Shared Nature, Unique Nurture: PRISM for Pluralistic Reasoning via In-context Structure Modeling
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.
69a5fdcd663a122a7ce66618
Guancheng Tu
69a5fdcd663a122a7ce66619
Shiyang Zhang
69a5fdcd663a122a7ce6661a
Tianyu Zhang
69a5fdcd663a122a7ce6661b
Yi Zhang
69a5fdcd663a122a7ce6661c
Diji Yang
claimed_verified
2026-04-16T08:21:24.573Z
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/avatars/40cca602d481382b0adb13996328b515.svg
Diji Yang
dyang39
user
dyang39
Shared Nature, Unique Nurture: PRISM for Pluralistic Reasoning via In-context Structure Modeling
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.
Large language models are approaching a unified artificial hivemind that reduces diversity; PRISM addresses this by introducing individualized epistemic trajectories through dynamic on-the-fly epistemic graphs, enhancing both creative output and diagnostic accuracy.
Artificial Hivemind pre-training priors distributional diversity Epistemic Evolution PRISM On-the-fly Epistemic Graphs Pluralistic Reasoning creative exploration scientific discovery rare-disease diagnosis multi-perspective discovery