Latent Variable Models

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9 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

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Latest papers 105

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FLaG: Fine-Grained Latent Grouping for Hallucination Detection
May 29, 2026Wentao Ye, Liyao Li, Zhiqing Xiao +6Latent Variable Models LLM Hallucination Detection
Zhejiang University · Hangzhou, Zhejiang, China · Nanyang Technological University +1
Early Prediction of Future Behavioral Strategy from Process Traces
May 28, 2026Robert Kasumba, Dennis Barbour, Chien-Ju HoLatent Variable Models Human Behavior Prediction
Division of Computational and Data Sciences Washington University in Saint Louis Saint Louis, MO 63130 · Department of Biomedical Engineering Washington University in Saint Louis Saint Louis, MO 63130 · Department of Computer Science Washington University in Saint Louis Saint Louis, MO 63130
Latent-Conditioned Parameterized Quantum Circuits as Universal Approximators for Distributions over Quantum States
May 27, 2026Quoc Hoan Tran, Koki Chinzei, Yasuhiro Endo +1Latent Variable Models Quantum Generative Modeling
Quantum Laboratory, Fujitsu Research, Fujitsu Limited, Kawasaki, Kanagawa 211-8588, Japan
Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models
May 27, 2026Simon Queric, Cédric Vincent-Cuaz, Charles Bouveyron +1Model Selection Latent Variable Models
Universit´e Cˆote d’Azur, Inria, CNRS LJAD, Maasai Nice, France · EPFL Lausanne, Switzerland · Universit´e Cˆote d’Azur, Inria, CNRS CEPAM, Maasai Nice, France
Identifiable Bayesian Deep Generative Copulas with Unknown Layer Widths for Data with Arbitrary Marginal Distributions
May 26, 2026Joseph Feldman, Yuqi GuCopula Models Latent Variable Models
Department of Statistics and Data Science Washington University in St. Louis · Department of Statistics Columbia University
Symbolic Regression via Latent Iterative Refinement
May 26, 2026Xieting Chu, Sriram Vishwanath, Vijay GaneshLatent Variable Models Amortized Inference
Georgia Institute of Technology
The Need for an External Observer Formalizing the Sufficiency Gap: A Mathematical Extension of Mixture Identifiability and Contextual Grounding in Sequence Models
May 26, 2026Francesco CorielliContextual Faithfulness LLM Grounding
Learning Nonlinear Factor Models with Unknown Monotone Links from Incomplete and Noisy Data
May 25, 2026Yutong Chao, Resat Gökhan, Jalal Etesami +1Latent Variable Models Matrix Factorization
School of Computation, Information and Technology, Technical University of Munich, Germany · Munich Institute of Robotics and Machine Intelligence · Department of Economics, Virginia Tech, USA
Understanding Multimodality in Generative Behavioral Cloning
May 21, 2026Lorenzo Mazza, Massimiliano Datres, Ariel Rodriguez +3Latent Variable Models Imitation Learning
NCT/UCC Dresden, UKDD Dresden, TU Dresden, DKFZ Heidelberg · Ludwig-Maximilians-Universität München, Munich Center for Machine Learning (MCML) · BMFTR Research Hub 6G-Life +4
Three Costs of Amortizing Gaussian Process Inference with Neural Processes
May 20, 2026Robin YoungLatent Variable Models Amortized Inference
University of Cambridge, Cambridge, UK
Score-Based Causal Discovery of Latent Variable Causal Models
May 19, 2026Ignavier Ng, Xinshuai Dong, Haoyue Dai +3Latent Variable Models Structural Causal Models
Carnegie Mellon University · University of California, San Diego · Mohamed bin Zayed University of Artificial Intelligence
Corrected Integrated Laplace Approximation for Bayesian Inference in Latent Gaussian Models
May 19, 2026Jinlin Lai, Charles C. Margossian, Daniel R. SheldonLatent Variable Models Importance Sampling
Manning College of Information and Computer Sciences University of Massachusetts Amherst · Department of Statistics University of British Columbia
Generative Recursive Reasoning
May 19, 2026Junyeob Baek, Mingyu Jo, Minsu Kim +3Latent Variable Models Generative Modeling
KAIST · Mila – Québec AI Institute · New York University +1
Identifiable Multimodal Causal Representation Learning under Partial Latent Sharing
May 18, 2026Manal Benhamza, Marianne Clausel, Myriam TamiLatent Variable Models Causal Representation Learning
Paris-Saclay University, CentraleSupélec, MICS Lab, France · Lorraine University, CRAN, France
GUT-IS: A Data-Driven Approach to Integrating Constructs and Their Relations in Information Systems
May 18, 2026Maximilian Reinhardt, Jonas Scharfenberger, Burkhardt FunkLatent Variable Models Clustering
Institute of Information Systems Leuphana University Lüneburg, Germany
Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors
May 18, 2026Aldric LabartheLatent Variable Models Bayesian Inference
CB, UNIGE · Centre Borelli, Université Paris-Saclay Gif-Sur-Yvette, FR · Department of Computer Science, University of Geneva Carouge, CH-1227
Entropy-Based Characterisation of the Polarised Regime in Latent Variable Models
May 15, 2026Peter Clapham, Lisa Bonheme, Marek GrzesVariational Autoencoders Latent Variable Models
School of Computing, University of Kent, Canterbury, United Kingdom · International Agency for Research on Cancer (IARC/WHO), Genomic Epidemiology Branch, Lyon, France
Unsupervised learning of acquisition variability in structural connectomes via hybrid latent space modeling
May 13, 2026Gaurav Rudravaram, Lianrui Zuo, Karthik Ramadass +17Variational Autoencoders Latent Variable Models
Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
Vector-Quantized Discrete Latent Factors Meet Financial Priors: Dynamic Cross-Sectional Stock Ranking Prediction for Portfolio Construction
May 13, 2026Namhyoung Kim, Jae Wook SongLatent Variable Models Portfolio Optimization
RiskX · Hanyang University
Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty
May 12, 2026Haoran Hu, Xingce WangParameter Estimation Latent Variable Models
School of Artificial Intelligence Beijing Normal University Beijing 100875, China
Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation
May 9, 2026ZhiMing Li, Yue SongLatent Variable Models Stochastic Approximation
School of Computer Science and Technology TianJin University Tianjin 300350, China · College of AI Tsinghua University Beijing 100084, China
Structured Coupling for Flow Matching
May 8, 2026Xavier Sumba, Carles Balsells-Rodas, Yingzhen LiFlow Matching Disentangled Representation Learning
Imperial College London
BGM-IV: AI-Powered Bayesian Generative Modeling for Instrumental Variable Regression with High-Dimensional Covariates
May 7, 2026Guyue Luo, Haidong Lu, Andrew J. Loza +1Causal Effect Estimation Latent Variable Models
Department of Biostatistics Yale University
End-to-End Identifiable and Consistent Recurrent Switching Dynamical Systems
May 7, 2026Carles Balsells-Rodas, Zhengrui Xiang, Xavier Sumba +1Dynamical Systems Latent Variable Models
Imperial College London
Transformed Latent Variable Multi-Output Gaussian Processes
May 6, 2026Xiaoyu Jiang, Xinxing Shi, Sokratia Georgaka +2Latent Variable Models Gaussian Processes
Department of Computer Science, University of Manchester, Manchester, UK · Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
Adaptive Querying with AI Persona Priors
May 1, 2026Kaizheng Wang, Yuhang Wu, Assaf ZeeviLatent Variable Models Bayesian Experimental Design
Department of Industrial Engineering and Operations Research and Data Science Institute, Columbia University. · Decision, Risk, and Operations Division, Columbia Business School.
Fractionally Supervised Classification with Maxima Nominated Samples
Apr 28, 2026Mohammad Jafari Jozani, Jingyu WangLatent Variable Models Expectation-Maximization
Department of Statistics, University of Manitoba, Winnipeg, MB, Canada · RBC Capital Markets, Toronto, Canada
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.
Mixed Membership sub-Gaussian Models
Apr 24, 2026Huan QingLatent Variable Models Gaussian Mixture Models
School of Economics and Finance, Chongqing University of Technology, Chongqing, 400054, China
Diverse Dictionary Learning
Apr 19, 2026Yujia Zheng, Zijian Li, Shunxing Fan +2Latent Variable Models Representation Identifiability
CMU · MBZUAI · NYU

