Multimodal Large Language Models have achieved strong performance in single-video understanding, yet their ability to reason across multiple videos remains limited. Existing approaches typically concatenate multiple videos into a single input and perform direct inference, which introduces training–inference mismatch, information loss from frame compression, and a lack of explicit cross-video coordination. Meanwhile, current multi-video benchmarks primarily emphasize event-level comparison, leaving identity-level matching, fine-grained discrimination, and structured multi-step reasoning underexplored. To address these gaps, we introduce MVX-Bench, a Multi-Video Cross-Dimension Benchmark that reformulates 11 classical computer vision tasks into a unified multi-video question-answering framework, comprising 1,442 questions over 4,255 videos from diverse real-world datasets. We further propose SAMA, a Skill-Augmented Agentic Framework for Multi-Video Understanding, which integrates visual tools, task-specific skills, and a conflict-aware verification mechanism to support iterative and structured reasoning. Experiments show that SAMA improves substantially over its visual perception backbone and achieves the best average accuracy on MVX-Bench. Ablations confirm the benefits of skill-guided orchestration and conflict-aware verification.

Video Large Multimodal Models (VLMMs) have achieved impressive progress in video understanding, yet they remain limited in adaptive reasoning—the ability to revise conclusions when new evidence emerges. In reality, conclusions are rarely set in stone; additional context can strengthen or weaken an initial inference. To capture this capability, we introduce Defeasible Video Entailment (DVidE), a new task that challenges models to think like doubters, requiring them to dynamically update entailment judgments as evidence evolves. In DVidE, given a video premise and a textual hypothesis, the models must determine whether a new update strengthens or weakens the hypothesis (classification task) or generates a coherent update that modifies the entailment relationship (generation task). For solving the classification task, we propose the Chain of Counterfactual Thought (CoCT) framework, utilizing counterfactual reasoning, ASR-enhanced video content, and rationale refinement to reduce inference bias. For the generation task, we develop a framework that combines ASR output with a Large Language Model (LLM) to produce coherent, contextually relevant updates aligned with the intended strengthener or weakener goals. We also introduce a benchmark dataset with human annotations and an LLM-based evaluation metric for assessing generative performance. Experimental results show substantial improvements across multiple VLMM baselines, demonstrating that CoCT significantly enhances both classification and generation performance and strengthens the dynamic reasoning capabilities of video-language models.