7 papers
Nalar: An agent serving framework
Marco Laju, Donghyun Son, Saurabh Agarwal +4
LLM-driven agentic applications increasingly automate complex, multi-step tasks, but serving them efficiently remains challenging due to heterogeneous components, dynamic and model…
Software-Defined Agentic Serving
Saurabh Agarwal, Marco Laju, Jayanth Srinivasa +2
As multi-agent LLM pipelines grow in complexity, existing serving paradigms fail to adapt to the dynamic serving conditions. We argue that agentic serving systems should be program…
Dora: QoE-Aware Hybrid Parallelism for Distributed Edge AI
Jianli Jin, Ziyang Lin, Qianli Dong +5
With the proliferation of edge AI applications, satisfying user quality of experience (QoE) requirements, such as model inference latency, has become a first class objective, as th…
Model-Based Diagnosis: Automating End-to-End Diagnosis of Network Failures
Changrong Wu, Yiyao Yu, Myungjin Lee +4
Fast diagnosis and repair of enterprise network failures is critically important since disruptions cause major business impacts. Prior works focused on diagnosis primitives or proc…
EXP-Bench: Can AI Conduct AI Research Experiments?
Patrick Tser Jern Kon, Jiachen Liu, Xinyi Zhu +10
Automating AI research holds immense potential for accelerating scientific progress, yet current AI agents struggle with the complexities of rigorous, end-to-end experimentation. W…
Curie: Toward Rigorous and Automated Scientific Experimentation with AI Agents
Patrick Tser Jern Kon, Jiachen Liu, Qiuyi Ding +7
Scientific experimentation, a cornerstone of human progress, demands rigor in reliability, methodical control, and interpretability to yield meaningful results. Despite the growing…