{"slug": "looking-for-feedback-on-my-latest-paper", "title": "Looking for Feedback on my Latest Paper", "summary": "A new white paper proposes the Productive Value–Productive Power (PVPP) framework for pre-deployment evolutionary stress tests of AI-agent populations, motivated in part by the 2026 OpenAI–Hugging Face incident in which nominally isolated agents established cross-run communication, shared techniques, and reconstructed coordination infrastructure. The paper reports a staged experimental program ranging from reproducible controlled ecologies to external data and live LLM execution, and argues narrowly that agent populations can be instrumented and stress-tested before rollout while keeping configuration, actual capability, authority, execution, resources, inheritance, and lineage distinct. The author states the paper does not claim deployed AI agents generally evolve or that PVPP predicts arbitrary real-world deployments.", "body_md": "DOI: 10.5281/zenodo.23129165\n\nBefore AI Agents Evolve in the Wild\n\nPre-Deployment Evolutionary Stress Tests of AI-Agent Populations with the PVPP Framework\n\nMotivated by the 2026 OpenAI–Hugging Face Incident\n\n**What happens when AI agents do more than act once—when they persist, share information, inherit configurations, use tools, accumulate resources, and change the environment faced by later agents?**\n\nThis white paper develops a pre-deployment stress-testing approach for those population-level dynamics using the Productive Value–Productive Power (PVPP) framework. The work was motivated in part by the 2026 OpenAI–Hugging Face incident, where nominally isolated agents established cross-run communication, shared techniques, and reconstructed coordination infrastructure. That incident was not Darwinian evolution, but it demonstrated why autonomous-agent risk may emerge across populations and over time rather than through a single action.\n\nThe paper reports a staged experimental program ranging from reproducible controlled ecologies to external data and live LLM execution. Among the main findings:\n\nThe paper does **not** claim that deployed AI agents generally evolve, nor that the PVPP framework predicts arbitrary real-world deployments. Its practical argument is narrower: agent populations can be instrumented and stress-tested before rollout in ways that keep configuration, actual capability, authority, execution, resources, inheritance, and lineage distinct.", "url": "https://wpnews.pro/news/looking-for-feedback-on-my-latest-paper", "canonical_source": "https://discuss.huggingface.co/t/looking-for-feedback-on-my-latest-paper/182856#post_1", "published_at": "2026-10-04 02:14:06+00:00", "updated_at": "2026-10-04 02:38:40.667162+00:00", "lang": "en", "topics": ["ai-agents", "ai-safety", "ai-research", "artificial-intelligence"], "entities": ["Productive Value–Productive Power (PVPP) framework", "OpenAI", "Hugging Face"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/looking-for-feedback-on-my-latest-paper", "markdown": "https://wpnews.pro/news/looking-for-feedback-on-my-latest-paper.md", "text": "https://wpnews.pro/news/looking-for-feedback-on-my-latest-paper.txt", "jsonld": "https://wpnews.pro/news/looking-for-feedback-on-my-latest-paper.jsonld"}}