Selected work

Representative Papers

Evaluating Agent Skills for Version-Specific Plugin Migration: A Retrospective Study

Sep 24, 2026

This study addresses the limitation that skill evaluations of coding agents during plugin migration frequently rely on diagnostic scores, making it difficult to verify whether agents genuinely satisfy target-version contracts. To overcome this, we propose a traceable evaluation framework that conducts retrospective studies using static migration archives to link aggregated rewards with contract-level evidence. By integrating task-guided statistics, execution probes, and multi-model independent rescoring with weighted Kappa analysis, the approach systematically reveals scoring biases. Our findings demonstrate that although skill enhancements increase average rewards, such gains are concentrated and accompanied by scoring deficiencies. Nevertheless, corrected estimates confirm positive benefits, and multi-judge rescoring exhibits high agreement, thereby providing reliable checkpoints for auditing agent capabilities.

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Multimodal Adaptive Expert Selection with Text Routing and Ordinal Prototype Optimization for Sentiment Analysis

Aug 31, 2026

为解决多模态情感分析中语义复杂性和情感强度层次结构问题，提出MAESTRO框架，通过文本引导的专家选择和序数原型优化方法来动态调整和优化多模态表示。

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CFMD: Dynamic Cross-layer Feature Fusion for Salient Object Detection

Apr 02, 2025

To address computational redundancy and ambiguous boundary reconstruction in cross-layer feature pyramid networks (CFPNs) for salient object detection, this paper proposes the Context-aware Feature Mamba-based Dynamic fusion network (CFMD). Methodologically, CFMD introduces two key components: (1) a Context-aware Feature Long-range Memory Aggregation module (CFLMA) built upon the Mamba architecture, enabling efficient, long-range dependency modeling and dynamic weight allocation; and (2) an Adaptive Dynamic Upsampling unit (CFLMD) that combines bilinear initialization with a tunable receptive field mechanism to faithfully restore spatial details without degradation. Evaluated on three major benchmarks, CFMD achieves consistent improvements in both accuracy and efficiency: F-measure and E-measure increase significantly, boundary precision improves by 3.2% (Fβ) and 4.1% (Berkeley-DB), while inference speed rises by 18% (FPS), demonstrating superior trade-offs between real-time performance and pixel-level segmentation fidelity.

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Recent publications

Latest Papers

Evaluating Agent Skills for Version-Specific Plugin Migration: A Retrospective Study

Sep 24, 2026

This study addresses the limitation that skill evaluations of coding agents during plugin migration frequently rely on diagnostic scores, making it difficult to verify whether agents genuinely satisfy target-version contracts. To overcome this, we propose a traceable evaluation framework that conducts retrospective studies using static migration archives to link aggregated rewards with contract-level evidence. By integrating task-guided statistics, execution probes, and multi-model independent rescoring with weighted Kappa analysis, the approach systematically reveals scoring biases. Our findings demonstrate that although skill enhancements increase average rewards, such gains are concentrated and accompanied by scoring deficiencies. Nevertheless, corrected estimates confirm positive benefits, and multi-judge rescoring exhibits high agreement, thereby providing reliable checkpoints for auditing agent capabilities.

0 citationsRead paper

Multimodal Adaptive Expert Selection with Text Routing and Ordinal Prototype Optimization for Sentiment Analysis

Aug 31, 2026

为解决多模态情感分析中语义复杂性和情感强度层次结构问题，提出MAESTRO框架，通过文本引导的专家选择和序数原型优化方法来动态调整和优化多模态表示。

0 citationsRead paper

CFMD: Dynamic Cross-layer Feature Fusion for Salient Object Detection

Apr 02, 2025

To address computational redundancy and ambiguous boundary reconstruction in cross-layer feature pyramid networks (CFPNs) for salient object detection, this paper proposes the Context-aware Feature Mamba-based Dynamic fusion network (CFMD). Methodologically, CFMD introduces two key components: (1) a Context-aware Feature Long-range Memory Aggregation module (CFLMA) built upon the Mamba architecture, enabling efficient, long-range dependency modeling and dynamic weight allocation; and (2) an Adaptive Dynamic Upsampling unit (CFLMD) that combines bilinear initialization with a tunable receptive field mechanism to faithfully restore spatial details without degradation. Evaluated on three major benchmarks, CFMD achieves consistent improvements in both accuracy and efficiency: F-measure and E-measure increase significantly, boundary precision improves by 3.2% (Fβ) and 4.1% (Berkeley-DB), while inference speed rises by 18% (FPS), demonstrating superior trade-offs between real-time performance and pixel-level segmentation fidelity.

0 citationsRead paper