Large language models (LLMs) are no longer just language generators—they are increasingly used to simulate human behavior, perspectives, and demographic variation across social domains, from public opinion surveys to experimental research. Amid this shift, the use of LLMs to simulate historical narratives has emerged as a timely frontier. It is crucial to scrutinize the asymmetries these models embed when framing, interpreting, and retelling the past. Building on prior work that defines caricature as the combination of individuation and exaggeration, we analyze LLM-generated responses across 197 historically significant events—each featuring a directly and an indirectly affected persona. We find that LLMs reliably distinguish persona-based responses from neutral baselines, and that directly affected personas consistently exhibit higher exaggeration—amplifying identity-specific portrayals. Beyond lexical patterns, personas often frame the same event in conflicting ways—especially in military, political, and morally charged contexts. Grammatical analysis further reveals that direct personas adopt more passive constructions in institutional contexts, but shift to active framing when emotional immediacy is foregrounded. Our findings show how subtle asymmetries in tone, stance, and emphasis—not overt toxicity—can quietly, yet systematically, distort how history is told and remembered.