Negotiating the Punchline: Contextual Meme Understanding via Discrete Semantic Energy Minimization

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.