Introduction: Merging Spatial Computing with AI Assistants
Over the past few months, the line between the digital and physical worlds has grown increasingly blurred. Platforms like ByteDance’s SeedRealtime have demonstrated that a single model can simultaneouslysee, hear, and speakin real time, paving the way for truly spatial AI assistants. But what does this mean for developers creating interactive experiences or products based on conversational AI?
In this article, I’ll show you how to combinespatial computingwith the latestfull-duplex LLMsto build AI assistants that understand a user’s visual, audio, and spatial context. You’ll learn effective prompting techniques, explore a practical code example, and discover the 2026 trends reshaping the industry.
What makes an AI assistant "spatial" today
1. Sensors and multimodal inputs
- Depth cameras and LiDAR provide real-time 3D coordinates.
- Array microphones capture directional speech and ambient sound.
- Wearable devices (gloves, glasses) add gesture and hand-tracking capabilities.
These data streams create aspatial representation
2. Full-duplex LLMs: see, hear, and respond
The launch of SeedRealtime (August 2026) marked a turning point. This model fuses video, audio, and text into a single encoder-decoder, eliminating the need for separate pipelines. As a result, an assistant can:
- Detect an object (e.g., a cup on a desk) from a single frame.
- Listen to the user’s question about that object.
- Generate a response that integrates both visual reasoning and auditory context.
All processes occur in a single step, reducing latency and complexity.
Designing prompts for spatial AI assistants
Prompts for these systems must encode three dimensions: visual, auditory, and spatial. Here’s a practical template:
Prompt template
{
"prompt": "",
"system": "You are a spatial AI assistant that receives real-time video, audio, and coordinate streams.",
"context": {
"video_description": "[output from visual model, e.g., 'a wooden desk with a red cup']",
"audio_transcript": "[transcription of captured audio, e.g., 'how do I fill the cup?']",
"spatial_coords": "[3D position of the cup, e.g., (0.5, 0.2, 0.9)]"
},
"user_query": "[user's question]",
"instructions": "Provide a concise response that integrates visual, audio, and spatial data."
}
"
}This pattern ensures the model receives coordinated information rather than disjointed data.
Practical example: A remote collaboration assistant
Imagine building an assistant that helps two users in different locations manipulate virtual objects together. With a full-duplex LLM, we can:
- Capture video streams from both users.
- Extract objects and gestures in real time.
- Convert audio to text and locate it spatially.
- Generate contextual captions and collaboration suggestions.
Code mockup (Python)
import torch
from seed_realtime import SeedRealtimeModel
# Initialize the model (single encoder-decoder)
model = SeedRealtimeModel()
# Simulate a single step of multimodal input
video_frame = capture_depth_frame() # np.ndarray (H, W, 3)
audio_chunk = capture_audio() # np.ndarray (mono, 16k)
spatial_tag = {'id': 'cup_red', 'bbox': (0.5, 0.2, 0.9, 0.1), 'center': (0.5, 0.2, 0.9)}
# Prepare the prompt
prompt = f"""
Video: describe the main object in this image.
Audio: transcribe recent speech.
Spatial position: {spatial_tag}
User asks: how can we fill this cup together?
"""
# Generate response
response = model.generate(prompt, video=video_frame, audio=audio_chunk)
print(response)
# Expected output: "You can add a cube of ice to the red cup..."This snippet demonstrates the minimal integration required: a single model call with video, audio, and spatial context. No additional preprocessing or prompt concatenation is needed.
Current trends shaping spatial computing
- Instagram AI(2026) is already using full-duplex models to generate contextual captions based on real-time audio and video, powering its "Explore" suggestions.
- Sentiment analysison IMDb, now powered by DistilBERT LoRA and calibrated TF-IDF, shows how combining classical and modern pipelines can improve interpretability for spatial applications (e.g., analyzing a user’s sentiment while interacting with a virtual product).
- Real-time occlusion reasoningis becoming a standard feature in AR toolkits, enabling assistants to correctly hide avatars when physical objects block them.
These developments indicate that multimodal, full-duplex models will soon become the standard for any assistant aiming to truly understand its surroundings.
Best practices for developers and prompt engineers
1. Maintain spatial consistency
Normalize all coordinates to a single reference system (e.g., meters from the camera). Use depth tags to indicate distance and planarity.
2. Simplify prompts
Avoid overly long phrases. An effective prompt for a spatial assistant is typically under 200 tokens and explicitly includes the three context modules.
3. Test latency
With a full-duplex LLM, even a few extra milliseconds add up. Run A/B tests with 30 fps video and 48 kHz audio inputs to ensure end-to-end latency stays under 200 ms.
4. Security and privacy
Since video and audio data are continuous, implement automatic data deletion after each session and consider on-device inference for raw streams.
Conclusion: From theory to spatial experience
2026 provides us with unprecedented tools to merge spatial computing with conversational intelligence. With models like SeedRealtime, the boundaries between "seeing," "hearing," and "speaking" blur, enabling developers to create assistants that not only respond but truly understand the physical environment and human intentions.
Key takeaways
- Use a full-duplex LLM (e.g., SeedRealtime) to handle video, audio, and spatial context in a single model.
- Design prompts that explicitly include visual descriptions, audio transcriptions, and 3D coordinates.
- Simplify integration with a single model call and keep latency under 200 ms.
- Stay ahead of user expectations in 2026 by following current trends (Instagram AI, sentiment analysis with DistilBERT LoRA).