Challenge: Large Language Models (LLMs) have greatly influenced the development of Large Multi-modal Video Models.
Approach: They propose a benchmark to assess the proficiency of Large Multi-modal Video Models (LMMs) in detecting and localizing anomalies and inconsistencies in videos.
Outcome: The proposed benchmark assesses the proficiency of Video-LMMs in detecting and localizing anomalies and inconsistencies in videos.

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VALU: A Benchmark for Video Anomaly Temporal Localization and Understanding at Multiple Semantic Levels (2026.acl-long)

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Challenge: Recent advances in Video Large Language Models (Video-LLMs) enhance the ability of VAU models to describe and interpret anomalies.
Approach: They propose a benchmark that explicitly defines anomalies across five semantic levels and provides detailed temporal boundaries and detailed textual descriptions for each.
Outcome: The proposed benchmark defines anomalies across five semantic levels and provides detailed descriptions for each.
MVTamperBench: Evaluating Robustness of Vision-Language Models (2025.findings-acl)

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Challenge: Multimodal Large Language Models (MLLMs) have been a key advance in video understanding but their vulnerability to adversarial tampering remains underexplored.
Approach: They evaluate MLLMs against five prevalent tampering techniques to assess their robustness . they use a tampered video format to examine the vulnerability of ML models .
Outcome: The benchmark evaluates MLLMs against five prevalent tampering techniques based on 19 video manipulation tasks.
AD-LLM: Benchmarking Large Language Models for Anomaly Detection (2025.findings-acl)

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Challenge: Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring.
Approach: They propose a benchmark that evaluates how large language models (LLMs) can help with NLP anomaly detection.
Outcome: The proposed model can perform zero-shot detection without tasks-specific training, data augmentation and model selection, and it can suggest unsupervised AD models.
A Culturally-diverse Multilingual Multimodal Video Benchmark & Model (2025.emnlp-main)

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Challenge: Large multimodal models have gained attention for their effectiveness to understand and generate descriptions of visual content.
Approach: They propose a multilingual Video LMM benchmark to evaluate video LMMs across 14 languages . they also introduce a machine translated multilingual video training set .
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NLP-ADBench: NLP Anomaly Detection Benchmark (2025.findings-emnlp)

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Challenge: Anomaly detection (AD) is an important machine learning task, but its effectiveness in detecting harmful content, phishing attempts, and spam reviews is limited.
Approach: They introduce NLP-ADBench, the most comprehensive NLP anomaly detection benchmark to date . it includes eight curated datasets and 19 state-of-the-art algorithms .
Outcome: The NLP-ADBench benchmark includes 19 state-of-the-art methods and 8 curated datasets . no single model dominates across all datasets, indicating need for automated model selection .
VideoPASTA: 7K Preference Pairs That Matter for Video-LLM Alignment (2025.emnlp-main)

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Challenge: Video-language models excel at understanding video content but struggle with spatial relationships, temporal ordering, and cross-frame continuity.
Approach: They propose a framework that trains video-LLMs to distinguish accurate representations from carefully crafted adversarial examples.
Outcome: Experiments show that VideoPASTA improves performance without human annotation or captioning . the framework can be used on various state-of-the-art video-LLMs with no human annotation .
TC-Bench: Benchmarking Temporal Compositionality in Conditional Video Generation (2025.findings-acl)

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Challenge: Existing video generation models struggle to interpret compositional changes and synthesize components across different time steps.
Approach: They propose a temporal compositionality benchmark that uses text prompts and ground truth videos to evaluate compositional changes in video.
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INFACT: A Diagnostic Benchmark for Induced Faithfulness and Factuality Hallucinations in Video-LLMs (2026.acl-long)

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Challenge: Existing benchmarks only evaluate models in clean settings due to hallucinations .
Approach: They propose a diagnostic benchmark that evaluates models in four modes for faithfulness and factuality.
Outcome: The proposed benchmark evaluates models in four modes: Base (clean), Visual Degradation, Evidence Corruption, and Temporal Intervention for order-sensitive items.
CAMEL-Bench: A Comprehensive Arabic LMM Benchmark (2025.findings-naacl)

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Challenge: Recent years have witnessed a significant interest in developing large multimodal models capable of performing various visual reasoning and understanding tasks.
Approach: They propose to use Arabic as a language to evaluate large multi-modal models capable of performing visual reasoning and understanding tasks.
Outcome: The proposed benchmark comprises eight diverse domains and 38 sub-domains to represent a large population of over 400 million speakers.
MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues (2026.findings-acl)

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Challenge: Existing evaluation benchmarks for Multimodal Large Language Models (MLLMs) focus on single-turn question answering, overlooking the complexity of multi-turn dialogues in real-world scenarios.
Approach: They propose a video understanding benchmark for MLLMs in multi-turn dialogues that assesses six core competencies that focus on perceptivity and interactivity.
Outcome: The MT-Video-Bench evaluates 1,000 multi-turn dialogues from diverse domains and reveals significant performance discrepancies and limitations in handling multi-turned video dialogues.

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