---
license: other
license_name: brsx-open-license
license_link: https://brsxlabs.gt.tc/brsxlicense.html
pipeline_tag: zero-shot-classification
tags:
- Ising
- quantum
- phy
- physics
---

# IsingBreaker

## Overview

IsingBreaker is an experimental symbolic sequence classification model developed by BRSX-Labs.

The model analyzes sequences composed of four symbolic tokens:

```text
U D + -
```

and estimates the degree of structural order present within the sequence.

The goal is not language modeling, but pattern recognition, periodicity detection, and symbolic structure analysis.

---

## Classification Labels

### Absolute

Perfect repeating motifs and highly ordered structures.

Examples:

```text
UDUDUDUDUDUDUDUD...
UD+-UD+-UD+-UD+...
UU++DD--UU++DD--...
```

---

### Maybe

Mostly ordered structures containing small local perturbations.

Examples:

```text
UDUDUDUDUDDDUDUD...
UD+-UD++UD+-UD+-...
UU++DD--UU+DDD--...
```

---

### NoN

Chaotic or non-periodic structures.

Examples:

```text
U+D--DU+U-+D++UD...
+-U-++UU+D+-DDUU...
```

---

## Architecture

IsingBreaker uses a hybrid Mixture-of-Experts architecture composed of four independent expert branches:

### CNN Expert

Captures local motifs and short-range symbolic structures.

Specialized for:

* Local repetition
* Motif detection
* Symbol blocks

---

### GRU Expert

Captures sequential dependencies and order-sensitive patterns.

Specialized for:

* Temporal relationships
* Sequence continuity
* Ordered transitions

---

### Transformer Expert

Captures long-range interactions between distant symbols.

Specialized for:

* Global structure
* Long-distance dependencies
* Pattern consistency

---

### Mamba Expert

Provides efficient state-space sequence modeling.

Specialized for:

* Long-context symbolic reasoning
* Efficient memory retention
* Sequence compression

---

## Expert Fusion

Outputs from all four experts are combined through a learned gating mechanism.

The model dynamically allocates attention between experts depending on the structure of the input sequence.

Example expert activity:

```text
CNN         0.28
GRU         0.24
Transformer 0.22
Mamba       0.26
```

---

## Model Information

* Architecture: GenoLiteHybrid
* Parameters: ~88 Million
* Context Length: 64
* Vocabulary Size: 4
* Classes: 3

---

## Dataset

Training dataset:

* 1,500 Absolute samples
* 1,500 Maybe samples
* 1,500 NoN samples

Total:

```text
4,500 unique samples
```

All samples are unique and shuffled before training.

---

## Performance

Benchmark Accuracy:

```text
93%+
```

The model demonstrates reliable separation between:

* Fully ordered structures
* Partially corrupted structures
* Chaotic structures

while generalizing to unseen motif combinations.

---

## Example

Input:

```text
UDUD-UDUD+UDUD-UDUD+UDUD-UDUD+
```

Prediction:

```text
Absolute
```

Confidence:

```text
0.94+
```

---

## License

brsx-open-license

---

## Author

BRSX-Labs