3 papers
cs.LG2026
MOSAIC: Efficient Mixture-of-Agent Scheduling via Adaptive Aggregation and Inference Concurrency
Saptarshi Mitra, Yifan Zhang, Rachid Karami +5
Mixture-of-Agents (MoA) systems improve reasoning accuracy by routing each query to multiple expert LLMs and aggregating their outputs. Efficiently executing this workload on limit…
cs.CL2025
Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection
Yeshwanth Venkatesha, Souvik Kundu, Priyadarshini Panda
Parameter Efficient Fine-Tuning (PEFT) has become the de-facto approach in adapting Large Language Models (LLMs) for downstream tasks in Natural Language Processing. However, its a…
cs.RO2025
Fast and Cost-effective Speculative Edge-Cloud Decoding with Early Exits
Yeshwanth Venkatesha, Souvik Kundu, Priyadarshini Panda
Large Language Models (LLMs) enable various applications on edge devices such as smartphones, wearables, and embodied robots. However, their deployment often depends on expensive c…