1 citations · 3 across the 17 of their papers we have counts for
5 papers · 1 filter
On the Clock: Towards Punctual and Productive Time-Budgeted AI Agents
Aaron Wang, Neelabh Madan, Vlad Sobal +5
We study whether small LLM agents can operate effectively under explicit wall-clock time budgets by both respecting the allocated runtime and using available time productively. We…
How LLMs Might Think
Joseph Gottlieb, Ethan Kemp, Matthew Trager
Do large language models (LLMs) think? Daniel Stoljar and Zhihe Vincent Zhang have recently developed an argument from rationality for the claim that LLMs do not think. We contend,…
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…