Selected work

Representative Papers

Lucid Dreaming for World Models: Learning to Doubt Imagination and Decide by Trust

Sep 29, 2026

This study addresses the unreliability of imagined planning in world models, where overconfidence in unseen state-action pairs misleads decision-making. To mitigate this, we propose LucidWM, a framework that introduces subjective logic to assign degrees of doubt to categorical latent transitions, enabling uncertainty estimation without additional parameters. By accumulating trust over multi-step trajectories to reweight returns, the method integrates doubt into the imagination process, thereby guiding reinforcement learning policy optimization. The effectiveness of this approach is validated against four baseline models and seventeen uncertainty readouts. In navigation tasks, LucidWM reduces the number of steps required for goal attainment from 362 to 190, significantly enhancing the robustness of trust-based decision-making.

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Detecting Glaucoma Across Multi-ethnic Myopic and Non-Myopic Populations Using an Uncertainty-Aware Vision Transformer: A Multicentre Model Development and Validation Study

Sep 24, 2026

This study addresses the performance degradation of AI-based glaucoma detection in multi-ethnic and highly myopic populations caused by data distribution shifts. We develop an uncertainty-aware model based on the ViT-B/16 architecture, trained on 56,000 fundus images and validated across 16 independent global datasets. The core contributions include the first unified modeling framework encompassing multi-ethnic cohorts as well as both myopic and non-myopic populations, alongside the integration of predictive uncertainty estimation to enhance clinical trustworthiness. Experimental results demonstrate an internal AUROC of 98.7% and external generalization ranging from 86.4% to 99.6%. Notably, the model significantly outperforms general ophthalmologists in diagnostic accuracy within myopic scenarios, exhibiting exceptional cross-population robustness.

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Recent publications

Latest Papers

Lucid Dreaming for World Models: Learning to Doubt Imagination and Decide by Trust

Sep 29, 2026

This study addresses the unreliability of imagined planning in world models, where overconfidence in unseen state-action pairs misleads decision-making. To mitigate this, we propose LucidWM, a framework that introduces subjective logic to assign degrees of doubt to categorical latent transitions, enabling uncertainty estimation without additional parameters. By accumulating trust over multi-step trajectories to reweight returns, the method integrates doubt into the imagination process, thereby guiding reinforcement learning policy optimization. The effectiveness of this approach is validated against four baseline models and seventeen uncertainty readouts. In navigation tasks, LucidWM reduces the number of steps required for goal attainment from 362 to 190, significantly enhancing the robustness of trust-based decision-making.

0 citationsRead paper

Detecting Glaucoma Across Multi-ethnic Myopic and Non-Myopic Populations Using an Uncertainty-Aware Vision Transformer: A Multicentre Model Development and Validation Study

Sep 24, 2026

This study addresses the performance degradation of AI-based glaucoma detection in multi-ethnic and highly myopic populations caused by data distribution shifts. We develop an uncertainty-aware model based on the ViT-B/16 architecture, trained on 56,000 fundus images and validated across 16 independent global datasets. The core contributions include the first unified modeling framework encompassing multi-ethnic cohorts as well as both myopic and non-myopic populations, alongside the integration of predictive uncertainty estimation to enhance clinical trustworthiness. Experimental results demonstrate an internal AUROC of 98.7% and external generalization ranging from 86.4% to 99.6%. Notably, the model significantly outperforms general ophthalmologists in diagnostic accuracy within myopic scenarios, exhibiting exceptional cross-population robustness.

0 citationsRead paper