7 citations · 21 across the 27 of their papers we have counts for
36 papers
What do Language Models Learn and When? The Implicit Curriculum Hypothesis
Emmy Liu, Kaiser Sun, Millicent Li +4
Large language models (LLMs) can perform remarkably complex tasks, yet the fine-grained details of how these capabilities emerge during pretraining remain poorly understood. Scalin…
ComUICoder: Component-based Reusable UI Code Generation for Complex Websites via Semantic Segmentation and Element-wise Feedback
Jingyu Xiao, Jiantong Qin, Shuoqi Li +5
Multimodal Large Language Models (MLLMs) have demonstrated strong performance on the UI-to-code task, which aims to generate UI code from design mock-ups. However, when applied to…
Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation
Jen-tse Huang, Chang Chen, Shiyang Lai +3
Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues. While Multimodal Large Language…
AI Deception: Risks, Dynamics, and Controls
Boyuan Chen, Sitong Fang, Jiaming Ji +56
As intelligence increases, so does its shadow. AI deception, in which systems induce false beliefs to secure self-beneficial outcomes, has evolved from a speculative concern to an…
Deep Research: A Systematic Survey
Zhengliang Shi, Yiqun Chen, Haitao Li +23
Large language models (LLMs) have rapidly evolved from text generators into powerful problem solvers. Yet, many open tasks demand critical thinking, multi-source, and verifiable ou…
ComboBench: Can LLMs Manipulate Physical Devices to Play Virtual Reality Games?
Shuqing Li, Jiayi Yan, Chenyu Niu +5
Virtual Reality (VR) games require players to translate high-level semantic actions into precise device manipulations using controllers and head-mounted displays (HMDs). While huma…