Permutation-Equivariant 2D State Space Models: Theory and Canonical Architecture for Multivariate Time Series
arXiv 2603.08753•6cdacca7743a08cb23202472dc068a9b8541f267f4c97e1c5322f8ca41919725
AI alignmentBayesian neural networksConant-AshbyMidicothPPMVI-2D-SSMalignment verificationbalancing (regularization)causal inferencecompressiondenoisingflow matchinggeneralitygenerative modelshypergraph observersmodel architecturemultivariate time seriesnatural gradientpermutation-equivariantreversible sampling','Metropolis-Hastings','MMD','Thompson-SAMPLsimulation-based inferencesoundnessstate-space modelstractabilitytrilemma
Paper metadata
- arXiv ID
- 2603.08753
- Version
- Not specified by this published record
- Category
- Statistics — Machine Learning (stat.ML)
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- Source ID
- arxiv_stat_ml
- Record identifier
- 6cdacca7743a08cb23202472dc068a9b8541f267f4c97e1c5322f8ca41919725
- Enrichment time
- 2026-03-11T07:24:02Z
- AI-assisted enrichment
- Yes
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