Embedding Consistency Regulation for Persistent Semantic Routing in Compact Language Models

arXiv:2601.00543v2 Announce Type: replace-cross Abstract: Language models are increasingly deployed under tight memory and compute constraints, where semantic routes (e.g., language, tone, or intent) may be assigned before decoding. However, these routes may not remain reflected in generated responses. We study this phenomenon as \emph{persistent semantic routing}. We investigate this property using Embedding Consistency Regulation (ECR) as a minimal probe. ECR derives a short discrete prefix for each input using a frozen embedding model and applies the same prefix construction during both training and inference. In controlled prefix-assignment experiments, ECR achieves 99.93\% $\pm$ 0.12\% language routing accuracy, while random, shuffled, and dummy controls remain near chance (34--36\%). This gap is not reliably captured by likelihood: some controls achieve competitive NLL despite losing routing consistency. We introduce Response Routing Recoverability (RRR), an encoder-independent diagnostic that tests whether assigned routes remain observable after generation. Routing recoverability persists across multilingual settings, unsupervised partitions, reduced precision, and transfer tasks. These findings establish routing persistence as a distinct and measurable property of language models, complementary to task performance and likelihood.

Abstract:Language models are increasingly deployed under tight memory and compute constraints, where semantic routes (e.g., language, tone, or intent) may be assigned before decoding. However, these routes may not remain reflected in generated responses. We study this phenomenon as \emph{persistent semantic routing}. We investigate this property using Embedding Consistency Regulation (ECR) as a minimal probe. ECR derives a short discrete prefix for each input using a frozen embedding model and applies the same prefix construction during both training and inference. In controlled prefix-assignment experiments, ECR achieves 99.93\% $\pm$ 0.12\% language routing accuracy, while random, shuffled, and dummy controls remain near chance (34--36\%). This gap is not reliably captured by likelihood: some controls achieve competitive NLL despite losing routing consistency. We introduce Response Routing Recoverability (RRR), an encoder-independent diagnostic that tests whether assigned routes remain observable after generation. Routing recoverability persists across multilingual settings, unsupervised partitions, reduced precision, and transfer tasks. These findings establish routing persistence as a distinct and measurable property of language models, complementary to task performance and likelihood.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2601.00543 [cs.CL]

(or arXiv:2601.00543v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2601.00543 Focus to learn more arXiv-issued DOI via DataCite

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Embedding Consistency Regulation for Persistent Semantic Routing in Compact Language Models

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