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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...
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.
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...
This paper shows for the first time that visual token pruning enhances the robustness of Multimodal Large Language Models (MLLMs), mitigating vulnerabilities such as jailbreak attacks and hallucinations, and maintains strong results on general datasets like MME.
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