4 papers
STaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models
Xin Yan, Aqiang Wang, Zhenglin Wan +2
Diffusion large language models (DLLMs) have recently emerged as a promising alternative to autoregressive LLMs by generating text through iterative masked denoising with bidirecti…
R3-VAE: Reference Vector-Guided Rating Residual Quantization VAE for Generative Recommendation
Qiang Wan, Ze Yang, Dawei Yang +8
Generative Recommendation (GR) has gained traction for its merits of superior performance and cold-start capability. As the vital role in GR, Semantic Identifiers (SIDs) represent…
LLM-Integrated Bayesian State Space Models for Multimodal Time-Series Forecasting
Sungjun Cho, Changho Shin, Suenggwan Jo +3
Forecasting in the real world requires integrating structured time-series data with unstructured textual information, but existing methods are architecturally limited by fixed inpu…
TARDIS: Mitigating Temporal Misalignment via Representation Steering
Changho Shin, Xinya Yan, Suenggwan Jo +3
Language models often struggle with temporal misalignment, performance degradation caused by shifts in the temporal distribution of data. Continuously updating models to avoid degr…