arXiv:2605.20784v1 Announce Type: cross Abstract: Spatial reasoning requires both location-bound computation and location-invariant structure: agents must make local moves while preserving route, object, or constraint-level plans. We propose interaction locality, a task-geometry-aware framework for measuring whether information flow stays within nearby cells or semantic segments, or crosses them. We instantiate the framework with sparse-autoencoder feature ablations and finite-noise activation patching, with structural Jacobian and attention checks reported in the appendix, and apply it to HRM
The continuous drive for more efficient and robust AI, particularly in spatial reasoning, necessitates fundamental advances in understanding how information flows within complex models.
This research provides a framework for analyzing AI's internal mechanics, which is critical for developing more capable and reliable AI systems, especially for embodied intelligence and agentic applications.
Our ability to design and debug spatial reasoning in AI models can be significantly improved by a systematic understanding of interaction locality, leading to more robust and explainable AI.
- · AI researchers
- · Robotics developers
- · AI hardware manufacturers
- · AI safety researchers
- · Developers of opaque black-box AI models
- · Sectors reliant on inefficient spatial reasoning
Improved architectures for spatial reasoning in AI models, leading to greater efficiency and accuracy.
Accelerated development of advanced AI agents and humanoid robots capable of sophisticated real-world interaction.
New benchmarks and diagnostic tools becoming standard practice in AI development, raising the bar for model interpretability and reliability across the industry.
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Read at arXiv cs.LG