StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts
On the map: LLM reasoning → Spatial and embodied reasoning
Abstract (arXiv)
Inferring spatial relations in natural language is a crucial ability an intelligent system should possess. The bAbI dataset tries to capture tasks relevant to this domain (task 17 and 19). However, these tasks have several limitations. Most importantly, they are limited to fixed expressions, they are limited in the number of reasoning steps required to solve them, and they fail to test the robustness of models to input that contains irrelevant or redundant information. In this paper, we present a new Question-Answering dataset called StepGame for robust multi-hop spatial reasoning in texts. Our experiments demonstrate that state-of-the-art models on the bAbI dataset struggle on the StepGame dataset. Moreover, we propose a Tensor-Product based Memory-Augmented Neural Network (TP-MANN) specialized for spatial reasoning tasks. Experimental results on both datasets show that our model outperforms all the baselines with superior generalization and robustness performance.
Key facts. Cited by 68 papers in the map (data through 14 Oct 2026). Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. Builds on 24 of its 44 references that are in the map. First posted on arXiv 18 Apr 2022.
Builds on
24 of 44 references.
References · 24
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12 more
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Citing papers per year: 2022: 3; 2023: 14; 2024: 21; 2025: 10; 2026: 20.
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- 2022FOLIO: Natural Language Reasoning with First-Order Logiccited by 235
- 2025BIG-Bench Extra Hardcited by 125
- 2024Mind's Eye of LLMs: Visualization-of-Thought Elicits Spatial Reasoning in Large Language Modelscited by 66
- 2023Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Textcited by 59
- 2024TopViewRS: Vision-Language Models as Top-View Spatial Reasonerscited by 49
- 2022Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoningcited by 34
- 2024Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Spatial Reasoningcited by 34
- 2024CityGPT: Empowering Urban Spatial Cognition of Large Language Modelscited by 30
- 2024Advancing Spatial Reasoning in Large Language Models: An In-Depth Evaluation and Enhancement Using the StepGame Benchmarkcited by 27
- 2023Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinkingcited by 19
- 2024Reframing Spatial Reasoning Evaluation in Language Models: A Real-World Simulation Benchmark for Qualitative Reasoningcited by 17
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- 2026Spatial Reasoning via Modality Switching Between Language and Symbolic Representations30 Jun 2026
- 2026Mind the Gap Between Spatial Reasoning and Acting! Step-by-Step Evaluation of Agents With Spatial-Gym10 Apr 2026
- 2026STAR: Mitigating Cascading Errors in Spatial Reasoning via Turn-point Alignment and Segment-level DPO1 Apr 2026
- 2026Text-to-Stage: Spatial Layouts from Long-form Narratives18 Mar 2026
- 2026Grid Spatial Understanding: A Dataset for Textual Spatial Reasoning over Grids, Embodied Settings, and Coordinate Structures18 Mar 2026
- 2026SpatialText: A Pure-Text Cognitive Benchmark for Spatial Understanding in Large Language Models3 Mar 2026
- 2025ORIGAMISPACE: Benchmarking Multimodal LLMs in Multi-Step Spatial Reasoning with Mathematical Constraints23 Nov 2025
- 2025DecompSR: A dataset for decomposed analyses of compositional multihop spatial reasoning4 Nov 2025
- 2025A Comparative Study of Neurosymbolic AI Approaches to Interpretable Logical Reasoning5 Aug 2025
- 2025Evaluating the Ability of Large Language Models to Reason about Cardinal Directions, Revisited16 Jul 2025
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- 2024Instruction Tuning With Loss Over Instructionscited by 40
- 2023DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningcited by 35
- 2023Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learnercited by 23
- 2024Quantifying the Capabilities of LLMs across Scale and Precisioncited by 19
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- 2023Rethinking Semi-supervised Learning with Language Modelscited by 17
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- 2026Using profiles of cognitive capability to assess AI suitability for workplace tasks26 Aug 2026
- 2026ReRef-3D: A Benchmark for Spatial Referring Expression-Guided 3D Scene Rearrangement17 Aug 2026
- 2026GeoBenchLLM: A Comprehensive Benchmark for Evaluating LLMs on Geo-Related Tasks7 Aug 2026
- 2026OmniLayout: A Schematic-Coupled Multimodal Benchmark for Constraint-Aware Geometric Reasoning in PCB Layout3 Jul 2026
- 2026CombEval: A Framework for Evaluating Combinatorial Counting in Large Language Models18 Jun 2026
- 2026Positional versus Symbolic Attention Heads: Learning Dynamics, RoPE Geometry, and Length Generalization29 May 2026
- 2026How Do LLMs and VLMs Understand Viewpoint Rotation Without Vision? An Interpretability Study16 Apr 2026
- 2026Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration?4 Feb 2026
- 2025FoR-SALE: Frame of Reference-guided Spatial Adjustment in LLM-based Diffusion Editing27 Sep 2025
- 2024STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis27 Jun 2024
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