Multimodal fusion requires integrating information from multiple sources that may conflict depending on context. Existing fusion approaches typically rely on static assumptions about source reliability, limiting their ability to resolve conflicts when a modality becomes unreliable due to situational factors such as sensor degradation or class-specific corruption. We introduce C$^2$MF, a context-specfic credibility-aware multimodal fusion framework that models per-instance source reliability using a Conditional Probabilistic Circuit (CPC). We formalize instance-level reliability through Context-Specific Information Credibility (CSIC), a KL-divergence-based measure computed exactly from the CPC. CSIC generalizes conventional static credibility estimates as a special case, enabling principled and adaptive reliability assessment. To evaluate robustness under cross-modal conflicts, we propose the Conflict benchmark, in which class-specific corruptions deliberately induce discrepancies between different modalities. Experimental results show that C$^2$MF improves predictive accuracy by up to 29% over static-reliability baselines in high-noise settings, while preserving the interpretability advantages of probabilistic circuit-based fusion.

翻译：多模态融合需要整合来自多个可能因情境不同而产生冲突的信息源。现有融合方法通常依赖于关于源可靠性的静态假设，这限制了它们在因传感器退化或类别特定损坏等情境因素导致某一模态不可靠时解决冲突的能力。我们提出C²MF，一种基于条件概率电路（CPC）对每个实例的源可靠性进行建模的情境感知可信多模态融合框架。我们通过情境特定信息可信度（CSIC）形式化实例级可靠性，CSIC是一种基于KL散度的度量，可通过CPC精确计算。CSIC将传统的静态可信度估计作为特例进行推广，从而实现原则性的自适应可靠性评估。为了评估跨模态冲突下的鲁棒性，我们提出了冲突基准，其中类别特定损坏会故意引发不同模态之间的差异。实验结果表明，在高噪声环境下，C²MF相较于基于静态可靠性的基线方法，预测准确率提升高达29%，同时保持了基于概率电路融合的可解释性优势。