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arxiv: 2605.21832 · v1 · pith:DAOAXU2Onew · submitted 2026-05-20 · 💻 cs.AI

FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation

Xinhang Yuan , Zexi Huang , Anjia Cao , Xudong Lu , Zikai Wang , Penghao Zhou , Chang Liu , Wentao Guo
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Qinglei Wang
This is my paper

Pith reviewed 2026-05-22 08:17 UTC · model grok-4.3

classification 💻 cs.AI
keywords livestreaming recommendationmultimodal semantic codesID-free rankingcold-start problemhierarchical codescross-domain encoderindustrial recommender systems
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The pith

FLUID replaces ephemeral item IDs with hierarchical multimodal codes from short videos and livestreams in large-scale ranking.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Recommender systems normally assign each item a unique ID embedding that gathers signals from many user interactions. Livestream rooms, however, last only tens of minutes, so their IDs stay in a permanent cold-start state and ranking models cannot generalize well. FLUID trains a single multimodal encoder on both short videos and livestreams to produce discrete hierarchical LUCID codes. It then feeds slice-level and room-level codes as separate tokens into a late-fusion, ID-free ranker that is warmed up in stages during online training. When run at production scale on a billion-user platform, the system records measurable lifts in watch time, cold-start views, and active hours.

Core claim

FLUID is the first framework to retire the candidate-side item ID completely from a production livestreaming ranker. It couples a cross-domain multimodal encoder, trained jointly on short videos and livestreams to emit discrete hierarchical LUCID codes, with a late-fusion architecture that treats slice-level and room-level codes as independent tokens and stabilizes training through staged warmup under incremental online updates.

What carries the argument

LUCID codes: discrete hierarchical semantic tokens generated by a cross-domain multimodal encoder jointly trained on short videos and livestreams; these tokens substitute for item ID embeddings inside an ID-free late-fusion ranking model.

If this is right

  • The ID-free ranker generalizes to newly created live rooms that have never accumulated interaction data.
  • Joint training on short videos and livestreams produces codes that transfer semantic information across the two domains.
  • Staged warmup during online incremental training keeps the model stable after the removal of item ID embeddings.
  • Production deployment on platforms serving over one billion users yields gains of +0.55% Quality Watch Duration and +2.05% Cold-Start Room Views.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same code-generation approach could be tested on other short-lived content such as stories or temporary events where persistent IDs are unavailable.
  • Removing item IDs may reduce memory footprint and embedding table size in very large catalogs.
  • Cross-domain training might improve consistency when users move between short-form video and live content within the same app.

Load-bearing premise

The LUCID codes can capture and replace the collaborative signals that would normally come from user interactions with persistent item IDs.

What would settle it

An online A/B test on new live rooms that shows equal or lower cold-start room views when LUCID codes are removed compared with the ID-based baseline would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.21832 by Anjia Cao, Chang Liu, Penghao Zhou, Qinglei Wang, Wentao Guo, Xinhang Yuan, Xudong Lu, Zexi Huang, Zikai Wang.

Figure 1
Figure 1. Figure 1: Overview of FLUID. Top: a cross-domain multimodal encoder (SigLip2 ViT + Qwen3-Embedding) jointly trained on livestreams and short videos produces a 128-d slice embedding 𝑧, which RQ-KMeans discretizes into a 4-level codeword tuple— LUCID—with room-level LUCID obtained by per-level majority voting over slices in a session. Bottom: slice- and room-level LUCID enter the production ranker as independent candi… view at source ↗
Figure 2
Figure 2. Figure 2: Item ID embedding ℓ2 norm vs. live-room age, ag￾gregated over one day of production traffic. The norm fails to converge within the typical ∼45-minute room lifetime. semantic codes more compatible with ID-based ranking [7, 19– 21, 47], following the semantic-id line opened by TIGER [26] and re￾cent prefix-𝑛-gram parameterizations [48]. End-to-end multimodal recommenders [18, 36, 43] further train the multim… view at source ↗
Figure 3
Figure 3. Figure 3: Three-stage warmup procedure for FLUID. Each [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Live slices and short videos grouped by LUCID: [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Converged learned gate weight on the item ID under [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
read the original abstract

