LogicGaze：通过反事实验证基准测试视觉叙事中的因果一致性
LogicGaze: Benchmarking Causal Consistency in Visual Narratives via Counterfactual Verification
AI总结：
LogicGaze通过反事实验证基准测试，评估视觉语言模型在视觉叙事中因果一致性验证的能力，揭示了现有模型在处理因果链和幻觉问题上的不足。
摘要：
尽管顺序推理增强了视觉语言模型（VLMs）执行复杂多模态任务的能力，但它们在将这些推理链与实际视觉证据相结合时的可靠性仍不够深入研究。我们引入了LogicGaze，一种新的基准框架，旨在严格检验VLMs是否能通过视觉输入验证顺序因果链，特别是针对普遍存在的幻觉问题。从ShareGPT4Video的4万个视频片段和Flickr30k图像子集精心挑选，LogicGaze将因果序列与视觉上矛盾但语言上合理的扰动相结合，迫使模型验证每一步推理的真实性。我们的三重评估协议——因果验证、 grounded叙述合成和扰动拒绝——揭示了最先进的VLMs如Qwen2.5-VL-72B存在显著漏洞。LogicGaze倡导稳健、可信的多模态推理，所有资源均在匿名仓库中公开。
英文摘要：
While sequential reasoning enhances the capability of Vision-Language Models (VLMs) to execute complex multimodal tasks, their reliability in grounding these reasoning chains within actual visual evidence remains insufficiently explored. We introduce LogicGaze, a novel benchmark framework designed to rigorously interrogate whether VLMs can validate sequential causal chains against visual inputs, specifically targeting the pervasive issue of hallucination. Curated from 40,000 video segments from ShareGPT4Video and a subset of Flickr30k imagery, LogicGaze integrates causal sequences with visually contradictory yet linguistically plausible perturbations, compelling models to verify the authenticity of each reasoning step. Our tripartite evaluation protocol - Causal Validation, Grounded Narrative Synthesis, and Perturbation Rejection - exposes significant vulnerabilities in state-of-the-art VLMs such as Qwen2.5-VL-72B. LogicGaze advocates for robust, trustworthy multimodal reasoning, with all resources publicly available in an anonymized repository.
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