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cs.CL2026
MoME: Mixture-of-Memory Embeddings for Context-Aware Sparse Lookup
Muchen Li, Leonid Sigal, Renjie Liao
Scaling large language models efficiently has motivated sparse capacity mechanisms such as Mixture-of-Experts and, more recently, conditional memory: token-indexed embedding tables…
cs.CV2026
CoRDS: Coreset-based Representative and Diverse Selection for Streaming Video Understanding
Ailar Mahdizadeh, Puria Azadi, Muchen Li +2
Streaming video understanding with large vision-language models (VLMs) requires a compact memory that can support future reasoning over an ever-growing visual history. A common sol…
cs.CV2026
SynMulti: Synthetic-to-Real Learning for Multimodal Video Understanding
Tanzila Rahman, Renjie Liao, Leonid Sigal
Training multimodal large language models (MLLMs) for video understanding requires large-scale annotated data spanning diverse tasks such as object counting, question answering, an…