The Cognitive Categorical Transformer: Category-Theoretic Inductive Biases for Language Modeling
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. Under a matched-step protocol (215,000 optimizer steps, matched data, matched optimizer and schedule) on WikiText-103, CCT reaches 21.27 validation perplexity, compared with 24.19 for an identically fine-tuned GPT-2 Small baseline. The architecture therefore contributes a 2.92 PPL (12% relative) reduction beyon
The continuous advancements in AI research, particularly in large language models, necessitate exploration of new architectural paradigms to overcome existing limitations and improve performance.
This research signifies a novel approach to enhancing language models by integrating cognitive science and category theory, potentially leading to more efficient and robust AI systems.
The explicit incorporation of cognitive and mathematical inductive biases into transformer architectures represents a shift from purely data-driven model improvements, potentially enabling more generalizable and interpretable AI.
- · AI researchers
- · Cognitive science
- · NLP applications
- · AI-driven product developers
- · Purely empirical model development
- · Legacy transformer architectures
Improved performance and efficiency of large language models for various tasks.
Development of new AI models that leverage deeper theoretical understandings from cognitive science and mathematics.
Acceleration of AI agent capabilities through more sophisticated and human-like reasoning models.
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Read at arXiv cs.AI