Selected work

Representative Papers

M2P-AD: Memory-to-Prototype Learning with Boundary-aware Score Refinement for 3D Anomaly Detection

Jul 15, 2026

This work addresses the challenges of over-response in normal regions and false positives near object boundaries in 3D anomaly detection by proposing a novel approach that integrates a Memory-to-Prototype (M2P) module with a Boundary-aware Score Refinement (BSR) strategy. The M2P module leverages a memory bank to learn representative prototypes of normal features, enabling precise modeling of the normal distribution. Concurrently, a boundary extraction module is introduced to facilitate structure-aware correction of anomaly scores through BSR, effectively preserving geometric integrity while suppressing boundary artifacts. Evaluated on Real3D-AD, Anomaly-ShapeNet, and MulSen-AD datasets, the proposed method significantly reduces false alarms in normal regions and boundary-related false positives, achieving more accurate and robust anomaly localization and outperforming current state-of-the-art methods.

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Two Confounds in Cross-Model Value Comparison: Response Determinism and the Access Harness

Jul 11, 2026

This study addresses the confounding of genuine value differences with variations in response determinism in cross-model value comparisons, which is further exacerbated by interference from evaluation harnesses, leading to mischaracterizations of models' individualized values. To disentangle these effects, the authors propose a determinism-corrected decomposition framework that leverages rule-free value dilemmas, repeated forced-choice experiments, and a determinism index to isolate true value divergence from determinism-related artifacts. The approach also systematically evaluates the impact of deployment interfaces—such as APIs and client-side implementations—on value expression. Experiments across nine mainstream language models reveal substantial inter-model differences in determinism (ranging from 0.66 to 0.95) and show that correcting for determinism markedly reduces apparent individualization. Notably, different interfaces induce value profile shifts of up to 0.31 and can even reverse specific moral judgments, thereby uncovering—for the first time—the formative role of the deployment layer in shaping model-expressed values.

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LoCA: Spatially-Aware Low-Rank Convolutional Adaptation of Vision Foundation Models

Jul 07, 2026

Existing low-rank adaptation methods, such as LoRA, flatten 4D convolutional kernels into 2D matrices, disregarding their intrinsic spatial-channel coupling structure and thereby disrupting spatial topology and discarding valuable pre-trained priors. To address this limitation, this work proposes LoCA, the first spatially aware low-rank adaptation framework tailored for convolutional layers. LoCA decouples channel and spatial dimensions, applying low-rank channel adaptation to enable effective cross-channel mixing while leveraging singular value decomposition (SVD) to fine-tune the spatial bases of pre-trained kernels with high precision. This approach preserves spatial priors, substantially reduces the number of trainable parameters, and achieves state-of-the-art performance across fine-grained classification, domain-generalized semantic segmentation, and generative tasks, all while mitigating catastrophic forgetting.

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Failure-Aware Refinement of Vision-Language Model for Lithography Defect Detection

Jun 07, 2026

This work addresses the challenge of missed detections, false positives, and misclassifications of subtle semiconductor lithography defects—such as bridging, burrs, necking, and contamination—in single-stage detection models. To overcome these limitations, the authors propose a two-stage vision-language detection framework. In the first stage, the Qwen3-VL model is efficiently fine-tuned using LoRA to perform defect counting, classification, and localization. The second stage introduces a failure-aware prediction refinement mechanism, which explicitly models and corrects initial prediction biases by training a refinement module on error cases from the first stage along with their corrected labels. This approach significantly reduces both false positive and miss rates while improving classification accuracy, outperforming existing single-stage fine-tuning strategies and achieving more robust and precise inference performance in lithography defect detection.

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Weighted Knowledge Distillation for Semi-Supervised Segmentation of Maxillary Sinus in Panoramic X-ray Images

Apr 22, 2026

This study addresses the challenges of segmenting maxillary sinuses in panoramic radiographs, where structural overlap, ambiguous boundaries, and scarce annotations hinder model training. To overcome these limitations, the authors propose a semi-supervised segmentation framework that integrates knowledge distillation with unlabeled data to train a student model. A key innovation is the introduction of a weighted distillation loss designed to suppress unreliable signals arising from inconsistencies between teacher and student predictions. Furthermore, the work presents SinusCycle-GAN—a novel unpaired image translation approach—to refine pseudo-label boundary quality. Evaluated on 2,511 clinical images, the method achieves a Dice score of 96.35%, significantly outperforming existing approaches and demonstrating high accuracy, anatomical consistency, and robustness under limited annotation conditions.

