Perceive, Interact, Reason: Building Tool-Augmented Visual Agents for Spatial Reasoning
📰 ArXiv cs.AI
Learn to build visual agents with spatial reasoning capabilities using tool-augmented perception, interaction, and reasoning
Action Steps
- Implement a PERIA agent using a vision-language model as the foundation
- Augment the agent with tools for active evidence acquisition and multi-step visual interaction
- Train the agent on spatial reasoning tasks that require fine-grained visual evidence
- Evaluate the agent's performance on tasks that involve perception, interaction, and reasoning
- Integrate the PERIA agent with other AI systems to enhance their spatial reasoning capabilities
Who Needs to Know This
AI researchers and engineers working on vision-language models and spatial reasoning tasks can benefit from this article to improve their models' capabilities
Key Insight
💡 Tool-augmented visual agents can improve spatial reasoning capabilities by actively acquiring and interacting with visual evidence
Share This
🤖 Introducing PERIA: a tool-augmented visual agent for spatial reasoning #AI #ComputerVision
Key Takeaways
Learn to build visual agents with spatial reasoning capabilities using tool-augmented perception, interaction, and reasoning
Full Article
Title: Perceive, Interact, Reason: Building Tool-Augmented Visual Agents for Spatial Reasoning
Abstract:
arXiv:2606.12830v1 Announce Type: cross Abstract: While recent vision-language models (VLMs) demonstrate strong multimodal understanding, they remain limited in spatial reasoning tasks that require active evidence acquisition and multi-step visual interaction. This limitation suggests that relying solely on implicit visual representations from vision encoders is insufficient for recovering fine-grained spatial evidence. We introduce PERception-Interaction-reason Agent (PERIA), a tool-augmented v
Abstract:
arXiv:2606.12830v1 Announce Type: cross Abstract: While recent vision-language models (VLMs) demonstrate strong multimodal understanding, they remain limited in spatial reasoning tasks that require active evidence acquisition and multi-step visual interaction. This limitation suggests that relying solely on implicit visual representations from vision encoders is insufficient for recovering fine-grained spatial evidence. We introduce PERception-Interaction-reason Agent (PERIA), a tool-augmented v
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