Demystifying Variance in Circuit Discovery of LLMs
Source published ·Modelwire updated
Original coverage: arXiv cs.LG ↗·How Modelwire adds context
The development
Mechanistic interpretability research has long struggled with a fundamental problem: circuit discovery methods that identify which model components drive specific behaviors produce inconsistent results across different data batches, prompt phrasings, and individual samples. This paper diagnoses the root causes of this variance in the current state-of-the-art EAP-IG method and proposes CEAP as an improved alternative. The work matters because unreliable circuit discovery undermines the entire interpretability agenda, making it harder for researchers to build trustworthy explanations of LLM reasoning. Solving variance is a prerequisite for mechanistic interpretability to move from academic exercise to practical tool for safety and debugging.
Modelwire’s AI-generated summary of coverage from arXiv cs.LG.
Modelwire analysis
ExplainerOur AI-generated reading of the wider context and the next developments to watch.
The paper's contribution isn't a faster circuit discovery method but a diagnostic one: it identifies *why* existing methods produce different answers on different runs, which is a prerequisite problem that has quietly invalidated a lot of prior interpretability work without anyone formally accounting for it.
This pairs directly with 'Scalable Circuit Learning for Interpreting Large Language Models' from the same day, which introduced CircuitLasso as a compute-efficient alternative to intervention-based circuit discovery. That paper solved the cost problem; this one addresses the reliability problem. Together they sketch a two-front effort to make circuit discovery actually usable: cheaper and more consistent. Neither paper alone closes the loop, but the convergence of both on the same date suggests the interpretability community is actively stress-testing the foundations of the method rather than just applying it to new models.
Watch whether CEAP gets adopted as a baseline in follow-on circuit discovery work over the next six months. If CircuitLasso or similar scalable methods begin citing CEAP for variance control, that signals the field is converging on a shared methodology rather than continuing to produce incomparable results.
This interpretation is generated from the summary above and the archive coverage cited below. Our methodology · Report an error
Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.LG
Scalable Circuit Learning for Interpreting Large Language Models
Mechanistic interpretability research has long struggled with the computational cost of mapping how language models produce outputs. CircuitLasso addresses this bottleneck by replacing expensive intervention-based methods with sparse linear regression, achieving comparable accuracy at a fraction of the compute. The technique works by learning relationships among sparse autoencoder features rather than raw neurons, making circuits…
MentionsEAP-IG · CEAP
How this coverage is produced
Modelwire uses AI to generate summaries and context from source headlines, snippets, and selected archive coverage. Automated checks do not verify every claim, and items are not routinely reviewed by a person before publication. Zacaria Solis operates the site. Read the linked source for the full evidence and report errors through our corrections process.
Modelwire summarizes, we don’t republish. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.