Recent progress in vision-language models has enabled processing of increasingly long video sequences, but handling extended token streams does not translate to understanding complex narrative structure in long videos. Existing long-video benchmarks focus on needle-in-a-haystack retrieval rather than evaluating how low-level actions form events, interact across time, and drive narratives, for example whether a model can connect an early job loss to a later relationship breakup despite intervening scenes or flashbacks. We introduce NEST (Narrative Event Structures in Time for Long Video Understanding), a dataset of 1,005 full-length movies (avg. 98 minutes), each annotated with about 103 multimodal narrative events grounded in visual content, dialogue, and audio. NEST links these events through relations that reflect narrative structure, including explicit temporal ordering, hierarchical composition, and long-range dependencies. We introduce baselines for event trigger detection (ETD), event localization (EL), event argument extraction (EAE), and event relation extraction (ERE). The benchmark is highly challenging for grounded event discovery, with the best performance across evaluated zero-shot models on main evaluation subsets remaining below 8% for ETD, 6% for EL, and 11% for EAE. In contrast, ERE is more tractable once events are given, reaching 34.23% F1 zero-shot and 49.20% F1 after fine-tuning.

Understanding how the same real-world event is represented across text, video, and audio remains a fundamental challenge in information extraction. While prior datasets have explored events within individual modalities or limited modality pairs, a setting that combines all three modalities remains largely unexplored. We introduce M³EC (Multimodal Multidocument Event Coreference), a new benchmark for multimodal, multidocument event extraction and coreference resolution. It is built from tightly related news clusters that combine eventful videos, audio files, and news articles. We also introduce a semi-automated annotation pipeline, AutoEventAnnotator, to generate automated annotations at scale. Additionally, we construct a human-annotated test set for robust evaluation. To our knowledge, this is the first benchmark to support cluster-level multidocument event extraction and event coreference across text, video, and audio. Evaluation with strong multimodal LLMs shows that the majority of them fail to extract valid events in video and particularly in audio. We further show that models finetuned on the annotations generated by our automated pipeline achieve consistent improvements, including more than +30 F1 on video event extraction, +15 F1 on audio event extraction, and +40 CoNLL F1 on end-to-end coreference resolution, highlighting the usefulness of M³EC for training and evaluation in multimodal event understanding.

Large Vision-Language Models (LVLMs) have achieved strong performance on vision-language tasks, particularly Visual Question Answering (VQA). While prior work has explored unimodal biases in VQA, the problem of selection bias in Multiple-Choice Question Answering (MCQA), where models may favor specific option tokens (e.g., “A”) or positions, remains underexplored. In this paper, we investigate both the presence and nature of selection bias in LVLMs through fine-grained MCQA benchmarks spanning easy, medium, and hard difficulty levels, defined by the semantic similarity of the options. We further propose an inference-time logit-level debiasing method that estimates an ensemble bias vector from general and contextual prompts and applies confidence-adaptive corrections to the model’s output. Our method mitigates bias without retraining and is compatible with frozen LVLMs. Extensive experiments across several state-of-the-art models reveal consistent selection biases that intensify with task difficulty, and show that our mitigation approach significantly reduces bias while improving accuracy in challenging settings. This work offers new insights into the limitations of LVLMs in MCQA and presents a practical approach to improve their robustness in fine-grained visual reasoning. Datasets and code are available at: https://github.com/Atabuzzaman/Selection-Bias-of-LVLMs