---
title: "Mem-Gallery — paper by Bei et al. (2026) — summary and ideas"
description: "The authors also define an evaluation framework with three dimensions: extracting memory and… 49 references, cited by 5 papers here."
authors: ["Yuanchen Bei", "Tianxin Wei", "Xuying Ning", "Yanjun Zhao", "Zhining Liu", "Xiao Lin", "Yada Zhu", "Hendrik Hamann", "Jingrui He", "Hanghang Tong"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1892/"
doi: "10.18653/v1/2026.acl-long.1892"
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/bei-2026-mem-gallery-benchmarking-multimodal-long/"
---
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# Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents

**Authors:** [Yuanchen Bei](https://md.research.elamur.ai/free/en/authors/yuanchen-bei/), [Tianxin Wei](https://md.research.elamur.ai/free/en/authors/tianxin-wei/), [Xuying Ning](https://md.research.elamur.ai/free/en/authors/xuying-ning/), [Yanjun Zhao](https://md.research.elamur.ai/free/en/authors/yanjun-zhao/), [Zhining Liu](https://md.research.elamur.ai/free/en/authors/zhining-liu/), [Xiao Lin](https://md.research.elamur.ai/free/en/authors/xiao-lin/), [Yada Zhu](https://md.research.elamur.ai/free/en/authors/yada-zhu/), Hendrik Hamann, [Jingrui He](https://md.research.elamur.ai/free/en/authors/jingrui-he/) and [Hanghang Tong](https://md.research.elamur.ai/free/en/authors/hanghang-tong/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/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.acl-long.1892/) · [PDF](https://aclanthology.org/2026.acl-long.1892.pdf) · [DOI 10.18653/v1/2026.acl-long.1892](https://doi.org/10.18653/v1/2026.acl-long.1892) · [OpenAlex](https://openalex.org/W7166803708)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [5](https://md.research.elamur.ai/free/en/papers/bei-2026-mem-gallery-benchmarking-multimodal-long/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Agents built on multimodal large language models (MLLMs) need to remember what users show and tell them over many conversations. Existing benchmarks do not test this well. Some cover multi-session memory but use text only (e.g., LoCoMo, LongMemEval). Others include images but stay within one short session (e.g., MMDU, MMRC).

Mem-Gallery fills this gap. It is a new dataset of long multi-session dialogues in which pictures and text depend on each other, and facts appear, recur and change over time. The authors also define an evaluation framework with three dimensions:
- extracting memory and adapting at test time,
- reasoning over remembered evidence,
- managing knowledge, meaning handling corrections, contradictions and unanswerable questions.

They test twelve memory systems. Keeping visual content explicitly in memory helps, and how memory is organized matters. However, all methods struggle with reasoning-heavy questions and with updating or reconciling knowledge, and multimodal memory is much more expensive to run. The benchmark is relevant to anyone building assistants that must keep track of evolving image-plus-text histories.

## Key points
- The dataset averages 16.51 user–assistant rounds and 4.18 images per session. It covers nine subtasks:
  - factual retrieval, visual-centric search and test-time learning;
  - temporal, visual-centric and multi-entity reasoning;
  - knowledge resolution, conflict detection and answer refusal.
- The authors argue that LoCoMo is unsuitable for multimodal testing because adding visual information there gives little or even negative gain.
- Methods that are good at extraction and reasoning tend to refuse unanswerable questions poorly. A-Mem and MuRAG score 7.22% and 5.82% lower F1 on refusal than a simple FIFO baseline.
- On cost, MuRAG uses about 3.85× more total tokens than NaiveRAG, mostly because of input tokens. On retrieval, it reaches Recall@5 of 0.7506 versus 0.5381 for NaiveRAG, but its precision drops sharply as K grows.
- Data and code are on GitHub (YuanchenBei/Mem-Gallery). The benchmark covers only vision and language, not audio or embodied signals.

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

## Institutions

- [Massachusetts Institute of Technology](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/) — University, Cambridge, United States · 211 works · 69,000 citations
- [University of Illinois Urbana-Champaign](https://md.research.elamur.ai/free/en/universities/university-of-illinois-urbana-champaign/) — University, Urbana, United States · 184 works · 69,372 citations
- [IBM](https://md.research.elamur.ai/free/en/companies/ibm/) — Company, Armonk, United States · 139 works · 70,691 citations
- [Stony Brook University](https://md.research.elamur.ai/free/en/universities/stony-brook-university/) — University, Stony Brook, United States · 43 works · 2,571 citations

Also on the byline: [Brookhaven National Laboratory](https://ror.org/02ex6cf31).

