2 papers
cs.LG2026
Spectral-MoE: Heterogeneous Per-Layer Expert Utilization in Dense-to-MoE Conversion
Zachary Broveak, Christian Kubicka, Thierry Tambe
Large language models impose substantial memory demands and arithmetic costs during inference. Dense-to-mixture-of-experts (MoE) conversion is a model optimization technique that a…
cs.AI2026
Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads
Yasmine Omri, Ziyu Gan, Zachary Broveak +6
LLM agents are increasingly deployed on long-horizon tasks requiring sustained reasoning over extended interaction histories. Realizing this at scale requires agents to persistentl…