2025/10/04 by Ruiying Ma, Ma, Ruiying, Chieh-Jan Mike Liang +5 · 4 citations
Computer Science · #Artificial Intelligence (cs.AI) #Cloud Computing and Resource Management #FOS: Computer and information sciences #Parallel Computing and Optimization Techniques #Software System Performance and Reliability
paper · pdf · doi:10.48550/arxiv.2510.03851
openalex publication_date 2025/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Designing system algorithms remains challenging, where the discontinuous nature of the solution space often forces system engineers to rely on generic heuristics at the expense of performance. We study whether LLMs can practically drive algorithm generation, and find that they are biased towards well-known generic designs, rather than making the creative leaps needed to navigate the discontinuous solution space. To address this limitation, we introduce MetaMuse, a framework for creative ideation built on three self-reflection principles: (1) quantifying solution diversity and usefulness in measurable performance space, rather than abstract idea space, (2) steering ideation through external stimuli, rather than internal randomness, and (3) constructing executable solutions using waypoint reasoning, rather than free-form chain-of-thought. Considering two critical online problems at a global cloud provider, extensive evaluations show that MetaMuse can generate high-performing solutions: it reduces cache misses by up to 35.76% in cache replacement and reduces bin usage by up to 30.93% in online bin packing.