arXiv Machine Learning

Generative Modeling of Discrete Latent Structures via Dynamic Policy Gradients

arXiv:2606. 07400v1 Announce Type: new Abstract: Many scientific problems require inferring unobserved mechanistic latent states from indirect observations.

arXiv AI

Jun 10

Flexible Flows for Biological Sequence Design

arXiv:2606. 10543v1 Announce Type: cross Abstract: Designing functional biological sequences requires navigating vast discrete spaces under strict evolutionary and biophysical constraints.

By Yogesh Verma, Dani Korpela, Harri L\"ahdesm\"aki, Vikas Garg

diffusion benchmarks

arXiv Machine Learning

Jun 10

Latent Guided Sampling for Combinatorial Optimization

arXiv:2506. 03672v2 Announce Type: replace-cross Abstract: Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature makes them computationally challenging.

By Sobihan Surendran (LPSM), Adeline Fermanian (LPSM), Sylvain Le Corff (LPSM)

diffusion reinforcement-learning benchmarks

arXiv Machine Learning

Jun 16

Structured Nonparametric Variational Inference for Dependent Latent Modeling

arXiv:2606. 15458v1 Announce Type: cross Abstract: Variational inference (VI) is a core engine of modern AI, enabling scalable approximate Bayesian learning and uncertainty-aware training of large probabilistic and generative models.

By Yuda Shao, Zhiling Gu, Shan Yu

computer-vision

arXiv AI

Jun 2

Variational Learning for Insertion-based Generation

arXiv:2606. 02133v1 Announce Type: cross Abstract: Non-monotonic sequence generation methods, such as masked diffusion models, provide a flexible alternative to left-to-right autoregressive modeling by allowing tokens to be generated in non-fixed and prescribed orders.

By Yangtian Zhang, Zhe Wang, Arthur Gretton, Rex Ying, David van Dijk, Michalis K. Titsias, Jiaxin Shi

diffusion

arXiv AI

Aug 12

JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures

arXiv:2602. 17162v3 Announce Type: replace Abstract: Genomic Foundation Models (GFMs) typically rely on Masked Language Modeling (MLM) or Next-Token Prediction (NTP) to learn the "Laws of Nature".

By Ariel Larey, Elay Dahan, Amit Bleiweiss, Raizy Kellerman, Guy Leib, Omri Nayshool, Dan Ofer, Tal Zinger, Dan Dominissini, Gideon Rechavi, Nicole Bussola, Simon Lee, Shane O'Connell, Dung Hoang, Marissa Wirth, Alexander W. Charney, Nati Daniel, Yoli Shavit

rag diffusion benchmarks safety

arXiv Machine Learning

Jun 2

Efficient Synthetic Network Generation via Latent Embedding Reconstruction

arXiv:2606. 00934v1 Announce Type: cross Abstract: Network data are ubiquitous across the social sciences, biology, and information systems.

By Feifan Jiang, Yinan Bu, Shihao Wu, Gongjun Xu, Ji Zhu

rag diffusion efficiency

arXiv AI

Jun 18

Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation

arXiv:2512. 09185v4 Announce Type: replace-cross Abstract: Understanding disease progression is a central clinical challenge with direct implications for early diagnosis and personalized treatment.

By Hao Chen, Rui Yin, Yifan Chen, Qi Chen, Chao Li

diffusion benchmarks safety

arXiv AI

Jul 9

Exploration of Fast-Slow Latent Recurrence for Train-Short, Test-Long Generalization

arXiv:2604. 01577v3 Announce Type: replace-cross Abstract: We study out of distribution generalization in streaming tasks where models are trained on short sequences but must operate over much longer, unknown horizons under bounded memory.

By Shota Takashiro, Masanori Koyama, Takeru Miyato, Yusuke Iwasawa, Yutaka Matsuo, Kohei Hayashi

llms reinforcement-learning safety

arXiv AI

Jun 16

Efficient Flow Matching using Latent Variables

arXiv:2505. 04486v4 Announce Type: replace-cross Abstract: Flow matching models have shown great potential in image generation tasks among probabilistic generative models.

By Anirban Samaddar, Yixuan Sun, Viktor Nilsson, Sandeep Madireddy

diffusion multimodal benchmarks safety

arXiv Machine Learning

Jul 30

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings

arXiv:2605. 16836v2 Announce Type: replace-cross Abstract: Hypergraphs provide a principled framework for modeling polyadic interactions, with applications in recommendation systems, social networks, and molecular modeling.

By Xinyi Hong, Shuntuo Xu, Zhou Yu

rag

arXiv Machine Learning

Aug 6

RiboSphere: Learning Unified and Efficient Representations of RNA Structures

arXiv:2603. 19636v2 Announce Type: replace Abstract: Accurate RNA structure modeling remains difficult because RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D structures are comparatively scarce.

By Zhou Zhang, Hanqun Cao, Cheng Tan, Fang Wu, Pheng Ann Heng, Tianfan Fu

llms diffusion efficiency benchmarks

arXiv Machine Learning

Jun 29

Textual Belief States for World Models: Identifiable Representation Learning Under Strict Mediation

arXiv:2606. 27681v1 Announce Type: new Abstract: World models in partially observed environments rely on latent representations that summarize interaction history, but in many modern LLM-based architectures predictive performance fails to reflect representation quality due to history bypass, rendering the latent state unidentifiable.

By Xiang Gao, Kaiwen Dong, Yuguang Yao, Padmaja Jonnalagedda, Kamalika Das

llms reinforcement-learning

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