3 papers
cs.LG2026
Shape Mutating Expert Compression:LorExperts and BTExperts
Inesh Chakrabarti, Sourjya Roy, Bowen Bao +3
Mixture-of-Experts (MoE) language models deliver high capacity at low per-token compute, but deploying them cheaply requires compressing their many expert weight matrices. Expert p…
cs.LG2025
KV-CAR: KV Cache Compression using Autoencoders and KV Reuse in Large Language Models
Sourjya Roy, Shrihari Sridharan, Surya Selvam +1
As Large Language Models (LLMs) scale in size and context length, the memory requirements of the key value (KV) cache have emerged as a major bottleneck during autoregressive decod…
cs.LG2025
Experts are all you need: A Composable Framework for Large Language Model Inference
Shrihari Sridharan, Sourjya Roy, Anand Raghunathan +1
Large Language Models (LLMs) have achieved state-of-the-art accuracies in a variety of natural language processing (NLP) tasks. However, this success comes at the cost of increased…