arXiv:2605.08934v2 Announce Type: replace Abstract: Mechanistic interpretability aims to explain neural model behaviour by reverse-engineering learned computational structure into human-understandable components. Without a formal framework, however, mechanistic explanations cannot be objectively verified, compared, or composed. We introduce compositional interpretability, a category-theoretic framework grounded in the principles of compositionality and minimum description length. Compositional interpretations are pairs of syntactic and semantic mappings that must commute to enforce consistency
The rapid advancement and deployment of large neural networks necessitate more robust, verifiable, and explainable AI systems, pushing research towards formal interpretability frameworks.
A formal framework for interpreting AI models could enable more reliable, auditable, and deployable AI, particularly in critical applications where trust and transparency are paramount.
The proposed 'compositional interpretability' framework offers a mathematically grounded approach to understanding neural network behavior, moving beyond ad-hoc mechanistic explanations towards provable and composable insights.
- · AI safety researchers
- · Developers of critical AI systems
- · Regulatory bodies
- · Academic AI research institutions
- · Black box AI developers
- · Systems reliant solely on empirical AI validation
- · AI models lacking transparent design
More rigorous methods for validating AI outputs and understanding model decisions will emerge.
This could lead to a 'gold standard' for AI interpretability, influencing regulatory requirements and industry best practices.
The ability to formally compose and verify AI interpretations might accelerate the development of truly robust and provably safe AI agents and complex autonomous systems.
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Read at arXiv cs.LG