Grounding from an AI and Cognitive Science Lens
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Abstract (arXiv)
Grounding is a challenging problem, requiring a formal definition and different levels of abstraction. This article explores grounding from both cognitive science and machine learning perspectives. It identifies the subtleties of grounding, its significance for collaborative agents, and similarities and differences in grounding approaches in both communities. The article examines the potential of neuro-symbolic approaches tailored for grounding tasks, showcasing how they can more comprehensively address grounding. Finally, we discuss areas for further exploration and development in grounding.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 2 of its 18 references that are in the map. First posted on arXiv 19 Feb 2024.
Builds on
2 of 18 references.
References · 2
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Citing papers per year: 2024: 2; 2025: 2; 2026: 2.
6 papers.
- 2025Agentic Systems in Radiology: Design, Applications, Evaluation, and Challengescited by 5
- 2024Neurosymbolic AI for Enhancing Instructability in Generative AIcited by 3
- 2024Neurosymbolic AI approach to Attribution in Large Language Modelscited by 3
- 2025ThematicPlane: Bridging Tacit User Intent and Latent Spaces for Image Generationcited by 2
- 2026Neurosymbolic Retrievers for Retrieval-augmented Generationcited by 1
- 2026Understanding Generative AI-mediated User Engagement with Academic Library Resourcescited by 0
- 2026Understanding Generative AI-mediated User Engagement with Academic Library Resources22 Jul 2026
- 2026Neurosymbolic Retrievers for Retrieval-augmented Generation8 Jan 2026
- 2025Agentic Systems in Radiology: Design, Applications, Evaluation, and Challenges10 Oct 2025
- 2025ThematicPlane: Bridging Tacit User Intent and Latent Spaces for Image Generation8 Aug 2025
- 2024Neurosymbolic AI approach to Attribution in Large Language Models30 Sep 2024
- 2024Neurosymbolic AI for Enhancing Instructability in Generative AI26 Jul 2024