Opening the Black Box: Interpretable LLMs via Semantic Resonance Architecture (arxiv.org)

Researchers introduced the Semantic Resonance Architecture (SRA), an interpretable Mixture-of-Experts (MoE) design that replaces opaque learned gates with a Chamber of Semantic Resonance (CSR) that routes tokens by cosine similarity to trainable "semantic anchors." To encourage diverse, distinct expert roles, they add a Dispersion Loss that pushes anchors toward orthogonality. SRA was evaluated on WikiText-103 and, under a matched active-parameter constraint (29.0M), achieved a validation perplexity of 13.41—better than a dense baseline (14.13) and a standard MoE (13.53). Crucially, SRA dramatically reduces dead experts (1.0% vs 14.8%) and yields semantically coherent expert specializations, unlike the noisy patterns typical of standard MoEs. This work is significant because it demonstrates a practical path to making sparse, efficient LLMs more transparent and controllable without sacrificing performance. By using similarity-based routing tied to human-interpretable anchors, SRA enables clearer attribution of token routing and expert responsibilities—helpful for debugging, auditing, and targeted fine-tuning. The Dispersion Loss and anchor design also improve utilization and stability, suggesting semantic routing could become a standard tool for building modular, inspectable language models that retain MoE efficiency while offering better diagnostics and governance.