arXiv:2605.03847v2 Announce Type: replace Abstract: Distributed collaborative intelligence (DCI), encompassing edge-to-edge architectures, federated learning, transfer learning, and swarm systems, creates environments in which emergent risk is structurally unavoidable: locally correct decisions by individual agents compose into globally unacceptable behavioral trajectories under uncertainty. Existing approaches such as constrained optimization, safe reinforcement learning, and runtime assurance evaluate acceptability at the level of individual actions rather than across behavioral trajectories
The proliferation of complex, multi-agent AI systems necessitates foundational research into their dependable operation, as current approaches are insufficient for emergent risks.
This work addresses a critical bottleneck for the safe and widespread deployment of advanced AI, especially in high-stakes environments, by focusing on global behavioral trajectories rather than just individual actions.
The focus for ensuring AI dependability will broaden from individual agent safety to complex systems-level behavioral reliability, requiring new mathematical and architectural frameworks.
- · AI safety researchers
- · Developers of distributed AI systems
- · Industries deploying autonomous systems
- · Companies relying solely on reactive AI safety measures
- · Developers neglecting system-level AI safety
- · Early, undependable DCI deployments
Increased investment in formal methods and verification for distributed AI systems.
New regulatory frameworks emerging for AI systems that mandate trajectory-level dependability assessments.
Public trust in AI systems improving as demonstrable guarantees of global behavioral safety become possible, leading to broader adoption across critical sectors.
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Read at arXiv cs.AI