Model Revolution

Preprints that challenges the foundational assumptions of a field, proposing bold new concepts or alternative frameworks with potential to replace the dominant paradigm.

Linear instability of plane Couette and Poiseuille flows

URL https://arxiv.org/pdf/2506.04242.pdf Stage Model Revolution Paradigm framing Hydrodynamic Stability Highlights This preprint challenges the established paradigm in hydrodynamic stability regarding the linear instability of plane Couette and Poiseuille flows. The traditional assumption of longitudinal periodicity of disturbances is questioned, leading to new theoretical predictions for critical Reynolds numbers. The authors provide an alternative model that […]

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Cracking the Undruggable: Discovery of a Mutation-Induced Cryptic Pocket in TP53 C238Y for Precision Oncology

URL https://assets-eu.researchsquare.com/files/rs-6651642/v1/564bb168-231f-48c3-8fd7-d3cbf5797c65.pdf Stage Model Revolution Paradigm framing The established paradigm of mutant TP53 being "undruggable" due to a lack of stable binding pockets. Highlights This preprint presents compelling computational evidence challenging the current paradigm by demonstrating the existence of a mutation-induced cryptic pocket in the TP53 C238Y mutant. While the in silico findings are robust

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Sentiment Simulation using Generative AI Agents

URL https://arxiv.org/pdf/2505.22125.pdf Stage Model Revolution Paradigm framing The preprint challenges the existing paradigm of sentiment analysis as retrospective classification based on surface-level linguistic features. It proposes a shift towards prospective and dynamic sentiment simulation grounded in psychological principles, utilizing generative AI agents embodied with psychographic profiles. Highlights This preprint presents a compelling case for a

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Evidence for complex fixed points in pandemic data

URL https://assets-eu.researchsquare.com/files/rs-70238/v1_covered.pdf?c=1631840663 Stage Model Revolution / Paradigm Change Paradigm framing The preprint attempts to revolutionize the existing paradigm of epidemiological modeling, by introducing complex fixed points into the Renormalization Group (RG) framework. It challenges the conventional compartmental models and complex network diffusion techniques, which primarily focus on initial growth, by proposing a unified way to

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