Multimodal Stance Detection (MSD) is a crucial task for understanding public opinion on social media.Existing methods predominantly operate by learning to fuse modalities.They lack an explicit reasoning process to discern how inter-modal dynamics, such as irony or conflict, collectively shape the user's final stance, leading to frequent misjudgments.To address this, we advocate for a paradigm shift from learning to fuse to learning to reason.We introduce MIND, a Meta-cognitive Intuitivereflective Network for Dual-reasoning.Inspired by the dual-process theory of human cognition, MIND operationalizes a self-improving loop.It first generates a rapid, intuitive hypothesis by querying evolving Modality and Semantic Experience Pools.Subsequently, a meta-cognitive reflective stage uses Modality-CoT and Semantic-CoT to scrutinize this initial judgment, distill superior adaptive strategies, and evolve the experience pools themselves.These dual experience structures are continuously refined during training and recalled at inference to guide robust and context-aware stance decisions.Extensive experiments on the MMSD benchmark demonstrate that our MIND significantly outperforms most baseline models and exhibits strong generalization.

Contextual meme understanding decodes implicit meaning from ambiguous visual metaphors and social context, which is crucial for online communication analysis. However, existing linear reasoning paradigms reductively cast this process as deterministic decoding, overlooking the fundamental reality that meme interpretation necessitates the dynamic alignment of multimodal cues. Lacking mechanisms to measure and correct misalignment, these static models inevitably allow initial perceptual failures to cascade into irreversible hallucinations. To address this, we propose Semantic Energy Entropy Descent (SEED), a framework that reformulates contextual meme understanding as an energy minimization problem within a discrete semantic space. Specifically, SEED constructs a Convergent Heterogeneous Thought Tree (CHTT) as the optimization workspace, where an Evaluator Agent quantifies the semantic inconsistency of hypotheses via a Semantic Energy Mechanism. By calculating a Discrete Semantic Gradient as structured feedback, the framework activates a Supplier Agent to retrieve knowledge and context evidence and a Reasoner Agent to refine explanations, thereby orchestrating Semantic Descent Dynamics that iteratively drive the reasoning trajectory to converge on a stable, low-entropy interpretation. Experimental results show that SEED consistently outperforms strong baselines on both classification and generation, reducing logical hallucinations and improving cultural grounding.