Modern recommender systems rely heavily on ID-based collaborative filtering: each item is represented by a unique ID embedding that accumulates collaborative signals from user interactions. Livestreaming recommendation, however, faces a unique challenge in this paradigm: a live room typically broadcasts for only tens of minutes, so its item ID remains poorly learned in a persistent cold-start state and ID-centric ranking models fail to generalize. We present FLUID, the first framework to fully retire the candidate-side item ID from a production-scale livestreaming ranker. FLUID couples a cross-domain multimodal encoder, jointly trained on short videos and livestreams to produce discrete hierarchical codes (LUCID), with a late-fusion, ID-free design that injects slice-level and room-level LUCID as independent tokens, stabilized by a staged warmup under online incremental training. Deployed on our industrial livestreaming recommenders with a cross-platform combined user base of over one billion globally, FLUID delivers significant online gains of +0.55% Quality Watch Duration, +2.05% Cold-Start Room Views, and +0.05% Active Hours.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript presents FLUID, a framework that retires candidate-side item ID embeddings from a production livestreaming ranker. It couples a cross-domain multimodal encoder (jointly trained on short videos and livestreams) that outputs discrete hierarchical codes called LUCID with a late-fusion ID-free architecture that injects slice-level and room-level LUCID tokens as independent features, stabilized by staged warmup under online incremental training. The paper reports online A/B gains of +0.55% Quality Watch Duration, +2.05% Cold-Start Room Views, and +0.05% Active Hours after deployment on a platform with a combined user base exceeding one billion.

Significance. If the substitution of LUCID codes for ID embeddings holds under rigorous validation, the work is significant for industrial recommender systems. It directly addresses the cold-start failure mode that arises when live rooms broadcast for only tens of minutes, offering a scalable, cross-domain semantic alternative to persistent ID-based collaborative filtering. The reported large-scale deployment and cross-platform gains constitute a practical contribution that could influence design choices for other ephemeral-content ranking problems.

major comments (2)
  1. [Abstract] Abstract: the reported online A/B gains (+0.55% QWD, +2.05% CSRV, +0.05% AH) are stated without any accompanying experimental details—test population size, experiment duration, baseline models, statistical tests, or ablation results that isolate the contribution of the LUCID tokens versus an ID-based counterpart. This information is load-bearing for the central claim that the discrete hierarchical codes fully substitute for collaborative signals previously carried by item IDs.
  2. [Method (LUCID encoder and late-fusion design)] The weakest assumption—that LUCID codes generated from the cross-domain multimodal encoder can capture and replace interaction-derived collaborative signals—is asserted but not supported by any ablation that compares ranking performance with and without candidate-side ID embeddings or that measures how much of the observed lift is attributable to the semantic codes versus other architectural changes.
minor comments (1)
  1. [Abstract] The expansion of the acronym LUCID is not given in the abstract even though the term is introduced as a key contribution.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback on our work. The comments highlight important aspects of experimental transparency and validation that we address point by point below. We have prepared revisions to strengthen the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the reported online A/B gains (+0.55% QWD, +2.05% CSRV, +0.05% AH) are stated without any accompanying experimental details—test population size, experiment duration, baseline models, statistical tests, or ablation results that isolate the contribution of the LUCID tokens versus an ID-based counterpart. This information is load-bearing for the central claim that the discrete hierarchical codes fully substitute for collaborative signals previously carried by item IDs.

    Authors: We agree that the abstract would be strengthened by additional context on the online A/B evaluation. In the revised version we will expand the abstract to note the experiment duration (multiple weeks of incremental deployment), the baseline as the prior production ID-based ranker, and that gains were assessed for statistical significance via standard hypothesis testing. Full population sizes and exact p-values remain subject to confidentiality constraints typical of industrial deployments, but we will clarify that the reported lifts reflect live traffic on a platform serving over one billion users. revision: yes

  2. Referee: [Method (LUCID encoder and late-fusion design)] The weakest assumption—that LUCID codes generated from the cross-domain multimodal encoder can capture and replace interaction-derived collaborative signals—is asserted but not supported by any ablation that compares ranking performance with and without candidate-side ID embeddings or that measures how much of the observed lift is attributable to the semantic codes versus other architectural changes.

