Shared Semantics, Divergent Mechanisms: Unsupervised Feature Discovery by Aligning Semantics and Mechanisms
arXiv:2606.08236v1 Announce Type: cross Abstract: As large language models are increasingly deployed in high-stakes settings, there is a growing need for tools that audit not only model outputs but also the internal computations that produce them. Circuit analysis is a central approach in mechanistic interpretability, but it is typically target-conditioned, explaining a single prompt paired with a chosen completion. This target-conditioned setup can obscure heterogeneity across a model's continuation distribution. We introduce distribution-level unsupervised feature discovery, which clusters s
As large language models become ubiquitous in critical applications, the urgency to understand and audit their internal mechanisms intensifies, driving new research in interpretability.
This research provides a foundational step towards more transparent and reliable AI systems, which is crucial for their adoption in high-stakes environments and for regulatory compliance.
The ability to uncover model features at a distribution level, rather than point-conditioned, offers a more comprehensive understanding of AI behavior and potential biases.
- · AI interpretability researchers
- · High-stakes AI deployers
- · AI auditors
- · Responsible AI developers
- · Black-box AI vendors
Improved understanding and debugging of complex AI models.
Development of new tools and methodologies for dynamic AI monitoring and control.
Potentially leading to regulatory frameworks that mandate distribution-level explainability for critical AI systems.
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Read at arXiv cs.LG