"""
LiqMamba: Liquid-Mamba Image Generator

A novel lightweight architecture combining:
- Liquid Time-Constant (CfC) networks for adaptive continuous-time gating
- Mamba-2 State Space Duality (SSD) for linear-time sequence processing
- Flow Matching for stable image generation
- Multi-directional 2D scans for image understanding
- ConFIG gradient stabilization (from PINN research)

Key innovations:
1. CfC-Gated Mamba blocks: Replace static nonlinearities with learnable
   continuous-time dynamics that adapt computation depth per-token
2. Liquid State Modulation: The SSM state transition is modulated by CfC
   dynamics, giving the model ODE-inspired expressivity
3. Physics-informed training: ConFIG gradient composition prevents 
   competing loss terms from destabilizing training
4. Extremely lightweight: ~25M params, trainable on Colab free tier

Paper References:
- CfC: "Closed-form Continuous-time Neural Networks" (Hasani et al., 2021)
- Mamba-2: "Transformers are SSMs" (Dao & Gu, 2024) 
- DiM: "Diffusion Mamba" (Teng et al., 2024)
- ConFIG: "Towards Conflict-free Training of PINNs" (Liu et al., 2024)
"""

__version__ = "0.1.0"