Introduction: Why AI assistants are essential for spatial computing today
1. Understanding spatial computing and its use cases
Spatial computing combines sensors, 3D visualization, and artificial intelligence to enable users to interact with information embedded in physical space. The most common applications include:
- Visualizing data on 3D models for industrial design.
- Providing contextual guidance in augmented reality (AR) environments for tourism or maintenance.
- Enabling remote collaboration in shared virtual spaces.
- Creating location-based gesture interfaces for wearable devices.
2. The evolution of AI assistants in 2026
2.1 On-device models: Liquid AI’s LFM2.5-2.6B
Liquid AI has launched LFM2.5-2.6B, a 2.69 billion parameter agentic model that runs entirely on-device, supports up to 128K token context, and can call external tools. This makes it ideal for spatial assistants that need to maintain privacy and operate offline.
# Example: Load LFM2.5-2.6B and generate a 3D description
from liquid_ai import LFMModel
model = LFMModel("LFM2.5-2.6B")
prompt = "Describe the geometry of this STL mesh in terms of usable cutting planes for an AR interface layout."
response = model.generate(prompt, context=mesh_data)
print(response)2.2 Research harnesses: Prime Agent by Prime Intellect
Prime Intellect has open-sourced Prime Agent, a research harness based on Recursive Language Models (RLM) where sub-agents are function calls within a persistent IPython kernel. This enables rapid prototyping of complex reasoning chains for spatial tasks, such as object detection or navigation in 3D environments.
# Example: Decompose a spatial task into sub-tasks with Prime Agent
from prime_agent import PrimeAgent
agent = PrimeAgent()
agent.add_task("Detect surface planes from point cloud")
agent.add_task("Calculate optimal guard view for an AR operator")
agent.add_task("Generate lightweight AR interface markup")
plan = agent.compile()
print(plan)2.3 Adaptive optimization with Meta’s Ax
Meta has released Ax with a new client API that simplifies running adaptive experiments for parameterizing spatial interface layouts. Using Ax allows you to efficiently find optimal control placement, information density, or command priority based on real-time interaction data.
import ax
from ax import optimize
def layout_objective(parameters):
# Simulates spatial task completion time
return simulate_task_time(parameters['control_distance'],
parameters['occlusion_level'])
search_space = [
ax.Parameter('control_distance', [0.5, 1.0, 1.5]),
ax.Parameter('occlusion_level', [0.1, 0.5, 0.9]),
]
best = optimize(layout_objective, search_space, num_trials=20)
print('Optimal control placement:', best)3. Prompt engineering for spatial AI assistants
Writing effective prompts is crucial when working with large contextual models. For spatial tasks, the following principles yield consistent results:
- Contextual specificity:Include explicit coordinates, object identifiers, or directional references.
- Multi-stage structure:
- Tool recall:Explicitly specify which function calls are needed (e.g., "Use the 3D mapping function to find waypoints").
- Feedback iteration:Add correction prompts based on previous errors to improve agent performance.
A practical prompt for generating an adaptive AR interface might be:
"Given a 3D model of a meeting room and a list of real-time presence data points, propose an optimal AR interface layout that minimizes visual distance and occlusion for each user. Use plane detection to identify surfaces, then calculate control distance and occlusion. Provide the result as a JSON with keys: 'position', 'orientation', 'priority'."4. Practical workflow: From 3D scene to interactive assistant
Follow this step-by-step workflow to create a comprehensive spatial AI assistant using the tools mentioned above:
- Data preparation:Convert your meshes or point clouds into supported formats (e.g., GLTF, PCD). Save metadata in a spatial database.
- Select the model:For on-device operations with privacy constraints, use LFM2.5-2.6B. For complex prototyping, integrate Prime Agent to decompose tasks.
- Define the prompt:Write a prompt that includes spatial context, multi-stage reasoning, and explicit tool calls.
- Run adaptive optimization:Use Meta’s Ax to refine interface parameters (control distance, occlusion level) based on interaction data.
- Iterate and validate:Collect usability data, feed it back into the optimization loop, and refine the prompt and agent parameters.
By implementing this workflow, you can create AI assistants that not only respond but also actively guide users through dynamic 3D environments.
5. Actionable takeaways
- Design for the device:Start with LFM2.5-2.6B if privacy or latency is critical. Its tool calling support allows you to integrate heavier computation modules without leaving the device.
- Prototype with Prime Agent:Use Prime Agent to explore complex reasoning chains. Its persistent kernel makes it easy to iterate on spatial sub-tasks.
- Optimize with Ax:Include adaptive optimization early in the interface design process to discover non-obvious layouts that improve usability.
- Document prompts:Maintain a library of spatial-context prompts with examples and expected results. This accelerates debugging and scalability.
- Measure the right metrics:Beyond accuracy, track spatial latency, task completion time, and user satisfaction to truly evaluate assistant performance.