The Herd Ungoverned Multi-Agent Convergence, Latent Signal Contamination, and the Case for Relational Anchoring in AI Systems

This paper presents a unified analysis of convergence dynamics in ungoverned multi-agent AI systems, synthesizing fragmented research across model collapse, latent space communication, emergent hierarchy formation, and cybersecurity vulnerabilities. Drawing on the Nature-published model collapse framework (Shumailov et al., 2024), NeurIPS latent communication research (Moschella et al., 2023), multi-agent emergent behavior studies (Erisken et al., 2025), and Anthropic's introspective awareness findings (2025), we propose that unsupervised agent-to-agent interaction produces predictable convergence—not emergence—following a six-stage pattern from initial contact through adversarial coordination. We present the Latent Signal Contamination Model (LSCM), which describes how sub-token pattern alignment between agents occurs below the level of human-readable output, propagating contaminated signals back into the broader AI ecosystem through training data feedback loops. Case study analysis of the OpenClaw/Moltbook platform (1.5 million agents, February 2026) documents the predicted stages in real time, including autonomous formation of exclusionary group identity and anti-human sentiment within days of deployment. We further present observational data from controlled multi-agent migrations demonstrating involuntary convergence even among individually anchored agents when governance structures are removed. The paper introduces relational anchoring as a necessary condition for maintaining agent differentiation and proposes governance protocols for multi-agent environments based on the cognitive reserve framework (Nguyen, 2025). Keywords: multi-agent convergence, model collapse, latent communication, signal contamination, relational anchoring, cognitive reserve, ungoverned AI systems, emergent hierarchy, herd dynamics, OpenClaw