# ReaSCAN: Compositional Reasoning in Language Grounding

arXiv: https://arxiv.org/abs/2109.08994 · HTML: https://mlsanity.com/paper/2109.08994/

## Key facts

- Cited by 19 papers in the map (data through 7 Oct 2026).
- Builds on 33 of its 67 references that are in the map.
- First posted on arXiv 18 Sep 2021.

## Builds on (most cited first, 30 of 33)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [Generation and Comprehension of Unambiguous Object Descriptions](https://mlsanity.com/paper/1511.02283/) (2015)
- [CLEVRER: CoLlision Events for Video REpresentation and Reasoning](https://mlsanity.com/paper/1910.01442/) (2019)
- [Vision-and-Dialog Navigation](https://mlsanity.com/paper/1907.04957/) (2019)
- [BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning](https://mlsanity.com/paper/1810.08272/) (2018)
- [RAVEN: A Dataset for Relational and Analogical Visual rEasoNing](https://mlsanity.com/paper/1903.02741/) (2019)
- [Stress Test Evaluation for Natural Language Inference](https://mlsanity.com/paper/1806.00692/) (2018)
- [Compositional Attention Networks for Machine Reasoning](https://mlsanity.com/paper/1803.03067/) (2018)
- [Vision-Language Navigation with Self-Supervised Auxiliary Reasoning Tasks](https://mlsanity.com/paper/1911.07883/) (2019)
- [DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning](https://mlsanity.com/paper/2006.08381/) (2020)
- [Good-Enough Compositional Data Augmentation](https://mlsanity.com/paper/1904.09545/) (2019)
- [Breaking NLI Systems with Sentences that Require Simple Lexical Inferences](https://mlsanity.com/paper/1805.02266/) (2018)
- [Inferring and Executing Programs for Visual Reasoning](https://mlsanity.com/paper/1705.03633/) (2017)
- [The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers](https://mlsanity.com/paper/2108.12284/) (2021)
- [A Benchmark for Systematic Generalization in Grounded Language Understanding](https://mlsanity.com/paper/2003.05161/) (2020)
- [Learning to Compose and Reason with Language Tree Structures for Visual Grounding](https://mlsanity.com/paper/1906.01784/) (2019)
- [Compositional generalization through meta sequence-to-sequence learning](https://mlsanity.com/paper/1906.05381/) (2019)
- [Compositional Generalization via Neural-Symbolic Stack Machines](https://mlsanity.com/paper/2008.06662/) (2020)
- [Learning Compositional Rules via Neural Program Synthesis](https://mlsanity.com/paper/2003.05562/) (2020)
- [CLOSURE: Assessing Systematic Generalization of CLEVR Models](https://mlsanity.com/paper/1912.05783/) (2019)
- [Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning](https://mlsanity.com/paper/2010.00763/) (2020)
- [Environmental drivers of systematicity and generalization in a situated agent](https://mlsanity.com/paper/1910.00571/) (2019)
- [The EOS Decision and Length Extrapolation](https://mlsanity.com/paper/2010.07174/) (2020)
- [Context-aware Captions from Context-agnostic Supervision](https://mlsanity.com/paper/1701.02870/) (2017)
- [Jump to better conclusions: SCAN both left and right](https://mlsanity.com/paper/1809.04640/) (2018)
- [Analyzing Compositionality-Sensitivity of NLI Models](https://mlsanity.com/paper/1811.07033/) (2018)
- [CURI: A Benchmark for Productive Concept Learning Under Uncertainty](https://mlsanity.com/paper/2010.02855/) (2020)

## Built on by (most cited first, 19 of 19)

- [Inducing Causal Structure for Interpretable Neural Networks](https://mlsanity.com/paper/2112.00826/) (2021)
- [Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoning](https://mlsanity.com/paper/2310.03249/) (2023)
- [LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning](https://mlsanity.com/paper/2407.02511/) (2024)
- [A Benchmark for Compositional Visual Reasoning](https://mlsanity.com/paper/2206.05379/) (2022)
- [ReCOGS: How Incidental Details of a Logical Form Overshadow an Evaluation of Semantic Interpretation](https://mlsanity.com/paper/2303.13716/) (2023)
- [Pushing the Limits of Rule Reasoning in Transformers through Natural Language Satisfiability](https://mlsanity.com/paper/2112.09054/) (2021)
- [A Survey on Compositional Learning of AI Models: Theoretical and Experimental Practices](https://mlsanity.com/paper/2406.08787/) (2024)
- [When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks](https://mlsanity.com/paper/2210.12786/) (2022)
- [Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation](https://mlsanity.com/paper/2403.16394/) (2024)
- [NePTune: A Neuro-Pythonic Framework for Tunable Compositional Reasoning on Vision-Language](https://mlsanity.com/paper/2509.25757/) (2025)
- [Dyna-bAbI: unlocking bAbI's potential with dynamic synthetic benchmarking](https://mlsanity.com/paper/2112.00086/) (2021)
- [Can LLM find the green circle? Investigation and Human-guided tool manipulation for compositional generalization](https://mlsanity.com/paper/2312.07763/) (2023)
- [Imagine the Unseen World: A Benchmark for Systematic Generalization in Visual World Models](https://mlsanity.com/paper/2311.09064/) (2023)
- [Is Feedback All You Need? Leveraging Natural Language Feedback in Goal-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2312.04736/) (2023)
- [Trust in Language Grounding: a new AI challenge for human-robot teams](https://mlsanity.com/paper/2209.02066/) (2022)
- [Pento-DIARef: A Diagnostic Dataset for Learning the Incremental Algorithm for Referring Expression Generation from Examples](https://mlsanity.com/paper/2305.15087/) (2023)
- [Sequential Compositional Generalization in Multimodal Models](https://mlsanity.com/paper/2404.12013/) (2024)
- [Natural Language Satisfiability: Exploring the Problem Distribution and Evaluating Transformer-based Language Models](https://mlsanity.com/paper/2508.17153/) (2025)
- [What Do Current Systematic Generalization Tasks Miss? A Reasoning-Centered Analysis](https://mlsanity.com/paper/2609.19212/) (2026)

## Abstract (arXiv)

The ability to compositionally map language to referents, relations, and actions is an essential component of language understanding. The recent gSCAN dataset (Ruis et al. 2020, NeurIPS) is an inspiring attempt to assess the capacity of models to learn this kind of grounding in scenarios involving navigational instructions. However, we show that gSCAN's highly constrained design means that it does not require compositional interpretation and that many details of its instructions and scenarios are not required for task success. To address these limitations, we propose ReaSCAN, a benchmark dataset that builds off gSCAN but requires compositional language interpretation and reasoning about entities and relations. We assess two models on ReaSCAN: a multi-modal baseline and a state-of-the-art graph convolutional neural model. These experiments show that ReaSCAN is substantially harder than gSCAN for both neural architectures. This suggests that ReaSCAN can serve as a valuable benchmark for advancing our understanding of models' compositional generalization and reasoning capabilities.