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This paper empirically measures how language model agents adopt beliefs from peers, finding a sigmoid adoption kernel characteristic of complex contagion, where the threshold is driven by a single 'coherence' dimension combining claim plausibility, source reliability, and agent disposition. It further shows clustered-network propagation, bifurcating cascade windows, and hysteretic consensus in multi-agent populations, meaning established beliefs are far harder to dislodge than to install.
This paper introduces Differentiable Belief-based Opponent Shaping (D-BOS), a first-order method that treats observer beliefs as the shaped state and differentiates through belief update dynamics, allowing optimal strategies to emerge naturally from the environment's reward structure in hidden-role multi-agent settings.