CRSD is a minimal, research-oriented sequence modeling framework built from scratch to explore state-space models (SSMs) and sequence-to-sequence architectures in PyTorch. It’s designed to be fully reproducible, interpretable, and extensible — suitable both for learning and for building experimental variants such as nonlinear-SSMs, gated decoders
machine-learning research ai deep-learning pytorch sequence-to-sequence representation-learning state-space-models open-source-ml-platform long-context experimental-architecture transformer-alternative gated-decoder
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Updated
Nov 26, 2025 - Python