The Partners You Keep Decide What You See: When Reputation-Driven Partner Choice Reshapes Exposure in LLM Societies

Large language model (LLM) agents can evaluate peers and select future partners, thereby choosing whose behavior remains observable. This makes behavioral trajectories ambiguous because they can reflect both learning from others and changes in exposure. We formalize this exposure-selection confound in a reputation-based partner market with finite partner budgets and observer-specific trust. Cross-role exposure is the primary structural variable. Matched-random retention isolates selection, and forced exploration intervenes on exposure. In a one-shot model with role-balanced reputation supply and role-aligned compatibility, Top-$K$ retention yields no more cross-role exposure than matched-random retention. Lower exposure then preserves a larger stationary behavioral gap. A broadcast cross-role subsidy reverses this ordering once its strength exceeds the compatibility bonus. It reaches the integration oracle when cross-role candidates also have uniformly high reputation. Across 1,749 reference-process runs, activation follows partner scarcity and selection precision rather than population size. The result extends to four groups and a continuous trait. Across ten paired seeds per family, DeepSeek shows a modest but detectable exposure reduction, whereas MiniMax and GPT remain near matched-random retention. DeepSeek also follows its reported trust ranking more closely. Instruction-conditioned agents exhibit strong exposure lock-in in all three families. Perturbation-removal experiments separate persistent individual memory from network-mediated persistence, but do not establish a self-sustaining exposure–behavior loop. These results provide an identification framework and measurable activation conditions for determining when reputation-driven selection changes what agents see.