arXiv:2607. 14240v1 Announce Type: new Abstract: Current alignment approaches typically focus on emulating human behavior using static representations of human preferences, failing to capture the dynamic, context-dependent nature of real-world human-AI interactions.
By Valerie Chen, Cleotilde Gonzalez, Anita Williams Woolley, Michael Lee, Tongshuang Wu, Vincent Conitzer, Aarti Singh
arXiv:2604. 21827v2 Announce Type: replace Abstract: In accomplishing complex tasks, human cognition typically progresses from abstract to concrete (e.
By Nathanael Jo, Zoe De Simone, Mitchell Gordon, Ashia Wilson
arXiv:2606. 12754v1 Announce Type: cross Abstract: Are large language models (LLMs) bad at capturing human judgment?
By Danica Dillion, Chen Cecilia Liu, Baihui Wang, Daniele Barolo, Tanmay Rajore, Niket Tandon, Pranathi Ravikumar, Kurt Gray
arXiv:2608. 10327v1 Announce Type: new Abstract: Can AI systems be aligned to human values?
By Andrew Smart, Shazeda Ahmed, Jackie Kay, Jimmy Tobin, Kris Shrishak, Abeba Birhane
We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems.
arXiv:2608. 12325v1 Announce Type: new Abstract: Autonomous reasoning is among the most scientifically and economically motivating topics in AI today.
By Rachel Lawrence, Jacqueline Maasch