1 citations · 2 across the 10 of their papers we have counts for
7 papers · 1 filter
Training Language Models To Be Coherent Decision-Makers
Khurram Yamin, Xavier Fernandes, Paul Koch +2
Reliable decision-making requires more than accurate prediction: a model must preserve its beliefs, apply the relevant utilities, and recognize when the information needed to justi…
Bayesian Intelligence from the Outside
Alex Smolin, Bryan Wilder
Inferring intelligence from observable behavior is a foundational challenge in artificial intelligence. We develop a theory of Bayesian intelligence for agents such as language mod…
Beliefs and Behavior in Language Models
Alex Smolin, Bryan Wilder
There is significant uncertainty about whether abstractions like beliefs or desires usefully describe the behavior of large language models (LLMs). In addition to the inherent scie…
High-Stakes Decisions with Language Models: Insights from Emergency Triage
Khurram Yamin, Christopher Kelly, Bryan Wilder +1
High-stakes decisions under uncertainty, such as medical emergency triage, require more than accurate predictions. They depend on estimating the likelihood of alternative outcomes…
What Must Generalist Agents Remember?
Khurram Yamin, Namrata Deka, Maitreyi Swaroop +3
This paper develops a formal account of what generalist agents must store in memory in order to act near-optimally across multiple environments and goals. It shows that when two do…
Can Revealed Preferences Clarify LLM Alignment and Steering?
Khurram Yamin, Jingjing Tang, Eric Horvitz +1
LLMs are increasingly used to make or support high-stakes decisions under uncertainty, where alignment depends not only on factual accuracy but on how models weigh tradeoffs betwee…
When Agents Say One Thing and Do Another: Validating Elicited Beliefs from LLMs
Khurram Yamin, Jingjing Tang, Santiago Cortes-Gomez +3
Large language models (LLMs) are increasingly deployed in high-stakes settings where good decisions require forming beliefs over the probability of unknown outcomes. However, it is…