Exploring and Evaluating Multimodal Knowledge Reasoning Consistency of Multimodal Large Language Models (2025)

Abstract: In recent years, multimodal large language models (MLLMs) have achieved significant breakthroughs, enhancing understanding across text and vision. However, current MLLMs still face challenges in effectively integrating knowledge across these modalities during multimodal knowledge reasoning, leading to inconsistencies in reasoning outcomes. To systematically explore this issue, we propose four evaluation tasks and construct a new dataset. We conduct a series of experiments on this dataset to analyze and compare the extent of consistency degradation in multimodal knowledge reasoning within MLLMs. Based on the experimental results, we identify factors contributing to the observed degradation in consistency. Our research provides new insights into the challenges of multimodal knowledge reasoning and offers valuable guidance for future efforts aimed at improving MLLMs.

## introduction currently, multimodal large language models (mllms) @xcite have garnered significant attention for their ability to integrate multiple data modalities, such as text, images, and audio, thereby enhancing the model's capability in cross-modal understanding and reasoning @xcite . despite the progress mllms have made in specific reasoning tasks such as language understanding and image recognition, significant challenges remain in multimodal knowledge reasoning tasks that involve knowledge fusion across modalities. a major limitation is their insufficient ability to effectively integrate knowledge across different modalities, resulting in inconsistencies in reasoning outcomes, making it difficult for mllms to maintain reliable performance in complex reasoning tasks. to evaluate the reasoning capabilities of mllms, researchers have proposed numerous benchmark datasets that assess model performance across various tasks @xcite . however, many of these benchmarks primarily focus on evaluating the model's ability to interpret superficial visual information, such as object recognition @xcite , multi-class identification @xcite , and basic image description @xcite . while these tasks provide insights into the model's perceptual understanding, they fall short in assessing its capability to perform complex reasoning that requires deep integration of both visual and textual knowledge. as a result, existing evaluation frameworks may not fully capture the true reasoning potential of mllms, particularly in scenarios where the model needs to synthesize multimodal knowledge to derive nuanced inferences. existing large multimodal models still face significant challenges in maintaining reasoning consistency. experiments(see motivated by the above observation, we propose four evaluation tasks (see section 3.2 for details of all tasks.) and construct a new dataset to study the consistency problem encountered by mllm in multimodal reasoning. specifically, we introduce tasks involving multiple images and multiple reasoning hops to thoroughly investigate this issue. our dataset can serve as a common benchmark for complex multimodal knowledge reasoning. we systematically evaluate various popular mllms using our dataset and analyze the factors contributing to the inconsistency. the contributions of our work can be summarized as follows: 1) we discover that mllms suffer from inconsistency in multimodal knowledge reasoning. 2) we construct a multimodal, multiimage, multi-hop, multi-task dataset for evaluating multimodal knowledge reasoning. 1 . 3) based on the experimental results, we analyzed the causes of mllm inconsistency and found that consistency is affected by factors such as the number of inference hops and inference relations. ## dataset construction the text data used in the experiments is sourced from the mquake dataset @xcite , which is designed for knowledge graph editing and contains multiple data instances based on triples (s, r, o), where s represents the subject, r represents the relation, and o represents the object. we construct our data based on the knowledge triples before knowledge editing. the dataset's triple relations cover various levels of reasoning tasks, including two-hop, three-hop, and four-hop reasoning tasks. for each subject s and object o in the triple, we crawled ten relevant images from google. these images together with the text data triplets constitute the basic dataset d with a size of 3,770. we also decompose all multi-hop data in the original dataset d into two-hop dataset d t , with a size of 3,240. tasks. below, we detail the construction of each task dataset; single-image recognition for single-image recognition task, we utilize all four-hop data points in d and construct nhop reasoning data by truncating first n hops in the four hop question and selecting an image corresponding to the s in the first hop as the image input. multi-image recognition for the multi-image recognition task, we utilize all data in two-hop dataset d t but focus only on the first hop to reduce the difficulty of multi-image reasoning. we use gpt-4o to sort the crawled images and entities according to relevance, and use the clip model to filter images with a threshold value lower than 0.7, and perform sampling checks on these filtered images. sampling found that for person-type entities, the images crawled have high relevance after filtering, while for some non-person-type entities, some images with low relevance will be crawled, such as when searching for iphone, images of the apple logo will be crawled. therefore, we manually screened the images of some non-person-type entities @xcite to ensure that the images and entities can be correctly matched. we selected the three most relevant images as relevant images and an irrelevant image as a distractor. these four images constituted the input images. in addition, when constructing specific questions, the initial mquake dataset has provided some single-hop and multi-hop questions. when designing questions for other tasks, we let gpt-4o refer to these questions to better understand the reasoning relationship and generate high-quality questions. we also sampled and checked the results generated for each task to ensure the rationality of the designed questions. multi-image retrieval for the multi-image retrieval task, we also utilize all data in two-hop dataset d t . we construct both single-hop (using the first hop) and two-hop questions based on d t . we select the image most relevant to the entity to be retrieved as the input and randomly selected other images of the same type of entity as interference options. for text problems, we used gpt-4o to generate two prompts for each type of retrieval reasoning data based on forward retrieval and backward retrieval reasoning chains. knowledge association for the knowledge association task, we reused the images and texts from the multi-image recognition and multi-image forward retrieval tasks. using gpt-4o, we generated two question prompts, requiring the model to complete the multi-image recognition task from the first four images and the multi-image forward retrieval task from the second four images. the amount of data, number of images, and number of reasoning hops for different tasks are shown in the for the textual questions, the mquake dataset provides reasoning questions. we also used gpt4o to generate two distinct questions per data point. to increase diversity and enhance robustness, we randomly selected one question during testing, allowing us to build a diverse dataset covering multihop, multi-image, multi-task knowledge reasoning, for robustly evaluating multimodal knowledge reasoning ability of mllms. to illustrate our work more intuitively, when checking the correctness of an answer, we use aliases to match model output more accurately. we extracted synonyms and aliases for each candidate answer from wikipedia and created a keyvalue (kv) table containing the candidate answers and their corresponding aliases. each entry in this table records a candidate answer and its list of synonyms or aliases. since most words in our dataset (such as names of people and places) have clear aliases or variants, it effectively covers a variety of expressions that the model may use. during the evaluation process, we compare the model's output with each entry in the candidate answer and alias table. if the model's output matches any of the candidate answers or their synonyms/aliases, it is considered correct. this approach evaluates the model's ability to reason in natural language based on semantics rather than exact word matching. in addition, we also sampled and verified the output results of the model. we sampled 50 output results and found that in 96 percent of cases, the model output was completely consistent with this matching method. we then used gpt-4o to expand the alias of the answer, sampled and verified 50 output results again, and confirmed that all the output answers of the model were covered, in order to verify the rationality of the evaluation method. ## conclusion in this research, we discover the consistency problem in multimodal knowledge reasoning in mllms. we construct multiple tasks and design a multi-hop, multi-image, multi-task benchmark for evaluating consistency in multimodal knowledge reasoning. we find that current mllms struggle to maintain consistency when faced with complex reasoning task. the analysis further reveals multiple factors affecting consistency, including reasoning hops, relation type, task type and reasoning process, pointing out directions for future research.