most citedAI Evaluation Should Require Standardized Item-Level Data Releases

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Fidelity-Diversity-Consistency (FDC): Data Pruning for Remote Sensing Change Detection

Dongyao Zhu, Ranga Raju Vatsavai

Despite the success of data pruning (DP) in reducing training data sizes and improving downstream model performance in classification and segmentation tasks, its potential in remot…

cs.AI20261 cited

AI Evaluation Should Require Standardized Item-Level Data Releases

Han Jiang, Susu Zhang, Dongyao Zhu +6

This position paper argues that standardized item-level benchmark data should become the default infrastructure for AI evaluation. Current evaluations suffer from underspecified it…

cs.CV2026

Leveraging Latent Visual Reasoning in Silence

Dongyao Zhu, Zhen Wang, Xi Xiao +7

Latent visual reasoning involves visual evidence more directly in multimodal reasoning by inserting continuous latent tokens before textual generation. However, the necessity of th…

cs.CL2025

PICACO: Pluralistic In-Context Value Alignment of LLMs via Total Correlation Optimization

Han Jiang, Dongyao Zhu, Xiaoyuan Yi +3

In-Context Learning has shown great potential for aligning Large Language Models (LLMs) with human values, helping reduce harmful outputs and accommodate diverse preferences withou…

cs.CV2023

Rethinking Data Distillation: Do Not Overlook Calibration

Dongyao Zhu, Bowen Lei, Jie Zhang +4

Neural networks trained on distilled data often produce over-confident output and require correction by calibration methods. Existing calibration methods such as temperature scalin…