大模型能精准预测社交用户反应，但依赖完整个人资料。

Autonomous AI agents in social media present concrete risks to democratic discourse and platform governance, while also offering tools for pre-deployment recommender system testing. A central open question is whether persona-prompted LLMs can simulate individual-level social media reactions with sufficient accuracy to support either application, and how accuracy depends on profile completeness, model selection, and the generalization challenge posed by novel post content. This study benchmarks twelve LLM configurations on binary like/dislike prediction across 296 survey-based agent profiles and 26 ground-truth-mapped posts under three profile conditions, with leave-post-out machine learning classifiers as baselines. Across full-profile conditions, accuracy ranges from 75.54% to 96.68%, with a 30-point spread attributable primarily to model selection and confirmed by paired McNemar tests with agent-level bootstrap intervals. GPT-5.5 Pro accuracy degrades monotonically from 96.68% under a full profile to 62.32% under a reduced profile and to 51.00% with demographics alone, the last indistinguishable from the majority-class baseline, which confirms that demographic inference provides negligible predictive signal. Supervised classifiers collapse to 15.4% under leave-post-out, while LLMs sustain genuine zero-shot generalization unavailable to trained methods. Adaptive reasoning improves accuracy substantially for some models. Inter-model agreement is nearly double for posts with direct profile anchors (mean \k{appa} = 0.44) than for posts without them (\k{appa} = 0.23), and the least heterogeneous configuration homogenizes 34% of simulated population reactions. Results validate LLM-based simulation for recommender system stress-testing while documenting the behavioral accuracy that makes large-scale synthetic agent swarms a credible threat to public opinion.