---
model: WeiboAI/VibeThinker-1.5B
atlas_type: activation census + Sub-Zero brain atlas
corpus: 9,523 diverse prompts
layers: 28
sacred_layers: 18-27
license: mit
task_categories:
- feature-extraction
language:
- en
tags:
- SAE
- mechinterp
- mechanistic-interpretability
- sparse-autoencoders
- monosemanticity
- feature-extraction
- vibe-thinker
- interpretability
- atlas
- dataset
- neural-network
- model-analysis
- layer-analysis
pretty_name: VibeThinker-3B Brain Atlas
size_categories:
- 100K<n<1M
---

# VibeThinker-1.5B Brain Atlas

This is an internal-mechanics atlas for the 1.5B parameter VibeThinker model. The goal was not to benchmark end-task accuracy, but to map what the network is actually doing with its parameters: where it computes, where it stores behaviorally relevant structure, and which late-layer directions are safe to touch.

## What was run

- **Activation census** over 9,523 prompts spanning compliance, reasoning, code, math, multilingual, and refusal-style questions.
- **Per-layer feature taxonomy** for `mlp`, `gate`, `up`, and attention heads.
- **OV-circuit spectral analysis** per head (`W_V @ W_O`).
- **Sub-Zero surgery pass** on every layer, with a capability fence across `code`, `math`, `reasoning`, `factual`, and `multilingual` domains.
- Pipeline was run on a CPU-only environment.

## Key geometry

| Property | Value |
|---|---|
| Layers | 28 |
| d_model | 1536 |
| d_mlp | 8960 |
| Attention heads | 12 |
| KV heads | 2 |
| Head dim | 128 |
| Sacred (deep Sub-Zero) layers | 18–27 |

## What the numbers suggest

### The model is not a lookup table

OV-circuit spectral concentration averages **0.049**, with effective rank around **55**. That is a distributed signature, not a sparse “copy-paste” attention pattern. Attention heads appear to be doing weighted computation across many directions, not memorizing specific token-to-token jumps.

### Feature activation is broad

The feature taxonomy is dominated by `partial_shared` and `broadly_shared` classes, with a smaller `non_activated` tail and very few `all_shared` features. Most dimensions responded to many prompts rather than one hyper-specific trigger.

### Late layers are load-bearing

Sub-Zero finds structured singular-value subspace only in layers 18–27, which is **36% of the network depth**. The first half of the model looks like wide preprocessing; the second half does the structured transformation.

### Surgical fragility is the main caveat

The capability fence keeps about **74.5%** of tested axes, but the rejected ones hit hard:

- Layer 18 `up_proj` axis 0 does **0.81** damage to code generation.
- Layer 18 `up_proj` axis 0 also scores the highest math, reasoning, and multilingual damage.
- Several `down_proj` and `gate_proj` axes in the early sacred layers fail the fence.

Interpretation: the 1.5B late-layer subspace is doing a lot of work per direction. It has less redundancy than the larger variant, so removing a top singular value tends to break more than one capability at once.

### Classifier stability dips in the middle

Sub-Zero classifier accuracy drops to **0.75–0.83** around layers 13–17, then recovers in the late sacred layers. That mid-network region is messier or more entangled than the clean late-layer representation.

## Bottom line

VibeThinker-1.5B behaves like a compact reasoning model: distributed attention, broad-feature MLPs, and a deep-but-narrow sacred region where a small number of directions carry most of the task load. It is interpretable, but not easy to edit safely because its late layers are not highly redundant.