Memes have emerged as a popular form of multimodal online communication, where their interpretation heavily depends on the specific context in which they appear. Current approaches predominantly focus on isolated meme analysis, either for harmful content detection or standalone interpretation, overlooking a fundamental challenge: the same meme can express different intents depending on its conversational context. This oversight creates an evaluation gap: although humans intuitively recognize how context shapes meme interpretation, Large Vision Language Models (LVLMs) can hardly understand context-dependent meme intent. To address this critical limitation, we introduce MemeReaCon, a novel benchmark specifically designed to evaluate how LVLMs understand memes in their original context. We collected memes from five different Reddit communities, keeping each meme’s image, the post text, and user comments together. We carefully labeled how the text and meme work together, what the poster intended, how the meme is structured, and how the community responded. Our tests with leading LVLMs show a clear weakness: models either fail to interpret critical information in the contexts, or overly focus on visual details while overlooking communicative purpose. MemeReaCon thus serves both as a diagnostic tool exposing current limitations and as a challenging benchmark to drive development toward more sophisticated LVLMs of the context-aware understanding.

Recent advances in large language models have demonstrated remarkable performance on Contextual Question Answering (CQA). However, prior approaches typically employ elaborate reasoning strategies regardless of question complexity, leading to low adaptability. Recent efficient test-time scaling methods introduce budget constraints or early stop mechanisms to avoid overthinking for straightforward questions. But they add human bias to the reasoning process and fail to leverage models’ inherent reasoning capabilities. To address these limitations, we present T²: Think-to-Think, a novel framework that dynamically adapts reasoning depth based on question complexity. T² leverages the insight that if an LLM can effectively solve similar questions using specific reasoning strategies, it can apply the same strategy to the original question. This insight enables to adoption of concise reasoning for straightforward questions while maintaining detailed analysis for complex problems. T² works through four key steps: decomposing questions into structural elements, generating similar examples with candidate reasoning strategies, evaluating these strategies against multiple criteria, and applying the most appropriate strategy to the original question. Experimental evaluation across seven diverse CQA benchmarks demonstrates that T² not only achieves higher accuracy than baseline methods but also reduces computational overhead by up to 25.2%.

Conversational humor is the key to capturing dialogue semantics and dialogue comprehension, which is usually generated in multiple modalities, such as linguistic rhetoric (textual modality), exaggerated facial expressions or movements (visual modality), and quirky intonation (acoustic modality). However, existing multimodal corpora for conversation humor are coarse-grained, and the modality is insufficient to support the conversational humor recognition task. This paper designed an annotation scheme for multimodal humor datasets, and constructed a corpus based on a Chinese sitcom for conversational humor recognition, named MUCH. The MUCH corpus consists of 34,804 utterances in total, and 7,079 of them are humorous. We employed both unimodal and multimodal methods to test our MUCH corpus. Experimental results showed that the multimodal approach could achieve 75.94% in terms of F1-score and surpassed the performance of most unimodal methods, which demonstrated that the MUCH corpus was effective for multimodal humor recognition tasks.