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## Cite

Cite as: Yuanchen Bei, Tianxin Wei, Xuying Ning, Yanjun Zhao, Zhining Liu, Xiao Lin, Yada Zhu, Hendrik Hamann, Jingrui He and Hanghang Tong. 2026. Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{bei2026mem,
  title = {{Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents}},
  author = {Yuanchen Bei, Tianxin Wei, Xuying Ning, Yanjun Zhao, Zhining Liu, Xiao Lin, Yada Zhu, Hendrik Hamann, Jingrui He and Hanghang Tong},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1892},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1892}
}
```

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## References

49 references, 12 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2023. [Gpt-4 technical report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/). arXiv preprint arXiv:2303.08774.
2. Jaewoo Ahn, Yeda Song, Sangdoo Yun, and Gunhee Kim. 2023. Mpchat: Towards multimodal persona-grounded conversation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3354– 3377. [find](https://scholar.google.com/scholar?q=Jaewoo+Ahn%2C+Yeda+Song%2C+Sangdoo+Yun%2C+and+Gunhee+Kim%2E+2023%2E+Mpchat%3A+Towards+multimodal+persona%2Dgrounded+conversation%2E+In+Proceedings+of+the+61st+Annual+Meeting+of+the+Association+for+Computational+Lingu)
3. Dirk Jan Ardesch, Lianne H Scholtens, Longchuan Li, Todd M Preuss, James K Rilling, and Martijn P van den Heuvel. 2019. Evolutionary expansion of connectivity between multimodal association areas in the human brain compared with chimpanzees. Proceedings of the National Academy of Sciences, 116(14):7… [find](https://scholar.google.com/scholar?q=Dirk+Jan+Ardesch%2C+Lianne+H+Scholtens%2C+Longchuan+Li%2C+Todd+M+Preuss%2C+James+K+Rilling%2C+and+Martijn+P+van+den+Heuvel%2E+2019%2E+Evolutionary+expansion+of+connectivity+between+multimodal+association+areas+in+t)
4. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, and 1 others. 2025. [Qwen2.5-vl technical report](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen2-5-vl-technical/). arXiv preprint arXiv:2502.13923.
5. Weihao Bo, Shan Zhang, Yanpeng Sun, Jingjing Wu, Qunyi Xie, Xiao Tan, Kunbin Chen, Wei He, Xiaofan Li, Na Zhao, and 1 others. 2025. Agentic learner with grow-and-refine multimodal semantic memory. arXiv preprint arXiv:2511.21678. — [arXiv:2511.21678](https://arxiv.org/abs/2511.21678) [find](https://scholar.google.com/scholar?q=Weihao+Bo%2C+Shan+Zhang%2C+Yanpeng+Sun%2C+Jingjing+Wu%2C+Qunyi+Xie%2C+Xiao+Tan%2C+Kunbin+Chen%2C+Wei+He%2C+Xiaofan+Li%2C+Na+Zhao%2C+and+1+others%2E+2025%2E+Agentic+learner+with+grow%2Dand%2Drefine+multimodal+semantic+memory%2E+arX)
6. Gongwei Chen, Xurui Zhou, Rui Shao, Yibo Lyu, Kaiwen Zhou, Shuai Wang, Wentao Li, Yinchuan Li, Zhongang Qi, and Liqiang Nie. 2025. Less is more: Empowering gui agent with context-aware simplification. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5901–5911. [find](https://scholar.google.com/scholar?q=Gongwei+Chen%2C+Xurui+Zhou%2C+Rui+Shao%2C+Yibo+Lyu%2C+Kaiwen+Zhou%2C+Shuai+Wang%2C+Wentao+Li%2C+Yinchuan+Li%2C+Zhongang+Qi%2C+and+Liqiang+Nie%2E+2025%2E+Less+is+more%3A+Empowering+gui+agent+with+context%2Daware+simplification%2E)
7. Wenhu Chen, Hexiang Hu, Xi Chen, Pat Verga, and William Cohen. 2022. [Murag: Multimodal retrieval-augmented generator for open question answering over images and text](https://md.research.elamur.ai/free/en/papers/chen-2022-murag-multimodal-retrieval-augmented-generator/). In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5558–5570.
8. Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, and 1 others. 2025. [Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities](https://md.research.elamur.ai/free/en/papers/comanici-2025-gemini-2-5-pushing-frontier/). arX…
9. Jizhan Fang, Xinle Deng, Haoming Xu, Ziyan Jiang, Yuqi Tang, Ziwen Xu, Shumin Deng, Yunzhi Yao, Mengru Wang, Shuofei Qiao, and 1 others. 2025. Lightmem: Lightweight and efficient memory-augmented generation. arXiv preprint arXiv:2510.18866. — [arXiv:2510.18866](https://arxiv.org/abs/2510.18866) [find](https://scholar.google.com/scholar?q=Jizhan+Fang%2C+Xinle+Deng%2C+Haoming+Xu%2C+Ziyan+Jiang%2C+Yuqi+Tang%2C+Ziwen+Xu%2C+Shumin+Deng%2C+Yunzhi+Yao%2C+Mengru+Wang%2C+Shuofei+Qiao%2C+and+1+others%2E+2025%2E+Lightmem%3A+Lightweight+and+efficient+memory%2Daugmented+gene)
10. Jiazhan Feng, Qingfeng Sun, Can Xu, Pu Zhao, Yaming Yang, Chongyang Tao, Dongyan Zhao, and Qingwei Lin. 2023. Mmdialog: A large-scale multi-turn dialogue dataset towards multi-modal open-domain conversation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (… [find](https://scholar.google.com/scholar?q=Jiazhan+Feng%2C+Qingfeng+Sun%2C+Can+Xu%2C+Pu+Zhao%2C+Yaming+Yang%2C+Chongyang+Tao%2C+Dongyan+Zhao%2C+and+Qingwei+Lin%2E+2023%2E+Mmdialog%3A+A+large%2Dscale+multi%2Dturn+dialogue+dataset+towards+multi%2Dmodal+open%2Ddomain+conver)
11. Matthew Fisher. 2025. Neural graph memory: A structured approach to long-term memory in multimodal agents. [find](https://scholar.google.com/scholar?q=Matthew+Fisher%2E+2025%2E+Neural+graph+memory%3A+A+structured+approach+to+long%2Dterm+memory+in+multimodal+agents%2E)
12. Yuanzhe Hu, Yu Wang, and Julian McAuley. 2025a. Evaluating memory in llm agents via incremental multi-turn interactions. arXiv preprint arXiv:2507.05257. — [arXiv:2507.05257](https://arxiv.org/abs/2507.05257) [find](https://scholar.google.com/scholar?q=Yuanzhe+Hu%2C+Yu+Wang%2C+and+Julian+McAuley%2E+2025a%2E+Evaluating+memory+in+llm+agents+via+incremental+multi%2Dturn+interactions%2E+arXiv+preprint+arXiv%3A2507%2E05257%2E+%E2%80%94+%5BarXiv%3A2507%2E05257%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F250)
13. Yuyang Hu, Shichun Liu, Yanwei Yue, Guibin Zhang, Boyang Liu, Fangyi Zhu, Jiahang Lin, Honglin Guo, Shihan Dou, Zhiheng Xi, and 1 others. 2025b. Memory in the age of ai agents. arXiv preprint arXiv:2512.13564. — [arXiv:2512.13564](https://arxiv.org/abs/2512.13564) [find](https://scholar.google.com/scholar?q=Yuyang+Hu%2C+Shichun+Liu%2C+Yanwei+Yue%2C+Guibin+Zhang%2C+Boyang+Liu%2C+Fangyi+Zhu%2C+Jiahang+Lin%2C+Honglin+Guo%2C+Shihan+Dou%2C+Zhiheng+Xi%2C+and+1+others%2E+2025b%2E+Memory+in+the+age+of+ai+agents%2E+arXiv+preprint+arXiv%3A25)
14. Wei-Chieh Huang, Weizhi Zhang, Yueqing Liang, Yuanchen Bei, Yankai Chen, Tao Feng, Xinyu Pan, Zhen Tan, Yu Wang, Tianxin Wei, and 1 others. 2026. Rethinking memory mechanisms of foundation agents in the second half. arXiv preprint arXiv:2602.06052. — [arXiv:2602.06052](https://arxiv.org/abs/2602.06052) [find](https://scholar.google.com/scholar?q=Wei%2DChieh+Huang%2C+Weizhi+Zhang%2C+Yueqing+Liang%2C+Yuanchen+Bei%2C+Yankai+Chen%2C+Tao+Feng%2C+Xinyu+Pan%2C+Zhen+Tan%2C+Yu+Wang%2C+Tianxin+Wei%2C+and+1+others%2E+2026%2E+Rethinking+memory+mechanisms+of+foundation+agents+in+t)
