---
license: mit
language:
  - en
  - zh
tags:
  - liquid-neural-networks
  - microtubules
  - long-context
  - text-generation
  - o1
  - edge
pipeline_tag: text-generation
---

# MT-LNN — Microtubule-Inspired Liquid Neural Network (O1-48M)

**Honest status first:** research artifact, not a general assistant. This is the
**O1-48M** attention-free edge model trained from scratch — the O-Series line of
the [AwareLiquid](https://github.com/AwareLiquid/M1) research project. It does
**not** aim to match frontier models on dialogue or benchmarks; its claims are
about **memory form-factor and efficiency**, and every number below is measured
and reproducible.

## What it is

- **48M params**, attention-free, from scratch — no base model
- **O(1) constant inference state**: 0.381 MB flat state from 512 to 1,048,576
  tokens (vs a KV cache growing to 3,072 MB — 8,063× smaller)
- Continuous-time **liquid core** with learnable time constants
  (13 protofilaments × 5 time-scales), true parallel-scan recurrence
- CPU-friendly: the live demo at [awareliquid.ai/demo](https://awareliquid.ai/demo)
  runs on CPU

## Measured results (multi-seed, reproducible)

| Result | Number |
|---|---|
| Cross-window associative recall (fast-weight memory) | **0.56** (3 seeds) vs **0.000** for attention/LoRA |
| O(1) inference state @1M tokens | **0.381 MB** constant, 8,063× smaller than KV |
| Robustness to irregular sampling (NASA battery SoH) | **+7.7%** degradation @80% dropped samples (LSTM +31.1%, GRU +32.8%) |
| Language-modeling quality | **Not** an advantage: 125M-scale WikiText-103 PPL 88.93 ± 0.33 vs Transformer 78.86 ± 0.25 |

The architecture's case rests on memory form-factor and efficiency, **not** on
quality per parameter. Full honest analysis: [RESULTS.md](https://github.com/AwareLiquid/M1/blob/main/RESULTS.md).

## Weights

This checkpoint is published on GitHub Releases:
[o1-48m-v1](https://github.com/AwareLiquid/M1/releases/tag/o1-48m-v1).

Serve it:

```bash
CKPT_PATH=o1_48m_serve.pt TOKENIZER=gpt2 python -m uvicorn serve.server:app
```

## Paper

- [English PDF](https://github.com/AwareLiquid/M1/blob/main/mt_lnn_v2_reliable_long_pretraining_arxiv.pdf)
- [中文 PDF](https://github.com/AwareLiquid/M1/blob/main/mt_lnn_v2_reliable_long_pretraining_arxiv_zh.pdf)

## Related

- Code: [AwareLiquid/M1](https://github.com/AwareLiquid/M1) (MIT)
- Live demo: [awareliquid.ai/demo](https://awareliquid.ai/demo)
- Model family: [awareliquid.ai](https://awareliquid.ai/#models)