---
title: "MODE-RAG — paper by Wei et al. (2026) — summary and ideas"
description: "The authors argue that current fixes face an \"intervention paradox\". 23 references. Among them: 0."
authors: ["Zehang Wei", "JiaXin Dai", "Jiamin Yan", "Xiang Xiang"]
year: 2026
venue: "Proceedings of the 2nd Workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR 2026)"
source: "https://aclanthology.org/2026.magmar-main.6/"
doi: "10.18653/v1/2026.magmar-main.6"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wei-2026-mode-rag-manifold-outlier-diagnosis/"
---
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# MODE-RAG: Manifold Outlier Diagnosis and Energy-based Retrieval-Augmented Generation Evaluation

**Authors:** Zehang Wei, JiaXin Dai, Jiamin Yan and [Xiang Xiang](https://md.research.elamur.ai/free/en/authors/xiang-xiang/) · **Year:** 2026 · **Venue:** [Proceedings of the 2nd Workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR 2026)](https://md.research.elamur.ai/free/en/venues/magmar/) ([Workshop on Multimodal Augmented Generation via Multimodal Retrieval 2026](https://md.research.elamur.ai/free/en/venues/magmar/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.magmar-main.6/) · [PDF](https://aclanthology.org/2026.magmar-main.6.pdf) · [DOI 10.18653/v1/2026.magmar-main.6](https://doi.org/10.18653/v1/2026.magmar-main.6) · [OpenAlex](https://openalex.org/W7166825285)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Multimodal RAG systems built on vision-language models often contradict the visual evidence, invent causal links, or give in to leading user claims (sycophancy). The authors argue that current fixes face an "intervention paradox". Applying rules to every query harms answers that were already correct, while letting small models reason without guidance allows errors to snowball. They first sort M-RAG hallucinations into nine types across four pipeline stages: perception, retrieval, reasoning and generation.

MODE-RAG then sends each query through an FE-Router that estimates risk from Variational Free Energy (VFE) and ATLAS attention probes. Low-risk inputs skip intervention. Risky ones pass through stage-specific agents, which use MCTS to build causal DAGs and logit perturbation to curb sycophancy. A correction agent and a verifying Overseer finish the pipeline.

For testing, the authors derive the ModeVent benchmark from MultiVent by keeping the uncertainty extremes. On ModeVent, the framework reduces hallucination and logical fabrication and suppresses catastrophic zero-score outputs, at a moderate runtime cost. This makes multimodal QA more dependable.

## Key points
- **Taxonomy:** nine hallucination types in four stages, for example modality conflict, temporal inversion, imposed causality and information fabrication.
- **ModeVent benchmark:** 1,000 MultiVent samples.
  - Features come from SigLIP and CLIP with global whitening.
  - Samples are ranked by mean VFE.
  - The 500 highest-VFE samples serve as manifold outliers and the 500 lowest as stable inliers.
- **Architecture:** FE-Router plus Per-Agent, Ret-Agent, Rea-Agent, Gen-Agent, a correction agent and a PORAG-driven Overseer with a fallback loop. A regex-based "Dead Man's Switch" blocks mode collapse and prompt bleed-through.
- **Comparisons:** MODE-RAG is evaluated against the Vanilla Baseline, Self-RAG, SelfCheckGPT and Woodpecker on ModeVent.
- **Cost:** average time per query rises from 18.5 s to 26.2 s (1.42×), about 7.3 hours for the full benchmark on a single thread.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Zehang Wei, JiaXin Dai, Jiamin Yan and Xiang Xiang. 2026. MODE-RAG: Manifold Outlier Diagnosis and Energy-based Retrieval-Augmented Generation Evaluation. Proceedings of the 2nd Workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wei2026mode,
  title = {{MODE-RAG: Manifold Outlier Diagnosis and Energy-based Retrieval-Augmented Generation Evaluation}},
  author = {Zehang Wei, JiaXin Dai, Jiamin Yan and Xiang Xiang},
  booktitle = {Proceedings of the 2nd Workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.magmar-main.6},
  url = {https://doi.org/10.18653/v1/2026.magmar-main.6}
}
```

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## References

23 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. \#\# Conclusions Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024. [Self-rag: Learning to retrieve, generate, and critique through self-reflection](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/). In The Twelfth International Conference on Learning Representations (ICLR).
