Interpretable Models Train Transparency In
A new training approach treats interpretability as a capability optimized with the language model rather than a post-hoc explanation applied to an opaque system. If capability scales without losing the interpretable structure, regulated and high-stakes deployments could gain a different trust architecture. This remains one research result; independent replication, competitive performance at larger scale or use in a deployed model would confirm the hypothesis.