{"total":1,"items":[{"citing_arxiv_id":"2607.14786","ref_index":24,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Inferring Non-Normal Amplification Geometry from Multivariate Time Series","primary_cat":"physics.data-an","submitted_at":"2026-07-16T10:02:39+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A moving-window ridge-regression pipeline fits local linear operators, extracts a dominant 2D input-response plane by optimization or commutator methods, and tracks R = K/Kc(Δ), recovering this reduced non-normal geometry from finite multivariate time series with far fewer samples than full operator","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}