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This paper proposes MIDAS, a unified framework for incomplete multimodal sentiment analysis that uses mutual information disentanglement and uncertainty-aware fusion to robustly represent and integrate modalities under missing-data conditions.
An intuitive, visual introduction to information theory covering entropy, mutual information, and channel capacity, assuming only basic probability. The paper explains fundamental limits of compression and transmission.
Proposes MI-EPO, an information-theoretic framework for multi-objective alignment of large language models that uses mutual information to enhance exploration and ensure generated responses are distinguishable and aligned with different preference vectors, achieving stable trade-offs across conflicting objectives.
InfoShield introduces a privacy-preserving method for speech representations in mental health screening using information-theoretic optimization, reducing sensitive attribute inference while maintaining diagnostic accuracy. A novel TimeAwareMINE estimator addresses temporal-static misalignment in sequential speech.
InfoAtlas is a foundation model that directly estimates mutual information in a single forward pass, achieving 100x speedup over traditional neural estimators while matching accuracy. It is pretrained on synthetic data and generalizes to real-world scenarios.
This paper introduces PromptNCE, a method that uses large language models and contrastive prompts to estimate pointwise mutual information zero-shot, achieving high correlation with human-derived ground truth across three datasets.
OmniISR proposes a unified framework combining centralized and federated learning via intermediate supervision and regularization at hidden layers, offering theoretical convergence guarantees and reducing the CL–FL gap by 22.60%.
This paper introduces a spectral diagnostic method to detect hidden coalitions in multi-agent AI systems by analyzing internal neural representations via mutual information, addressing critical AI safety and alignment challenges.
ArXiv preprint quantifies layered RTL and LTR constraints in the Voynich Manuscript, showing 97 % of cross-boundary mutual information lies in specific grapheme transitions and that simple generative models cannot simultaneously reproduce all observed structural signatures.