DynaCat-Diffusion: Iterative Semantic Categorization for Open-World Language Generation Article Swipe
Recent work by Wang (2025) argues that “AI = Dynamic Categorization,” framing language modeling as the real-time construction of temporary semantic prototypes that guide token selection. The Transformer implements this via self-referential attention, where Value (V) vectors—crucially—encode response semantics (“what should be said next”) rather than input semantics. However, this paradigm remains a one-step decision process, lacking the capacity for iterative refinement. In this paper, we propose DynaCat-Diffusion, a hybrid framework that unifies dynamic categorization with diffusion-inspired prototype refinement. Our core insight is that intelligent generation requires not only building a semantic prototype but also improving it through multi-step optimization. We present a minimal implementation where an initial goal-oriented prototype (from QKV attention) is refined in embedding space via a stable, interpolation-based diffusion process. Experiments confirm that this approach preserves coherence in high-certainty contexts while enabling natural diversity in ambiguous ones (e.g., “bye” → “see” or “later”). We argue that iterative semantic categorization represents a necessary evolution toward robust open-world intelligence.
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- Type
- preprint
- Landing Page
- https://doi.org/10.5281/zenodo.17894271
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7114932145