Create README.md

README.md

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+---
+tags:
+- state-space-models
+- liquid-networks
+- sequence-modeling
+- classics-revival
+- experimental
+license: apache-2.0
+library_name: pytorch
+---
+
+# Liquid State Space Model - The Classics Revival
+
+**Continuous-Time Adaptive Sequence Processing with Learned Dynamics**
+
+**Experimental Research Code** - Functional but unoptimized, expect rough edges
+
+## What Is This?
+
+Liquid State Space Model enhances traditional state space models with liquid neural network dynamics and adaptive time constants. The system learns content-dependent time evolution, making it naturally adaptive to different sequence characteristics and potentially more efficient than transformers for long sequences.
+
+**Core Innovation**: Time constants and state dynamics adapt based on input content, creating a continuous-time sequence processor that adjusts its temporal behavior to match data requirements.
+
+## Architecture Highlights
+
+- **Adaptive Time Constants**: Learn content-dependent evolution speeds
+- **Continuous-Time Dynamics**: Proper differential equation integration
+- **HiPPO Initialization**: Theoretically grounded memory representation
+- **Liquid Evolution**: Neural ODEs for state transitions
+- **Efficient Long Sequences**: O(L) complexity vs O(L²) attention
+- **Language Model Ready**: Drop-in transformer replacement
+
+## Quick Start
+```python
+from liquid_state_space import LiquidSSMLanguageModel
+
+# Create liquid SSM language model
+model = LiquidSSMLanguageModel(
+    vocab_size=32000,
+    d_model=512,
+    state_dim=256,
+    num_layers=6,
+    max_seq_len=2048
+)
+
+# Process sequences
+input_ids = torch.randint(0, 32000, (batch_size, seq_len))
+outputs = model(input_ids, labels=target_ids)
+
+# Generate text
+generated = model.generate(
+    input_ids[:1],
+    max_length=100,
+    temperature=1.0
+)
+```
+
+## Current Status
+- **Working**: Adaptive time constants, continuous dynamics, HiPPO matrices, language modeling, text generation
+- **Rough Edges**: No optimization for very long sequences (>4k), numerical stability could be improved
+- **Still Missing**: Distributed training, advanced initialization schemes, memory compression
+- **Performance**: Competitive with small transformers, needs scaling validation
+- **Memory Usage**: Lower than transformers for long sequences, higher for short ones
+- **Speed**: Good sequential processing, benefits from specialized ODE solvers
+
+## Mathematical Foundation
+The core state space model follows:
+```
+dx/dt = A(t,x)·x + B·u
+y = C·x + D·u
+```
+
+With adaptive time constants:
+```
+τ(x,u) = base_τ × (1 + η·MLP([x;u]))
+effective_dt = min(target_dt, min(τ)/10)
+```
+
+HiPPO matrices initialize A for optimal memory:
+```
+A_ij = √(2i+1)√(2j+1)  if i > j
+A_ii = -(2i+1)
+```
+
+Liquid evolution uses:
+```
+dx/dt = -x/τ + A·x + B·u + noise·exploration_rate
+```
+
+## Research Applications
+- **Long-range sequence modeling**
+- **Time series prediction with adaptive dynamics**
+- **Scientific computing with learned ODEs**
+- **Efficient transformer alternatives**
+- **Continuous-time natural language processing**
+
+## Installation 
+```bash
+pip install torch numpy scipy
+# Download liquid_state_space.py from this repo
+```
+
+## The Classics Revival Collection
+
+Liquid State Space Model is part of a larger exploration of foundational algorithms enhanced with modern neural techniques:
+
+-  Evolutionary Turing Machine
+-  Hebbian Bloom Filter  
+-  Hopfield Decision Graph
+-  Liquid Bayes Chain
+- **Liquid State Space Model** ← You are here
+-  Möbius Markov Chain
+-  Memory Forest
+
+## Citation
+```bibtex
+@misc{liquidssm2025,
+  title={Liquid State Space Model: Continuous-Time Adaptive Sequence Processing},
+  author={Jae Parker 𓅸 1990two},
+  year={2025},
+  note={Part of The Classics Revival Collection}
+}
+```