arXiv AI June 12, 2026 By Aruna Dey, Suraj Biswas

Is It You or Your Environment? A Bayesian Inference Framework for Genomically-Anchored Personalized Physiological Interpretation

arXiv:2606. 13556v1 Announce Type: new Abstract: Personalized health AI systems face a fundamental cold-start problem: machine learning models for physiological interpretation require weeks of individual behavioral data before they can distinguish constitutional variation from environmentally driven deviation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 14

CDFM: Towards a General-Purpose Causal Discovery Foundation Model

arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.

By Jie Qiao, Ruichu Cai, Zijian Li, Weilin Chen, Pengfei Hua, Boyan Xu, Zhengming Chen, Zhifeng Hao, Peng Cui

arXiv Machine Learning

Jun 2

Annotation-Informed Block-Sparse Bayesian Modeling for cis-Expression Prediction

arXiv:2606. 00483v1 Announce Type: cross Abstract: Genotype-based cis-expression prediction depends on accurately modeling local regulatory architecture.

By Lei Huang, Hui Shen, Kuan-Jui Su, Chuan Qiu, Martha Isabel Gonzalez-Ramirez, Anqi Liu, Zhe Luo, Yun Gong, Yipu Zhang, Dawei Li, Chaoyang Zhang, Hong-Wen Deng

efficiency

arXiv Machine Learning

Jun 18

Shrinkage priors for Bayesian Substitute Confounders

arXiv:2606. 18535v1 Announce Type: cross Abstract: Multi-cause observational studies contain information about unmeasured confounding through the dependence structure among causes.

By Yordan P. Raykov, Hengrui Luo, Justin D. Strait, Wasiur R. KhudaBukhsh

arXiv Machine Learning

Jul 9

Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation

arXiv:2601. 14590v3 Announce Type: replace Abstract: Counterfactual explanations (CFEs) provide human-centric interpretability by identifying the minimal, actionable changes required to alter a machine learning model's prediction.

By Shovito Barua Soumma, Asiful Arefeen, Stephanie M. Carpenter, Melanie Hingle, Hassan Ghasemzadeh

llms fine-tuning multimodal safety

arXiv AI

Jul 23

Causal dictionary learning reveals and validates transcription-factor binding features in genomic language models

arXiv:2607. 19618v1 Announce Type: cross Abstract: Genomic language models achieve strong performance across regulatory-genomics tasks, yet what these models internally represent remains opaque, and the field lacks a principled procedure for verifying that an apparent ``concept'' inside a model is real rather than an artifact of sequence composition.

By Sarwan Ali

llms nlp safety

arXiv Machine Learning

Jun 8

Robustly estimating heterogeneity in factorial data using Rashomon Partitions

arXiv:2404. 02141v5 Announce Type: replace-cross Abstract: In both observational data and randomized control trials, researchers select statistical models to articulate how the outcome of interest varies with combinations of observable covariates.

By Aparajithan Venkateswaran, Anirudh Sankar, Arun G. Chandrasekhar, Tyler H. McCormick

Related stories

CDFM: Towards a General-Purpose Causal Discovery Foundation Model

Annotation-Informed Block-Sparse Bayesian Modeling for cis-Expression Prediction

Shrinkage priors for Bayesian Substitute Confounders

Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation

Causal dictionary learning reveals and validates transcription-factor binding features in genomic language models

Robustly estimating heterogeneity in factorial data using Rashomon Partitions