# ArchAgent: Agentic AI-driven Computer Architecture Discovery

arXiv: https://arxiv.org/abs/2602.22425 · HTML: https://mlsanity.com/paper/2602.22425/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 72 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 7 of 7)

- [Mathematical exploration and discovery at scale](https://mlsanity.com/paper/2511.02864/) (2025)
- [An Imitation Learning Approach for Cache Replacement](https://mlsanity.com/paper/2006.16239/) (2020)
- [The Championship Simulator: Architectural Simulation for Education and Competition](https://mlsanity.com/paper/2210.14324/) (2022)
- [The GAP Benchmark Suite](https://mlsanity.com/paper/1508.03619/) (2015)
- [HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation](https://mlsanity.com/paper/2105.01585/) (2021)
- [CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design](https://mlsanity.com/paper/2506.03474/) (2025)
- [A Joint Learning Approach to Hardware Caching and Prefetching](https://mlsanity.com/paper/2510.10862/) (2025)

## Built on by (most cited first, 6 of 6)

- [What Do Evolutionary Coding Agents Evolve?](https://mlsanity.com/paper/2605.20086/) (2026)
- [Agentic Architect: An Agentic AI Framework for Architecture Design Exploration and Optimization](https://mlsanity.com/paper/2604.25083/) (2026)
- [ArchAgent v2: A Case Study with the Data Prefetching Championship](https://mlsanity.com/paper/2608.09874/) (2026)
- [Automated Membership Inference Attacks (AutoMIA): Discovering MIA Signal Computations using LLM Agents](https://mlsanity.com/paper/2603.19375/) (2026)
- [AgentDSE: Reasoning-Augmented Architectural Design Space Exploration](https://mlsanity.com/paper/2606.21836/) (2026)
- [DuoDiscover: Scaling Autonomous Discovery Beyond the Evaluation Bottleneck](https://mlsanity.com/paper/2610.17184/) (2026)

## Abstract (arXiv)

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 within the confines of an established cache replacement policy design competition. In two days without human intervention, ArchAgent generated a policy achieving a 5.3% IPC speedup improvement over the prior SoTA on public multi-core Google Workload Traces. On the heavily-explored single-core SPEC06 workloads, it generated a policy in just 18 days showing a 0.9% IPC speedup improvement over the existing SoTA (a similar "winning margin" as reported by the existing SoTA). ArchAgent achieved these gains 3-5x faster than prior human-developed SoTA policies. Agentic flows also enable "post-silicon hyperspecialization" where agents tune runtime-configurable parameters exposed in hardware policies to further align the policies with a specific workload (mix). Exploiting this, we demonstrate a 2.4% IPC speedup improvement over prior SoTA on SPEC06 workloads. Finally, we outline broader implications for computer architecture research in the era of agentic AI. For example, we demonstrate the phenomenon of "simulator escapes", where the agentic AI flow discovered and exploited a loophole in a popular microarchitectural simulator - a consequence of the fact that these research tools were designed for a (now past) world where they were exclusively operated by humans acting in good-faith.