Bayesian Mixture Models

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Test-Time Adaptation of Reasoning Strategies with Bayesian Nonparametric Memory
Oct 5, 2026Keshav Ramji, Tahira Naseem, Ramón Fernandez AstudilloMemory-Augmented Language Models Bayesian Mixture Models
IBM Research AI
Sharp Minimax Regret for Infinite-Memory Logistic Prediction
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Purdue University
PYPM-GGD: Pitman-Yor Process Mixture with Generalized Gaussian Density using ADAM
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Distributional Determinantal Point Process for Repulsive Clustering of Distributions
Jul 23, 2026Khai Nguyen, Yang Ni, Elizabeth Juarez-Colunga +1Bayesian Mixture Models Clustering
Department of Statistics and Data Sciences, University of Texas at Austin · Department of Biostatistics and Informatics, University of Colorado Anschutz · Department of Mathematics, University of Texas at Austin
DP-BOA: Dirichlet-Process Birth-or-Assign for On-the-Fly Category Discovery
Jul 15, 2026Peiyan Gu, Zixin Teng, Xuming HeBayesian Mixture Models Gaussian Mixture Models
ShanghaiTech University, Shanghai, China · Shanghai Engineering Research Center of Intelligent Vision and Imaging
DP-Splat: Bayesian Nonparametric Complexity Control for Gaussian Splatting
Jul 12, 2026Aqi Dong3D Gaussian Splatting Bayesian Mixture Models
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BACH: A Bayesian Admixture of Contrastive Heads for Multi-Interest Two-Tower Retrieval
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TDGT: A Tabular Data Generation Toolkit supporting adaptive GPU-accelerated Bayesian mixture models, diffusion-based models, and latent-space generative modeling
Jun 30, 2026Vasileios C. Pezoulas, Nikolaos S. Tachos, Eleni Georga +3Bayesian Mixture Models Bayesian Inference
SYNTHAINA AI, Ioannina, Greece
Variational Consensus Monte Carlo for Bayesian Mixture
Jun 17, 2026Julie Fendler, Francesca L. Crowe, Tom Marshall +2Markov Chain Monte Carlo Federated Learning Aggregation
MRC Biostatistics Unit, University of Cambridge · Institute of Applied Health Research, University of Birmingham
Folded Transport MCMC: Eliminating Label Switching by Sampling on a Fundamental Domain
Jun 3, 2026Jun HuMarkov Chain Monte Carlo Bayesian Mixture Models
Wuhan University of Technology
Heterogeneous Variational Inference for Markov Degradation Hazard Models: Discretized Mixture with Interpretable Clusters
Apr 27, 2026Takato YasunoLatent Variable Models Markov Models
Digital Business Department, Yachiyo Engineering Co., Ltd.
On Bayesian Softmax-Gated Mixture-of-Experts Models
Apr 22, 2026Nicola Bariletto, Huy Nguyen, Nhat Ho +1Bayesian Mixture Models Bayesian Inference
The University of Texas at Austin
Inverse Problem for Partial Differential Equations with Jump Discontinuities in Coefficients by Two-stage Physics-Informed Deep Learning and Statistical Mixture Models
Oct 16, 2025Zhikun Zhang, Guanyu Pan, Xiangjun Wang +2Bayesian Mixture Models Physics-Informed ML
School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710072, China · School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China · SandGold AI Research, Guangzhou 510642, China +3

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Test-Time Adaptation of Reasoning Strategies with Bayesian Nonparametric Memory

Keshav Ramji, Tahira Naseem, Ramón Fernandez Astudillo

IBM Research AI

Memory-Augmented Language Models Bayesian Mixture Models Bayesian Inference Efficient Language Model Reasoning Test-Time Training Metacognition in Language Models

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While modern large language models (LLMs) have been trained to reason through verbalized chains-of-thought, the generation cost grows substantially due to suboptimal paths to reach the final answer. Furthermore, as new insights are discovered while observing various input queries (e.g. through self-reflection), limited mechanisms exist for carrying forward these findings to be applied to subsequent problems. One can view the list of such strategies or behaviors as a growing cheatsheet, with elements retrieved from this memory module at inference-time. In this work, we consider structured cheatsheets, with learned clusters of behaviors. We introduce a Hierarchical Dirichlet Process Gaussian Mixture Model (HDP-GMM) over behavior embeddings, which shares components across domains while allowing domain-specific mixing weights, and uses the posterior predictive to retrieve relevant behaviors for a query; we call this a

\textit{Bayesian Cheatsheet}

. This mechanism allows for cheap adaptation in an online test-time training (TTT) setting, softly updating the mixture's sufficient statistics following each sample and enabling the creation of new components when the synthesized behaviors are sufficiently novel. We demonstrate that Bayesian Cheatsheet achieves clear performance gains relative to existing memory modules across reasoning benchmarks such as AIME'25, Omni-MATH, and PhysReason, even in the cold-start setting. We show that the Bayesian Cheatsheet is an adaptively reorganizing memory module, as behaviors can be re-assigned to components through a single step of collapsed Gibbs sampling. Our findings highlight the value of Bayesian-inspired memory modules for effective test-time adaptation and the role of structure in metacognitive reasoning.

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