AUTOBUS is a neuro-symbolic architecture that uses AI agents to generate executable logic programs from business instructions and knowledge graphs for end-to-end process automation with human supervision.
Neuro-symbolic ai in 2024: A systematic review
6 Pith papers cite this work. Polarity classification is still indexing.
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A neuro-symbolic system pairing LLMs for symbolic reasoning with neural delta controllers for execution delivers over 70% step reduction and up to 8.83x speedup in language-guided planar object manipulation while remaining robust to LLM quality.
A systematic review of neuro-symbolic AI in cybersecurity finds that deeper integration and causal reasoning improve performance across intrusion detection and vulnerability tasks, while identifying barriers and a research roadmap.
Explores options for using LLMs to scale deliberation and empower marginalized groups via systemic-functional linguistics concepts while cautioning against over- and under-claiming.
Position paper proposing compliance-by-construction for neuro-symbolic agents in regulated process automation and calling for research on associated challenges.
A homoglyph substitution method perturbs introductory CS theory problems to make them unsolvable by current AI tools while preserving semantic meaning.
citing papers explorer
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Autonomous Business System via Neuro-symbolic AI
AUTOBUS is a neuro-symbolic architecture that uses AI agents to generate executable logic programs from business instructions and knowledge graphs for end-to-end process automation with human supervision.
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Neuro-Symbolic Control with Large Language Models for Language-Guided Spatial Tasks
A neuro-symbolic system pairing LLMs for symbolic reasoning with neural delta controllers for execution delivers over 70% step reduction and up to 8.83x speedup in language-guided planar object manipulation while remaining robust to LLM quality.
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Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities
A systematic review of neuro-symbolic AI in cybersecurity finds that deeper integration and causal reasoning improve performance across intrusion detection and vulnerability tasks, while identifying barriers and a research roadmap.
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The Almost Intelligent Revolution: Options for Scaling Up Deliberation and Empowering People with AI
Explores options for using LLMs to scale deliberation and empower marginalized groups via systemic-functional linguistics concepts while cautioning against over- and under-claiming.
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Neuro-Symbolic Agents for Regulated Process Automation: Challenges and Research Agenda
Position paper proposing compliance-by-construction for neuro-symbolic agents in regulated process automation and calling for research on associated challenges.
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Homoglyph-based Adversarial Perturbation of Introductory Computer Science Theory Problems
A homoglyph substitution method perturbs introductory CS theory problems to make them unsolvable by current AI tools while preserving semantic meaning.