Heng Guo

The paper proposes using GUI agents, both as objective evaluators and subjective playtesters, to significantly improve the generation of playable games from prompts, demonstrating a 66.8% rubric pass-rate with a novel iterative framework.

The paper introduces Pocket-Dentist, an efficiency-aware benchmark and model that demonstrates that compact, smaller Vision-Language Models (VLMs) can outperform larger models in accuracy while drastically reducing computational cost for on-device dental image understanding.

The paper demonstrates that audio-language models often ignore conflicting audio evidence in favor of text, and proposes a training-free decoding rule, GACL, that significantly improves faithfulness by correcting this arbitration bias.

RAMP is a method for improving click-through rate and conversion rate prediction accuracy in privacy-constrained settings by using a personalized pathway, a non-personalized pathway, and a prediction-alignment architecture.