TRAINING-FREE POSITIVE SPATIOTEMPORAL ENHANCEMENT FOR MITIGATING TEMPORAL HALLUCINATIONS IN VIDEO-LLMS

Video Large Language Models (Video-LLMs) recognize objects and scenes reliably, yet they still misread event order, motion direction and object-state transitions, which produces *temporal hallucinations*. We refer to the change of the same object's state across frames as *object-centric motion*. Existing training-free approaches boost *per-frame* object-centric evidence to reduce hallucination. Yet, they do not explicitly boost *cross-frame* object-centric motions, the temporal relationships of the same object. Our empirical analysis suggests that this information is partially decodable from object-level visual representations yet, the final layer does not decode them in the final prediction. From this diagnosis, we propose Feature Enhance, a training-free scheme that strengthens object-centric visual evidence by emphasizing foreground regions and making inter-frame changes more explicit in the visual representations. It reinforces foreground regions that persist across frames and injects foreground-gated inter-frame feature residuals, leaving the model parameters untouched. Across 3 backbones, LLaVA-OneVision, Qwen2.5-VL and LLaVA-Video, Feature Enhance improves average accuracy over the corresponding unmodified model in all 6 backbone and benchmark combinations on VideoHallucer and EventHallusion, with gains of up to 4.67 and 4.00 percentage points.