---
license: cc-by-4.0
tags:
  - sparse-autoencoder
  - interpretability
  - sonar
  - automatic-interpretability
---

# SONAR SAEs — autointerp results

Automatic interpretability outputs (latent explanations and held-out
classifier scores) produced from the scaled-up BatchTopK SAE in:

> **Interpretability of Text Auto-Encoders using Sparse Auto-Encoders: A Sandbox for Interpreting Neuralese.**
> Nicky Pochinkov & Jason Rich Darmawan, EACL 2026 (submitted).

## Protocol

For each SAE latent we:

1. Selected the top-10 sentences with the highest activation values.
2. Prompted **GPT-OSS-120B** (`openai/gpt-oss-120b`) to generate a
   short natural-language description of what the latent appears to
   detect.
3. Re-prompted the same model on a shuffled set of 12 sentences (8
   random + 4 top-activating) to classify each as matching or not
   matching the generated explanation.
4. Reported accuracy / precision / recall / F1 against the held-out
   labels.

**Caveat (also stated in the paper).** Because explanation generation
and evaluation use the same model, high F1 should be read as
self-consistency rather than external semantic validation.

## Files

Each subdirectory is one SAE run id and contains per-latent JSON
records of the form:

```json
{
  "latent_id": 41816,
  "top_activations": [...],
  "explanation": "sentences that include a lexical item referring to a cat",
  "scores": {"f1": 1.0, "precision": 1.0, "recall": 1.0, "accuracy": 1.0},
  "heldout_examples": [...]
}
```

## Related

- [`nickypro/sonar-saes-large`](https://huggingface.co/nickypro/sonar-saes-large) — the SAE these results were generated from