collaborators

6 papers

cs.LG2026

KLAS: Using Similarity to Stitch Neural Networks for Improved Accuracy-Efficiency Tradeoffs

Debopam Sanyal, Anantharaman Iyer, Alind Khare +5

Given the wide range of deployment targets, flexible model selection is essential for optimizing performance within a given compute budget. Recent work demonstrates that stitching…

cs.DC2026

Sutradhara: An Intelligent Orchestrator-Engine Co-design for Tool-based Agentic Inference

Anish Biswas, Kanishk Goel, Srivarshinee S +5

Agentic applications are LLMs that iteratively invoke external tools to accomplish complex tasks. Such tool-based agents are rapidly becoming the dominant paradigm for deploying la…

cs.AI2026

AgentRx: Diagnosing AI Agent Failures from Execution Trajectories

Shraddha Barke, Arnav Goyal, Alind Khare +3

AI agents often fail in ways that are difficult to localize because executions are probabilistic, long-horizon, multi-agent, and mediated by noisy tool outputs. We address this gap…

cs.DC2025

Serving Heterogeneous LoRA Adapters in Distributed LLM Inference Systems

Shashwat Jaiswal, Shrikara Arun, Anjaly Parayil +8

Low-Rank Adaptation (LoRA) has become the de facto method for parameter-efficient fine-tuning of large language models (LLMs), enabling rapid adaptation to diverse domains. In prod…

cs.DC2025

ModServe: Modality- and Stage-Aware Resource Disaggregation for Scalable Multimodal Model Serving

Haoran Qiu, Anish Biswas, Zihan Zhao +9

Large multimodal models (LMMs) demonstrate impressive capabilities in understanding images, videos, and audio beyond text. However, efficiently serving LMMs in production environme…

cs.LG2025

STRIDE: Structure and Embedding Distillation with Attention for Graph Neural Networks

Anshul Ahluwalia, Payman Behnam, Rohit Das +4

Recent advancements in Graph Neural Networks (GNNs) have led to increased model sizes to enhance their capacity and accuracy. Such large models incur high memory usage, latency, an…