mlsanityData to 7 Oct 2026

Neuro-Symbolic VQA: A review from the perspective of AGI desiderata

arXiv 2104.06365 ↗First posted 13 Apr 2021cs.LG cs.CVcited by 4

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Abstract (arXiv)

An ultimate goal of the AI and ML fields is artificial general intelligence (AGI); although such systems remain science fiction, various models exhibit aspects of AGI. In this work, we look at neuro-symbolic (NS)approaches to visual question answering (VQA) from the perspective of AGI desiderata. We see how well these systems meet these desiderata, and how the desiderata often pull the scientist in opposing directions. It is my hope that through this work we can temper model evaluation on benchmarks with a discussion of the properties of these systems and their potential for future extension.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 28 of its 34 references that are in the map. First posted on arXiv 13 Apr 2021.

Builds on

28 of 34 references.

References · 28

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16 more
  1. 2016Visual Relationship Detection with Language Priorscited by 333
  2. 2015Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networkscited by 305
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2022Neuro-Symbolic Learning: Principles and Applications in Ophthalmologycited by 7
  2. 2025A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectivescited by 5
  3. 2024Bridging the Gap: Representation Spaces in Neuro-Symbolic AIcited by 0
  4. 2026PointVG-R: Internalizing Geometric Reasoning in MLLMs for Precise Pointing Localization via Visual Chain of Thoughtcited by 0