Scaling Inherently Interpretable Language Models
A new paradigm where scaling language models enhances rather than obscures internal interpretability.
Interpretability is often treated as a tax on capability: language models are trained as opaque systems, then explained after the fact, with methods whose reliability is difficult to establish. In this work, we challenge this premise. Rather than reverse-engineering a model, we m...
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This research breaks the traditional trade-off between model performance and transparency by proving that interpretability can be a core constraint during training. By building explainability directly into the architecture, developers can diagnose and correct AI behaviors without requiring expensive, inefficient post-hoc analysis.
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