I’ve been working on a tool for ambitious mechanistic interpretability called ATLAS. What started as an exploratory technique for the ARC White-Box Estimation Challenge turned into a different kind of tool entirely, so I figured I’d drop it in here, as there’s been a bit of mech interp discussion recently.
Playing around with Hoel’s Causal Emergence 2.0, I arrived at ATLAS by treating a forward pass as a program rolling through a field of constraints and reading that pass at the grain where the model’s causation lives. The net has an internal geometry that can be read with machine-exact precision, and that precision means that looking into the black box doesn’t require methods that disturb the computation like ablation or activation patching. Instead, when you use exact accounting, you can know the effect of a weight-change edit before you actually run it by solving it in closed form and predicting the result rather than observing it.
Looking for collaborators with experience in the field to check it out and give feedback. Current model works on basic MLPs, GPT-2, Tracr, Pythia. The in repo demo goes up to Qwen2.5-72B-Instruct (though I’ve run larger models, 72B is what’s in there right now).
Current capabilities include circuit discovery and zero-run surgical weight edits (demoed on a grokking model and GPT-2), among a number of other features, all done without ablation, approximation, dictionaries, or fitting.
I’ve got a separate repo going for using untrained GPT-2 nets as programmatic breadboards as an extension of the technique to explore the various interactions of valenceand drives, but figured I’d start with the basics here in this repo to gauge interest as that’s a related but secondary project.
I’ve been working on a tool for ambitious mechanistic interpretability called ATLAS. What started as an exploratory technique for the ARC White-Box Estimation Challenge turned into a different kind of tool entirely, so I figured I’d drop it in here, as there’s been a bit of mech interp discussion recently.
Playing around with Hoel’s Causal Emergence 2.0, I arrived at ATLAS by treating a forward pass as a program rolling through a field of constraints and reading that pass at the grain where the model’s causation lives. The net has an internal geometry that can be read with machine-exact precision, and that precision means that looking into the black box doesn’t require methods that disturb the computation like ablation or activation patching. Instead, when you use exact accounting, you can know the effect of a weight-change edit before you actually run it by solving it in closed form and predicting the result rather than observing it.
Looking for collaborators with experience in the field to check it out and give feedback. Current model works on basic MLPs, GPT-2, Tracr, Pythia. The in repo demo goes up to Qwen2.5-72B-Instruct (though I’ve run larger models, 72B is what’s in there right now).
Current capabilities include circuit discovery and zero-run surgical weight edits (demoed on a grokking model and GPT-2), among a number of other features, all done without ablation, approximation, dictionaries, or fitting.
I’ve got a separate repo going for using untrained GPT-2 nets as programmatic breadboards as an extension of the technique to explore the various interactions of valence and drives, but figured I’d start with the basics here in this repo to gauge interest as that’s a related but secondary project.
Repo is here: https://github.com/OperatorPhoenix/ATLAS