Agents
New work from Google DeepMind and collaborators. Agentic algorithm discovery keeps getting demonstrated on problems with fast rewards. Micro…
New work from Google DeepMind and collaborators. Agentic algorithm discovery keeps getting demonstrated on problems with fast rewards. Microarchitecture has vast search spaces, strict hardware budgets
New work from Google DeepMind and collaborators. Agentic algorithm discovery keeps getting demonstrated on problems with fast rewards. Microarchitecture has vast search spaces, strict hardware budgets, and simulation times measured in hours, which is why the original ArchAgent stopped at single-level cache replacement. ArchAgent v2 scales to multi-level data prefetching with two additions. Cascaded evolutionary search subdivides the space by sequentially evolving and freezing prefetchers at individual cache levels. A hardware-realizability feedback loop embeds real-time size estimation into evolution, so candidates that cannot fit the budget get pruned during search. Under identical 4th Data Prefetching Championship rules, the discovered three-level prefetcher beats the winning hand-designed solution. 3.8% geometric mean IPC speedup over baseline, 0.3% over prior champion BertiGO, widening to 4.6% against 2.6% on low-bandwidth single-core configurations. Multi-core evolution remains blocked by simulation latency slowing the evolution loop. Paper: https://arxiv.org/abs/2608.09874 Track more trending AI papers in our academy: https://academy.dair.ai/
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Source: DAIR.AI (X) | 2026-08-15