Semantically-Aligned Reasoning for Safe AGI via Knowledge-Based Deep Models
Noor ul Ain Zaidi, Sundas Fatima · Journal of Computational Science and Applications (JCSA) ISSN 3079-0867 (Onilne) · 2025
A hybrid neuro-symbolic framework is presented in this work with the goal of enhancing the safety, dependability, and interpretability of reasoning in large language models (LLMs), which will ultimately aid in the development of reliable artificial general intelligence (AGI).The proposed strategy integrates structured knowledge representations, such as ontologies, symbolic planners, and formal logic, with deep learning-based natural language comprehension and Chain-of-Thought (CoT) reasoning. The framework aims to combine the semantic rigor of symbolic systems with the generative flexibility of LLMs to ensure semantic alignment with human ideals, improve reasoning faithfulness, and lessen hallucinations. Adversarial robustness testing, logic-based inference validation, and a benchmark suite for assessing coherence, honesty, and alignment are important elements. A functioning prototype that may be applied to high-stakes decision-making domains, including healthcare and autonomous systems, as well as assessment metrics and tools, are among the anticipated results.