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Chau Yuen

6 papers indexed

arxivcs.LGcs.AI2026-07-20

CoCurve: Cross-Module Co-Pruning Curvature for Training-Free Structured LLM Pruning

Zhiren Gong, Zihao Zeng, Zijie Wang, Tiantong Wang, Chau Yuen, Wei Yang Bryan Lim

Structured pruning compresses large language models (LLMs) by removing whole computational units, such as attention heads and feed-forward (FFN) channel groups. Most training-free methods, however, rank these units independently, implicitly treating the loss from pruning a set as…

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arxivcs.LG2026-07-15

Task-Oriented Sensing and Covert Transmissions for Collaborative Multi-AUV Systems

Xueyao Zhang, Chenyang Yan, Bo Yang, Xuelin Cao, Zhiwen Yu, Bin Guo, et al.

In underwater covert cooperative missions, autonomous underwater vehicles (AUVs) often cannot rely on active sonar to continuously obtain complete information, since active sensing and frequent communications increase the risk of exposure. As a result, AUVs primarily rely on pass…

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arxivcs.CVcs.AI2026-07-08

CarbonCLIP: Enhance Carbon Prediction from Satellite Imagery via Integrated Street-View Semantics and Temporal Context Training

Zeru Yang, Fang-Ying Gong, Steve H. L. Yim, Chau Yuen

Accurately estimating urban carbon emissions is critical for sustainable urban planning, yet many existing approaches remain difficult to apply consistently across cities due to data-source heterogeneity and the lack of fine-grained semantic-temporal context in remote sensing dat…

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arxivcs.LGcs.AI2026-07-02

Conditional Co-Ablation: Recovering Self-Repair Backups in Transformer Circuits

Zhiren Gong, Zihao Zeng, Chau Yuen, Wei Yang Bryan Lim

Mechanistic interpretability often relies on component-level interventions to discover how a model produces a behavior. This guides attribution, capability knockout, and model pruning downstream to operate by scoring each unit by the effect of ablation in isolation. Such first-or…

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arxivcs.LGcs.AI2026-06-26

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants

Anushiya Arunan, Xin Li, Yan Qin, U-Xuan Tan, Nhu Khue Vuong, Xiaoli Li, et al.

Multimodal industrial anomaly inspection assistants are a critical component of next-generation smart factories, enabling interactive vision-language-based querying. However, multimodal large language models remain impractical for on-site deployment due to prohibitive computation…

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