheng Wang, Andrew Wang +4
Recently, the practice of augmenting LLM agent capability with skills has gained prevalence. We explore the cost effective adaptation of agents to novel domains by means of learnin…
Non-Parametric Machine Text Detection via Multi-View Gaussian Processes
Aleem Khan, Nicholas Andrews
Adversarial conditions such as paraphrasing and targeted style transfer sharply degrade the accuracy of machine text detectors. A document, however, carries multiple complementary…
Unsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion
Rafael Rivera Soto, Barry Chen, Nicholas Andrews
The rapid development of large language models (LLMs) has raised concerns about misuse such as plagiarism, misinformation, and automated influence operations, motivating the need f…
Inducing Artificial Uncertainty in Language Models
Sophia Hager, Simon Zeng, Nicholas Andrews
In safety-critical applications, language models should be able to characterize their uncertainty with meaningful probabilities. Many uncertainty quantification approaches require…
Can Coding Agents Reproduce Findings in Computational Materials Science?
Ziyang Huang, Yi Cao, Ali K. Shargh +15
Large language models are increasingly deployed as autonomous coding agents and have achieved remarkably strong performance on software engineering benchmarks. However, it is uncle…