{"paper":{"title":"Weak and Strong Data Informativity with Structural Priors for System Analysis","arxiv_id":"2610.38459","primary_cat":"eess.SY","submitted_at":"2026-10-28T08:37:02+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. No sentence in this record was written by a language model.","schema":"pith.compiled-record/v0","status":"compiled","abstract":{"source":"arXiv listing abstract v1","sentences":[{"i":0,"p":1.0,"id":"s0","own":false,"role":"background","text":"When measurement data are insufficient for unique system identification, data informativity evaluates whether a dataset determines a property across all systems consistent with the data.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.06,"main_claim_p":0.04,"quantitative":false,"quantitative_p":0.05,"role_probabilities":{"gap":0.0,"method":0.0,"result":0.0,"background":1.0,"limitation":0.0,"future_work":0.0,"contribution":0.0,"interpretation":0.0}},{"i":1,"p":1.0,"id":"s1","own":true,"role":"contribution","text":"This paper introduces a unified framework distinguishing between weak and strong data informativity under prior structural knowledge described by affine constraints: data are weakly informative if at least one system consistent with the data and prior knowledge possesses the target property, and strongly informative if every consistent system satisfies it.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.93,"main_claim_p":0.22,"quantitative":false,"quantitative_p":0.22,"role_probabilities":{"gap":0.0,"method":0.0,"result":0.0,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":1.0,"interpretation":0.0}},{"i":2,"p":0.59,"id":"s2","own":false,"role":"interpretation","text":"The latter is consistent with the notion of data informativity in the existing literature.","hedge":{"p":0.82,"label":"hedged_possibility"},"own_p":0.06,"main_claim_p":0.03,"quantitative":false,"quantitative_p":0.18,"role_probabilities":{"gap":0.0,"method":0.0,"result":0.0,"background":0.4,"limitation":0.0,"future_work":0.0,"contribution":0.01,"interpretation":0.59}},{"i":3,"p":0.93,"id":"s3","own":true,"role":"method","text":"By explicitly parameterizing all consistent systems as affine functions of free parameters via the Moore-Penrose pseudoinverse, we characterize these notions for fundamental system properties-including controllability, stabilizability, and stability.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.95,"main_claim_p":0.08,"quantitative":false,"quantitative_p":0.05,"role_probabilities":{"gap":0.0,"method":0.93,"result":0.0,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.07,"interpretation":0.0}},{"i":4,"p":0.71,"id":"s4","own":true,"role":"result","text":"This formulation establishes that verifying weak and strong informativity, when tractable, reduces to rank tests of matrix pencils constructed from the data and prior knowledge.","hedge":{"p":0.97,"label":"asserted"},"own_p":0.91,"main_claim_p":0.18,"quantitative":false,"quantitative_p":0.08,"role_probabilities":{"gap":0.0,"method":0.17,"result":0.71,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.12,"interpretation":0.0}},{"i":5,"p":0.61,"id":"s5","own":true,"role":"result","text":"Our findings show that structural priors can lower data-excitation requirements and enable reliable analysis even when data fail to be strongly informative.","hedge":{"p":0.98,"label":"asserted"},"own_p":0.94,"main_claim_p":0.67,"quantitative":false,"quantitative_p":0.25,"role_probabilities":{"gap":0.0,"method":0.0,"result":0.61,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.0,"interpretation":0.39}},{"i":6,"p":0.96,"id":"s6","own":true,"role":"contribution","text":"Finally, the framework is extended to a class of noisy data.","hedge":{"p":0.99,"label":"asserted"},"own_p":0.88,"main_claim_p":0.04,"quantitative":false,"quantitative_p":0.03,"role_probabilities":{"gap":0.0,"method":0.02,"result":0.0,"background":0.0,"limitation":0.0,"future_work":0.02,"contribution":0.96,"interpretation":0.0}}]},"compiled":{"main_claim":{"p":1.0,"text":"Our findings show that structural priors can lower data-excitation requirements and enable reliable analysis even when data fail to be 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