The transformer architecture (V aswani et al., 2017) has revolutionized the field of machine learning,

Table 9) and identifying reflections (Error #20 in Table 13) are also noted.

Our comprehensive analysis of 20K real-world interactions reveals important insights into the failure cases of top-performing VLMs.

Citation networks are critical infrastructures of modern science, serving as intricate webs of past literature and enabling researchers to navigate the knowledge production system. To mine information hiding in the link space of such networks, predicting which previous papers (candidates) will a new paper (query) cite is a critical problem that has long been studied. However, an important gap remains unaddressed: the roles of a paper's citations vary significantly, ranging from foundational knowledge basis to superficial contexts.

This work was done during the internship at Microsoft Research Asia.

However, it becomes increasingly challenging with larger models because updating all parameters is impractical.

Recent advancements in large multimodal language models have demonstrated remarkable proficiency across a wide range of tasks. Y et, these models still struggle with understanding the nuances of human humor through juxtaposition, particularly when it involves nonlinear narratives that underpin many jokes and humor cues. This paper investigates this challenge by focusing on comics with contradictory narratives, where each comic consists of two panels that create a humorous contradiction.