Beyond Visual Enhancement: Adaptive Multi-Context Steering to Mitigate LVLM Hallucinations

The paper introduces AIMS (Adaptive Information Multi-source Steering), a lightweight, training‑free framework that dynamically balances visual, prefilled textual, and generated contexts during decoding to reduce hallucinations in Large Vision‑Language Models (LVLMs). By constructing compact prototypes for each context domain and estimating their affinities with the current query, AIMS assigns head‑wise steering weights that guide adaptive context integration without extra training or forward passes. Experiments across multiple LVLMs and decoding strategies show that AIMS effectively mitigates object hallucination while preserving general multimodal performance.

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• llms
• multimodal
• safety