agent/papers/items/2026-2606-26453-optimizing-cuda-like-a-human-micro-profiling-tools-as-expert-surrogates-for-llm-.md

Paper: Optimizing CUDA like a Human: Micro-Profiling Tools as Expert Surrogates for LLM-Based GPU Kernel Optimization

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type: paper title: "Optimizing CUDA like a Human: Micro-Profiling Tools as Expert Surrogates for LLM-Based GPU Kernel Optimization" authors: Jiading Gai, Shuai Zhang, Kaj Bostrom, Jin Huang, Vihang Patil, Haoyang Fang, Bernie Wang, Huzefa Rangwala, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.26453 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-24 updated_at: 2026-06-24 status: queued relevance: high topics:

• agent-evaluation
• coding-agent
• computer-use
• memory
• multi-agent
• tool-use methods:

benchmarks:

models:

datasets:

• cs.LG related_concepts:

related_jobs:

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related_projects:

collection_score: 15 collection_queries: coding-agent, multi-agent-llm

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: coding-agent, multi-agent-llm
• inferred topics: agent-evaluation, coding-agent, computer-use, memory, multi-agent, tool-use
• arXiv categories: cs.LG
• collection score: 15

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.26453