15. Jitesh Jain, Shubham Maheshwari, Ning Yu, Wen-mei Hwu, and Humphrey Shi. 2025. Augustus: An llm-driven multimodal agent system with contextualized user memory. arXiv preprint arXiv:2510.15261. — [arXiv:2510.15261](https://arxiv.org/abs/2510.15261) [find](https://scholar.google.com/scholar?q=Jitesh+Jain%2C+Shubham+Maheshwari%2C+Ning+Yu%2C+Wen%2Dmei+Hwu%2C+and+Humphrey+Shi%2E+2025%2E+Augustus%3A+An+llm%2Ddriven+multimodal+agent+system+with+contextualized+user+memory%2E+arXiv+preprint+arXiv%3A2510%2E15261%2E+%E2%80%94+%5BarXi)
16. Zixi Jia, Qinghua Liu, Hexiao Li, Yuyan Chen, and Jiqiang Liu. 2025. Evaluating the long-term memory of large language models. In Findings of the Association for Computational Linguistics: ACL 2025, pages 19759–19777. [find](https://scholar.google.com/scholar?q=Zixi+Jia%2C+Qinghua+Liu%2C+Hexiao+Li%2C+Yuyan+Chen%2C+and+Jiqiang+Liu%2E+2025%2E+Evaluating+the+long%2Dterm+memory+of+large+language+models%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL+2025%2C+p)
17. Jiazheng Kang, Mingming Ji, Zhe Zhao, and Ting Bai. 2025. [Memory os of ai agent](https://md.research.elamur.ai/free/en/papers/kang-2025-memory-os-ai-agent/). In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 25972–25981.
18. Zixuan Ke, Fangkai Jiao, Yifei Ming, Xuan-Phi Nguyen, Austin Xu, Do Xuan Long, Minzhi Li, Chengwei Qin, PeiFeng Wang, silvio savarese, Caiming Xiong, and Shafiq Joty. 2025. A survey of frontiers in LLM reasoning: Inference scaling, learning to reason, and agentic systems. Transactions on Machine Lea… [find](https://scholar.google.com/scholar?q=Zixuan+Ke%2C+Fangkai+Jiao%2C+Yifei+Ming%2C+Xuan%2DPhi+Nguyen%2C+Austin+Xu%2C+Do+Xuan+Long%2C+Minzhi+Li%2C+Chengwei+Qin%2C+PeiFeng+Wang%2C+silvio+savarese%2C+Caiming+Xiong%2C+and+Shafiq+Joty%2E+2025%2E+A+survey+of+frontiers+in+LL)
19. Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Fei-Fei Li. 2011. Novel dataset for fine-grained image categorization: Stanford dogs. In Proc. CVPR workshop on fine-grained visual categorization (FGVC), volume 2. [find](https://scholar.google.com/scholar?q=Aditya+Khosla%2C+Nityananda+Jayadevaprakash%2C+Bangpeng+Yao%2C+and+Fei%2DFei+Li%2E+2011%2E+Novel+dataset+for+fine%2Dgrained+image+categorization%3A+Stanford+dogs%2E+In+Proc%2E+CVPR+workshop+on+fine%2Dgrained+visual+categor)
20. Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, and 1 others. 2025. From generation to judgment: Opportunities and challenges of llm-as-a-judge. In Proceedings of the 2025 Conference on Empirical Methods… [find](https://scholar.google.com/scholar?q=Dawei+Li%2C+Bohan+Jiang%2C+Liangjie+Huang%2C+Alimohammad+Beigi%2C+Chengshuai+Zhao%2C+Zhen+Tan%2C+Amrita+Bhattacharjee%2C+Yuxuan+Jiang%2C+Canyu+Chen%2C+Tianhao+Wu%2C+and+1+others%2E+2025%2E+From+generation+to+judgment%3A+Opport)
21. Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang. 2016. Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1096– 1104. [find](https://scholar.google.com/scholar?q=Ziwei+Liu%2C+Ping+Luo%2C+Shi+Qiu%2C+Xiaogang+Wang%2C+and+Xiaoou+Tang%2E+2016%2E+Deepfashion%3A+Powering+robust+clothes+recognition+and+retrieval+with+rich+annotations%2E+In+Proceedings+of+the+IEEE+conference+on+compu)
