3 papers
cs.CL2026
Hearing Between the Lines: Unlocking the Reasoning Power of LLMs for Speech Evaluation
Arjun Chandra, Kevin Miller, Venkatesh Ravichandran +2
Large Language Model (LLM) judges exhibit strong reasoning capabilities but are limited to textual content. This leaves current automatic Speech-to-Speech (S2S) evaluation methods…
cs.CV2025
BabyVLM-V2: Toward Developmentally Grounded Pretraining and Benchmarking of Vision Foundation Models
Shengao Wang, Wenqi Wang, Zecheng Wang +20
Early children's developmental trajectories set up a natural goal for sample-efficient pretraining of vision foundation models. We introduce BabyVLM-V2, a developmentally grounded…
cs.CV2025
BabyVLM: Data-Efficient Pretraining of VLMs Inspired by Infant Learning
Shengao Wang, Arjun Chandra, Aoming Liu +2
Human infants rapidly develop visual reasoning skills from minimal input, suggesting that developmentally inspired pretraining could significantly enhance the efficiency of vision-…