> For the complete documentation index, see [llms.txt](https://paper.lingyunyang.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://paper.lingyunyang.com/reading-notes/conference/icml-2026.md).

# ICML 2026

## Meta Info

Homepage: <https://icml.cc/Conferences/2026>

Paper list: <https://icml.cc/virtual/2026/papers.html?filter=titles>

## Papers

### Large Language Models (LLMs)

* LLM Inference
  * Agentic Inference
    * ThunderAgent: A Fast, Simple, and Program-Aware Agentic Inference System \[[Paper](https://openreview.net/forum?id=kR4iOTaAOJ)] \[[arXiv](https://arxiv.org/abs/2602.13692)] \[[Code](https://github.com/ThunderAgent-org/ThunderAgent)] \[[Homepage](https://thunderagent.ai)] \[[ICML](https://icml.cc/virtual/2026/poster/62040)]
      * GaTech & UIUC & CMU & Together AI
      * **Spotlight**
      * Introduce **ThunderAgent**, an agentic inference and rollout system that represents workflows as LLM Programs with unified KV-cache, state, and tool-resource metadata.
      * Use program-aware scheduling and tool resource management to improve KV-cache reuse, reduce memory imbalance, and prepare tool environments asynchronously.
  * Workload Characterization
    * Characterizing Agents in Production \[[Paper](https://openreview.net/forum?id=mWxEAgz3xu)] \[[arXiv](https://arxiv.org/abs/2512.04123)] \[[ICML](https://icml.cc/virtual/2026/oral/71172)]
      * UC Berkeley & Intesa Sanpaolo & UIUC & Stanford & IBM Research
      * **Oral**
      * Present **CAP**, a systematic study of production LLM agents using 20 interviews and a survey of 306 practitioners across 26 domains.
      * Find that production agents often use simple, controllable approaches: bounded step counts before human intervention, off-the-shelf prompting, and human evaluation.