Neurocognitive Governance Model for Autonomous AI Agents
A recent publication on arXiv introduces a neurocognitive governance model for autonomous AI agents. This model leverages human self-governance mechanisms, such as executive function and inhibitory control, to align with the reasoning of LLM-driven agents. It seeks to fill the governance void present in existing methods like runtime guardrails and post-hoc audits, which view governance as an external limitation instead of an ingrained principle. The objective of the paper is to avert unsafe and irreversible actions by agents in sectors such as enterprise, healthcare, and safety-critical domains.
Key facts
- arXiv:2604.25684v1
- Published on arXiv
- Proposes neurocognitive governance framework
- Draws on human self-governance processes
- Maps executive function and inhibitory control to LLM reasoning
- Addresses governance gap in autonomous AI agents
- Current approaches include runtime guardrails, training-time alignment, post-hoc auditing
- Targets enterprise, healthcare, safety-critical environments
Entities
Institutions
- arXiv