NeuroSkill(tm): Proactive Real-Time Agentic System Capable of Modeling Human State of Mind
- Published
- Source
- arXiv
- Paper number
- 121
- Field
- Brain-Computer Interface / Agents
- arXiv ID
- 2603.03212
Key points
- Existing generative AI agents lack a fine-grained understanding of human cognitive and emotional states, which leads to generic and often inappropriate interactions.
- Although the amount of personal biosignal data from BCI devices is increasing, it is often controlled by large companies, which limits personal utility and data ownership.
- Current AI agents are mainly reactive and do not actively adapt in real time to changing human emotional and cognitive states.
- NeuroSkill combines BCI data collection, a local EXG foundation model for neural embeddings, and multimodal alignment with text context to build a continuously updated representation of the dynamic human state of mind.
- NeuroLoop, an LLM harness running on edge devices, uses the modeled state of mind to enable proactive, personalized agentic interaction, tool use, and protocol execution.
- A simple Markdown-based skill layer lets non-developers easily extend and modify agent behavior, which improves accessibility and broad personalization.
Paper links
External research summaries. These are not HDATF publications or measured product results.