mlsanityData to 7 Oct 2026

Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI

arXiv 2401.01040 ↗First posted 2 Jan 2024cs.AI cs.ARcited by 24

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Abstract (arXiv)

The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, have significantly impacted various aspects of our lives. However, the current challenges surrounding unsustainable computational trajectories, limited robustness, and a lack of explainability call for the development of next-generation AI systems. Neuro-symbolic AI (NSAI) emerges as a promising paradigm, fusing neural, symbolic, and probabilistic approaches to enhance interpretability, robustness, and trustworthiness while facilitating learning from much less data. Recent NSAI systems have demonstrated great potential in collaborative human-AI scenarios with reasoning and cognitive capabilities. In this paper, we provide a systematic review of recent progress in NSAI and analyze the performance characteristics and computational operators of NSAI models. Furthermore, we discuss the challenges and potential future directions of NSAI from both system and architectural perspectives.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 24 of its 36 references that are in the map. First posted on arXiv 2 Jan 2024.

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24 of 36 references.

References · 24

  1. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 723
  2. 2019The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervisioncited by 369
  3. 2018Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understandingcited by 304
  4. 2019Deep Learning for Symbolic Mathematicscited by 266
  5. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  6. 2019RAVEN: A Dataset for Relational and Analogical Visual rEasoNingcited by 225
  7. 2017Learning Explanatory Rules from Noisy Datacited by 210
  8. 2020Logic Tensor Networkscited by 197
  9. 2017Neural-Symbolic Learning and Reasoning: A Survey and Interpretationcited by 195
  10. 2019Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoningcited by 149
  11. 2019Neural Logic Machinescited by 144
  12. 2020Logical Neural Networkscited by 118
12 more
  1. 2020Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspectivecited by 93
  2. 2020Stratified Rule-Aware Network for Abstract Visual Reasoningcited by 72
  3. 2020Thinking Fast and Slow in AIcited by 67
  4. 2022A Neuro-vector-symbolic Architecture for Solving Raven's Progressive Matricescited by 64
  5. 2018Ontology Reasoning with Deep Neural Networkscited by 40
  6. 2022NeuPSL: Neural Probabilistic Soft Logiccited by 34
  7. 2022Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computingcited by 34
  8. 2021Neuro-Symbolic AI: An Emerging Class of AI Workloads and their Characterizationcited by 22
  9. 2023PyReason: Software for Open World Temporal Logiccited by 16
  10. 2021Analyzing and Improving Fault Tolerance of Learning-Based Navigation Systemscited by 5
  11. 2022Roofline Model for UAVs: A Bottleneck Analysis Tool for Onboard Compute Characterization of Autonomous Unmanned Aerial Vehiclescited by 3
  12. 2022Robotic Computing on FPGAs: Current Progress, Research Challenges, and Opportunitiescited by 2

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Citing papers per year: 2024: 6; 2025: 11; 2026: 7.

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  1. 2024Towards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecturecited by 7
  2. 2024Dspy-based Neural-Symbolic Pipeline to Enhance Spatial Reasoning in LLMscited by 6
  3. 2024ProSLM : A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answeringcited by 5
  4. 2025A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectivescited by 5
  5. 2026Egocentric Co-Pilot: Web-Native Smart-Glasses Agents for Assistive Egocentric AIcited by 3
  6. 2024H3DFact: Heterogeneous 3D Integrated CIM for Factorization with Holographic Perceptual Representationscited by 2
  7. 2024Towards certifiable AI in aviation: landscape, challenges, and opportunitiescited by 2
  8. 2025Foundations of a Developmental Design Paradigm for Integrated Continual Learning, Deliberative Behavior, and Comprehensibilitycited by 2
  9. 2025Symbolic Intermediaries as a Linguistic-Numerical Interface for LLM-Driven Geometric Reasoningcited by 2
  10. 2024Building Trustworthy AI: Transparent AI Systems via Large Language Models, Ontologies, and Logical Reasoning (TranspNet)cited by 1
  11. 2025NSFlow: An End-to-End FPGA Framework with Scalable Dataflow Architecture for Neuro-Symbolic AIcited by 1
  12. 2025On the Parallels Between Evolutionary Theory and the State of AIcited by 1
12 more
  1. 2026A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classificationcited by 1
  2. 2025MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Acceleratorscited by 0
  3. 2025Evolutionary Developmental Biology Can Serve as the Conceptual Foundation for a New Design Paradigm in Artificial Intelligencecited by 0
  4. 2025Bridging the Gap Between Scientific Laws Derived by AI Systems and Canonical Knowledge via Abductive Inference with AI-Noethercited by 0
  5. 2025From Craft to Constitution: A Governance-First Paradigm for Principled Agent Engineeringcited by 0
  6. 2025Multi-task GINN-LP for Multi-target Symbolic Regressioncited by 0
  7. 2025A Neuro-Symbolic Framework for Accountability in Public-Sector AIcited by 0
  8. 2026REASON: Accelerating Probabilistic Logical Reasoning for Scalable Neuro-Symbolic Intelligencecited by 0
  9. 2026SciTaRC: A Plan-Annotated Scientific Tabular QA Benchmark for Language Reasoning and Complex Computationcited by 0
  10. 2026ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learningcited by 0
  11. 2026Neuro-Symbolic Agents for Regulated Process Automation: Challenges and Research Agendacited by 0
  12. 2026A Task-Centric Ontology and Deterministic Domain Rules as a Verifiable Core for AI-Assisted Chemistry Problem Solvingcited by 0