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Physics-based phenomenological characterization of cross-modal bias in multimodal models
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Physics-based phenomenological characterization of cross-modal bias in multimodal models

#Algorithmic fairness #Multimodal bias #Physics-based characterization #Large language models #Cross-modal bias #Transformer dynamics #Explainable AI #AAAI2026

📌 Key Takeaways

  • Researchers developed a physics-based approach to characterize cross-modal bias in multimodal AI models
  • The method focuses on physical entities experienced during training rather than traditional symbolic approaches
  • Experiments showed multimodal inputs can reinforce modality dominance rather than mitigate it
  • The research received a Best Paper Award at AAAI2026's BiasinAI track

📖 Full Retelling

Researchers led by Hyeongmo Kim and seven colleagues from various institutions developed a physics-based approach to characterize cross-modal bias in multimodal large language models, submitting their findings to arXiv on February 24, 2026, where it received a Best Paper Award at the BiasinAI track in AAAI2026, addressing concerns that inconspicuous distortions in multimodal interaction dynamics can lead to systematic bias in AI systems. The paper introduces a novel framework for understanding and addressing algorithmic fairness in multimodal AI systems, arguing that while recent advances in multimodal large language models have made significant progress in understanding, reasoning, and generation, they can still exhibit systematic biases not fully captured by conventional analysis methods. The researchers shift from traditional cognitivist symbolic accounts or metaphysical approaches to a phenomenological explainable methodology that focuses on the physical entities that machines experience during training and inference, developing a surrogate physics-based model that describes transformer dynamics including semantic network structure and self-/cross-attention mechanisms. Through multi-input diagnostic experiments including perturbation-based analyses of emotion classification using Qwen2.5-Omni and Gemma 3n, and dynamical analysis of Lorenz chaotic time-series prediction, the team demonstrated that across two architecturally distinct MLLMs, multimodal inputs can actually reinforce modality dominance rather than mitigate it, revealing structured error-attractor patterns under systematic label perturbation that complement their dynamical analysis.

🏷️ Themes

AI fairness, Multimodal systems, Physics-based modeling

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Mentioned Entities

Fairness (machine learning)

Measurement of algorithmic bias

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Explainable artificial intelligence

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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.20624 [Submitted on 24 Feb 2026] Title: Physics-based phenomenological characterization of cross-modal bias in multimodal models Authors: Hyeongmo Kim , Sohyun Kang , Yerin Choi , Seungyeon Ji , Junhyuk Woo , Hyunsuk Chung , Soyeon Caren Han , Kyungreem Han View a PDF of the paper titled Physics-based phenomenological characterization of cross-modal bias in multimodal models, by Hyeongmo Kim and 7 other authors View PDF HTML Abstract: The term 'algorithmic fairness' is used to evaluate whether AI models operate fairly in both comparative (where fairness is understood as formal equality, such as "treat like cases as like") and non-comparative (where unfairness arises from the model's inaccuracy, arbitrariness, or inscrutability) contexts. Recent advances in multimodal large language models are breaking new ground in multimodal understanding, reasoning, and generation; however, we argue that inconspicuous distortions arising from complex multimodal interaction dynamics can lead to systematic bias. The purpose of this position paper is twofold: first, it is intended to acquaint AI researchers with phenomenological explainable approaches that rely on the physical entities that the machine experiences during training/inference, as opposed to the traditional cognitivist symbolic account or metaphysical approaches; second, it is to state that this phenomenological doctrine will be practically useful for tackling algorithmic fairness issues in MLLMs. We develop a surrogate physics-based model that describes transformer dynamics (i.e., semantic network structure and self-/cross-attention) to analyze the dynamics of cross-modal bias in MLLM, which are not fully captured by conventional embedding- or representation-level analyses. We support this position through multi-input diagnostic experiments: 1) perturbation-based analyses of emotion classification using Qwen2.5-Omni and Gemma 3n, and 2) dynamical analysis of Lore...
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