Object hallucination in Multimodal Large Language Models (MLLMs) often appears in two forms: omitting objects that are present in the image and generating objects that are absent. Existing training-free mitigation methods usually apply a uniform intervention to both cases. We find that this strategy leads to an imbalanced effect: it can reduce some omission errors, but often introduces new fabrications. To understand this behavior, we analyze spatial attention patterns and layer-wise attention dynamics during generation. Our results suggest that omissions often occur when the model attends to the relevant visual regions but gradually loses visual confidence during decoding, whereas fabrications are more closely related to deep-layer semantic priors overriding visual evidence. Based on this observation, we propose Visual Potential Field Calibration (VPFC), a training-free hidden-state calibration method. VPFC locates an intervention region using the spatial centroid of high-confidence visual tokens and applies a lightweight hidden-state update to adjust the model’s generative confidence. Experiments on multiple hallucination benchmarks show that VPFC consistently improves over existing training-free methods while maintaining inference efficiency close to standard greedy decoding.

Multimodal Large Language Models (MLLMs) encode images into dense visual token sequences, creating substantial attention and KV-cache overhead. Existing visual token compression methods reduce this cost by pruning low-saliency tokens in shallow layers, but this practice couples temporary computational deactivation with irreversible information deletion. We show that this coupling breaks an intrinsic inference pattern in MLLMs. Through layer-wise attention analysis, we identify Visual Backtracking: visual pivots remain spatially stable, yet in middle layers they actively retrieve context from tokens that appeared unimportant earlier. Thus, early low-saliency tokens can be inactive for computation while still being useful for later reasoning. Motivated by this finding, we propose ReTrace, a backtracking-aware compression framework that introduces a dormant token state. ReTrace caches non-pivot tokens outside expensive layer-wise computation, restores them during the backtracking window, and prunes only after their information has been absorbed. Experiments on Qwen2.5-VL and InternVL3 demonstrate nearly 90% reductions in FLOPs and KV cache, a 2.83 prefilling speedup, and strong performance retention across complex vision-language benchmarks.