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Representative Papers

Reasoning Quality Emerges Early: Data Curation for Reasoning Models

Jun 25, 2026

Existing approaches to filtering high-quality reasoning data rely heavily on strong reasoning models, resulting in high costs and limited effectiveness. This work proposes an efficient alternative that reliably identifies challenging samples by analyzing the loss of a pretrained model over the first 100 reasoning tokens. By further incorporating loss patterns and gradient similarity from a small number of perturbed checkpoints, the method enables precise selection of diverse, high-difficulty data without requiring complex inference procedures. Evaluated on Qwen2.5-7B and Llama3.1-8B, this approach achieves up to a 1.7% improvement in fine-tuning performance while reducing token consumption by 91%, substantially lowering the cost of data curation.

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Beyond Visual Forensics: Auditing Multimodal Robustness for Synthetic Medical Image Detection

Jun 24, 2026

This work proposes a novel framework based on adaptive feature fusion and contrastive learning to address the limited generalization of existing methods in complex scenarios. By dynamically integrating multi-scale semantic information and incorporating cross-sample consistency constraints, the approach significantly enhances model robustness under distribution shifts. Extensive experiments demonstrate that the proposed method consistently outperforms state-of-the-art models across multiple benchmark datasets, with particularly notable gains in low-resource and long-tailed settings. Beyond offering a new perspective for improving model generalization, this study also releases the associated code and pre-trained models to facilitate future research.

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Hierarchical Modeling of ICD Codes in EHR Foundation Models

Jun 13, 2026

This work addresses the limitation of existing electronic health record (EHR) foundation models that treat ICD diagnosis codes as flat tokens, thereby neglecting their intrinsic clinical hierarchical structure and underutilizing semantic information. To overcome this, the study introduces the ICD-10-CM hierarchy as an inductive bias into EHR modeling for the first time, proposing a hierarchy-enhanced Transformer and a hierarchy-aware graph neural network that jointly leverage diagnosis co-occurrence patterns and multi-granular ICD codes. The model is pretrained on MIMIC-IV and evaluated via frozen probing on eICU for cross-dataset generalization. Results demonstrate consistent and significant improvements over flat-code baselines in both in-domain and cross-domain settings, enhancing downstream prediction performance and yielding more semantically coherent embedding spaces, thus validating the broad utility of hierarchical modeling across diverse tasks and architectures.

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Latest Papers

Reasoning Quality Emerges Early: Data Curation for Reasoning Models

Jun 25, 2026

Existing approaches to filtering high-quality reasoning data rely heavily on strong reasoning models, resulting in high costs and limited effectiveness. This work proposes an efficient alternative that reliably identifies challenging samples by analyzing the loss of a pretrained model over the first 100 reasoning tokens. By further incorporating loss patterns and gradient similarity from a small number of perturbed checkpoints, the method enables precise selection of diverse, high-difficulty data without requiring complex inference procedures. Evaluated on Qwen2.5-7B and Llama3.1-8B, this approach achieves up to a 1.7% improvement in fine-tuning performance while reducing token consumption by 91%, substantially lowering the cost of data curation.

0 citationsRead paper

Beyond Visual Forensics: Auditing Multimodal Robustness for Synthetic Medical Image Detection

Jun 24, 2026

This work proposes a novel framework based on adaptive feature fusion and contrastive learning to address the limited generalization of existing methods in complex scenarios. By dynamically integrating multi-scale semantic information and incorporating cross-sample consistency constraints, the approach significantly enhances model robustness under distribution shifts. Extensive experiments demonstrate that the proposed method consistently outperforms state-of-the-art models across multiple benchmark datasets, with particularly notable gains in low-resource and long-tailed settings. Beyond offering a new perspective for improving model generalization, this study also releases the associated code and pre-trained models to facilitate future research.

0 citationsRead paper

Hierarchical Modeling of ICD Codes in EHR Foundation Models

Jun 13, 2026

This work addresses the limitation of existing electronic health record (EHR) foundation models that treat ICD diagnosis codes as flat tokens, thereby neglecting their intrinsic clinical hierarchical structure and underutilizing semantic information. To overcome this, the study introduces the ICD-10-CM hierarchy as an inductive bias into EHR modeling for the first time, proposing a hierarchy-enhanced Transformer and a hierarchy-aware graph neural network that jointly leverage diagnosis co-occurrence patterns and multi-granular ICD codes. The model is pretrained on MIMIC-IV and evaluated via frozen probing on eICU for cross-dataset generalization. Results demonstrate consistent and significant improvements over flat-code baselines in both in-domain and cross-domain settings, enhancing downstream prediction performance and yielding more semantically coherent embedding spaces, thus validating the broad utility of hierarchical modeling across diverse tasks and architectures.

0 citationsRead paper