3 papers
cs.IR2026
What Makes a Good Semantic ID for Generative Recommendation? A Reproducibility Study
Yufei Chen, Junchen Fu, Jujia Zhao +2
Generative recommendation has emerged as an active research direction, where items are commonly represented by semantic IDs (SIDs): discrete codes generated token by token. Despite…
cs.IR2026
Closing the Indexing-Decoding Gap in Multimodal Generative Retrieval via Prefix Retention Optimization
Yufei Chen, Zihan Wang, Yubao Tang +3
Multimodal generative retrieval formulates multimodal retrieval as discrete identifier generation, eliminating the need for explicit similarity search over external embeddings. Exi…
cs.LG2026
Riemannian MeanFlow for One-Step Generation on Manifolds
Zichen Zhong, Haoliang Sun, Yukun Zhao +2
Flow Matching enables simulation-free training of generative models on Riemannian manifolds, yet sampling typically still relies on numerically integrating a probability-flow ODE.…