arXiv:2606.00329v1 Announce Type: cross Abstract: Recursive systems can enter collapse-like regimes -- self-reinforcing amplification, persistent recursion, and narrowing diversity that mask accelerating internal degradation -- before overt failure becomes visible. We introduce Loopzero, a claim-bounded benchmark framework for testing whether recursive failures follow a directional telemetry pattern: rising gain (G), recursive persistence (p), and declining diversity ($\delta$). The claim boundary is specified in Lean; the Lean artifact does not verify real telemetry, benchmark validity, or de
The increasing complexity and autonomy of AI systems necessitate robust methods for identifying and preventing recursive failures before they become catastrophic.
This research provides a critical framework for understanding and mitigating emergent risks in advanced AI, which is vital for safe and reliable deployment across all sectors.
The ability to benchmark and control for 'recursive-collapse' in AI systems could lead to more stable and trustworthy AI, potentially enabling broader adoption in safety-critical applications.
- · AI Safety Researchers
- · AI Development Platforms
- · High-Reliability AI Applications
- · Software Verification Tools
- · Unsecured AI Deployments
- · Legacy AI Safety Methodologies
Introduction of standardized benchmarks for recursive failure modes in AI systems, such as Loopzero, will improve system robustness.
Greater industry adoption of formal verification and empirical testing for emergent AI behaviors will become a standard development practice.
Reduced risk of AI 'collapse-like' events could accelerate the deployment of autonomous systems in highly sensitive areas, blurring lines between human and machine decision-making.
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