Knot So Simple: A Minimalistic Environment for Spatial Reasoning
On the map: LLM reasoning → Spatial and embodied reasoning
Abstract (arXiv)
We propose KnotGym, an interactive environment for complex, spatial reasoning and manipulation. KnotGym includes goal-oriented rope manipulation tasks with varying levels of complexity, all requiring acting from pure image observations. Tasks are defined along a clear and quantifiable axis of complexity based on the number of knot crossings, creating a natural generalization test. KnotGym has a simple observation space, allowing for scalable development, yet it highlights core challenges in integrating acute perception, spatial reasoning, and grounded manipulation. We evaluate methods of different classes, including model-based RL, model-predictive control, and chain-of-thought reasoning, and illustrate the challenges KnotGym presents. KnotGym is available at https://github.com/lil-lab/knotgym.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. Builds on 23 of its 38 references that are in the map. First posted on arXiv 23 May 2025.
Builds on
23 of 38 references.
Closest ancestors · 1
Same direction, newest first
- 2022Visual Spatial Reasoning30 Apr
Other references · 22
- 2023GPT-4 Technical Reportcited by 18,396
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,457
- 2020D4RL: Datasets for Deep Data-Driven Reinforcement Learningcited by 1,427
- 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 783
- 2023TD-MPC2: Scalable, Robust World Models for Continuous Controlcited by 512
- 2023Faith and Fate: Limits of Transformers on Compositionalitycited by 503
- 2018A Corpus for Reasoning About Natural Language Grounded in Photographscited by 459
- 2024VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generationcited by 295
- 2019Compositionality decomposed: how do neural networks generalise?cited by 265
- 2025EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agentscited by 221
- 2022Exploring Length Generalization in Large Language Modelscited by 206
10 more
- 2024Stream of Search (SoS): Learning to Search in Languagecited by 145
- 2020From Local Structures to Size Generalization in Graph Neural Networkscited by 124
- 2020SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulationcited by 121
- 2022RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networkscited by 72
- 2020Learning to Unknotcited by 40
- 2025Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challengescited by 31
- 2021Symbolic Brittleness in Sequence Models: on Systematic Generalization in Symbolic Mathematicscited by 27
- 2021Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policiescited by 11
- 2020Learning Topological Motion Primitives for Knot Planningcited by 6
- 2022lilGym: Natural Language Visual Reasoning with Reinforcement Learningcited by 3
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Citing papers per year: 2026: 2.
2 papers.
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- 2026The Gordian Knot for VLMs: Diagrammatic Knot Reasoning as a Hard Benchmark
- 2026IMBench: A Benchmark for Intuitive Robotic Manipulation
- 2025SEM: Enhancing Spatial Understanding for Robust Robot Manipulation
- 2026Test-Time Spatial Reasoning for Robot Manipulation Using Generative Real-to-Sim
- 2026Spatially Grounded Long-Horizon Task Planning in the Wild
- 2025From Seeing to Doing: Bridging Reasoning and Decision for Robotic Manipulation
- 2026CLASP: Closed-loop Asynchronous Spatial Perception for Open-vocabulary Desktop Object Grasping
- 2026ThinkingVLA: Interleaved Vision and Language Reasoning for Robotic Manipulation