arXiv:2606.11770v1 Announce Type: new Abstract: Spatial reasoning remains a challenge for Multimodal Large Language Models (MLLMs), as it requires reliable multi-hop inference over both intermediate states and state transitions. Current studies often leave intermediate states unverified and treat state transitions as implicit processes, which limits reliability in multi-hop spatial reasoning. To address this, we propose State-aware Visualization-of-Thought (SVoT), a reinforcement learning framework that generates interleaved, verifiable intermediate states and visualizations. SVoT integrates t
The continuous advancements in AI research, particularly in addressing complex reasoning for MLLMs, drive the emergence of solutions like SVoT as existing methods show limitations.
Improving spatial reasoning in MLLMs is crucial for developing more capable AI agents that can interact effectively with the physical world and perform multi-step tasks reliably.
The explicit generation of verifiable intermediate states and visualizations through reinforcement learning marks a step towards more transparent, reliable, and interpretable AI reasoning.
- · AI/ML researchers
- · Robotics developers
- · Generative AI platforms
- · Developers of unreliable black-box AI systems
SVoT improves the reliability and interpretability of spatial reasoning in multimodal large language models.
Enhanced spatial reasoning could accelerate the development of autonomous systems and embodied AI agents capable of complex physical interactions.
More reliable autonomous agents may lead to greater adoption across industries, reshaping workflows and human-computer interaction paradigms.
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Read at arXiv cs.AI