# Symbolic Grounding Reveals Representational Bottlenecks in Abstract Visual Reasoning

arXiv: https://arxiv.org/abs/2604.21346 · HTML: https://mlsanity.com/paper/2604.21346/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 23 Apr 2026.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 11 of 11)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [When and why vision-language models behave like bags-of-words, and what to do about it?](https://mlsanity.com/paper/2210.01936/) (2022)
- [Re-Thinking Inverse Graphics With Large Language Models](https://mlsanity.com/paper/2404.15228/) (2024)
- [Bongard in Wonderland: Visual Puzzles that Still Make AI Go Mad?](https://mlsanity.com/paper/2410.19546/) (2024)
- [CLIP Under the Microscope: A Fine-Grained Analysis of Multi-Object Representation](https://mlsanity.com/paper/2502.19842/) (2025)
- [How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks](https://mlsanity.com/paper/2507.01955/) (2025)
- [A Cognitive Paradigm Approach to Probe the Perception-Reasoning Interface in VLMs](https://mlsanity.com/paper/2501.13620/) (2025)
- [A Neurosymbolic Agent System for Compositional Visual Reasoning](https://mlsanity.com/paper/2506.07778/) (2025)
- [Solving the Clustering Reasoning Problems by Modeling a Deep-Learning-Based Probabilistic Model](https://mlsanity.com/paper/2403.03173/) (2024)
- [NSA: Neuro-symbolic ARC Challenge](https://mlsanity.com/paper/2501.04424/) (2025)

## Built on by (most cited first, 1 of 1)

- [Language-Guided Abstraction for Visual Reasoning](https://mlsanity.com/paper/2606.12847/) (2026)

## Abstract (arXiv)

Vision--language models (VLMs) often fail on abstract visual reasoning benchmarks such as Bongard problems, raising the question of whether the main bottleneck lies in reasoning or representation. We study this on Bongard-LOGO, a synthetic benchmark of abstract concept learning with ground-truth generative programs, by comparing end-to-end VLMs on raw images with large language models (LLMs) given symbolic inputs derived from those images. Using symbolic inputs as a diagnostic probe rather than a practical multimodal architecture, our Componential--Grammatical (C--G) paradigm reformulates Bongard-LOGO as a symbolic reasoning task based on LOGO-style action programs or structured descriptions. LLMs achieve large and consistent gains, reaching mid--90s accuracy on Free-form problems, while a strong visual baseline remains near chance under matched task definitions. Ablations on input format, explicit concept prompts, and minimal visual grounding show that these factors matter much less than the shift from pixels to symbolic structure. These results identify representation as a key bottleneck in abstract visual reasoning and show how symbolic input can serve as a controlled diagnostic upper bound.