    Authors: The referee correctly notes the absence of explicit offline ablations isolating LUCID from ID embeddings. Our primary evidence is the production deployment itself, where the system operates without candidate-side IDs and still delivers the reported gains, particularly in cold-start scenarios. To address this directly, the revised manuscript will include a new offline ablation subsection using pre-transition logged data, comparing an ID-augmented variant against the ID-free LUCID design on ranking metrics for both warm and cold items. This will quantify the semantic codes' contribution relative to residual architectural factors. revision: yes

standing simulated objections not resolved
  • Exact test population sizes and proprietary baseline configurations, which cannot be disclosed due to industrial confidentiality policies.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper describes FLUID as a framework that retires candidate-side item IDs by coupling a cross-domain multimodal encoder producing discrete hierarchical LUCID codes with a late-fusion ID-free architecture using slice- and room-level tokens plus staged warmup. No equations, derivations, or load-bearing steps are presented in the abstract or high-level claims that reduce the substitution of collaborative signals to a self-definition, fitted input renamed as prediction, or self-citation chain. The central premise is framed as an empirical engineering result validated by online A/B lifts on a billion-user platform, remaining self-contained against external benchmarks without internal reduction to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Reviewed from abstract only; no explicit free parameters, axioms, or invented entities beyond the high-level introduction of LUCID codes are stated in the provided text.

invented entities (1)
  • LUCID codes no independent evidence
    purpose: Discrete hierarchical multimodal semantic representations intended to replace item ID embeddings
    Introduced in the abstract as the core output of the cross-domain encoder; no independent evidence or falsifiable prediction supplied.

pith-pipeline@v0.9.0 · 5758 in / 1325 out tokens · 56553 ms · 2026-05-22T08:17:32.336250+00:00 · methodology

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Reference graph

Works this paper leans on

52 extracted references · 52 canonical work pages · 9 internal anchors

  1. [2]

    Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A Neural Probabilistic Language Model.Journal of Machine Learning Research3 (2003), 1137–1155

    work page 2003

  2. [3]

    Anjia Cao, Xing Wei, and Zhiheng Ma. 2025. Flame: Frozen large language models enable data-efficient language-image pre-training. InProceedings of the Computer FLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation Conference acronym ’XX, June 03–05, 2018, Woodstock, NY Table 7: Ablation on candidate-side ...

    work page 2025

  3. [4]

    Haonan Chen, Hong Liu, Yuping Luo, Liang Wang, Nan Yang, Furu Wei, and Zhicheng Dou. 2025. Moca: Modality-aware continual pre-training makes better bidirectional multimodal embeddings.arXiv preprint arXiv:2506.23115(2025)

    work page arXiv 2025

  4. [5]

    Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah

    work page

  5. [6]

    InProceedings of the 1st Workshop on Deep Learning for Recommender Systems(Boston, MA, USA) (DLRS 2016)

    Wide & Deep Learning for Recommender Systems. InProceedings of the 1st Workshop on Deep Learning for Recommender Systems (DLRS@RecSys). ACM, 7–10. doi:10.1145/2988450.2988454

    work page doi:10.1145/2988450.2988454

  6. [7]

    Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. InProceedings of the 10th ACM Conference on Recommender Systems (RecSys). ACM, Boston, MA, USA, 191–198. doi:10.1145/ 2959100.2959190

    work page arXiv 2016

  7. [8]

    Xiuqi Deng, Lu Xu, Xiyao Li, Jinkai Yu, Erpeng Xue, Zhongyuan Wang, Di Zhang, Zhaojie Liu, Yang Song, Guorui Zhou, Na Mou, and Shen Jiang. 2024. EM3: End-to-End Training of Multimodal Model and Ranking Model.arXiv preprint arXiv:2404.06078(2024)

    work page arXiv 2024

  8. [9]

