3 citations · 3 across the 3 of their papers we have counts for
7 papers
VLIC: Vision-Language Models As Perceptual Judges for Human-Aligned Image Compression
Kyle Sargent, Ruiqi Gao, Philipp Henzler +5
Evaluations of image compression performance which include human preferences have generally found that naive distortion functions such as MSE are insufficiently aligned to human pe…
ENACT: Evaluating Embodied Cognition with World Modeling of Egocentric Interaction
Qineng Wang, Wenlong Huang, Yu Zhou +8
Embodied cognition argues that intelligence arises from sensorimotor interaction rather than passive observation. It raises an intriguing question: do modern vision-language models…
VAGEN: Reinforcing World Model Reasoning for Multi-Turn VLM Agents
Kangrui Wang, Pingyue Zhang, Zihan Wang +13
A key challenge in training Vision-Language Model (VLM) agents, compared to Language Model (LLM) agents, lies in the shift from textual states to complex visual observations. This…
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
Zihan Wang, Kangrui Wang, Qineng Wang +15
Training large language models (LLMs) as interactive agents presents unique challenges including long-horizon decision making and interacting with stochastic environment feedback.…
WorldScore: A Unified Evaluation Benchmark for World Generation
Haoyi Duan, Hong-Xing Yu, Sirui Chen +2
We introduce the WorldScore benchmark, the first unified benchmark for world generation. We decompose world generation into a sequence of next-scene generation tasks with explicit…
Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization
Kyle Sargent, Kyle Hsu, Justin Johnson +2
Since the advent of popular visual generation frameworks like VQGAN and latent diffusion models, state-of-the-art image generation systems have generally been two-stage systems tha…