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.