7 papers
Scaling Latent Reasoning via Looped Language Models
Rui-Jie Zhu, Zixuan Wang, Kai Hua +30
Modern LLMs are trained to "think" primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training and under-leverages pre-training…
When 2D Tasks Meet 1D Serialization: On Serialization Friction in Structured Tasks
Chung-Hsiang Lo, Lu Li, Diji Yang +4
In the LLM era, many symbolic and structured problems are presented to models through 1D text serialization. Yet some such problems are natively two-dimensional: their relevant rel…
Large Language Models Explore by Latent Distilling
Yuanhao Zeng, Ao Lu, Lufei Li +3
Generating diverse responses is crucial for test-time scaling of large language models (LLMs), yet standard stochastic sampling mostly yields surface-level lexical variation, limit…
Culture-Aware Humorous Captioning: Multimodal Humor Generation across Cultural Contexts
Run Xu, Lu Li, Rongzhao Zhang +1
Recent multimodal large language models have shown promising ability in generating humorous captions for images, yet they still lack stable control over explicit cultural context,…
STRICT: Stress Test of Rendering Images Containing Text
Tianyu Zhang, Xinyu Wang, Lu Li +5
While diffusion models have revolutionized text-to-image generation with their ability to synthesize realistic and diverse scenes, they continue to struggle to generate consistent…
MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation
Lu Li, Tianyu Zhang, Zhiqi Bu +7
Model merging has emerged as an effective approach to combine multiple single-task models into a multitask model. This process typically involves computing a weighted average of th…