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

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 by creating a persistent visual anchor. We extract a robust evidence direction (**CARD**) directly from the model's prefill residual updates, and inject it into the decoding process. This injection is modulated by an adaptive gate, the **Beta Gate**, which acts as a trust mechanism and ensures the visual reminder is applied only when necessary. Experiments on LLaVA-1.5 (7B/13B), Idefics2, InstructBLIP, and Qwen2.5-VL demonstrate that RUDDER consistently mitigates hallucination (with greedy decoding, RUDDER reduces CHAIR$_S$ by an average of **24.4%** and CHAIR$_i$ by **23.6%** relative) and scales effectively across architectures, all while maintaining **>96.0%** throughput. The code is available at https://github.com/Akko000/RUDDER-Residual-Update-Directed-DEcoding-Regulation-