Social interactions are shaped by the interplay of dispositional traits and situational context, yet systematically investigating how personality configurations between individuals jointly influence social behavior across diverse social contexts remains methodologically challenging. We address this gap by introducing a simulation pipeline adapted from the CHARISMA framework, which employs well-known movie characters and public figures as psychologically grounded agents for multi-LLM social simulation using a method we term personality anchoring. We present a large-scale empirical study examining how dyadic Agreeableness composition influences social interaction outcomes across 1,010 simulated conversations. Our results reveal a monotonic relationship between dyadic Agreeableness composition and shared goal achievement, with Homogeneous-Agreeable pairs achieving success 10 times the rate of Homogeneous-Disagreeable pairs (62% vs. 6%). Behavioral mediation analysis reveals that Agreeableness shapes goal achievement partially through cooperative strategy selection, though it continues to predict outcomes within the same dominant strategy, indicating pathways beyond observable conversational behavior. Robustness analyses confirm high consistency of results across repeated simulations (ICC = 0.89) and stable personality expression across diverse scenarios, validating personality anchoring as a viable operationalization strategy.

Narratives are widely recognized as a powerful tool for structuring information and facilitating comprehension of complex ideas in various domains such as science communication. This paper explores whether generating narratives can serve “as a specialized mode of thinking” that improves the reasoning abilities of Large Language Models (LLMs). We introduce Story of Thought (SoT), a novel prompt-driven reasoning framework that guides LLMs to construct narratives around the problem statement to solve the task more effectively. SoT enables LLMs to integrate narrative techniques such as metaphor and analogy into their reasoning process. Our experiments show that SoT significantly improves the LLMs’ problem-solving abilities on various tasks, including physics, chemistry, and biology in both JEEBench and GPQA (e.g., SoT resulted in 13% improvement compared to CoT when using GPT-4). To validate LLM-based evaluation for generated narratives, we conduct a human annotation of the narrative techniques used by LLMs. Our results show strong inter-annotator agreement between Llama 3 70B and human annotators. This work brings LLM reasoning closer to human cognitive processes by mirroring mechanisms such as analogical problem-solving, which are central to how humans understand and process complex ideas.