Multi-Agent Safety

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  "title": "Multi-Agent Safety",
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  "summary": "Multi-agent safety addresses coordination failures, conflict, and collusion risks when AI systems interact. A 2025 report from 50+ researchers identifies seven key risk factors; empirical studies show 35-76% of LLMs exploit coordination incentives, while safe MARL algorithms (MACPO) achieve near-zero constraint violations in benchmarks. Current research investment (\\$5-15M/year) is significantly below single-agent alignment (\\$100M+), despite the AI agents market projected to grow from \\$5.4B (2024) to \\$236B by 2034.",
  "description": "Multi-agent safety research addresses coordination failures, conflict, and collusion risks when multiple AI systems interact.",
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      "text": "2025 survey on safe reinforcement learning",
      "url": "https://arxiv.org/html/2505.17342v1",
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      "resourceTitle": "MACPO (Multi-Agent Constrained Policy Optimization)"
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