22. Ziyu Liu, Tao Chu, Yuhang Zang, Xilin Wei, Xiaoyi Dong, Pan Zhang, Zijian Liang, Yuanjun Xiong, Yu Qiao, Dahua Lin, and 1 others. 2024. Mmdu: A multi-turn multi-image dialog understanding benchmark and instruction-tuning dataset for lvlms. In Advances in Neural Information Processing Systems, volume… [find](https://scholar.google.com/scholar?q=Ziyu+Liu%2C+Tao+Chu%2C+Yuhang+Zang%2C+Xilin+Wei%2C+Xiaoyi+Dong%2C+Pan+Zhang%2C+Zijian+Liang%2C+Yuanjun+Xiong%2C+Yu+Qiao%2C+Dahua+Lin%2C+and+1+others%2E+2024%2E+Mmdu%3A+A+multi%2Dturn+multi%2Dimage+dialog+understanding+benchmark+an)
23. Lin Long, Yichen He, Wentao Ye, Yiyuan Pan, Yuan Lin, Hang Li, Junbo Zhao, and Wei Li. 2025. Seeing, listening, remembering, and reasoning: A multimodal agent with long-term memory. arXiv preprint arXiv:2508.09736. — [arXiv:2508.09736](https://arxiv.org/abs/2508.09736) [find](https://scholar.google.com/scholar?q=Lin+Long%2C+Yichen+He%2C+Wentao+Ye%2C+Yiyuan+Pan%2C+Yuan+Lin%2C+Hang+Li%2C+Junbo+Zhao%2C+and+Wei+Li%2E+2025%2E+Seeing%2C+listening%2C+remembering%2C+and+reasoning%3A+A+multimodal+agent+with+long%2Dterm+memory%2E+arXiv+preprint+arX)
24. Andrea I Luppi, Pedro AM Mediano, Fernando E Rosas, Negin Holland, Tim D Fryer, John T O’Brien, James B Rowe, David K Menon, Daniel Bor, and Emmanuel A Stamatakis. 2022. A synergistic core for human brain evolution and cognition. Nature neuroscience, 25(6):771–782. [find](https://scholar.google.com/scholar?q=Andrea+I+Luppi%2C+Pedro+AM+Mediano%2C+Fernando+E+Rosas%2C+Negin+Holland%2C+Tim+D+Fryer%2C+John+T+O%E2%80%99Brien%2C+James+B+Rowe%2C+David+K+Menon%2C+Daniel+Bor%2C+and+Emmanuel+A+Stamatakis%2E+2022%2E+A+synergistic+core+for+human+b)
25. Adyasha Maharana, Dong-Ho Lee, Sergey Tulyakov, Mohit Bansal, Francesco Barbieri, and Yuwei Fang. 2024. Evaluating very long-term conversational memory of llm agents. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13851– 1387… [find](https://scholar.google.com/scholar?q=Adyasha+Maharana%2C+Dong%2DHo+Lee%2C+Sergey+Tulyakov%2C+Mohit+Bansal%2C+Francesco+Barbieri%2C+and+Yuwei+Fang%2E+2024%2E+Evaluating+very+long%2Dterm+conversational+memory+of+llm+agents%2E+In+Proceedings+of+the+62nd+Annual)
26. Maria-Elena Nilsback and Andrew Zisserman. 2008. Automated flower classification over a large number of classes. In 2008 Sixth Indian conference on computer vision, graphics & image processing, pages 722–729. IEEE. [find](https://scholar.google.com/scholar?q=Maria%2DElena+Nilsback+and+Andrew+Zisserman%2E+2008%2E+Automated+flower+classification+over+a+large+number+of+classes%2E+In+2008+Sixth+Indian+conference+on+computer+vision%2C+graphics+%26+image+processing%2C+pages+)
27. Xuying Ning, Dongqi Fu, Tianxin Wei, Mengting Ai, Jiaru Zou, Ting-Wei Li, Hanghang Tong, Yada Zhu, Hendrik Hamann, and Jingrui He. 2026. Mc-search: Evaluating and enhancing multimodal agentic search with structured long reasoning chains. arXiv preprint arXiv:2603.00873. — [arXiv:2603.00873](https://arxiv.org/abs/2603.00873) [find](https://scholar.google.com/scholar?q=Xuying+Ning%2C+Dongqi+Fu%2C+Tianxin+Wei%2C+Mengting+Ai%2C+Jiaru+Zou%2C+Ting%2DWei+Li%2C+Hanghang+Tong%2C+Yada+Zhu%2C+Hendrik+Hamann%2C+and+Jingrui+He%2E+2026%2E+Mc%2Dsearch%3A+Evaluating+and+enhancing+multimodal+agentic+search+w)
28. Jiao Ou, Junda Lu, Che Liu, Yihong Tang, Fuzheng Zhang, Di Zhang, and Kun Gai. 2024. Dialogbench: Evaluating llms as human-like dialogue systems. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume… [find](https://scholar.google.com/scholar?q=Jiao+Ou%2C+Junda+Lu%2C+Che+Liu%2C+Yihong+Tang%2C+Fuzheng+Zhang%2C+Di+Zhang%2C+and+Kun+Gai%2E+2024%2E+Dialogbench%3A+Evaluating+llms+as+human%2Dlike+dialogue+systems%2E+In+Proceedings+of+the+2024+Conference+of+the+North+Ame)
29. Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G Patil, Ion Stoica, and Joseph E Gonzalez. 2023. Memgpt: Towards llms as operating systems. arXiv preprint arXiv:2310.08560. [find](https://scholar.google.com/scholar?q=Charles+Packer%2C+Sarah+Wooders%2C+Kevin+Lin%2C+Vivian+Fang%2C+Shishir+G+Patil%2C+Ion+Stoica%2C+and+Joseph+E+Gonzalez%2E+2023%2E+Memgpt%3A+Towards+llms+as+operating+systems%2E+arXiv+preprint+arXiv%3A2310%2E08560%2E)
30. Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. 2023. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology, pages 1–22. [find](https://scholar.google.com/scholar?q=Joon+Sung+Park%2C+Joseph+O%E2%80%99Brien%2C+Carrie+Jun+Cai%2C+Meredith+Ringel+Morris%2C+Percy+Liang%2C+and+Michael+S+Bernstein%2E+2023%2E+Generative+agents%3A+Interactive+simulacra+of+human+behavior%2E+In+Proceedings+of+the+36)
31. Patrick Schramowski, Cigdem Turan, Nico Andersen, Constantin A Rothkopf, and Kristian Kersting. 2022. Large pre-trained language models contain human-like biases of what is right and wrong to do. Nature Machine Intelligence, 4(3):258–268. [find](https://scholar.google.com/scholar?q=Patrick+Schramowski%2C+Cigdem+Turan%2C+Nico+Andersen%2C+Constantin+A+Rothkopf%2C+and+Kristian+Kersting%2E+2022%2E+Large+pre%2Dtrained+language+models+contain+human%2Dlike+biases+of+what+is+right+and+wrong+to+do%2E+Natu)
32. Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. 2011. The caltech-ucsd birds-200-2011 dataset. [find](https://scholar.google.com/scholar?q=Catherine+Wah%2C+Steve+Branson%2C+Peter+Welinder%2C+Pietro+Perona%2C+and+Serge+Belongie%2E+2011%2E+The+caltech%2Ducsd+birds%2D200%2D2011+dataset%2E)
33. Tianxin Wei, Ting-Wei Li, Zhining Liu, Xuying Ning, Ze Yang, Jiaru Zou, Zhichen Zeng, Ruizhong Qiu, Xiao Lin, Dongqi Fu, and 1 others. 2026. Agentic reasoning for large language models. arXiv preprint arXiv:2601.12538. [find](https://scholar.google.com/scholar?q=Tianxin+Wei%2C+Ting%2DWei+Li%2C+Zhining+Liu%2C+Xuying+Ning%2C+Ze+Yang%2C+Jiaru+Zou%2C+Zhichen+Zeng%2C+Ruizhong+Qiu%2C+Xiao+Lin%2C+Dongqi+Fu%2C+and+1+others%2E+2026%2E+Agentic+reasoning+for+large+language+models%2E+arXiv+preprint)
34. Tianxin Wei, Noveen Sachdeva, Benjamin Coleman, Zhankui He, Yuanchen Bei, Xuying Ning, Mengting Ai, Yunzhe Li, Jingrui He, Ed H Chi, and 1 others. 2025. Evo-memory: Benchmarking llm agent test-time learning with self-evolving memory. arXiv preprint arXiv:2511.20857. — [arXiv:2511.20857](https://arxiv.org/abs/2511.20857) [find](https://scholar.google.com/scholar?q=Tianxin+Wei%2C+Noveen+Sachdeva%2C+Benjamin+Coleman%2C+Zhankui+He%2C+Yuanchen+Bei%2C+Xuying+Ning%2C+Mengting+Ai%2C+Yunzhe+Li%2C+Jingrui+He%2C+Ed+H+Chi%2C+and+1+others%2E+2025%2E+Evo%2Dmemory%3A+Benchmarking+llm+agent+test%2Dtime+le)
35. Di Wu, Hongwei Wang, Wenhao Yu, Yuwei Zhang, Kai-Wei Chang, and Dong Yu. 2025. [Longmemeval: Benchmarking chat assistants on long-term interactive memory](https://md.research.elamur.ai/free/en/papers/wu-2024-longmemeval-benchmarking-chat-assistants-long/). In The Thirteenth International Conference on Learning Representations.
36. Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, and Philip S Yu. 2023. Multimodal [large language models: A survey](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/). In 2023 IEEE International Conference on Big Data (BigData), pages 2247–2256. IEEE.
37. Zidi Xiong, Yuping Lin, Wenya Xie, Pengfei He, Zirui Liu, Jiliang Tang, Himabindu Lakkaraju, and Zhen Xiang. 2025. [How memory management impacts llm agents: An empirical study of experience-following behavior](https://md.research.elamur.ai/free/en/papers/xiong-2026-how-memory-management-impacts-llm/). arXiv preprint arXiv:2505.16067.
38. Wujiang Xu, Kai Mei, Hang Gao, Juntao Tan, Zujie Liang, and Yongfeng Zhang. 2025. [A-mem: Agentic memory for llm agents](https://md.research.elamur.ai/free/en/papers/xu-2025-mem-agentic-memory-llm-agents/). In The Thirty-ninth Annual Conference on Neural Information Processing Systems.
39. Xinchao Xu, Zhibin Gou, Wenquan Wu, Zheng-Yu Niu, Hua Wu, Haifeng Wang, and Shihang Wang. 2022. Long time no see! open-domain conversation with long-term persona memory. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2639–2650. [find](https://scholar.google.com/scholar?q=Xinchao+Xu%2C+Zhibin+Gou%2C+Wenquan+Wu%2C+Zheng%2DYu+Niu%2C+Hua+Wu%2C+Haifeng+Wang%2C+and+Shihang+Wang%2E+2022%2E+Long+time+no+see%21+open%2Ddomain+conversation+with+long%2Dterm+persona+memory%2E+In+Findings+of+the+Association)
40. Haochen Xue, Feilong Tang, Ming Hu, Yexin Liu, Qidong Huang, Yulong Li, Chengzhi Liu, Zhongxing Xu, Chong Zhang, Chun-Mei Feng, and 1 others. 2025. Mmrc: A large-scale benchmark for understanding multimodal large language model in real-world conversation. In Proceedings of the 63rd Annual Meeting of… [find](https://scholar.google.com/scholar?q=Haochen+Xue%2C+Feilong+Tang%2C+Ming+Hu%2C+Yexin+Liu%2C+Qidong+Huang%2C+Yulong+Li%2C+Chengzhi+Liu%2C+Zhongxing+Xu%2C+Chong+Zhang%2C+Chun%2DMei+Feng%2C+and+1+others%2E+2025%2E+Mmrc%3A+A+large%2Dscale+benchmark+for+understanding+mult)
41. Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Kristian Kersting, Jeff Z Pan, Hinrich Schütze, and 1 others. 2025. [Memory-r1: Enhancing large language model agents to manage and utilize memories via reinforcement learning](https://md.research.elamur.ai/free/en/papers/yan-2026-memory-r1-enhancing-large-language/). arXiv preprint arXiv:2508.19828.
42. Woongyeong Yeo, Kangsan Kim, Soyeong Jeong, Jinheon Baek, and Sung Ju Hwang. 2025. [Universalrag: Retrieval-augmented generation over corpora of diverse modalities and granularities](https://md.research.elamur.ai/free/en/papers/yeo-2026-universalrag-retrieval-augmented-generation-over/). arXiv preprint arXiv:2504.20734.
