CMR-SPB: Cross-Modal Multi-Hop Reasoning over Text, Image, and Speech with Path Balance

Cross-modal multi-hop reasoning (CMR) is a valuable yet underexplored capability of multimodal large language models (MLLMs), entailing the integration of information from multiple modalities to produce a coherent output for a given context. We argue that existing benchmarks for evaluating this ability have critical shortcomings: (1) they largely overlook the speech modality, and (2) they exhibit heavily biased reasoning path distributions, which can severely undermine fair evaluation. To address these limitations, we introduce a novel benchmark -- Cross-Modal Multi-Hop Reasoning over Text, Image and Speech with Path Balance (CMR-SPB) -- designed to assess tri-modal multi-hop reasoning while ensuring both unbiased and diverse reasoning paths. Our experiments with the new dataset reveal consistent model failures in specific reasoning sequences and show that biased benchmarks risk misrepresenting model performance. Finally, based on our extensive analysis, we propose a new ECV (Extract, Connect, Verify) prompting technique that effectively mitigates the performance gap across different reasoning paths. Overall, we call for more careful evaluation in CMR to advance the development of robust multimodal AI.