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neurips-2026

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interactive, sub-16ms vector explainer built for the DataForge 2026 Pathway Track.It proves why classical State Space Models suffer exponential associative forgetting ($\rho(A)^L$) and visualizes how Pathway's Dragon Hatchling (BDH) synaptic plasticity and BDH CQ continuous latent reasoning eliminate interference without KV-cache explosion.

  • Updated Sep 3, 2026
  • JavaScript

Official code for "Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection" (NeurIPS 2026). RAMSAD selects the best anomaly detector for a new series by retrieving similar series from a knowledge base, with no selector training.

  • Updated Sep 30, 2026
  • Python

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