Reasoner: A Model of Thoughtful Processing Beyond Attention
Keywords: natural language processing, Bayesian inference, semantic primes, Reasoner model, AI reasoning
TL;DR: Introducing the Reasoner model, a novel AI framework using semantic primitives and Bayesian inference for interpretable and adaptable language understanding.
Abstract: The Reasoner model introduces a novel approach to language processing that surpasses the limitations of attention-based transformer models (Vaswani et al., 2017). Unlike transformers, which rely on token-level relationships and attention mechanisms, the Reasoner model integrates structured reasoning processes to achieve deeper contextual understanding. Leveraging the Natural Semantic Metalanguage (NSM) framework (Wierzbicka, 1996), it simplifies language into semantic primitives and employs Bayesian inference to iteratively update its understanding based on new information (Cohen, 2021; Sreedharan et al., 2023). This combination of semantic transparency, probabilistic reasoning, and vectorized representations positions the Reasoner as a highly interpretable and adaptable alternative to existing models. Comparative analysis highlights its ad-vantages in interpretability, scalability, and adaptability to complex linguistic tasks.
Submission Number: 9
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