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Entropy-Driven Multimodal Answering (EDMA) is proposed, which uses logical entropy to formalise multimodal QA as an iterative process of entropy reduction and to quantify the contribution of each modality in distinguishing correct from incorrect candidate answers.
Large language models (LLMs) can memorize sensitive, private, or copyrighted content during pre-training, making machine unlearning necessary for removing targeted knowledge. Recent preference optimization (PO)-based unlearning methods improve stability over gradient ascent (GA)-based methods by introducing alignment-s...
G-ReAct is a reasoning framework for deep search that organizes reasoning as state evolution over a fixed-topology query graph, transforming exploratory search driven by textual history into graph-guided reasoning under explicit constraints.
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