    Simon Funk. 2006. Netflix Update: Try This at Home. Blog post. https://sifter. org/~simon/journal/20061211.html

    work page arXiv 2006

  9. [10]

    Huan Gui, Ruoxi Wang, Ke Yin, Long Jin, Maciej Kula, Taibai Xu, Lichan Hong, and Ed H. Chi. 2023. HiFormer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems.arXiv preprint arXiv:2311.05884 (2023)

    work page arXiv 2023

  10. [11]

    Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: A factorization-machine based neural network for CTR prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI). 1725–1731

    work page 2017

  11. [12]

    Weiquan Huang, Aoqi Wu, Yifan Yang, Xufang Luo, Yuqing Yang, Liang Hu, Qi Dai, Chunyu Wang, Xiyang Dai, Dongdong Chen, et al . 2024. Llm2clip: Powerful language model unlocks richer visual representation.arXiv preprint arXiv:2411.04997(2024)

    work page arXiv 2024

  12. [13]

    Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. 2021. Scaling up visual and vision-language representation learning with noisy text supervision. InICML

    work page 2021

  13. [14]

    Ziyan Jiang, Rui Meng, Xinyi Yang, Semih Yavuz, Yingbo Zhou, and Wenhu Chen. 2024. Vlm2vec: Training vision-language models for massive multimodal embedding tasks.arXiv preprint arXiv:2410.05160(2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  14. [15]

    Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin. 2016. Field- Aware Factorization Machines for CTR Prediction. InProceedings of the 10th ACM Conference on Recommender Systems (RecSys). ACM, 43–50. doi:10.1145/2959100. 2959134

    work page doi:10.1145/2959100 2016

  15. [16]

    Wang-Cheng Kang and Julian McAuley. 2018. Self-Attentive Sequential Rec- ommendation. InProceedings of the 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 197–206. doi:10.1109/ICDM.2018.00035

    work page doi:10.1109/icdm.2018.00035 2018

  16. [17]

    Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Tech- niques for Recommender Systems.Computer42, 8 (2009), 30–37. doi:10.1109/ MC.2009.263

    work page 2009

  17. [18]

    Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. InInternational conference on machine learning. PMLR, 12888–12900

    work page 2022

  18. [19]

    Yueyang Liu, Jiangxia Cao, Shen Wang, Shuang Wen, Xiang Chen, Xiangyu Wu, Shuang Yang, Zhaojie Liu, Kun Gai, and Guorui Zhou. 2025. LLM-Alignment Live-Streaming Recommendation.arXiv preprint arXiv:2504.05217(2025)

    work page arXiv 2025

  19. [20]

    Yanze Liu, Tianxin Wang, Hongyu Zhou, Qing Liu, Yingjie Qin, Ruilong Su, Rui- wen Xu, Yanhua Huang, Weinan Zhang, and Yong Yu. 2025. DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System. InProceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM). ACM. doi:10.1145/3746252.3761529

    work page doi:10.1145/3746252.3761529 2025

  20. [21]

    Yifan Liu, Kangning Zhang, Xiangyuan Ren, Yanhua Huang, Jiarui Jin, Yingjie Qin, Ruilong Su, Ruiwen Xu, Yong Yu, and Weinan Zhang. 2024. AlignRec: Aligning and Training in Multimodal Recommendations. InProceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM). ACM, 1503–1512

    work page 2024

  21. [22]

    Xinchen Luo, Jiangxia Cao, Tianyu Sun, Jinkai Yu, Rui Huang, Wei Yuan, Hezheng Lin, Yichen Zheng, Shiyao Wang, Qigen Hu, et al. 2025. QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou. InProceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 5915–5922. arXiv:2411.11739

    work page arXiv 2025

  22. [23]

    Rui Meng, Ziyan Jiang, Ye Liu, Mingyi Su, Xinyi Yang, Yuepeng Fu, Can Qin, Zeyuan Chen, Ran Xu, Caiming Xiong, et al . 2025. Vlm2vec-v2: Advancing multimodal embedding for videos, images, and visual documents.arXiv preprint arXiv:2507.04590(2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  23. [24]

    Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherni- avskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kon- dratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao,...

