# Attention at Rest Stays at Rest: Breaking Visual Inertia for Cognitive Hallucination Mitigation

arXiv: https://arxiv.org/abs/2604.01989 · HTML: https://mlsanity.com/paper/2604.01989/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 26 of its 29 references that are in the map.
- First posted on arXiv 2 Apr 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, 26 of 26)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [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)
- [OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation](https://mlsanity.com/paper/2311.17911/) (2023)
- [Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs](https://mlsanity.com/paper/2407.21771/) (2024)
- [See What You Are Told: Visual Attention Sink in Large Multimodal Models](https://mlsanity.com/paper/2503.03321/) (2025)
- [MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation](https://mlsanity.com/paper/2410.11779/) (2024)
- [Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models](https://mlsanity.com/paper/2410.03577/) (2024)
- [ClearSight: Visual Signal Enhancement for Object Hallucination Mitigation in Multimodal Large language Models](https://mlsanity.com/paper/2503.13107/) (2025)
- [Reefknot: A Comprehensive Benchmark for Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Models](https://mlsanity.com/paper/2408.09429/) (2024)
- [Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models](https://mlsanity.com/paper/2402.15721/) (2024)
- [VASparse: Towards Efficient Visual Hallucination Mitigation via Visual-Aware Token Sparsification](https://mlsanity.com/paper/2501.06553/) (2025)
- [GThinker: Towards General Multimodal Reasoning via Cue-Guided Rethinking](https://mlsanity.com/paper/2506.01078/) (2025)
- [Visual Multi-Agent System: Mitigating Hallucination Snowballing via Visual Flow](https://mlsanity.com/paper/2509.21789/) (2025)
- [MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models](https://mlsanity.com/paper/2407.17379/) (2024)
- [HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding](https://mlsanity.com/paper/2409.20429/) (2024)
- [Mitigating Hallucinations in Multimodal Spatial Relations through Constraint-Aware Prompting](https://mlsanity.com/paper/2502.08317/) (2025)

## Abstract (arXiv)

While multimodal large language models demonstrate strong entity-level perception, faithfully grounding relational interactions between objects remains a persistent challenge. Although conventional visual grounding techniques attempt to resolve hallucinations by amplifying visual attention, strengthening overall visual signals fails to reliably correct relational errors. Tracing visual attention in relation descriptions reveals that correct responses tend to dynamically shift focus across regions, whereas hallucinated responses often linger on previously dominant evidence, exhibiting an undesirable textitvisual inertia. Further analysis shows that relation-prediction performance steadily deteriorates as more previous-step visual attention is carried into the current decoding step. We therefore introduce Inertia-aware Visual Excitation (IVE), an MLLM decoding method that dynamically recalibrates visual values using token-level attention history. By contrasting current attention against recent moving averages, IVE separates emergent tokens with rising relevance from persistently dominant inertia tokens, selectively reinforcing newly needed evidence while mildly attenuating contributions from repeatedly attended regions. Across three MLLMs and decoding strategies, IVE reduces relation hallucinations while preserving broader multimodal performance.