jspace-ring / README.md
Pixedar's picture
Dataset card
649266f verified
|
Raw History Blame Contribute Delete
2.25 kB
---
license: mit
tags:
- interpretability
- jacobian-lens
- j-space
- self-model
- qwen3
pretty_name: The ring (Strange Loop in J-space, Part 3)
---
# The ring: a latent loop that keeps a model of itself
Part 3 of [Strange Loop in J-space](https://github.com/Pixedar/jspace-strange-loop#part-3-the-ring).
A Qwen3 model reads one placeholder token at a time. At every step its J-space content, read at 70 % of depth with
the Neuronpedia Jacobian lens and non-negative pursuit on common English words (K = 25), is written back at 50 % of
depth. No word is sampled inside the loop. Each "mind" has an online self-model of its own transitions. Ten
conditions write back the self-model's surprise or its prediction, or controls of equal norm on the same schedule:
momentum, another mind's signal, the mind's own surprise from a random earlier step, random input, plain content, or
nothing. Minds with the same seed share every random draw.
**Pre-registration.** `code/` was uploaded at 19:03:34 UTC on 9 October 2026, before the first result. `code/stage2/`
(the exploratory gain ladder) was added after the Qwen3-4B result and is labelled as such.
## Layout
| Path | Contents |
|---|---|
| `qwen3-{1.7b,4b,8b,14b}/sweep/` | pilot sweep of the free substrate (gain × noise) |
| `qwen3-*/params.json` | the regime picked by the fixed rule |
| `qwen3-*/main/s*_*/ring.npz` | J-space content per step and mind (`C`), top atoms, self-model skill, step similarity |
| `qwen3-*/main/results.json`, `summary.txt` | the pre-registered tests |
| `qwen3-*/gains/g*/` and `gains/gains.json` | the exploratory ladder across each substrate's critical gain |
| `qwen3-4b-long/` | 2,000-step run |
| `ladder.json`, `ladder.txt` | all models in one table |
| `logs/` | the unattended job's logs |
## Result in one line
Feeding back the current surprise of a self-model that keeps learning keeps the trajectory exploring. It beats
momentum, the mind's own past surprise and random input of the same size, and freezing the self-model removes the
effect. Whether the surprise is the mind's own rather than another mind's matters only past the critical gain, where
plain momentum resists collapse better. Nothing self-like emerges. Details are in the repository README.