标题： 视觉-语言内省：通过可解释的双因果引导减轻多模态大语言模型的过度自信幻觉 显示英文标题

标题： Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering

摘要： 物体幻觉严重损害了多模态大语言模型的可靠性，通常源于认知内省的根本性失败，其中模型盲目信任语言先验而非具体的视觉证据。 现有的缓解方法仍然有限：对比解码方法仅表面运作而未纠正内部语义错位，而当前的潜在控制方法依赖于缺乏实例特定精度的静态向量。 我们引入了视觉-语言内省（VLI），一种无需训练的推理框架，模拟元认知自我修正过程。 VLI首先进行属性内省，通过概率冲突检测诊断幻觉风险并定位因果视觉锚点。 然后采用可解释的双向因果控制，主动调节推理过程，动态地将视觉证据与背景噪声分离，同时通过自适应校准中和盲目的信心。 VLI在先进模型上取得了最先进的性能，在MMHal-Bench上将物体幻觉率降低了12.67%，在POPE上提高了5.8%的准确性。 显示英文摘要

摘要： Object hallucination critically undermines the reliability of Multimodal Large Language Models, often stemming from a fundamental failure in cognitive introspection, where models blindly trust linguistic priors over specific visual evidence. Existing mitigations remain limited: contrastive decoding approaches operate superficially without rectifying internal semantic misalignments, while current latent steering methods rely on static vectors that lack instance-specific precision. We introduce Vision-Language Introspection (VLI), a training-free inference framework that simulates a metacognitive self-correction process. VLI first performs Attributive Introspection to diagnose hallucination risks via probabilistic conflict detection and localize the causal visual anchors. It then employs Interpretable Bi-Causal Steering to actively modulate the inference process, dynamically isolating visual evidence from background noise while neutralizing blind confidence through adaptive calibration. VLI achieves state-of-the-art performance on advanced models, reducing object hallucination rates by 12.67% on MMHal-Bench and improving accuracy by 5.8% on POPE.