arXiv:2511.02627v3 Announce Type: replace Abstract: We introduce DecompSR, decomposed spatial reasoning, a large benchmark dataset (over 5m datapoints) and generation framework designed to analyse compositional spatial reasoning ability. The generation of DecompSR allows users to independently vary several aspects of compositionality, namely: productivity (reasoning depth), substitutivity (entity and linguistic variability), overgeneralisation (input order, distractors) and systematicity (novel linguistic elements). DecompSR is built procedurally in a manner which makes it is correct by constr
The release of the DecompSR dataset and framework coincides with a growing focus in AI research on understanding and improving compositional reasoning, which is critical for more advanced AI systems.
This dataset offers a robust benchmark for evaluating AI models' ability to perform complex, multi-step spatial reasoning, directly addressing a current limitation in AI capabilities and fostering breakthroughs in general intelligence.
AI researchers now have a standardized, large-scale tool to systematically test and develop models for compositional reasoning, moving beyond simple pattern recognition towards more human-like cognitive functions.
- · AI researchers
- · Generative AI companies
- · Robotics industry
- · AI ethics and safety organizations
- · AI models lacking strong compositional reasoning
- · Companies relying on simplistic AI solutions
Improved performance of AI models in tasks requiring complex spatial and compositional understanding.
Accelerated development of more robust, general-purpose AI agents capable of navigating and interacting with complex physical environments.
Potential for AI systems to independently discover novel relationships and theories in fields like science and engineering due to enhanced reasoning abilities.
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