2 citations · 3 across the 4 of their papers we have counts for
10 papers
MobileWorldBench: Towards Semantic World Modeling For Mobile Agents
Shufan Li, Konstantinos Kallidromitis, Akash Gokul +3
World models have shown great utility in improving the task performance of embodied agents. While prior work largely focuses on pixel-space world models, these approaches face prac…
Accelerating Inference of Masked Image Generators via Reinforcement Learning
Pranav Subbaraman, Shufan Li, Siyan Zhao +1
Masked Generative Models (MGM)s demonstrate strong capabilities in generating high-fidelity images. However, they need many sampling steps to create high-quality generations, resul…
From Masks to Worlds: A Hitchhiker's Guide to World Models
Jinbin Bai, Yu Lei, Hecong Wu +7
This is not a typical survey of world models; it is a guide for those who want to build worlds. We do not aim to catalog every paper that has ever mentioned a ``world model". Inste…
PhysiX: A Foundation Model for Physics Simulations
Tung Nguyen, Arsh Koneru, Shufan Li +1
Foundation models have achieved remarkable success across video, image, and language domains. By scaling up the number of parameters and training datasets, these models acquire gen…
Mercury: Ultra-Fast Language Models Based on Diffusion
Inception Labs, Samar Khanna, Siddhant Kharbanda +10
We present Mercury, a new generation of commercial-scale large language models (LLMs) based on diffusion. These models are parameterized via the Transformer architecture and traine…
PredGen: Accelerated Inference of Large Language Models through Input-Time Speculation for Real-Time Speech Interaction
Shufan Li, Aditya Grover
Large Language Models (LLMs) are widely used in real-time voice chat applications, typically in combination with text-to-speech (TTS) systems to generate audio responses. However,…