{"paper":{"title":"FastCentNN: Accelerating Centroid Neural Network with Entropy Proxy","arxiv_id":"2607.13613","primary_cat":"cs.LG","submitted_at":"2026-07-15T09:00:12+00:00","version_compiled":"v1"},"notice":"Every quoted sentence is the authors' text from the arXiv listing abstract. Labels and probabilities are the outputs of classifier-1.13.0 on the question bank abstract-p1-20260923, exactly as returned. 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which serves as a training entropy proxy.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.77,"main_claim_p":0.14,"quantitative":false,"quantitative_p":0.05,"role_probabilities":{"gap":0.0,"method":0.1,"result":0.0,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.9,"interpretation":0.0}},{"i":2,"p":0.75,"id":"s2","own":true,"role":"result","text":"As a result, FastCentNN reduces unnecessary reassignment epochs while preserving the original winner-loser learning dynamics.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.71,"main_claim_p":0.22,"quantitative":false,"quantitative_p":0.11,"role_probabilities":{"gap":0.0,"method":0.03,"result":0.75,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.07,"interpretation":0.15}},{"i":3,"p":0.92,"id":"s3","own":true,"role":"method","text":"FastCentNN supports both absolute and stage-relative movement thresholds, allowing the splitting criterion to remain either fixed or adaptive throughout training.","hedge":{"p":0.99,"label":"asserted"},"own_p":0.65,"main_claim_p":0.05,"quantitative":false,"quantitative_p":0.08,"role_probabilities":{"gap":0.0,"method":0.92,"result":0.0,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.08,"interpretation":0.0}},{"i":4,"p":1.0,"id":"s4","own":true,"role":"result","text":"Experiments on some benchmark datasets show that FastCentNN consistently achieves clustering quality comparable to CentNN while reducing runtime by up to 16% on synthetic 2D datasets and about 5% on high-dimensional datasets.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.95,"main_claim_p":0.18,"quantitative":true,"quantitative_p":0.99,"role_probabilities":{"gap":0.0,"method":0.0,"result":1.0,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.0,"interpretation":0.0}},{"i":5,"p":0.97,"id":"s5","own":true,"role":"interpretation","text":"FastCentNN therefore provides a practical and efficient drop-in replacement for CentNN, retaining its 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