Scaling Inherently Interpretable Language Models

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 make interpretability a constraint of the training pipeline, optimized alongside the language modeling objective. Across three orders of magnitude of compute, on both autoregressive and diffusion language models, interpre

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ethics.ai (5 August 2026), “Scaling Inherently Interpretable Language Models,” evidence record 18395, https://ethics.ai/record/18395 (originally published by HuggingFace Daily Papers).

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