3 citations · 8 across the 17 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.LG2026
Benchmarking Composable Compression Techniques in Mixture-of-Experts LLMs
Afsara Benazir, Chen Chen, Rongxiao Qu +3
Mixture-of-Experts (MoE) LLMs scale model capacity efficiently through sparse activation, but their large expert parameter footprint, routing imbalance, and long-context KV-cache g…
cs.CV2026
Argus-Unified: Towards A Compact and Economical Unified Model for Image Understanding and Generation
Weiming Zhuang, Jiabo Huang, Jingtao Li +4
Unifying visual understanding and generation in one model holds immense promise, but remains challenging and expensive due to heavy compute and data demands and conflicts between t…
cs.CV2026
UniCompress: Token Compression for Unified Vision-Language Understanding and Generation
Ziyao Wang, Chen Chen, Jingtao Li +4
Unified models aim to support both understanding and generation by encoding images into discrete tokens and processing them alongside text within a single autoregressive framework.…