Ablating Archetypes: The Stability of Archetypal SAEs is an Artifact of Initialization and Metric Design
arXiv:2606.02061v1 Announce Type: new Abstract: Dictionary learning with sparse autoencoders (SAEs) produces overcomplete bases from neural network activations that are often interpretable and reduces polysemanticity. However, features from SAEs vary substantially across random seeds -- a problem known as instability. Archetypal SAEs (Fel et al., 2025) were proposed as a general dictionary-learning intervention for more reliable concept extraction, and report more stable dictionaries at the end of training. We demonstrate that the stability claimed by archetypal SAEs is a result of setting ide
This research is published as the field of AI interpretation and reliability continues to be a critical area of focus for robust AI development.
Improving the stability and interpretability of sparse autoencoders is crucial for building more reliable and understandable AI systems, particularly for concept extraction in large language models.
The understanding of archetypal SAEs' stability is refined, suggesting that previous claims might have been influenced by methodological factors rather than inherent architectural superiority.
- · AI interpretability researchers
- · Developers of foundational AI models
- · Users of interpretable AI systems
- · Archetypal SAEs (as a standalone, unchallenged solution)
- · Researchers relying on naive SAE initialization strategies
Further research will likely focus on more robust and truly stable methods for dictionary learning in sparse autoencoders.
This improved understanding could lead to more accurate and less variable concept extraction tools, enhancing the debugging and auditing of complex AI systems.
More reliable interpretability tools could increase trust in AI decision-making, potentially accelerating AI adoption in sensitive applications.
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Read at arXiv cs.LG