2 citations · 2 across the 9 of their papers we have counts for
9 papers
Contemporary AI lacks the imagination to diverge or negate in science
Honglin Bao, Siyang Wu, Xiao Liu +3
Bold claims that AI will accelerate scientific discovery have raced ahead of evidence from working scientists, yet large-scale, scientist-in-the-loop evidence is scarce. Here we mo…
LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arena
Qingchuan Yang, Simon Mahns, Sida Li +3
Forecasting is not only a fundamental intellectual pursuit but also is of significant importance to societal systems such as finance and economics. With the rapid advances of large…
Mapping Overlaps in Benchmarks through Perplexity in the Wild
Siyang Wu, Honglin Bao, Sida Li +2
We introduce benchmark signatures to characterize the capacity demands of LLM benchmarks and their overlaps. Signatures are sets of salient tokens from in-the-wild corpora whose mo…
Optimizing Multi-Round Enhanced Training in Diffusion Models for Improved Preference Understanding
Kun Li, Jianhui Wang, Yangfan He +10
Generative AI has significantly changed industries by enabling text-driven image generation, yet challenges remain in achieving high-resolution outputs that align with fine-grained…
TimeChat-Online: 80% Visual Tokens are Naturally Redundant in Streaming Videos
Linli Yao, Yicheng Li, Yuancheng Wei +11
The rapid growth of online video platforms, particularly live streaming services, has created an urgent need for real-time video understanding systems. These systems must process c…
Efficient Temporal Consistency in Diffusion-Based Video Editing with Adaptor Modules: A Theoretical Framework
Xinyuan Song, Yangfan He, Sida Li +10
Adapter-based methods are commonly used to enhance model performance with minimal additional complexity, especially in video editing tasks that require frame-to-frame consistency.…
TDRI: Two-Phase Dialogue Refinement and Co-Adaptation for Interactive Image Generation
Yuheng Feng, Jianhui Wang, Kun Li +5
Although text-to-image generation technologies have made significant advancements, they still face challenges when dealing with ambiguous prompts and aligning outputs with user int…
Prediction-Powered Adaptive Shrinkage Estimation
Sida Li, Nikolaos Ignatiadis
Prediction-Powered Inference (PPI) is a powerful framework for enhancing statistical estimates by combining limited gold-standard data with machine learning (ML) predictions. While…
Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion
Yangfan He, Sida Li, Jianhui Wang +11
Recent advancements in text-to-image (T2I) generation using diffusion models have enabled cost-effective video-editing applications by leveraging pre-trained models, eliminating th…