{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T07:06:35Z","timestamp":1789974395961,"version":"4.0.1"},"publisher-location":"Cham","reference-count":51,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032373205","type":"print"},{"value":"9783032373212","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-37321-2_29","type":"book-chapter","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T06:45:19Z","timestamp":1789973119000},"page":"527-544","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Omni-RRM: Advancing Omni Reward Modeling via\u00a0Automatic Rubric-Grounded Preference Synthesis"],"prefix":"10.1007","author":[{"given":"Zicheng","family":"Kong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dehua","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenbo","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alven","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiwei","family":"Ru","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixuan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuqing","family":"Bie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liuyu","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huijia","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaofeng","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"29_CR1","unstructured":"Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., Hui, B., Ji, L., Li, M., Lin, J., Lin, R., Liu, D., Liu, G., Lu, C., Lu, K., Ma, J., Men, R., Ren, X., Ren, X., Tan, C., