OpenKedge: Governing Agentic Mutation with Execution-Bound Safety and Evidence C

arXiv:2604.05080v1 Announce Type: cross Abstract: We present Nidus, a governance runtime that mechanizes the V-model for AI-assisted software delivery. In the self-hosting

arXiv:2604.05229v1 Announce Type: new Abstract: Agentic AI systems plan, use tools, maintain state, and produce multi-step trajectories with external effects. Those properties

arXiv:2606.12797v1 Announce Type: new Abstract: Agentic large language model systems that autonomously invoke tools, maintain persistent memory, and execute multi-step plans are

arXiv:2605.12963v1 Announce Type: new Abstract: As AI systems become increasingly capable, safety strategies must be evaluated not only by how much they reduce present risk, but

arXiv:2604.18652v1 Announce Type: cross Abstract: The transition of agentic AI from brittle prototypes to production systems is stalled by a pervasive crisis of craft. We suggest

arXiv:2605.00055v1 Announce Type: cross Abstract: We report a safety incident in a deployed multi-agent research system in which a primary AI agent installed 107 unauthorized

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment

arXiv:2605.15871v1 Announce Type: new Abstract: Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers.