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K-Models: a Flexible and Interpretable Method for Ordinal Clustering with Application to Antigen-Antibody Interaction Profiles

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arXiv:2605. 14828v1 Announce Type: new Abstract: Existing clustering methods for functional data often prioritize partitioning accuracy over interpretability, making it challenging to extract meaningful insights when the data-generating process follows a specific underlying structure and an ordinal relationship among clusters is suspected.

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Authors: Giulia Patan\`e, Alessandra Menafoglio, Alexander Krauth, Peter Fechner, Luca Dede', Bianca Maria Colosimo, Federica Nicolussi
Released: 2026-05-15 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because K-Models: a Flexible and Interpretable Method for Ordinal Clustering with Application to Antigen-Antibody Interaction Profiles overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)". Matching terms: under, eval, rate, binding, test, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.14828

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