2 papers
cs.AI2026
When Uncertainty Isn't Enough: An Empirical Study of Self-Correction in Code Generation
Pranav Rakasi, Maanas Lalwani, Arnav Srivastava +4
Large language models for code generation often produce incorrect solutions without reliable indicators of failure. We study whether uncertainty estimation methods developed for na…
cs.AI2025
The Steganographic Potentials of Language Models
Artem Karpov, Tinuade Adeleke, Seong Hah Cho +1
The potential for large language models (LLMs) to hide messages within plain text (steganography) poses a challenge to detection and thwarting of unaligned AI agents, and undermine…