arXiv:2607.02370v1 Announce Type: cross Abstract: Compiler missed optimizations refer to cases in which compilers failed to optimize certain code. It takes many compiler developers' efforts to implement or patch such missed optimizations. In this paper, we present a systematic study of how well agents patch compiler missed optimizations. We identify a significant challenge that patching a missed optimization requires more than just fixing the reported case, and instead requires generalizing to similar cases. We construct a benchmark of real-world LLVM missed optimization issues and compare age
The proliferation of advanced AI models and agentic capabilities makes their application to complex engineering problems like compiler optimization a natural immediate frontier.
This research explores AI agents' effectiveness in a highly specialized, foundational software development area, potentially accelerating compiler efficiency and reducing manual developer effort.
The development and maintenance of critical software infrastructure like compilers could be significantly augmented by AI, shifting how complex software bugs and optimizations are managed.
- · AI agent developers
- · Cloud computing providers
- · Software development companies
- · Hardware manufacturers
- · Manual compiler optimization specialists
- · Companies slow to adopt AI-driven development tools
AI agents demonstrate potential for automating highly specialized software patching and optimization tasks.
This could lead to significantly more efficient compilers, driving performance gains across all software stacks and potentially reducing compute costs.
The success of agent-based patching in this domain could accelerate adoption in other complex engineering fields, altering the landscape of high-skill technical labor.
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Read at arXiv cs.AI