Papers with Spatial reasoning
Neuro-symbolic Training for Reasoning over Spatial Language (2025.findings-naacl)
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| Challenge: | Spatial reasoning is essential for everyday human tasks and is crucial for robots to interact with their environment in a human-like manner. |
| Approach: | They propose to train language models to adhere to spatial reasoning rules as constraints . this allows them to capture the necessary level of abstraction for spatial reasoning . |
| Outcome: | The proposed technique improves language models in multi-hop spatial reasoning over text . it achieves higher accuracy than other competitive Spatial Question-answering benchmarks . |
Disentangling Extraction and Reasoning in Multi-hop Spatial Reasoning (2023.findings-emnlp)
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| Challenge: | Recent studies highlight the struggles even large language models encounter when it comes to performing spatial reasoning over text. |
| Approach: | They propose to disentangle spatial reasoning over text and compare them to state-of-the-art models with no explicit design for these parts. |
| Outcome: | The proposed models show that they can perform spatial reasoning over text and can generalize within real data domains. |
SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models (2024.acl-long)
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| Challenge: | Existing large language models (LLMs) do not perform well on the datasets. |
| Approach: | They propose to use a Spatial Reasoning Characterization framework and a spatial reasoning path framework to study spatial reasoning. |
| Outcome: | The proposed framework and datasets outperform state-of-the-art models in spatial reasoning. |
DepWiGNN: A Depth-wise Graph Neural Network for Multi-hop Spatial Reasoning in Text (2023.findings-emnlp)
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| Challenge: | Existing approaches for spatial reasoning in text overlook the gap between natural language and symbolic structures. |
| Approach: | They propose a novel depth-wise Graph Neural Network to aggregate spatial information over the depth dimension instead of the breadth dimension of the graph. |
| Outcome: | The proposed model outperforms existing methods on two multi-hop spatial reasoning datasets. |
A Benchmark for Reasoning with Spatial Prepositions (2023.emnlp-main)
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| Challenge: | Spatial reasoning is a fundamental building block of human cognition . large language models (LLMs) are not on par with advanced aspects of human cognitive domains . |
| Approach: | They propose a benchmark to assess inferential properties of statements with spatial prepositions . they use prompt engineering to test the performance of two large language models . |
| Outcome: | The proposed benchmark shows that none of the models reaches human performance. |
Jigsaw-Puzzles: From Seeing to Understanding to Reasoning in Vision-Language Models (2025.emnlp-main)
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| Challenge: | Existing vision-language models lack spatial reasoning capability, despite their ability to comprehend spatial arrangements and model structural relations. |
| Approach: | They propose a benchmark to evaluate vision-language models' spatial perception, structural understanding, and reasoning capabilities by minimizing reliance on domain-specific knowledge. |
| Outcome: | The proposed benchmark is based on 1,100 carefully curated real-world images with high spatial complexity. |
FoREST: Frame of Reference Evaluation in Spatial Reasoning Tasks (2025.emnlp-main)
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| Challenge: | Spatial reasoning is a fundamental aspect of human intelligence. |
| Approach: | They propose a framework to assess FoR comprehension in large language models (LLMs) by using the Frame of Reference Evaluation in Spatial Reasoning Tasks benchmark. |
| Outcome: | The proposed method improves overall performance across spatial reasoning tasks. |