7 citations · 10 across the 8 of their papers we have counts for
10 papers
Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering
Zizhen Wang, Bo Feng, Zhengfeng Lai +5
Evaluating video captioning remains a critical challenge for Visual Large Language Models (VLLMs). Existing metrics primarily rely on matching generated text against ground-truth r…
VSAS-Bench: Real-Time Evaluation of Visual Streaming Assistant Models
Pavan Kumar Anasosalu Vasu, Cem Koc, Fartash Faghri +6
Streaming vision-language models (VLMs) continuously generate responses given an instruction prompt and an online stream of input frames. This is a core mechanism for real-time vis…
Training LLMs Beyond Next Token Prediction - Filling the Mutual Information Gap
Chun-Hao Yang, Bo-Han Feng, Tzu-Yuan Lai +4
Autoregressive language models are usually trained with next-token prediction under a fixed left-to-right target order. This paper studies target-token organization as a training-t…
Alignment Without Understanding: A Message- and Conversation-Centered Approach to Understanding AI Sycophancy
Lihua Du, Xing Lyu, Lezi Xie +1
AI sycophancy is increasingly recognized as a harmful alignment, but research remains fragmented and underdeveloped at the conceptual level. This article redefines AI sycophancy as…
Driving Accurate Allergen Prediction with Protein Language Models and Generalization-Focused Evaluation
Brian Shing-Hei Wong, Joshua Mincheol Kim, Sin-Hang Fung +11
Allergens, typically proteins capable of triggering adverse immune responses, represent a significant public health challenge. To accurately identify allergen proteins, we introduc…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
Breaking Down Video LLM Benchmarks: Knowledge, Spatial Perception, or True Temporal Understanding?
Bo Feng, Zhengfeng Lai, Shiyu Li +4
Existing video understanding benchmarks often conflate knowledge-based and purely image-based questions, rather than clearly isolating a model's temporal reasoning ability, which i…
VERDI: VLM-Embedded Reasoning for Autonomous Driving
Bowen Feng, Zhiting Mei, Julian Ost +5
While autonomous driving (AD) stacks struggle with decision making under partial observability and real-world complexity, human drivers are capable of applying commonsense reasonin…
When Large Language Models are More PersuasiveThan Incentivized Humans, and Why
Jiacheng Liu, Francesco Salvi, Philipp Schoenegger +37
Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare the persuasiveness of two LLMs (…
StreamBridge: Turning Your Offline Video Large Language Model into a Proactive Streaming Assistant
Haibo Wang, Bo Feng, Zhengfeng Lai +6
We present StreamBridge, a simple yet effective framework that seamlessly transforms offline Video-LLMs into streaming-capable models. It addresses two fundamental challenges in ad…