Introduction: why spatial computing is transforming AI assistants
In the technology landscape of 2026,spatial computingis no longer a futuristic concept, but a daily reality that is redefining how we interact with AI assistants. Thanks to advances inmultimodal encoderssuch as H Company’s new NeoMME and the proliferation of open-source models like IFM’s K2 Horizon series, assistants can now understand and generate content that combines text, voice, and spatial signals in real time.
What are the benefits of a spatial AI assistant
Traditional assistants operate primarily on text or voice data. A spatial computing-enabled assistant can:
- Interpret hand gestures, eye tracking, and object positions.
- Provide location-based or 3D-map context for answers.
- Generate intuitive gesture- and spacing-based user interfaces.
These advantages translate into faster workflows, more immersive experiences, and more equitable access for users with diverse abilities.
NeoMME: the new standard for multimodal encoders
For developers, this means being able to integrate powerful spatial capabilities without extensive dedicated hardware. NeoMME’s open-source API is already available on Hugging Face and includes a new spatial tokenization module that interprets depth data and GPS coordinates.
Meta FAIR introduces Research Preference Models (RPMs)
As prompt engineers seek to optimize spatial assistants, Meta FAIR, in collaboration with the University of Oxford and University College London, has introduced Research Preference Models (RPMs). These models predict the success of a training experiment before GPUs even start computing, enabling developers to refine prompt sets more efficiently.
Using RPMs, you can quickly assess whether a new prompt schema will improve spatial understanding without running hundreds of hours of training. The RPM library is now integrated into Meta AI’s development toolkit and is compatible with NeoMME models.
Practical example: building a spatial AI assistant with NeoMME and RPMs
Below is a complete workflow showing how to combine NeoMME, RPMs, and a user-friendly interface.
Step 1: Install required components
pip install transformers==4.36.0
pip install hcompany-neomem==0.9.0
pip install meta-rpm==1.2.0Step 2: Initialize the model
from transformers import AutoModelForSequenceClassification
from hcompany_neomem import NeoMMERecognizer
from meta_rpm import PreferenceRanker
# Load NeoMME for spatial encoder
neomme = NeoMMERecognizer.from_pretrained("H-Company/NeoMME-260M")
# Configure RPM preference model for prompt optimization
rpm = PreferenceRanker.from_pretrained("MetaAI/RPM-base")Step 3: Write a prompt that includes spatial signals
An effective prompt should explicitly specify the desired spatial data type (e.g., GPS coordinates, depth, direction vectors). Here’s an example:
PROMPT = """
Analyze the following spatial sequence and provide a textual summary:
- Position: [lat, lon]
- Depth data: [d1, d2, d3]
- Gaze direction: [angle_x, angle_y]
Respond in Italian and keep the response under 50 tokens.
"""Step 4: Perform inference
tokens = neomme.encode_spatial([lat, lon, depth, gaze])
summary = neomme.generate(tokens, max_length=50)
print(summary)Step 5: Optimize with RPMs
Use RPMs to evaluate whether the prompt generates more accurate answers:
score = rpm.rank(PROMPT, summary)
if scorePractical tips for prompt engineers
- Be specific about space:Include coordinate systems (WGS84, ENU) and units of measure (meters, feet).
- Use structured markup:JSON tags within the prompt help the model identify fields.
- Test with RPMs:Run a quick preference check before a full training cycle.
- Iterate incrementally:Adjust the prompt based on RPM scores and latency metrics.
Conclusions: what are the next steps
The spatial AI assistant market is evolving rapidly. With models like NeoMME offering efficient multimodal processing and K2 Horizon providing scalable open-source options, 2026 is the ideal year to experiment.
If you’re a developer, start with the NeoMME development kit and integrate RPMs for continuous prompt optimization. If you’re a product manager, consider embedding spatial capabilities into your product interfaces for a competitive edge.
Concrete actions to take today
- Download the NeoMME SDK and complete the basic integration tutorial.
- Set up an RPM project to test your first spatial prompts.
- Build a prototype of a location-aware AI assistant that answers questions like “What’s the nearest restaurant to me that’s open now?”
- Join H Company’s Discord community to stay updated on the latest model releases.
Conclusion
Spatial computing is taking AI assistants to the next level: from simple text agents to contextual, situational partners. By leveraging new multimodal encoders like NeoMME and using preference models to fine-tune prompts, you can create AI experiences that truly understand the world around us. The future of AI assistants is spatial, and the present starts with a simple prompt.