arXiv:2606.00930v1 Announce Type: new Abstract: Mechanistic interpretability often assumes that probes identifying a representational signature also identify the circuit executing the corresponding computation. We show that this assumption can fail systematically in Mamba-2. Studying the state sink (disproportionate Delta-gate activation on boundary tokens, analogous to the attention sink), we find that single-bucket probes recover only a small execution layer while missing a much larger detection layer with the same representational signature. In Mamba-2, the state sink decomposes into two fu
The continuous development and interpretability challenges in advanced AI models like Mamba-2 necessitate ongoing research into understanding their internal mechanisms.
This research provides crucial insights into how AI models process information, directly impacting the development of more robust, predictable, and interpretable AI systems.
Our understanding of AI model interpretability becomes more nuanced, highlighting a potential systematic flaw in how probes are assumed to identify execution circuits versus mere detection layers.
- · AI safety researchers
- · Mechanistic interpretability community
- · Developers of next-generation AI architectures
- · Simpler mechanistic interpretability methods
Increased complexity in designing effective probes for AI model interpretation.
Leads to more sophisticated tools and methodologies for debugging and verifying AI model behavior.
Potentially accelerates the development of truly transparent and controllable AI, fostering greater trust in AI deployments.
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Read at arXiv cs.CL