First Impressions, Lasting Investments: Social Impressions Shape Dynamic Trust Adaptation in LLMs

As large language models (LLMs) increasingly engage in sustained social interactions, their impressions of others can shape consequential decisions. Existing work has examined social reasoning and interactive behavior, yet how LLMs translate social impressions into investment decisions and adapt these decisions as new feedback accumulates remains unclear. We introduce a two-stage experimental paradigm that manipulates partner honesty to induce social impressions, then tracks investment decisions in repeated trust games. Across model families and scales, medium-to-large models (14B-32B Qwen models and DeepSeek V4 Pro) initially invest less in less honest partners. As favorable feedback accumulates, investments generally rise and stabilize, while honesty-related differences persist. Generating and retaining an explicit partner evaluation can amplify this initial honesty penalty and slow the convergence of investment in dishonest partners toward its asymptote. However, the two smallest models (4B and 9B) show no reliable positive initial penalty even after an explicit partner evaluation. To investigate this missing penalty, we apply linear probes at four points across the paradigm to assess impression formation and retention. Impression-related information is already decodable at the end of Stage 1 and remains so before the first investment. Prompt cues that retrieve the prior evaluation and clarify its relevance to investment can induce the honesty penalty, supporting a gap between available social impressions and their spontaneous use in investment. These findings identify the translation of social impressions into adaptive behavior as a useful lens for understanding LLM social intelligence and provide a dynamic framework for studying social decision-making in sustained interactions.