3 papers
cs.IR2026
Training-free Adjustable Polynomial Graph Filtering for Ultra-fast Multimodal Recommendation
Yu-Seung Roh, Joo-Young Kim, Jin-Duk Park +1
Multimodal recommender systems improve the performance of canonical recommender systems with no item features by utilizing diverse content types such as text, images, and videos, w…
cs.AI2026
POP: Online Structural Pruning Enables Efficient Inference of Large Foundation Models
Yi Chen, Wonjin Shin, Shuhong Liu +6
Large foundation models (LFMs) achieve strong performance through scaling, yet current structural pruning methods derive fixed pruning decisions during inference, overlooking spars…
cs.CL2026
A Training-Free Large Reasoning Model-based Knowledge Tracing Framework for Unified Prediction and Prescription
Unggi Lee, Joo Young Kim, Ran Ju +2
Knowledge Tracing (KT) aims to estimate a learner's evolving mastery based on interaction histories. Recent studies have explored Large Language Models (LLMs) for KT via autoregres…