# Highlights from MLSys 2026

DevFeed: [Highlights from MLSys 2026](<https://devfeed.tech/articles/highlights-from-mlsys-2026-22573.md>)

Original publisher: [Read original article](<https://medium.com/capital-one-tech/highlights-from-mlsys-2026-5e6d9f226f3d?source=rss----3db3a67cb648---4>)

Author: Capital One Tech

Published: 2026-08-11T15:07:08Z

Content type: opinion

Language: en

Sources: [Capital One Tech](<https://devfeed.tech/sources/capital-one-tech.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [systems](<https://devfeed.tech/topics/systems.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Streaming](<https://devfeed.tech/topics/streaming.md>), [Cache](<https://devfeed.tech/topics/cache.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai-research](<https://devfeed.tech/tags/ai-research.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [large-language-model](<https://devfeed.tech/tags/large-language-model.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [research](<https://devfeed.tech/tags/research.md>), [retrieval-augmented-generation-rag](<https://devfeed.tech/tags/retrieval-augmented-generation-rag.md>), [science](<https://devfeed.tech/tags/science.md>)

## AI overview

Capital One's AI research team reviews themes and selected papers from MLSys 2026, focusing on efficient LLM serving, retrieval-augmented generation, cache management, model speculation, and agentic AI. The article highlights research on inference optimization, distributed compute and communication, streaming, and vector search.

## Source excerpt

Capital One's AI research team recaps MLSys 2026, including optimizing serving LLMs, RAG and agentic AI. The 9th Annual Conference on Machine Learning and Systems (MLSys) took place in May in Bellevue, Washington. MLSys is a highly selective interdisciplinary conference sitting at the intersection of machine learning (ML) and systems design. The conference highlights cutting-edge research that combines generative AI, natural language processing, computer vision and reinforcement learning with infrastructure, deployment and hardware optimizations to make AI faster, scalable and more performant. MLSys offered Capital One associates the opportunity to learn from world-class conference sessions presented by experts in the field. All the attending associates left brimming with new ideas and planned collaborations. Kel Vanee, MVP, Machine Learning Engineering, presented some of the work happening at Capital One on using AI to make AI more efficient. Takeaways and favorite papers from MLSys 2026 Some of the most prevalent topics at MLSys this year were on cache management, model speculation, retrieval augmented generation (RAG) and agentic AI. With a plethora of relevant and interesting talks, we had no shortage of papers to choose favorites from. While a complete list of the papers we loved would be far too long, here are a few standouts: Large language model inference optimization One of the leading themes this year was how to more efficiently serve LLM models. We especially liked the papers on reducing self-attention costs, such as MAC-Attention: a Match-Amend-Complete scheme for fast and accurate attention computation and BLASST: Dynamic BLocked Attention Sparsity via Softmax Thresholding. We found valuable insights in papers covering how best to overlap computation with communication, such as TokenWeave: Efficient Compute-Communication Overlap for Distributed LLM Inference, Stream2LLM: Overlap Context Streaming and Prefill for Reduced Time-to-First-Token and FlashAgen