arXiv:2606.04596v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are increasingly used for video understanding, yet their reliability under multi-video inputs remains poorly understood. We study positional bias in multi-video summarization, where the quality of a per-video summary can change with the video's input slot even when the underlying content is unchanged. We construct a benchmark from ActivityNet and News videos, covering Cooking, Domestic, Leisure, and News settings with two- and four-video inputs. We evaluate nine open-source and proprietary MLLMs and measur
The proliferation of MLLMs for complex tasks necessitates rigorous evaluation of their inherent biases and reliability, especially as they move into multi-modal applications.
Understanding positional bias in MLLMs is critical for deploying robust and fair AI systems in areas like content summarization and video analysis, impacting both user experience and trust.
This research highlights a new class of foundational reliability issues in MLLMs when handling multi-video inputs, prompting developers to account for subtle architectural limitations.
- · AI developers focused on model robustness and explainability
- · Companies building MLLM evaluation benchmarks
- · Ethical AI research organizations
- · MLLM developers overlooking positional bias
- · Applications relying solely on unverified MLLM multi-video summarization
- · Users receiving potentially biased summaries
MLLM developers will likely integrate more sophisticated input processing to mitigate positional bias.
New MLLM architectures may emerge that explicitly address and neutralize order-dependent sensitivities in multi-modal inputs.
Industry standards and certifications for MLLM reliability in multi-input tasks could become commonplace, influencing adoption and market perception.
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