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The Cognitive Categorical Transformer: Category-Theoretic Inductive Biases for Language Modeling

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arXiv:2605. 28864v1 Announce Type: new Abstract: The Cognitive Categorical Transformer (CCT) is a 306M-parameter architecture that augments a pretrained GPT-2 Small backbone with cognitively grounded components derived from category theory and several inspirations from cognitive science.

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Authors: Al Kari
Released: 2026-05-29 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because The Cognitive Categorical Transformer: Category-Theoretic Inductive Biases for Language Modeling overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, under, line, compare, alone, full, trained, language. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.28864

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