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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}
}
``` |