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.

Multimodal foundation models that integrate audio, vision, and language achieve strong performance on reasoning and generation tasks, yet their robustness to adversarial manipulation remains poorly understood. We study a realistic and underexplored threat model: untargeted, audio-only adversarial attacks on trimodal audio–video–language models. We analyze six complementary attack objectives that target different stages of multimodal processing, including audio encoder representations, cross-modal attention, hidden states, and output likelihoods. Across four state-of-the-art models and multiple benchmarks, we show that audio-only perturbations can induce severe multimodal failures, achieving up to 96% attack success rate. We further show that attacks can be successful at low perceptual distortions (LPIPS ≤ 0.08, SI-SNR ≥ 0 dB) and benefit more from extended optimization than increased data scale. We evaluate the feasibility of these attacks under physically realistic conditions by incorporating room impulse response (RIR) modeling, showing that audio-only perturbations remain effective under environmental transformations and thus highlight the practical risk of single-modality attacks in real-world multimodal systems. Transferability across models and encoders remains limited, while speech recognition systems such as Whisper primarily respond to perturbation magnitude, achieving >97% attack success under severe distortion. These results expose a previously overlooked single-modality attack surface in multimodal systems and motivate defenses that enforce cross-modal consistency. Our project website is available at https://aafiya-h.github.io/soundbreak/.

Fact-checking claims is a highly laborious task that involves understanding how each factual assertion within the claim relates to a set of trusted source materials. Existing approaches make sample-level predictions but fail to identify the specific aspects of the claim that are troublesome and the specific evidence relied upon. In this paper, we introduce a method and new benchmark for this challenging task. Our method predicts the fine-grained logical relationship of each aspect of the claim from a set of multimodal documents, which include text, image(s), video(s), and audio(s). We also introduce a new benchmark (M3DC) of claims requiring multimodal multidocument reasoning, which we construct using a novel claim synthesis technique. Experiments show that our approach outperforms other models on this challenging task on two benchmarks while providing finer-grained predictions, explanations, and evidence.