# Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning

arXiv: https://arxiv.org/abs/2010.00763 · HTML: https://mlsanity.com/paper/2010.00763/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 30 of 63)

- [Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models](https://mlsanity.com/paper/2209.07511/) (2022)
- [EgoTaskQA: Understanding Human Tasks in Egocentric Videos](https://mlsanity.com/paper/2210.03929/) (2022)
- [Unveiling Transformers with LEGO: a synthetic reasoning task](https://mlsanity.com/paper/2206.04301/) (2022)
- [Abstraction and Analogy-Making in Artificial Intelligence](https://mlsanity.com/paper/2102.10717/) (2021)
- [The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain](https://mlsanity.com/paper/2305.07141/) (2023)
- [A Benchmark for Compositional Visual Reasoning](https://mlsanity.com/paper/2206.05379/) (2022)
- [Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven's Progressive Matrices](https://mlsanity.com/paper/2201.12382/) (2022)
- [Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions](https://mlsanity.com/paper/2205.13803/) (2022)
- [A Review of Emerging Research Directions in Abstract Visual Reasoning](https://mlsanity.com/paper/2202.10284/) (2022)
- [Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus](https://mlsanity.com/paper/2403.11793/) (2024)
- [MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning](https://mlsanity.com/paper/2404.13591/) (2024)
- [Pointer Value Retrieval: A new benchmark for understanding the limits of neural network generalization](https://mlsanity.com/paper/2107.12580/) (2021)
- [MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models](https://mlsanity.com/paper/2502.00698/) (2025)
- [Explain Before You Answer: A Survey on Compositional Visual Reasoning](https://mlsanity.com/paper/2508.17298/) (2025)
- [Are Deep Neural Networks SMARTer than Second Graders?](https://mlsanity.com/paper/2212.09993/) (2022)
- [ReaSCAN: Compositional Reasoning in Language Grounding](https://mlsanity.com/paper/2109.08994/) (2021)
- [Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World](https://mlsanity.com/paper/2310.10207/) (2023)
- [MEWL: Few-shot multimodal word learning with referential uncertainty](https://mlsanity.com/paper/2306.00503/) (2023)
- [Bongard in Wonderland: Visual Puzzles that Still Make AI Go Mad?](https://mlsanity.com/paper/2410.19546/) (2024)
- [RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning](https://mlsanity.com/paper/2204.11167/) (2022)
- [Human Cognitive Benchmarks Reveal Foundational Visual Gaps in MLLMs](https://mlsanity.com/paper/2502.16435/) (2025)
- [Tracking Without Re-recognition in Humans and Machines](https://mlsanity.com/paper/2105.13351/) (2021)
- [Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detection](https://mlsanity.com/paper/2503.03562/) (2025)
- [V-LoL: A Diagnostic Dataset for Visual Logical Learning](https://mlsanity.com/paper/2306.07743/) (2023)
- [Multi-Viewpoint and Multi-Evaluation with Felicitous Inductive Bias Boost Machine Abstract Reasoning Ability](https://mlsanity.com/paper/2210.14914/) (2022)
- [Bongard-RWR+: Real-World Representations of Fine-Grained Concepts in Bongard Problems](https://mlsanity.com/paper/2508.12026/) (2025)
- [Toward Cognitive Supersensing in Multimodal Large Language Model](https://mlsanity.com/paper/2602.01541/) (2026)
- [Geoclidean: Few-Shot Generalization in Euclidean Geometry](https://mlsanity.com/paper/2211.16663/) (2022)
- [BDC-Adapter: Brownian Distance Covariance for Better Vision-Language Reasoning](https://mlsanity.com/paper/2309.01256/) (2023)
- [Imagine the Unseen World: A Benchmark for Systematic Generalization in Visual World Models](https://mlsanity.com/paper/2311.09064/) (2023)

## Abstract (arXiv)

Humans have an inherent ability to learn novel concepts from only a few samples and generalize these concepts to different situations. Even though today's machine learning models excel with a plethora of training data on standard recognition tasks, a considerable gap exists between machine-level pattern recognition and human-level concept learning. To narrow this gap, the Bongard problems (BPs) were introduced as an inspirational challenge for visual cognition in intelligent systems. Despite new advances in representation learning and learning to learn, BPs remain a daunting challenge for modern AI. Inspired by the original one hundred BPs, we propose a new benchmark Bongard-LOGO for human-level concept learning and reasoning. We develop a program-guided generation technique to produce a large set of human-interpretable visual cognition problems in action-oriented LOGO language. Our benchmark captures three core properties of human cognition: 1) context-dependent perception, in which the same object may have disparate interpretations given different contexts; 2) analogy-making perception, in which some meaningful concepts are traded off for other meaningful concepts; and 3) perception with a few samples but infinite vocabulary. In experiments, we show that the state-of-the-art deep learning methods perform substantially worse than human subjects, implying that they fail to capture core human cognition properties. Finally, we discuss research directions towards a general architecture for visual reasoning to tackle this benchmark.