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20242026
most citedGDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks

1 citations · 1 across the 8 of their papers we have counts for

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cs.AI2025

Reasoning Models Ace the CFA Exams

Jaisal Patel, Yunzhe Chen, Kaiwen He +4

Previous research has reported that large language models (LLMs) demonstrate poor performance on the Chartered Financial Analyst (CFA) exams. However, recent reasoning models have…

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.LG2025★ 1 cited

GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks

Tejal Patwardhan, Rachel Dias, Elizabeth Proehl +16

We introduce GDPval, a benchmark evaluating AI model capabilities on real-world economically valuable tasks. GDPval covers the majority of U.S. Bureau of Labor Statistics Work Acti…

cs.LG2025

Energy Consumption in Parallel Neural Network Training

Philipp Huber, David Li, Juan Pedro Gutiérrez Hermosillo Muriedas +4

The increasing demand for computational resources of training neural networks leads to a concerning growth in energy consumption. While parallelization has enabled upscaling model…

cs.CV2025

Modeling Urban Food Insecurity with Google Street View Images

David Li

Food insecurity is a significant social and public health issue that plagues many urban metropolitan areas around the world. Existing approaches to identifying food insecurity rely…

cs.LG2025

Large Language Model Compression via the Nested Activation-Aware Decomposition

Jun Lu, Tianyi Xu, Bill Ding +2

In this paper, we tackle the critical challenge of compressing large language models (LLMs) to facilitate their practical deployment and broader adoption. We introduce a novel post…

cs.CV2025

Medical Semantic Segmentation with Diffusion Pretrain

David Li, Anvar Kurmukov, Mikhail Goncharov +2

Recent advances in deep learning have shown that learning robust feature representations is critical for the success of many computer vision tasks, including medical image segmenta…