most citedOn the Partitioning of GPU Power among Multi-Instances

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2025

Detecting Silent Failures in Multi-Agentic AI Trajectories

Divya Pathak, Harshit Kumar, Anuska Roy +3

Multi-Agentic AI systems, powered by large language models (LLMs), are inherently non-deterministic and prone to silent failures such as drift, cycles, and missing details in outpu…

cs.CL2025

Unsupervised Cycle Detection in Agentic Applications

Felix George, Harshit Kumar, Divya Pathak +3

Agentic applications powered by Large Language Models exhibit non-deterministic behaviors that can form hidden execution cycles, silently consuming resources without triggering exp…

cs.AI2025★ 1 cited

ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks

Saurabh Jha, Rohan Arora, Yuji Watanabe +40

Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench, a…

cs.DC2025★ 2 cited

On the Partitioning of GPU Power among Multi-Instances

Tirth Vamja, Kaustabha Ray, Felix George +1

Efficient power management in cloud data centers is essential for reducing costs, enhancing performance, and minimizing environmental impact. GPUs, critical for tasks like machine…

cs.DC2025

Metric Criticality Identification for Cloud Microservices

Akanksha Singal, Divya Pathak, Kaustabha Ray +3

Modern cloud-native applications built on microservice architectures present unprecedented challenges for system monitoring and alerting. Site Reliability Engineers (SREs) face the…