# Grounding from an AI and Cognitive Science Lens

arXiv: https://arxiv.org/abs/2402.13290 · HTML: https://mlsanity.com/paper/2402.13290/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision](https://mlsanity.com/paper/1904.12584/) (2019)
- [Grounding 'Grounding' in NLP](https://mlsanity.com/paper/2106.02192/) (2021)

## Built on by (most cited first, 6 of 6)

- [Agentic Systems in Radiology: Design, Applications, Evaluation, and Challenges](https://mlsanity.com/paper/2510.09404/) (2025)
- [Neurosymbolic AI for Enhancing Instructability in Generative AI](https://mlsanity.com/paper/2407.18722/) (2024)
- [Neurosymbolic AI approach to Attribution in Large Language Models](https://mlsanity.com/paper/2410.03726/) (2024)
- [ThematicPlane: Bridging Tacit User Intent and Latent Spaces for Image Generation](https://mlsanity.com/paper/2508.06065/) (2025)
- [Neurosymbolic Retrievers for Retrieval-augmented Generation](https://mlsanity.com/paper/2601.04568/) (2026)
- [Understanding Generative AI-mediated User Engagement with Academic Library Resources](https://mlsanity.com/paper/2607.20328/) (2026)

## 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.