8 citations · 14 across the 8 of their papers we have counts for
9 papers
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…
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…
Re-FORC: Adaptive Reward Prediction for Efficient Chain-of-Thought Reasoning
Renos Zabounidis, Aditya Golatkar, Michael Kleinman +3
We propose Re-FORC, an adaptive reward prediction method that, given a query, enables prediction of the expected future rewards as a function of the number of future thinking token…
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…
Critical Learning Periods Emerge Even in Deep Linear Networks
Michael Kleinman, Alessandro Achille, Stefano Soatto
Critical learning periods are periods early in development where temporary sensory deficits can have a permanent effect on behavior and learned representations. Despite the radical…
Critical Learning Periods for Multisensory Integration in Deep Networks
Michael Kleinman, Alessandro Achille, Stefano Soatto
We show that the ability of a neural network to integrate information from diverse sources hinges critically on being exposed to properly correlated signals during the early phases…
Gacs-Korner Common Information Variational Autoencoder
Michael Kleinman, Alessandro Achille, Stefano Soatto +1
We propose a notion of common information that allows one to quantify and separate the information that is shared between two random variables from the information that is unique t…
Usable Information and Evolution of Optimal Representations During Training
Michael Kleinman, Alessandro Achille, Daksh Idnani +1
We introduce a notion of usable information contained in the representation learned by a deep network, and use it to study how optimal representations for the task emerge during tr…