Changelog¶

All notable changes to Hyperbolix will be documented in this file.

Unreleased ¶

MkDocs Material documentation system
Complete API reference documentation
Getting Started guide
CI/CD workflow for documentation builds
Positional encoding layers for hyperbolic Transformers:
- lorentz_residual: Lorentzian midpoint-based residual connection
- hope: Hyperbolic Rotary Positional Encoding (functional)
- HyperbolicRoPE: NNX module wrapper for HOPE
- HypformerPositionalEncoding: Learnable positional encoding with HTCLinear
Class-based manifold API with automatic dtype casting (Poincare, Hyperboloid, Euclidean)
Isometry mappings between Poincaré ball and hyperboloid models
Manifold structural protocol for type-safe manifold dispatch
Causal attention masking (causal=True) for all three hyperbolic attention variants:
- HyperbolicSoftmaxAttention: lower-triangular -inf mask before softmax
- HyperbolicFullAttention: lower-triangular -inf mask on Lorentzian similarity scores
- HyperbolicLinearAttention: O(N) cumulative-sum recurrence via jax.lax.scan (Katharopoulos et al. 2020), keeping O(N) complexity in causal mode
Tiny Shakespeare character-level benchmark (benchmarks/bench_shakespeare_attention.py) comparing all four model variants (Euclidean + 3 hyperbolic) with causal attention

Breaking: Manifold public functions renamed to private (dist() → _dist()); use class methods instead
Replaced with_precision() wrapper with Poincare(dtype=jnp.float64) pattern

Pure JAX implementation of hyperbolic manifolds (Euclidean, Poincaré, Hyperboloid)
13+ neural network layers (linear, convolutional, regression)
Hypformer components: HTC/HRC with curvature-change support
4 hyperbolic activation functions (ReLU, Leaky ReLU, Tanh, Swish)
Riemannian optimizers (RSGD, RAdam) with automatic manifold detection
Wrapped normal distributions for VAEs
Comprehensive test suite (1,400+ tests)
CI/CD pipeline with benchmarking
vmap-native API design

Based on research by Ganea et al. (2018), Bécigneul & Ganea (2019), Bdeir et al. (2023)