Adaptive Residual-Update Steering for Low-Overhead Hallucination Mitigation in Large Vision Language Models

arXiv:2511.10292v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) typically process visual inputs as a prefix to the language decoder. As the model autoregressively generates text, this initial visual information inevitably undergoes "dilution" leading the model to over-rely on language priors and hallucinate objects. Existing interventions attempt to correct this by contrasting logits or iteratively refining outputs, but they incur prohibitive latency costs. We propose Residual-Update Directed DEcoding Regulation (RUDDER), a framework that counters visual dilution

Source: arXiv cs.AI — read the full report at the original publisher.

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