Recent multimodal large language models (MLLMs) increasingly incorporate explainable reasoning for emotion understanding. However, reasoning based mainly on observable affective cues can reduce emotion understanding to superficial cue-label associations, giving rise to the Clever Hans effect. Such shortcuts become unre...
Jia Li, Yi-Chao He, Yang-Chen Yu et al.· 0 citations
Experimental results on the AVI Challenge 2026 validation set show that this framework reduces mean squared error (MSE) by approximately 25% compared with the baseline, and consistent improvements on the official test set demonstrate that the method achieves the best performance.
Jia Li, Qian-Cheng Chen, Wei Wang et al.· 0 citations
This work proposes a multimodal reasoning framework for cross-domain PCBA visual question answering that converts standards-derived, real-world, and auxiliary PCB-domain data into a unified instruction format and constructs verified reasoning traces aligned with visual evidence, question semantics, candidate options, a...
Jia Li, Li Dai, Peng Jia et al.· 0 citations
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