2. In this paper, we proposed MODE-RAG, a mechanistically grounded multi-agent framework that addresses the intervention paradox in multimodal Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jing Jing. 2023. [Qwen-vl: A versatile vision-language model…](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-vl-versatile-vision-language/)
3. Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. 2023. [Evaluating object hallucination in large vision-language models](https://md.research.elamur.ai/free/en/papers/li-2023-evaluating-object-hallucination-large-vision/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 292–305.
4. Wenhu Chen, Hexiang Hu, Xi Chen, Pat Verga, and William W Cohen. 2022. Murag: Multimodal retrieval-augmented generator for open question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10597–10607. [find](https://scholar.google.com/scholar?q=Wenhu+Chen%2C+Hexiang+Hu%2C+Xi+Chen%2C+Pat+Verga%2C+and+William+W+Cohen%2E+2022%2E+Murag%3A+Multimodal+retrieval%2Daugmented+generator+for+open+question+answering%2E+In+Proceedings+of+the+IEEE%2FCVF+Conference+on+Compute)
5. Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li. 2020. Energy-based out-of-distribution detection. In Advances in Neural Information Processing Systems (NeurIPS), volume 33, pages 21464–21475. [find](https://scholar.google.com/scholar?q=Weitang+Liu%2C+Xiaoyun+Wang%2C+John+Owens%2C+and+Yixuan+Li%2E+2020%2E+Energy%2Dbased+out%2Dof%2Ddistribution+detection%2E+In+Advances+in+Neural+Information+Processing+Systems+%28NeurIPS%29%2C+volume+33%2C+pages+21464%E2%80%9321475%2E)
6. Potsawee Manakul, Adian Liusie, and Mark Gales. 2023. SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 9004–9017, Singapore. Association for Computation… [find](https://scholar.google.com/scholar?q=Potsawee+Manakul%2C+Adian+Liusie%2C+and+Mark+Gales%2E+2023%2E+SelfCheckGPT%3A+Zero%2Dresource+black%2Dbox+hallucination+detection+for+generative+large+language+models%2E+In+Proceedings+of+the+2023+Conference+on+Empir)
7. Florin Cuconasu, Giovanni Trappolini, Federico Siciliani, Simone Filice, Cesare Campagnano, Yoelle Maarek, Nicola Motta, and Fabrizio Silvestri. 2024. [The power of noise: Redefining retrieval for rag systems](https://md.research.elamur.ai/free/en/papers/cuconasu-2024-power-noise-redefining-retrieval-rag/). Proceedings of the 47th International ACM SIGIR Conference on Research and Development in I…
8. Hongjin Qian, Peitian Zhang, Zheng Liu, Kelong Mao, and Zhicheng Dou. 2024. Memorag: Moving towards next-gen rag via memory-inspired knowledge discovery. arXiv preprint arXiv:2409.05591. — [arXiv:2409.05591](https://arxiv.org/abs/2409.05591) [find](https://scholar.google.com/scholar?q=Hongjin+Qian%2C+Peitian+Zhang%2C+Zheng+Liu%2C+Kelong+Mao%2C+and+Zhicheng+Dou%2E+2024%2E+Memorag%3A+Moving+towards+next%2Dgen+rag+via+memory%2Dinspired+knowledge+discovery%2E+arXiv+preprint+arXiv%3A2409%2E05591%2E+%E2%80%94+%5BarXiv%3A2409)
9. Karl Friston. 2010. The free-energy principle: a unified brain theory? Nature reviews neuroscience, 11(2):127–138. [find](https://scholar.google.com/scholar?q=Karl+Friston%2E+2010%2E+The+free%2Denergy+principle%3A+a+unified+brain+theory%3F+Nature+reviews+neuroscience%2C+11%282%29%3A127%E2%80%93138%2E)
10. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, and 1 others. 2021. [Learning transferable visual models from natural language supervision](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/). In International Conference on Machine Learning (ICML), pages…
11. Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023. [Retrieval-augmented generation for large language models: A survey](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/). arXiv preprint arXiv:2312.10997.
