3 papers
cs.AI2026
A Positive Case for Faithfulness: LLM Self-Explanations Help Predict Model Behavior
Harry Mayne, Justin Singh Kang, Dewi Gould +3
LLM self-explanations are often presented as a promising tool for AI oversight, yet their faithfulness to the model's true reasoning process is poorly understood. Existing faithful…
cs.CR2025
LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoring
Chloe Li, Mary Phuong, Noah Y. Siegel
Trustworthy evaluations of dangerous capabilities are increasingly crucial for determining whether an AI system is safe to deploy. One empirically demonstrated threat is sandbaggin…
cs.LG2025
Advancing Event Forecasting through Massive Training of Large Language Models: Challenges, Solutions, and Broader Impacts
Sang-Woo Lee, Sohee Yang, Donghyun Kwak +1
Many recent papers have studied the development of superforecaster-level event forecasting LLMs. While methodological problems with early studies cast doubt on the use of LLMs for…