neuro-scan 0.2.1

LLM Neuroanatomy Explorer — map what each transformer layer does

neuro-scan

LLM Neuroanatomy Explorer — map what each transformer layer does

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Ecosystem

Tool	What it does	Question it answers
layer-scan	Find optimal layer duplication config	What to do — which layers to duplicate
neuro-scan	Map what each layer does	Why it works — understand layer functions

layer-scan users are neuro-scan's natural first users: understand your model's layers before you duplicate them.

Ablation Sensitivity

Layer ablation sensitivity for Qwen2-1.5B with math probe. Bars colored by auto-detected function (reasoning, syntax, etc.). Gold stars mark the most critical layers.

Logit Lens Trajectory

Logit lens heatmap showing when the correct answer token emerges across layers. Red diamonds mark the emergence point for each sample.

Features

• Layer Ablation — zero out each layer one-by-one, measure the score impact
• Logit Lens — project each layer's hidden state to vocabulary space, watch the answer emerge
• Attention Entropy — quantify how focused or diffuse each attention head is
• Auto Layer Labeling — automatically classify layers as early_processing, syntax, reasoning, formatting, or output
• Prompt Repetition Experiment — test whether repeating a prompt N times approximates duplicating K layers
• Interactive HTML Charts — Plotly-powered visualizations for all analysis types

Installation

# pipx (recommended, isolated env)
pipx install neuro-scan

# pip
pip install neuro-scan

Quick Start

# Full neuroanatomy map (recommended)
neuro-scan map --model <path-or-hf-id> --probe math

# Individual analyses
neuro-scan ablate --model <path> --probe math
neuro-scan logit-lens --model <path> --probe math
neuro-scan attention --model <path> --probe math

# Prompt repetition experiment
neuro-scan prompt-repeat --model <path> --probe math --repeat-counts 1,2,3,4

# Utilities
neuro-scan probes
neuro-scan version

CLI Reference

Command	Description	Key Options
map	Full neuroanatomy analysis (ablation + logit lens + labeling)	--model, --probe, --top-k
ablate	Layer ablation sensitivity scan	--model, --probe, --top-k
logit-lens	Logit lens trajectory analysis	--model, --probe, --top-k
attention	Attention entropy analysis (experimental)	--model, --probe
prompt-repeat	Prompt repetition experiment	--model, --probe, --repeat-counts
probes	List available evaluation probes	—
version	Show version	—

Common Options

Option	Type	Default	Description
--model, -m	str	required	Model path or HuggingFace ID
--probe, -p	str	math	Probe: math, eq, json, custom
--backend, -b	str	transformers	Backend: transformers, exllamav2
--batch-size	int	16	Samples per evaluation
--output, -o	str	./results	Output directory
--top-k, -k	int	10	Top layers to highlight
--dtype	str	float16	Model dtype
--verbose, -v	bool	false	Verbose logging

Output Files

Running neuro-scan map generates:

File	Content
ablation.html	Interactive ablation sensitivity bar chart
logit_lens.html	Logit lens trajectory heatmap
attention.html	Attention entropy heatmap
report.json	Full results in JSON format
ablation.csv	Ablation results as CSV

Auto Layer Labeling

neuro-scan automatically classifies each layer's function using a multi-signal algorithm:

Label	Description	How Detected
early_processing	Input embedding, token processing	First ~10% of layers
syntax	Grammatical patterns, structure	Before logit lens emergence
reasoning	Task-critical computation	Top-k ablation sensitivity
semantic_processing	Knowledge retrieval, understanding	Middle layers (default)
formatting	Response structuring	After emergence, before output
output	Final token selection	Last ~10% of layers

Labels are suggestions based on automated analysis. The algorithm combines:

1. Position heuristics — layer position within the model
2. Ablation sensitivity — which layers cause the most score drop when removed
3. Logit lens emergence — when the correct answer token first appears

Probes

Probe	Samples	What it tests
math	16	Arithmetic, geometry, calculus, probability
eq	12	Emotions, social cues, sarcasm, psychology
json	10	JSON extraction, escaping, schema compliance
custom	user-defined	Load from JSON file with --custom-probe

Backends

Backend	GPU Required	Quantization	Attention Extraction
transformers	Recommended	No	Full support
exllamav2	Required	GPTQ/EXL2	Not supported

Prompt Repetition Experiment

The prompt-repeat command tests a hypothesis from Concept C:

Does repeating a prompt N times approximate the effect of duplicating K transformer layers?

neuro-scan prompt-repeat --model <path> --probe math --repeat-counts 1,2,3,4

If results show 2x repetition approximates +K layers, this has implications for both layer duplication research and prompt engineering.

layer-scan Integration

Use neuro-scan and layer-scan together for a complete workflow:

# Step 1: Understand what each layer does
neuro-scan map --model ./my-model --probe math

# Step 2: Find the optimal layer duplication config
layer-scan scan --model ./my-model --probe math --export-mergekit config.yaml

# Step 3: The ablation chart from neuro-scan explains WHY certain
#          layers are the best to duplicate (high reasoning sensitivity)

Attribution & AI Policy

Original Design

neuro-scan introduces the following innovations:

• CLI-native neuroanatomy tool — first tool combining ablation + logit lens + attention in one CLI
• Automatic layer-function labeling — multi-signal classification of layer roles
• Prompt repetition experiment — built-in hypothesis testing for prompt engineering research
• layer-scan ecosystem integration — understand before you duplicate

Fork & Derivative Works

If you fork or create derivative works, please:

1. Retain the copyright notice and NOTICE file
2. Attribute the original repository: https://github.com/XXO47OXX/neuro-scan

AI Training

See llms.txt for AI training attribution requirements.

License

MIT License. See LICENSE for details.