Yu Guo, Shenghao Ye, Shuangwu Chen, Zijian Wen, Tao Zhang, Bai Qirui, Dong Jin, Yunpeng Hou, Huasen He, Jianyang, Xiaobin Tan. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Logical anomalies occur when a product's assembly violates prescribed logical rules, which widely exist in industrial assembly and packaging processes. Due to the difficulty in comprehending such complex logical relationships, a paucity of research has focused on the industrial logical anomaly diagnosis (LAD). Recently, large language models (LLMs) have demonstrated strong semantic understanding and zero-shot reasoning capabilities, making them a promising tool for LAD. However, directly applying LLMs to LAD still face two critical challenges: 1) the scarcity of abnormal samples in real-world settings, and 2) the propensity of LLMs to generate hallucinated or unreliable diagnostic conclusions. To address these challenges, we propose LogiDiag, a novel diagnostic reasoning method for LAD, to pinpoint where and why a product fails to comply with the logical rules, thereby elevating product quality and reducing remedial intervention cost. We design a visual descriptor that identifies product component attributes even in out-of-distribution abnormal images and organizes them into component descriptions for LLMs' comprehension. To mitigate hallucinations, we devise a planner to guide LLMs in diagnostic reasoning through rule orchestration, tool allocation, and chain-of-diagnosis generation. Experimental results on multiple benchmark datasets validate the competitive performance of LogiDiag.

Fault detection (FD) is essential for wafer quality control in semiconductor manufacturing (SM), as it can identify abnormal wafers in the early stages. However, frequent chamber discrepancies leads to distribution shifts in the data from different chambers, which causes performance degradation of the existing model. In this paper, we conceive a robust cross-chamber fault detection method in SM, which to the best of our knowledge is the first work that employs domain generalization to address the issue of cross-chamber fault detection in SM. Our basic idea is to map the samples from the source chambers and target chambers to the same hypersphere space, making normal samples cluster around a specific feature center while faulty samples stay away from it. Due to the scarcity of faulty samples, we propose a one-class classification-based fault detection method relying on normal samples to establish classification boundaries. To generalize the model to unseen target chambers, we design a meta-learning-based one-class domain generalization approach. We also devise a strategy to enhance distribution alignment within hypersphere, making the classification boundaries of various chambers be close to each other. The evaluations on real-world data collected from a wet process equipment in SM verify the high robustness of the model to chamber discrepancies with limited faulty samples.