Large Vision-Language Models (VLMs) often exhibit text inertia, where attention drifts from visual evidence toward linguistic priors, resulting in object hallucinations. Existing decoding strategies intervene only at the output logits and thus cannot correct internal reasoning drift, while recent internal-control methods based on heuristic head suppression or global steering vectors lack principled grounding. We introduce Conscious Gaze (CG-VLM), a training-free, inference-time framework that converts game-theoretic interpretability into actionable decoding control. A Cognitive Demand Sensor built on Harsanyi interactions estimates instantaneous vision-text synergy and identifies moments when visual grounding is necessary. Conditioned on this signal, a Focused Consensus Induction module selectively reorients mid-layer attention toward visual tokens before collapse into text priors. CG-VLM achieves state-of-the-art results on POPE and CHAIR across InstructBLIP, LLaVA, Qwen-VL, and mPLUG, while preserving general capabilities, demonstrating that token-level sensing enables precise, context-aware intervention without compromising foundational knowledge.

翻译：大型视觉语言模型（VLMs）常表现出文本惯性，即注意力从视觉证据漂移至语言先验，导致对象幻觉。现有解码策略仅干预输出逻辑值，无法纠正内部推理漂移；而近期基于启发式头抑制或全局导向向量的内部控制方法缺乏原理性基础。我们提出意识凝视（CG-VLM），一种免训练、推理时框架，将博弈论可解释性转化为可操作的解码控制。基于Harsanyi交互构建的认知需求传感器，可估计瞬时视觉-文本协同性，并识别需要视觉基础的关键时刻。基于此信号，聚焦共识归纳模块在注意力坍缩至文本先验前，选择性地将中间层注意力重定向至视觉标记。CG-VLM在InstructBLIP、LLaVA、Qwen-VL和mPLUG模型上，于POPE和CHAIR基准取得最先进结果，同时保持通用能力，证明标记级感知能实现精准、上下文感知的干预，且不损害基础知识。