This paper proposes ResOT, a training-free method to mitigate object hallucination in large vision-language models (LVLMs) at inference time. ResOT uses localized distribution alignment to repair representations by projecting hallucinated directions away from the faithful subspace and aligning the hallucinated distribution with the faithful one. This approach improves image caption quality and multimodal performance across multiple benchmarks.