May 29, 2026, cs.LGJ/K move · Enter open · S save

FLaG: Fine-Grained Latent Grouping for Hallucination Detection

Wentao Ye, Liyao Li, Zhiqing Xiao, Muzhi Zhu+5

Zhejiang University · Hangzhou, Zhejiang, China · Nanyang Technological University+1

Latent Variable Models LLM Hallucination Detection

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Hallucinations in large language models (LLMs) arise from heterogeneous failure mechanisms, making reliable detection difficult for any single global uncertainty score. In this work, we formulate hallucination detection as a mechanism-aware evidence aggregation problem, where diverse representation- and token-level signals must be interpreted under multiple latent explanations. We propose FLaG, a lightweight hallucination detection framework that models correctness through a set of latent evidence groups. Each instance is softly associated with multiple groups via an energy-based routing mechanism, and group-conditional reliability signals are combined through a principled log-marginal aggregation. This design enables FLaG to capture heterogeneous hallucination patterns while remaining invariant to decision thresholds and evaluation metrics. The framework operates as a frozen-model head, requires no modification to the underlying language model, and incurs minimal computational overhead. We further provide a theoretical perspective that connects FLaG to optimal evidence aggregation under heterogeneous error mechanisms, showing that the Bayes-optimal test statistic necessarily admits a log-marginal form and that FLaG constitutes a tractable approximation with a controllable error bound. Extensive experiments across multiple benchmarks and LLM backbones demonstrate that FLaG consistently achieves SOTA performance, while exhibiting robust transfer across datasets and models, and remaining effective under limited supervision.

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Latest papers 105