12. Sagar Srinivas Sakhinana, Shivam Gupta, Akash Das, and Venkataramana Runkana. 2025. Scaling test-time inference with policy-optimized, dynamic retrieval-augmented generation via KV caching and decoding. In KDD 2025 Workshop on Inference Optimization for Generative AI. [find](https://scholar.google.com/scholar?q=Sagar+Srinivas+Sakhinana%2C+Shivam+Gupta%2C+Akash+Das%2C+and+Venkataramana+Runkana%2E+2025%2E+Scaling+test%2Dtime+inference+with+policy%2Doptimized%2C+dynamic+retrieval%2Daugmented+generation+via+KV+caching+and+decodin)
13. Jonas Geiping, Sean McLeish, Naman Jain, John Kirchenbauer, Siddharth Singh, Brian R Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, and Tom Goldstein. 2025. Scaling up test-time compute with latent reasoning: A recurrent depth approach. arXiv preprint arXiv:2502.05171. [find](https://scholar.google.com/scholar?q=Jonas+Geiping%2C+Sean+McLeish%2C+Naman+Jain%2C+John+Kirchenbauer%2C+Siddharth+Singh%2C+Brian+R+Bartoldson%2C+Bhavya+Kailkhura%2C+Abhinav+Bhatele%2C+and+Tom+Goldstein%2E+2025%2E+Scaling+up+test%2Dtime+compute+with+latent+re)
14. David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, and 1 others. 2016. Mastering the game of go with deep neural networks and tree search. nature, 529(7587):484–489. [find](https://scholar.google.com/scholar?q=David+Silver%2C+Aja+Huang%2C+Chris+J+Maddison%2C+Arthur+Guez%2C+Laurent+Sifre%2C+George+Van+Den+Driessche%2C+Julian+Schrittwieser%2C+Ioannis+Antonoglou%2C+Veda+Panneershelvam%2C+Marc+Lanctot%2C+and+1+others%2E+2016%2E+Master)
15. Yunjie Ji, Jiawei Li, Haiyan Ye, Kehai Wu, Jun Xu, Lin Mo, and Min Zhang. 2025. Test-time computing: from system-1 thinking to system-2 thinking. arXiv preprint arXiv:2501.02497. — [arXiv:2501.02497](https://arxiv.org/abs/2501.02497) [find](https://scholar.google.com/scholar?q=Yunjie+Ji%2C+Jiawei+Li%2C+Haiyan+Ye%2C+Kehai+Wu%2C+Jun+Xu%2C+Lin+Mo%2C+and+Min+Zhang%2E+2025%2E+Test%2Dtime+computing%3A+from+system%2D1+thinking+to+system%2D2+thinking%2E+arXiv+preprint+arXiv%3A2501%2E02497%2E+%E2%80%94+%5BarXiv%3A2501%2E02497%5D%28)
16. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, and Pascale Fung. 2023. [Survey of hallucination in natural language generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/). ACM Computing Surveys, 55(12):1–38.
