Shared Nature, Unique Nurture: PRISM for Pluralistic Reasoning via In-context Structure Modeling

• URL: http://arxiv.org/abs/2602.21317v1
• Date: Tue, 24 Feb 2026 19:38:31 GMT
• Title: Shared Nature, Unique Nurture: PRISM for Pluralistic Reasoning via In-context Structure Modeling
• Authors: Guancheng Tu, Shiyang Zhang, Tianyu Zhang, Yi Zhang, Diji Yang,
• Abstract summary: PRISM is a model-agnostic system that augments Large Language Models with dynamic On-the-fly Epistemic Graphs.<n>On three creativity benchmarks, PRISM achieves state-of-the-art novelty and significantly expands distributional diversity.<n>Results demonstrate that PRISM successfully uncovers correct long-tail diagnoses that standard LLM miss.
• Score: 11.987225062711692
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are converging towards a singular Artificial Hivemind, where shared Nature (pre-training priors) result in a profound collapse of distributional diversity, limiting the distinct perspectives necessary for creative exploration and scientific discovery. To address this, we propose to equip models with inference-time Nurture (individualized epistemic trajectories) using Epistemic Evolution paradigm, progressing through explore, internalize, and express. We instantiate this via PRISM (Pluralistic Reasoning via In-context Structure Modeling), a model-agnostic system that augments LLM with dynamic On-the-fly Epistemic Graphs. On three creativity benchmarks, PRISM achieves state-of-the-art novelty and significantly expands distributional diversity. Moreover, we evaluate the real-world utility via a challenging rare-disease diagnosis benchmark. Results demonstrate that PRISM successfully uncovers correct long-tail diagnoses that standard LLM miss, confirming that its divergence stems from meaningful exploration rather than incoherent noise. Overall, this work establishes a new paradigm for Pluralistic AI, moving beyond monolithic consensus toward a diverse ecosystem of unique cognitive individuals capable of collective, multi-perspective discovery.

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