# Mask What Matters: Mitigating Object Hallucinations in Multimodal Large Language Models with Object-Aligned Visual Contrastive Decoding

arXiv: https://arxiv.org/abs/2602.11737 · HTML: https://mlsanity.com/paper/2602.11737/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Hallucination mitigation in LVLMs](https://mlsanity.com/mllm/hallucination-mitigation-in-lvlms/)

## Builds on (most cited first, 10 of 10)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge](https://mlsanity.com/paper/2206.01718/) (2022)
- [Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding](https://mlsanity.com/paper/2311.16922/) (2023)
- [Object Hallucination in Image Captioning](https://mlsanity.com/paper/1809.02156/) (2018)
- [Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention](https://mlsanity.com/paper/2406.12718/) (2024)
- [CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models](https://mlsanity.com/paper/2406.01920/) (2024)
- [Different Time, Different Language: Revisiting the Bias Against Non-Native Speakers in GPT Detectors](https://mlsanity.com/paper/2602.05769/) (2026)
- [Plasticity vs. Rigidity: The Impact of Low-Rank Adapters on Reasoning on a Micro-Budget](https://mlsanity.com/paper/2601.06677/) (2026)

## Built on by (most cited first, 3 of 3)

- [Instruction-Evidence Contrastive Dual-Stream Decoding for Grounded Vision-Language Reasoning](https://mlsanity.com/paper/2604.25809/) (2026)
- [Do We Really Need External Tools to Mitigate Hallucinations? SIRA: Shared-Prefix Internal Reconstruction of Attribution](https://mlsanity.com/paper/2605.14621/) (2026)
- [Diagnosing and Mitigating Perception-Decision Misalignment in Omni-LLMs via Modality Subspace Activation](https://mlsanity.com/paper/2608.14655/) (2026)

## Abstract (arXiv)

We study object hallucination in Multimodal Large Language Models (MLLMs) and improve visual contrastive decoding (VCD) by constructing an object-aligned auxiliary view. We leverage object-centric attention in self-supervised Vision Transformers. In particular, we remove the most salient visual evidence to construct an auxiliary view that disrupts unsupported tokens and produces a stronger contrast signal. Our method is prompt-agnostic, model-agnostic, and can be seamlessly plugged into the existing VCD pipeline with little computation overhead, i.e., a single cacheable forward pass. Empirically, our method demonstrates consistent gains on two popular object hallucination benchmarks across two MLLMs.