The Latency Gap in Autonomous Agents
As we saw in today's GitHub Trending, the community is shifting focus from "what can LLMs think" to "how fast can they act." Whether it's office automation or smart home control, latency is the invisible killer of user experience.
For smart home agents, a 2-second delay between voice command and action is unacceptable. But running full-scale LLMs for simple command parsing is overkill and slow.
Enter Domux
Domux (iflytek/domux) is an experimental, open-source model designed specifically for this "last mile" of smart home interaction. It’s not a general-purpose chatbot; it’s a specialized semantic parser optimized for extreme low latency.
Key Features
- < 150ms End-to-End Latency: Optimized for edge devices and local servers.
- Structured Slot Output: Converts free-form commands into a fixed 7-field pipe-delimited schema (
action|device|attribute|value|unit|room|floor). - High Accuracy: Achieves 98.37% result accuracy with 100% format compliance.
- Lightweight Base: Built on Gemma-4-E2B-it, making it suitable for on-device deployment without heavy cloud dependencies.
- Fuzzy Intent Handling: Understands commands like "make it brighter" without explicit values, mapping them to
adjustUpactions.
How It Works
Domux uses a combination of Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO) with custom reward functions to ensure it not only understands the intent but also outputs strictly formatted data that downstream systems (like RPA bots or IoT hubs) can execute immediately.
Example: From Voice to Action
| Input Command | Structured Output |
|---|---|
| "Turn on the living room light" | `turnOn |
| "Set AC to 24 degrees" | {% raw %}`set |
| "Make it brighter" | {% raw %}`adjustUp |
Why This Matters for Agents
In an Agent-driven workflow, the Perception -> Decision -> Action loop needs to be seamless. Domux sits in the Perception layer, ensuring that the agent’s understanding of user intent is instant. This allows the downstream Action layer (like {% raw %}astron-rpa or direct IoT APIs) to execute tasks with minimal friction.
It’s a great example of how specialized, lightweight models can outperform generalist LLMs in specific, latency-sensitive scenarios.
Get Started
Domux is in early stages (v0.1.0), and the team is open to community exploration. You can find the training code, reward plugins, and example datasets in the repo.
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