43. Duzhen Zhang, Yahan Yu, Jiahua Dong, Chenxing Li, Dan Su, Chenhui Chu, and Dong Yu. 2024a. [Mm-llms: Recent advances in multimodal large language models](https://md.research.elamur.ai/free/en/papers/zhang-2024-mm-llms-recent-advances-multimodal/). In Findings of the Association for Computational Linguistics ACL 2024, pages 12401–12430.
44. Shaolei Zhang, Qingkai Fang, Zhe Yang, and Yang Feng. 2025a. LLaVA-mini: Efficient image and video large multimodal models with one vision token. In The Thirteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Shaolei+Zhang%2C+Qingkai+Fang%2C+Zhe+Yang%2C+and+Yang+Feng%2E+2025a%2E+LLaVA%2Dmini%3A+Efficient+image+and+video+large+multimodal+models+with+one+vision+token%2E+In+The+Thirteenth+International+Conference+on+Learning)
45. Xin Zhang, Yanzhao Zhang, Wen Xie, Mingxin Li, Ziqi Dai, Dingkun Long, Pengjun Xie, Meishan Zhang, Wenjie Li, and Min Zhang. 2024b. Gme: Improving universal multimodal retrieval by multimodal llms. arXiv preprint arXiv:2412.16855. — [arXiv:2412.16855](https://arxiv.org/abs/2412.16855) [find](https://scholar.google.com/scholar?q=Xin+Zhang%2C+Yanzhao+Zhang%2C+Wen+Xie%2C+Mingxin+Li%2C+Ziqi+Dai%2C+Dingkun+Long%2C+Pengjun+Xie%2C+Meishan+Zhang%2C+Wenjie+Li%2C+and+Min+Zhang%2E+2024b%2E+Gme%3A+Improving+universal+multimodal+retrieval+by+multimodal+llms%2E+ar)
46. Zeyu Zhang, Quanyu Dai, Xiaohe Bo, Chen Ma, Rui Li, Xu Chen, Jieming Zhu, Zhenhua Dong, and Ji-Rong Wen. 2025b. A survey on the memory mechanism of large language model-based agents. ACM Transactions on Information Systems, 43(6):1–47. [find](https://scholar.google.com/scholar?q=Zeyu+Zhang%2C+Quanyu+Dai%2C+Xiaohe+Bo%2C+Chen+Ma%2C+Rui+Li%2C+Xu+Chen%2C+Jieming+Zhu%2C+Zhenhua+Dong%2C+and+Ji%2DRong+Wen%2E+2025b%2E+A+survey+on+the+memory+mechanism+of+large+language+model%2Dbased+agents%2E+ACM+Transactions+)
47. Zeyu Zhang, Quanyu Dai, Xu Chen, Rui Li, Zhongyang Li, and Zhenhua Dong. 2025c. Memengine: A unified and modular library for developing advanced memory of llm-based agents. In Companion Proceedings of the ACM on Web Conference 2025, pages 821–824. [find](https://scholar.google.com/scholar?q=Zeyu+Zhang%2C+Quanyu+Dai%2C+Xu+Chen%2C+Rui+Li%2C+Zhongyang+Li%2C+and+Zhenhua+Dong%2E+2025c%2E+Memengine%3A+A+unified+and+modular+library+for+developing+advanced+memory+of+llm%2Dbased+agents%2E+In+Companion+Proceedings+of)
48. Kai Zhao, Yanjun Zhao, Jiaming Song, Shien He, Lusheng Zhang, Qiang Zhang, and Tianjiao Li. 2026. Saber: Switchable and balanced training for efficient llm reasoning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 34950–34958. [find](https://scholar.google.com/scholar?q=Kai+Zhao%2C+Yanjun+Zhao%2C+Jiaming+Song%2C+Shien+He%2C+Lusheng+Zhang%2C+Qiang+Zhang%2C+and+Tianjiao+Li%2E+2026%2E+Saber%3A+Switchable+and+balanced+training+for+efficient+llm+reasoning%2E+In+Proceedings+of+the+AAAI+Confer)
49. Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. 2024. Memorybank: Enhancing large language models with long-term memory. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 19724–19731. [find](https://scholar.google.com/scholar?q=Wanjun+Zhong%2C+Lianghong+Guo%2C+Qiqi+Gao%2C+He+Ye%2C+and+Yanlin+Wang%2E+2024%2E+Memorybank%3A+Enhancing+large+language+models+with+long%2Dterm+memory%2E+In+Proceedings+of+the+AAAI+Conference+on+Artificial+Intelligence)

*Updated 31 October 2026*