neuro-symbolic

Automating tax calculations and optimisation is challenging because modern AI systems such as large language models operate probabilistically, while legal and financial reasoning requires deterministic compliance with statutory rules. This research presents a new approach to automate taxes in India through the Neuro-Symbolic Tax Optimizing Engine (NTOL) that combines the advantages of large langu…

This paper introduces MoE-TLM, a modular architecture for language intelligence based on domain-specialized micro-models coordinated through a neural routing system. In contrast to conventional large language models that rely on massive parameter scaling, the proposed approach distributes intelligence across multiple lightweight expert models, each trained for a specific domain. A central routing…

This Article asks what happens next. The model has encoded its knowledge of fraud as symbolic rules. V14 below a threshold means fraud. What happens when that relationship starts to change? Can the rules act as a canary? In other words: can neuro-symbolic concept drift monitoring work at inference time, without labels? Full architecture background in Hybrid Neuro-Symbolic Fraud Detection: Guiding…

Building Intelligent Agents with Neuro-Symbolic Concepts Author(s) Mao, Jiayuan; Tenenbaum, Joshua; Wu, JiajunAbstract This article presents a concept-centric paradigm for building agents that can learn continually and reason flexibly. The concept-centric agent utilizes a vocabulary of neuro-symbolic concepts. These concepts, such as object, relation, and action concepts, are grounded on sensory …

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