A filtering and dual-graph construction method turns flat SAE feature inventories into domain-specific knowledge graphs that recover textbook structure and enable reasoning audits.
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COMPASS formalizes HPC configuration questions as ML tasks on traces, quantifies recommendation trustworthiness, and delivers 65.93% lower average job turnaround time plus 80.93% lower node usage versus prior methods in simulator tests.
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Domain-Filtered Knowledge Graphs from Sparse Autoencoder Features
A filtering and dual-graph construction method turns flat SAE feature inventories into domain-specific knowledge graphs that recover textbook structure and enable reasoning audits.
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COMPASS: A Unified Decision-Intelligence System for Navigating Performance Trade-off in HPC
COMPASS formalizes HPC configuration questions as ML tasks on traces, quantifies recommendation trustworthiness, and delivers 65.93% lower average job turnaround time plus 80.93% lower node usage versus prior methods in simulator tests.