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Robust State-Conditional Feature-Weighted Jump Models for Temporal Clustering

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arXiv:2606. 13146v1 Announce Type: new Abstract: We propose a robust feature-weighted jump model for time-dependent clustering.

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Authors: Federico P. Cortese, Alessio Farcomeni
Released: 2026-06-12 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Robust State-Conditional Feature-Weighted Jump Models for Temporal Clustering overlaps with 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)", experiment "Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone", experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation". Matching terms: rate, control, another, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.13146

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