From the 1 of 12 linked papers with an AI index.
4 papers · 1 filter
How LLMs Might Think
Joseph Gottlieb, Ethan Kemp, Matthew Trager
The paper examines whether large language models can be said to think, arguing that while they likely do not engage in rational thought, they may exhibit a form of arational, purel…
Experience-Guided Adaptation of Inference-Time Reasoning Strategies
Adam Stein, Matthew Trager, Benjamin Bowman +4
Enabling agentic AI systems to adapt their problem-solving approaches based on post-training interactions remains a fundamental challenge. While systems that update and maintain a…
e1: Learning Adaptive Control of Reasoning Effort
Michael Kleinman, Matthew Trager, Alessandro Achille +2
Increasing the thinking budget of AI models can significantly improve accuracy, but not all questions warrant the same amount of reasoning. Users may prefer to allocate different a…
LATTS: Locally Adaptive Test-Time Scaling
Theo Uscidda, Matthew Trager, Michael Kleinman +3
One common strategy for improving the performance of Large Language Models (LLMs) on downstream tasks involves using a \emph{verifier model} to either select the best answer from a…