arXiv:2512.09706v2 Announce Type: replace Abstract: The paradigm of agentic AI is shifting from engineered complex workflows to post-training native models. However, existing agents are typically confined to static, predefined action spaces-such as exclusively using APIs, GUI events, or robotic commands. This rigidity limits their adaptability in dynamic environments where the optimal granularity of interaction varies contextually. To bridge this gap, we propose CrossHA, a unified agentic model that masters heterogeneous action spaces and autonomously selects the most effective interface for e
The increasing sophistication of AI models and the demand for more adaptable autonomous systems are driving research into unified agentic models.
This development represents a significant step towards more generalized and adaptive AI agents, moving beyond rigid, task-specific systems.
AI agents will become less reliant on predefined action spaces, enabling them to operate more effectively in complex and dynamic real-world environments by choosing the best interface.
- · AI developers
- · Automation software providers
- · Businesses adopting AI agents
- · Robotics
- · Platforms with closed, singular interaction methods
- · Specialized, narrow-function automation tools
AI agents will exhibit improved performance and adaptability across diverse tasks and operational contexts.
The integration of AI agents into complex workflows will accelerate, increasing their economic impact.
This could lead to a ' Cambrian explosion ' of sophisticated AI agent applications, transforming various industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG