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From the 1 of 24 linked papers with an AI index.

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cs.AI2026

Ostrakon-VL: Towards Domain-Expert MLLM for Food-Service and Retail Stores

Zhiyong Shen, Gongpeng Zhao, Jun Zhou +10

Multimodal Large Language Models (MLLMs) have recently achieved substantial progress in general-purpose perception and reasoning. Nevertheless, their deployment in Food-Service and…

cs.AI2025

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…

cs.AI2025

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…

cs.AI2025

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…

cs.AI2025

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…