Large language models (LLMs) offer new opportunities for simulating human decision making at scale, yet prompting-based approaches often fail to recover realistic population-level response distributions, particularly for heterogeneous demographic groups and high-entropy questions. This paper introduces context modeling with agentic AI, a structured simulation framework that explicitly constructs latent, individual-level decision contexts prior to response generation. By separating persona construction from decision making and introducing structured stochasticity through context sampling, the method captures within-group heterogeneity and mitigates mode collapse inherent in single-prompt simulations. Extensive empirical evaluation across population-level and group-conditioned settings shows that context modeling consistently improves simulation fidelity, with especially strong gains on high-entropy questions and demographic subpopulations, while preserving performance on low-entropy tasks. Detailed tail analysis further reveals that the primary improvement arises from recovering minority and low-probability response options that are systematically underrepresented by direct prompting. Overall, the results demonstrate that faithful behavioral simulation requires explicit modeling of contextual heterogeneity and structured uncertainty, establishing context modeling as a robust foundation for LLM-based behavioral simulation.

Computational narrative analysis aims to capture rhythm, tension, and emotional dynamics in literary texts. Existing large language models can generate long stories but overly focus on causal coherence, neglecting the complex story arcs and orchestration inherent in human narratives. This suggests a structural misalignment between model- and human-generated narratives.We therefore position narrative analysis as a diagnostic proxy for generation and propose VISTA Space, a high-dimensional framework for narrative orchestration that unifies human and model perspectives while jointly characterizing narrative function and structure in a common space.We further introduce LitVISTA, a structurally annotated benchmark grounded in literary texts, which operationalizes VISTA Space for systematic evaluation of models’ narrative orchestration capabilities. Under an oracle setting with gold event anchors, we evaluate frontier LLMs including GPT, Claude, Grok, and Gemini. Results reveal systematic deficiencies, as current models struggle to jointly capture narrative function and structure and fail to form an integrated global view of literary narrative orchestration. End-to-end analysis further shows that failures are dominated by anchor identification and localization errors. Even advanced thinking modes yield mixed and often limited gains for literary narrative understanding.