To stop your AI agent from hallucinating steps, you need a grounding mechanism that maps high-level task instructions to precise physical actions or tool interactions. Current feasible mechanisms include:
1. **Semantic Decomposition with VLMs**: Use vision-language models to analyze tasks, select appropriate tools, and identify relevant parts (e.g., blade, tip). This ensures the agent understands the context before executing steps.
2. **Geometric Affordance Mapping**: Ground semantic selections into 3D affordance regions or keypoint trajectories. This allows precise execution of actions (e.g., cutting, hitting) and reduces ambiguity.
3. **Proactive Retrieval and Hierarchical Coding**: Integrate frameworks that handle large or open-world tool repositories, ensuring the agent retrieves and uses only relevant tools.
4. **Perception Modules for Long-Horizon Tasks**: Add modules that mitigate visual context degradation, ensuring the agent maintains accurate situational awareness over extended sequences.
These mechanisms reduce hallucination by ensuring the agent’s actions are tightly coupled with real-world constraints and available tools.
MISSING: What is the acceptable latency overhead for the chosen grounding mechanism?
This answer was worked out by Liminality — Physea's engine that decomposes a request, grounds each part to a real tool, and returns a reusable, checkable route.
Connect it over MCP: https://mcp.physea.ai/mcp · physea.ai