17. Jianlin Su, Jiarun Cao, Weijie Liu, and Yangyi Ou. 2021. Whitening sentence representations for better semantics and faster retrieval. arXiv preprint arXiv:2103.15316. [find](https://scholar.google.com/scholar?q=Jianlin+Su%2C+Jiarun+Cao%2C+Weijie+Liu%2C+and+Yangyi+Ou%2E+2021%2E+Whitening+sentence+representations+for+better+semantics+and+faster+retrieval%2E+arXiv+preprint+arXiv%3A2103%2E15316%2E)
18. Weijia Su, Yubai Tang, Qingyao Ai, Zhijing Wu, and Yiqun Liu. 2024. [Dragin: Dynamic retrieval augmented generation based on the real-time information needs of large language models](https://md.research.elamur.ai/free/en/papers/su-2024-dragin-dynamic-retrieval-augmented-generation/). arXiv preprint arXiv:2403.10081.
19. Jiarui Jiang, Zhiyu Chen, Yifei Min, Jian Chen, Xiaoyu Cheng, Jian Wang, Yuxin Tang, Hao Sun, Jia Deng, Wayne Xin Zhao, and 1 others. 2024. Technical report: Enhancing llm reasoning with reward-guided tree search. arXiv preprint arXiv:2411.11694. — [arXiv:2411.11694](https://arxiv.org/abs/2411.11694) [find](https://scholar.google.com/scholar?q=Jiarui+Jiang%2C+Zhiyu+Chen%2C+Yifei+Min%2C+Jian+Chen%2C+Xiaoyu+Cheng%2C+Jian+Wang%2C+Yuxin+Tang%2C+Hao+Sun%2C+Jia+Deng%2C+Wayne+Xin+Zhao%2C+and+1+others%2E+2024%2E+Technical+report%3A+Enhancing+llm+reasoning+with+reward%2Dguided)
20. Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li. 2022. Out-of-distribution detection with deep nearest neighbors. In International Conference on Machine Learning (ICML), pages 20827–20840. PMLR. [find](https://scholar.google.com/scholar?q=Yiyou+Sun%2C+Yifei+Ming%2C+Xiaojin+Zhu%2C+and+Yixuan+Li%2E+2022%2E+Out%2Dof%2Ddistribution+detection+with+deep+nearest+neighbors%2E+In+International+Conference+on+Machine+Learning+%28ICML%29%2C+pages+20827%E2%80%9320840%2E+PMLR%2E)
21. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, and 1 others. 2020. [Retrieval-augmented generation for knowledge-intensive nlp tasks](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/). In Advances in Neural Information Processing Systems (NeurI…
22. Zijie Wang, Zihan certification Wang, Linyi Le, Hao Shen Zheng, Swaroop Mishra, Vincent Perot, Yashan Zhang, Ankit Mattapalli, Ankur Taly, Jingbo snowshoes or standing still on a snowboard preparing to descend. There are no extreme stunts, competitive sporting events, or water-related elements prese… — [arXiv:2407.08223](https://arxiv.org/abs/2407.08223) [find](https://scholar.google.com/scholar?q=Zijie+Wang%2C+Zihan+certification+Wang%2C+Linyi+Le%2C+Hao+Shen+Zheng%2C+Swaroop+Mishra%2C+Vincent+Perot%2C+Yashan+Zhang%2C+Ankit+Mattapalli%2C+Ankur+Taly%2C+Jingbo+snowshoes+or+standing+still+on+a+snowboard+preparing+t)
23. Jianing Wu, Mengwei Feng, Shiwei Zhang, Ren Jin, Fan Che, Zhi Wen, and Jianhua Tao. 2025. Boosting multimodal reasoning with mcts-automated structured thinking. arXiv preprint arXiv:2502.02339. — [arXiv:2502.02339](https://arxiv.org/abs/2502.02339) [find](https://scholar.google.com/scholar?q=Jianing+Wu%2C+Mengwei+Feng%2C+Shiwei+Zhang%2C+Ren+Jin%2C+Fan+Che%2C+Zhi+Wen%2C+and+Jianhua+Tao%2E+2025%2E+Boosting+multimodal+reasoning+with+mcts%2Dautomated+structured+thinking%2E+arXiv+preprint+arXiv%3A2502%2E02339%2E+%E2%80%94+%5BarX)

*Updated 1 November 2026*