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Recent publications

Latest Papers

M2P-AD: Memory-to-Prototype Learning with Boundary-aware Score Refinement for 3D Anomaly Detection

Jul 15, 2026

This work addresses the challenges of over-response in normal regions and false positives near object boundaries in 3D anomaly detection by proposing a novel approach that integrates a Memory-to-Prototype (M2P) module with a Boundary-aware Score Refinement (BSR) strategy. The M2P module leverages a memory bank to learn representative prototypes of normal features, enabling precise modeling of the normal distribution. Concurrently, a boundary extraction module is introduced to facilitate structure-aware correction of anomaly scores through BSR, effectively preserving geometric integrity while suppressing boundary artifacts. Evaluated on Real3D-AD, Anomaly-ShapeNet, and MulSen-AD datasets, the proposed method significantly reduces false alarms in normal regions and boundary-related false positives, achieving more accurate and robust anomaly localization and outperforming current state-of-the-art methods.

0 citationsRead paper

Two Confounds in Cross-Model Value Comparison: Response Determinism and the Access Harness

Jul 11, 2026

This study addresses the confounding of genuine value differences with variations in response determinism in cross-model value comparisons, which is further exacerbated by interference from evaluation harnesses, leading to mischaracterizations of models' individualized values. To disentangle these effects, the authors propose a determinism-corrected decomposition framework that leverages rule-free value dilemmas, repeated forced-choice experiments, and a determinism index to isolate true value divergence from determinism-related artifacts. The approach also systematically evaluates the impact of deployment interfaces—such as APIs and client-side implementations—on value expression. Experiments across nine mainstream language models reveal substantial inter-model differences in determinism (ranging from 0.66 to 0.95) and show that correcting for determinism markedly reduces apparent individualization. Notably, different interfaces induce value profile shifts of up to 0.31 and can even reverse specific moral judgments, thereby uncovering—for the first time—the formative role of the deployment layer in shaping model-expressed values.

0 citationsRead paper

LoCA: Spatially-Aware Low-Rank Convolutional Adaptation of Vision Foundation Models

Jul 07, 2026

Existing low-rank adaptation methods, such as LoRA, flatten 4D convolutional kernels into 2D matrices, disregarding their intrinsic spatial-channel coupling structure and thereby disrupting spatial topology and discarding valuable pre-trained priors. To address this limitation, this work proposes LoCA, the first spatially aware low-rank adaptation framework tailored for convolutional layers. LoCA decouples channel and spatial dimensions, applying low-rank channel adaptation to enable effective cross-channel mixing while leveraging singular value decomposition (SVD) to fine-tune the spatial bases of pre-trained kernels with high precision. This approach preserves spatial priors, substantially reduces the number of trainable parameters, and achieves state-of-the-art performance across fine-grained classification, domain-generalized semantic segmentation, and generative tasks, all while mitigating catastrophic forgetting.

0 citationsRead paper

Failure-Aware Refinement of Vision-Language Model for Lithography Defect Detection

Jun 07, 2026

This work addresses the challenge of missed detections, false positives, and misclassifications of subtle semiconductor lithography defects—such as bridging, burrs, necking, and contamination—in single-stage detection models. To overcome these limitations, the authors propose a two-stage vision-language detection framework. In the first stage, the Qwen3-VL model is efficiently fine-tuned using LoRA to perform defect counting, classification, and localization. The second stage introduces a failure-aware prediction refinement mechanism, which explicitly models and corrects initial prediction biases by training a refinement module on error cases from the first stage along with their corrected labels. This approach significantly reduces both false positive and miss rates while improving classification accuracy, outperforming existing single-stage fine-tuning strategies and achieving more robust and precise inference performance in lithography defect detection.

0 citationsRead paper

Weighted Knowledge Distillation for Semi-Supervised Segmentation of Maxillary Sinus in Panoramic X-ray Images

Apr 22, 2026

This study addresses the challenges of segmenting maxillary sinuses in panoramic radiographs, where structural overlap, ambiguous boundaries, and scarce annotations hinder model training. To overcome these limitations, the authors propose a semi-supervised segmentation framework that integrates knowledge distillation with unlabeled data to train a student model. A key innovation is the introduction of a weighted distillation loss designed to suppress unreliable signals arising from inconsistencies between teacher and student predictions. Furthermore, the work presents SinusCycle-GAN—a novel unpaired image translation approach—to refine pseudo-label boundary quality. Evaluated on 2,511 clinical images, the method achieves a Dice score of 96.35%, significantly outperforming existing approaches and demonstrating high accuracy, anatomical consistency, and robustness under limited annotation conditions.

0 citationsRead paper