ArchAgent: Agentic AI-driven Computer Architecture Discovery
Raghav Gupta; Akanksha Jain; Abraham Gonzalez; Alexander Novikov; Po-Sen Huang; Matej Balog; Marvin Eisenberger; Sergey Shirobokov · 2026 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Agile hardware design flows are a critically needed force multiplier to meet the exploding demand for compute. Recently, agentic generative AI systems have demonstrated significant advances in algorithm design, improving code efficiency, and enabling discovery across scientific domains. Bridging these worlds, we present ArchAgent, an automated computer architecture discovery system built on AlphaEvolve. We show ArchAgent's ability to automatically design/implement state-of-the-art (SoTA) cache replacement policies (architecting new mechanisms/logic, not only changing parameters), broadly withi
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Metadata source: arXiv