Multimodal UN commonsense: From Odd to Ordinary and Ordinary to Odd (2025)

Abstract: Commonsense reasoning in multimodal contexts remains a foundational challenge in artificial intelligence. We introduce Multimodal UNcommonsense (MUN), a benchmark designed to evaluate models’ ability to handle scenarios that deviate from typical visual or contextual expectations. MUN pairs visual scenes with surprising or unlikely outcomes described in natural language, prompting models to either rationalize seemingly odd images using everyday logic or uncover unexpected interpretations in ordinary scenes. To support this task, we propose a retrieval-based in-context learning (R-ICL) framework that transfers reasoning capabilities from larger models to smaller ones without additional training. Leveraging a novel Multimodal Ensemble Retriever (MER), our method identifies semantically relevant exemplars even when image and text pairs are deliberately discordant. Experiments show an average improvement of 8.3% over baseline ICL methods, highlighting the effectiveness of R-ICL in low-frequency, atypical settings. MUN opens new directions for evaluating and improving visual-language models’ robustness and adaptability in real-world, culturally diverse, and non-prototypical scenarios.

## introduction in everyday life, commonsense functions as an invisible framework, akin to "dark matter" in the universe. though we cannot directly perceive it, commonsense subtly influences our decisions, such as recognizing social norms or interpreting ambiguous situations @xcite . while this commonsense often leads to stable reasoning in familiar contexts, it can falter when confronted with visual or textual cues that fall outside typical experience or cultural familiarity figure 1 : multimodal uncommonsense reasoning aims to produce explanations that make given outcomes appear likely. for example, overripe bananas (an uncommon context) can still be used for baking a sweet, moist banana cake (a common outcome), while a bag on a bench (common context) leads to an arrest (uncommon outcome). this highlights the challenge of bridging visual cues with logical reasoning, as addressed in our multimodal uncommonsense (mun) dataset. @xcite . however, most existing benchmarks for commonsense reasoning evaluate models on frequent or prototypical cases that are wellcovered by large-scale english-language corpora and standard vision-language datasets @xcite @xcite @xcite . as a result, current ai systems exhibit significant brittleness when faced with rare, ambiguous, or culturallyspecific phenomena that lie beyond the training distribution. to address this gap, we introduce the multimodal uncommonsense (mun) benchmark: a human-curated dataset specifically designed to evaluate models' ability to reason about uncommon or counterintuitive outcomes in visual contexts. unlike prior datasets that emphasize prototypical commonsense, mun centers on visual situations that violate typical expectations, such as an overripe banana being preferable for baking, or culturally specific gestures like the indian head wobble ## multimodal uncommonsense (mun) to advance research in visual uncommonsense reasoning, we have constructed the benchmark multimodal uncommonsense (mun), created to challenge models with scenarios that diverge from standard visual or contextual expectations. inspired by prior work @xcite on uncommonsense reasoning, specifically abductive reasoning about unusual situations, our dataset adopts a structured context-results-explanation paradigm. in this framework, models are required to interpret an image-based context along with a textual scenario (the results) and then generate an explanation that reconciles the two. ## experiments we evaluate the effectiveness of our proposed retrieved in-context learning (icl) approach for multimodal uncommonsense reasoning using the mun dataset. our experimental study is organised to shed light on two core questions: rq1. how does the number of in-context examples (shots) affect model performance? rq2. what is the impact of retrieval-based examples compared to randomly selected ones? to establish robust baselines and ensure comprehensive evaluation, we benchmark several state-ofthe-art vision-language models (vlms) and utilize a multimodal ensemble retriever for our retrievalbased icl approach. ## conclusion we introduce the multimodal uncommonsense (mun) dataset to evaluate how vision-language models handle atypical scenarios that challenge commonsense reasoning. extensive experiments show that retrieved in-context learning (icl) examples, rather than randomly chosen ones, enhance model performance. by bridging unexpected visual cues with logical explanations, we successfully guide models to produce more coherent, contextually aligned reasoning. this approach enables more adaptive and reliable multimodal ai systems that are better equipped to understand uncommon events, cultural nuances, and low-frequency phenomena in real-world settings.