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Neurosymbolic AI: The 3rd Wave
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Current advances in Artificial Intelligence (AI) and Machine Learning (ML) have achieved unprecedented impact across research communities and industry. Nevertheless, concerns about trust, safety, interpretability and accountability of AI were raised by influential thinkers. Many have identified the need for well-founded knowledge representation and reasoning to be integrated with deep learning and for sound explainability. Neural-symbolic computing has been an active area of research for many years seeking to bring together robust learning in neural networks with reasoning and explainability via symbolic representations for network models. In this paper, we relate recent and early research results in neurosymbolic AI with the objective of identifying the key ingredients of the next wave of AI systems. We focus on research that integrates in a principled way neural network-based learning with symbolic knowledge representation and logical reasoning. The insights provided by 20 years of neural-symbolic computing are shown to shed new light onto the increasingly prominent role of trust, safety, interpretability and accountability of AI. We also identify promising directions and challenges for the next decade of AI research from the perspective of neural-symbolic systems.
Forward citations
Cited by 5 Pith papers
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Human-Inspired Neuro-Symbolic World Modeling and Logic Reasoning for Interpretable Safe UAV Landing Site Assessment
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Model-Driven Requirements Configuration with Three-Valued Uncertainty Scoring
An LLM-plus-symbolic-validator loop cuts structural requirement errors to 0.39% under a small model (0% under a frontier model) and quantifies ~25% of LLM choices as valid but indeterminate.
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Bridging Symbolic Control and Neural Reasoning in LLM Agents -- The Structured Cognitive Loop
A five-module LLM agent loop (retrieval, cognition, control, action, memory) is claimed to eliminate policy violations and redundant calls, though validation does not compare against real baselines.
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NRR-Core: Non-Resolution Reasoning as a Computational Framework for Contextual Identity and Ambiguity Preservation
A gated two-embedding toy model can output near-maximum uncertainty before context and resolve perfectly afterward, but the uncertainty is enforced by a hand-set gate.
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Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation
The paper is a literature review that classifies privacy-preserving AI techniques in dataspaces using a qualitative taxonomy of privacy, performance, and compliance ratings.
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