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This study proposes a model-free, noise-aware Bayesian clustering framework that, from a predictive Bayesian perspective, constructs a martingale posterior over clustering structures and quantifies clustering uncertainty without explicitly specifying a model.
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10/12/2026
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UnverifiedExperimental results show that the framework can effectively capture the dual uncertainty of clustering structure and noise.79% similarUnverifiedThe target-free carrier is responsible for absorbing the marginal token distributions predicted by the denoiser and constructing a joint model covering all unresolved tokens.78% similarUnverified扩散模型通过模拟从噪声到结构的去噪过程能有效采样配体的多种低能构象，DiffDock采用此核心技术路线77% similarUnverifiedThe framework can accommodate irregular cluster shapes and explicitly models noise as part of the posterior rather than simply discarding it as outliers.76% similarUnverifiedReasoning models can constructively understand never-before-seen scenarios by combining existing knowledge, possessing zero-shot generalization ability, which is difficult for pure perception models to achieve.74% similar
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