From the 1 of 8 linked papers with an AI index.
8 papers
OpRAG: A Resource-Deterministic Runtime for GPU-Backed Multi-Stage RAG Workflows
Arup Kumar Sarker, Mills Staylor, Aymen Alsaadi +3
Agentic retrieval-augmented generation (RAG) systems combine preprocessing, embedding, retrieval, memory access, context construction, generation, and vector-index updates. Althoug…
[AAFLOW+] Stateful Operator Abstraction with Zero-Copy Distributed KV Cache Orchestration for Multi-Agent Workflows
Arup Kumar Sarker, Alexander James Halpern, Mills Staylor +5
The paper presents AAFLOW+, a framework that treats key‑value (KV) caches as distributed objects, enabling zero‑copy sharing of model state across multi‑agent LLM workflows to cut…
AAFLOW: Scalable Patterns for Agentic AI Workflows
Arup Kumar Sarker, Mills Staylor, Aymen Alsaadi +3
Agentic workflows in large language model systems integrate retrieval, reasoning, and memory, but existing frameworks suffer from scalability and reproducibility limitations due to…
Combining Serverless and High-Performance Computing Paradigms to support ML Data-Intensive Applications
Mills Staylor, Arup Kumar Sarker, Gregor von Laszewski +3
Data is found everywhere, from health and human infrastructure to the surge of sensors and the proliferation of internet-connected devices. To meet this challenge, the data enginee…
AI Benchmark Democratization and Carpentry
Gregor von Laszewski, Wesley Brewer, Jeyan Thiyagalingam +28
Benchmarks are a cornerstone of modern machine learning, enabling reproducibility, comparison, and scientific progress. However, AI benchmarks are increasingly complex, requiring d…
An MLCommons Scientific Benchmarks Ontology
Ben Hawks, Gregor von Laszewski, Matthew D. Sinclair +6
Scientific machine learning research spans diverse domains and data modalities, yet existing benchmark efforts remain siloed and lack standardization. This makes novel and transfor…