SemCrys: Toward a Tokenizer-Native Semantic Substrate for Machine Cognition introduces Semantic Crystallization (SemCrys), a semantic-native representation framework for large language models in which recurrent semantic structures stabilize into canonical representational units integrated directly into tokenizer and embedding substrates. The paper argues that current tokenization systems optimize primarily for statistical lexical recurrence rather than stabilized semantic topology, forcing reasoning systems to repeatedly reconstruct recurring concepts from fragmented token surfaces. SemCrys reframes semantic compression as a representational-topological problem and proposes a dynamic, semantics-driven vocabulary learning framework governed by semantic recurrence, contextual stability, and representational reuse. The system introduces semantic atoms, semantic cooling dynamics, semantic topology organization, adaptive-to-canonical promotion systems, and constitutional semantic governance mechanisms designed to reduce semantic reconstruction overhead in transformer attention systems. The paper situates SemCrys within the historical lineage of Leibniz’s characteristica universalis, pasigraphy, philosophical language systems, lexicography, and symbolic representation traditions while extending these ideas into modern machine learning architectures. It further proposes evaluation pathways, backward-compatible deployment strategies, and semantic governance considerations for tokenizer-native semantic systems. SemCrys forms part of a broader semantic systems stack alongside: SemCrys0 — protocol-level semantic compression SemCrys — semantic-native representation substrate SemCrys-Secure — governed semantic access and authorization-scoped semantic interpretation The central claim of the work is that machine cognition may be materially improved when representational substrates are reorganized around stabilized meaning topology rather than purely statistical lexical recurrence.
Adam Ableman Mazurk (Mon,) studied this question.