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  24. [25]

    Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding.arXiv preprint arXiv:1807.03748(2018)

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  25. [26]

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. InICML

    work page 2021

  26. [27]

    Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q

    Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan H. Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q. Tran, Jonah Saber, Maheswaran Sathiamoorthy, Ed H. Chi, and Jonathon Shlens. 2023. Recommender Systems with Generative Retrieval. InAdvances in Neural Information Processing Systems 36 (NeurIPS)

    work page 2023

  27. [28]

    Steffen Rendle. 2010. Factorization Machines. InProceedings of the 2010 IEEE International Conference on Data Mining (ICDM). IEEE, 995–1000. doi:10.1109/ ICDM.2010.127 Conference acronym ’XX, June 03–05, 2018, Woodstock, NY Yuan∗, Huang∗, Cao∗, Lu∗ et al

    work page 2010

  28. [29]

    Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-Based Collaborative Filtering Recommendation Algorithms. InProceedings of the 10th International Conference on World Wide Web (WWW). ACM, Hong Kong, 285–295. doi:10.1145/371920.372071

    work page doi:10.1145/371920.372071 2001

  29. [30]

    Xiang-Rong Sheng, Feifan Yang, Litong Gong, Biao Wang, Zhangming Chan, Yujing Zhang, Yueyao Cheng, Yong-Nan Zhu, Tiezheng Ge, Han Zhu, Yuning Jiang, Jian Xu, and Bo Zheng. 2024. Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights. InProceed- ings of the 33rd ACM International Conference on Information ...

    work page doi:10.1145/3627673.3680068 2024

  30. [31]

    Anima Singh, Trung Vu, Raghunandan Keshavan, Nikhil Mehta, Xinyang Yi, Lichan Hong, Lukasz Heldt, Li Wei, Ed Chi, and Maheswaran Sathiamoorthy

    work page

  31. [32]

    Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations.arXiv preprint arXiv:2306.08121(2023)

    work page arXiv 2023

  32. [33]

    Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang

    work page

  33. [34]

    InProceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM)

    BERT4Rec: Sequential Recommendation with Bidirectional Encoder Rep- resentations from Transformer. InProceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 1441–1450. doi:10.1145/3357384.3357895

    work page doi:10.1145/3357384.3357895

  34. [35]

    Zhulin Tao, Xiaohao Liu, Yewei Xia, Xiang Wang, Lifang Yang, Xianglin Huang, and Tat-Seng Chua. 2022. Self-supervised learning for multimedia recommenda- tion.IEEE Transactions on Multimedia25 (2022), 5107–5116

    work page 2022

  35. [36]

    Michael Tschannen, Alexey Gritsenko, Xiao Wang, Muhammad Ferjad Naeem, Ibrahim Alabdulmohsin, Nikhil Parthasarathy, Talfan Evans, Lucas Beyer, Ye Xia, Basil Mustafa, et al. 2025. Siglip 2: Multilingual vision-language encoders with improved semantic understanding, localization, and dense features.arXiv preprint arXiv:2502.14786(2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  36. [37]

    Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & Cross Network for Ad Click Predictions. InProceedings of the ADKDD Workshop (ADKDD@KDD). ACM, 1–7. doi:10.1145/3124749.3124754

    work page doi:10.1145/3124749.3124754 2017

  37. [38]

    Zilin Xiao, Qi Ma, Mengting Gu, Chun-cheng Jason Chen, Xintao Chen, Vicente Ordonez, and Vijai Mohan. 2025. Metaembed: Scaling multimodal retrieval at test-time with flexible late interaction.arXiv preprint arXiv:2509.18095(2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  38. [39]

    Ruochen Yang, Yueyang Liu, Zijie Zhuang, Changxin Lao, Yuhui Zhang, Jiangxia Cao, Jia Xu, Xiang Chen, Haoke Xiao, Xiangyu Wu, et al . 2026. SARM: LLM- Augmented Semantic Anchor for End-to-End Live-Streaming Ranking.arXiv preprint arXiv:2602.09401(2026)

    work page arXiv 2026

  39. [40]

    Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. 2022. Coca: Contrastive captioners are image-text foundation models.arXiv preprint arXiv:2205.01917(2022)

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  40. [41]

    Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. 2023. Where to Go Next for Recommender Systems? ID- vs. Modality-Based Recommender Models Revisited. InProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). ACM, 2639–2649. doi:10.1145/3539618.3591932

    work page doi:10.1145/3539618.3591932 2023

  41. [42]

    Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Gong, Fangda Gu, Jiayuan He, Yinghai Lu, and Yu Shi. 2024. Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations. InProceedings of the 41st International Conference on Machine Learning (ICML) (Proceedings of Machine Learning Research, Vol. 235). PMLR, 58484–58509

    work page 2024

  42. [43]

    Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. 2023. Sig- moid loss for language image pre-training. InICCV

    work page 2023

  43. [44]

    Buyun Zhang, Liang Liao, Yonatan Koren, Andrey Malevich, Maxim Naumov, et al. 2024. Wukong: Towards a Scaling Law for Large-Scale Recommendation. InProceedings of the 41st International Conference on Machine Learning (ICML) (Proceedings of Machine Learning Research, Vol. 235). PMLR, 59421–59434

    work page 2024

  44. [45]

    Beichen Zhang, Pan Zhang, Xiaoyi Dong, Yuhang Zang, and Jiaqi Wang. 2024. Long-clip: Unlocking the long-text capability of clip. InECCV

    work page 2024

  45. [46]

    Chao Zhang et al. 2025. NoteLLM-2: Multimodal Large Representation Models for Recommendation. InProceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). ACM. Xiaohongshu

    work page 2025

  46. [47]

    Ruifeng Zhang, Zexi Huang, Zikai Wang, Ke Sun, Bohang Zheng, Yuchen Jiang, Zhe Chen, Zhen Ouyang, Huimin Xie, Phil Shen, et al. 2026. Zenith: Scaling up Ranking Models for Billion-scale Livestreaming Recommendation.arXiv preprint arXiv:2601.21285(2026)

    work page arXiv 2026

  47. [48]

    Xin Zhang, Yanzhao Zhang, Wen Xie, Mingxin Li, Ziqi Dai, Dingkun Long, Pengjun Xie, Meishan Zhang, Wenjie Li, and Min Zhang. 2024. GME: im- proving universal multimodal retrieval by multimodal LLMs.arXiv preprint arXiv:2412.16855(2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  48. [49]

    Yanzhao Zhang, Mingxin Li, Dingkun Long, Xin Zhang, Huan Lin, Baosong Yang, Pengjun Xie, An Yang, Dayiheng Liu, Junyang Lin, et al. 2025. Qwen3 embedding: Advancing text embedding and reranking through foundation models.arXiv preprint arXiv:2506.05176(2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  49. [50]

    Yuhan Zhao, Rui Chen, Qilong Han, Hongjun Li, and Li Chen. 2025. Aligning and Balancing ID and Multimodal Representations for Recommendation. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). ACM. doi:10.1145/3711896.3737275

    work page doi:10.1145/3711896.3737275 2025

  50. [51]

    Carolina Zheng, Minhui Huang, Dmitrii Pedchenko, Kaushik Rangadurai, Siyu Wang, Fan Xia, Gaby Nahum, Jie Lei, Yang Yang, Tao Liu, Zutian Luo, Xiaohan Wei, Dinesh Ramasamy, Jiyan Yang, Yiping Han, Lin Yang, Hangjun Xu, Rong Jin, and Shuang Yang. 2025. Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID. InProceedings of ...

    work page doi:10.1145/3705328.3748123 2025

  51. [52]

    Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Deep Interest Evolution Network for Click-Through Rate Prediction. InProceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI). 5941–5948. doi:10.1609/aaai.v33i01.33015941

    work page doi:10.1609/aaai.v33i01.33015941 2019

  52. [53]

    Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep Interest Network for Click- Through Rate Prediction. InProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM, 1059–1068. doi:10.1145/3219819.3219823

    work page doi:10.1145/3219819.3219823 2018