edifice

# Changelog

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

The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [0.1.1] - 2026-02-14

### Fixed

- **MoE top-k routing**: `top_k_forward` now uses `Nx.top_k` indices with one-hot mask for correct expert selection (was ignoring indices and averaging first k experts)
- **MoE hash routing**: `hash_forward` now properly selects expert by hash (was always returning first expert)
- **SwitchMoE routing**: Replaced soft weighted average with hard top-1 selection via straight-through estimator, restoring the sparsity that defines Switch Transformer
- **SchNet filter generation**: Added learned 2-layer filter-generating network (RBF -> Dense -> SiLU -> Dense) replacing naive mean aggregation
- **ConvNeXt layer scale**: Changed from frozen constant to learnable `Axon.param`, matching Liu et al. 2022
- **MessagePassing aggregate**: Added batch axes to `Nx.dot` for correct batched matrix multiplication
- **SNN docstring**: Corrected reset mechanism description from hard reset to soft reset (subtract threshold)

### Changed

- **KAN default basis**: Changed from `:sine` (Fourier features) to `:bspline` (cubic B-spline via Cox-de Boor), faithful to Liu et al. 2024. Previous bases (`:sine`, `:chebyshev`, `:fourier`, `:rbf`) remain available as options
- **TTT W_0 initialization**: Changed from `0.01 * Identity` to `:glorot_uniform` per Sun et al. 2024
- **TTT output RMS norm**: Made optional via `:output_rms_norm` option (default: `false`), was unconditionally applied

### Removed

- Unused `_x` and `_dt` parameters from Liquid `integrate_ode`

## [0.1.0] - 2026-02-14

### Added

- 92 registered architectures across 16 families
- Unified interface: `Edifice.build(:name, opts)` and `Edifice.list_architectures()`
- **Feedforward**: MLP, KAN (Kolmogorov-Arnold Networks), TabNet
- **Convolutional**: Conv1D/2D, ResNet, DenseNet, TCN, MobileNet, EfficientNet
- **Recurrent**: LSTM, GRU, xLSTM, MinGRU, MinLSTM, DeltaNet, TTT, Titans, Reservoir (ESN)
- **State Space Models**: Mamba (parallel scan), Mamba-2 (SSD), MambaCumsum, MambaHillisSteele, S4, S4D, S5, H3, Hyena, BiMamba, GatedSSM, Jamba, Zamba
- **Attention**: Multi-Head (sliding window, hybrid), GQA, Perceiver, FNet, LinearTransformer, Nystromformer, Performer, RetNet, RWKV-7, GLA, HGRN-2, Griffin/Hawk
- **Vision**: ViT, DeiT, Swin Transformer, U-Net, ConvNeXt, MLP-Mixer
- **Generative**: VAE, VQ-VAE, GAN (WGAN-GP), DDPM Diffusion, DDIM, DiT, Latent Diffusion, Consistency Model, Score SDE, Flow Matching, Normalizing Flows
- **Contrastive**: SimCLR, BYOL, Barlow Twins, MAE (Masked Autoencoder), VICReg
- **Graph**: GCN, GAT, GIN, GraphSAGE, Graph Transformer, PNA, SchNet
- **Sets**: DeepSets, PointNet
- **Energy**: EBM (contrastive divergence), Modern Hopfield Networks, Neural ODE
- **Probabilistic**: Bayesian Neural Networks, MC Dropout, Evidential Neural Networks
- **Memory**: Neural Turing Machine, Memory Networks
- **Meta**: MoE, Switch MoE, Soft MoE, LoRA, Adapter, Hypernetworks, Capsule Networks
- **Liquid**: Liquid Neural Networks (continuous-time ODE)
- **Neuromorphic**: SNN (LIF neurons), ANN2SNN conversion
- **Building Blocks**: RMSNorm, SwiGLU, FFN, RoPE, ALiBi, PatchEmbed, SinusoidalPE, AdaptiveNorm, CrossAttention
- 12 conceptual guide documents covering theory, evolution, and decision tables for all families
- `CONTRIBUTING.md` with architecture addition guide, test patterns, and Nx/Axon gotchas
- ~1160 tests covering all architecture families