[Proposal] Linear Artificial Tomography (LAT) scans

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
30/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Active
Tech stack
python
Domain
machine-learning

Research direction

The proposal adds a LAT utility for HookedTransformer using run_with_cache and resid_pre/resid_post projections. No specific files or tests are named; review the TransformerLens package and HookedTransformer hooks to find integration points. Done means a merged PR implementing the sketched API with direction extraction, scans, baselines, and visualization.

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Description

enhancement seen_by_maintainers TransformerBridge

Summary

Add a utility for running LAT scans on HookedTransformer models. A scan finds a concept direction at each layer and projects activations onto it, producing a layer-by-token readout of where the concept is represented. TransformerLens has the hooks for this, but no standard implementation.

Motivation

LAT is a common tool in representation engineering, and each project currently reimplements it. I used it to study emotion-associated directions in Qwen3-0.6B, where alignment rose in later layers. Interpreting that result required controls for norm growth and anisotropy that a shared implementation could provide by default. I already have the code written, would have to just be ported to the repository.

Proposed functionality

Direction extraction: Build per-layer concept vectors from contrastive or stimulus-based prompt sets, with PCA or difference-of-means options.
Scanning: Run run_with_cache over input text and project resid_pre / resid_post onto the vectors, returning a layer × token score tensor.
Baselines: Built-in random-direction and norm-matched controls, plus optional centering or whitening from neutral-prompt statistics.
Visualization: Layer-wise alignment curves and token-level heatmaps.
Interventions: Optional steering or ablation along extracted vectors for causal checks.

Example usage


python
scan = lat_scan(model, concept_prompts, neutral_prompts, layers="all")
scores = scan.project(text)
scan.plot(baseline="random")

The API is only a sketch, and I’m open to whatever fits the library’s conventions.

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