arXiv:2605.23393v1 Announce Type: new Abstract: Mechanistic interpretability of transformers requires identifying not just which components matter but how they compose into the computational route that produced a prediction. Both attention and MLP follow a shared key-value template $\phi(S)U$. We exploit this structure to develop Unpack, a backward recursion that decomposes credit through both sublayers, producing interaction strengths between any two components, named end-to-end paths with K/Q/V composition labels, and per-token attribution from a single forward pass, without intervention, gr
The increasing complexity and opacity of large AI models necessitate new methods for interpretability to ensure reliability and safety, driving current research in mechanistic interpretability.
Improved interpretability tools like 'Unpack' can unlock deeper understanding of AI model behavior, facilitate debugging, and accelerate the development of more robust AI systems, which is crucial for deployment in sensitive applications.
The ability to attribute token contributions and understand component interactions through a single decomposition offers a more efficient and comprehensive approach to mechanistic interpretability compared to previous methods requiring interventions.
- · AI researchers
- · AI safety organizations
- · Developers of critical AI applications
- · Opaque black-box AI model approaches
This research could lead to more trustworthy and explainable AI models, fostering greater adoption in critical sectors.
Enhanced interpretability might accelerate advancements in model optimization and efficiency by pinpointing inefficient or erroneous pathways.
A deeper understanding of AI internals could eventually inform new model architectures that are inherently more interpretable and controllable.
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