Unstable Features, Reproducible Subspaces: Understanding Seed Dependence in Sparse Autoencoders
arXiv:2606.12138v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are widely used to interpret neural network representations, but their utility depends on whether the learned features are reproducible across training runs. We study this question through \emph{feature stability}: for each SAE feature, we estimate the probability that a similar feature reappears in an independently trained SAE. This yields a scalable per-feature signal that separates stable from unstable features. In a large-scale study across seeds, models, layers, dictionary sizes, and SAE variants, we find a prono
This research addresses a critical foundational issue in AI interpretability as large models become ubiquitous, with the paper published in 2026 indicating future-forward research.
Understanding the reproducibility and stability of features in sparse autoencoders is crucial for reliable and trustworthy AI systems, particularly in sensitive applications.
This work provides a methodology to assess feature stability, allowing developers and researchers to distinguish robust AI insights from unreliable ones, improving model reliability.
- · AI safety researchers
- · AI interpretability tooling providers
- · Developers deploying explainable AI
- · Regulatory bodies
- · AI systems with unstable feature dependencies
- · Developers relying on black-box AI
- · Companies offering unverified AI explanations
Improved reliability and explainability of AI models through better understanding and mitigation of unstable features.
Increased adoption of interpretable AI techniques and potentially stricter standards for model validation in critical domains.
Accelerated development of AI systems that are provably robust and transparent, enhancing trust and enabling broader deployment in regulated industries.
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