3 papers
cs.CL2026
Long-Context Aware Upcycling: A New Frontier for Hybrid LLM Scaling
Parsa Ashrafi Fashi, Utkarsh Saxena, Mehdi Rezagholizadeh +7
Hybrid sequence models that combine efficient Transformer components with linear sequence modeling blocks are a promising alternative to pure Transformers, but most are still pretr…
cs.LG2025
Zebra-Llama: Towards Extremely Efficient Hybrid Models
Mingyu Yang, Mehdi Rezagholizadeh, Guihong Li +2
With the growing demand for deploying large language models (LLMs) across diverse applications, improving their inference efficiency is crucial for sustainable and democratized acc…
cs.CL2025
X-EcoMLA: Upcycling Pre-Trained Attention into MLA for Efficient and Extreme KV Compression
Guihong Li, Mehdi Rezagholizadeh, Mingyu Yang +2
Multi-head latent attention (MLA) is designed to optimize KV cache memory through low-rank key-value joint compression. Rather than caching keys and values separately, MLA stores t…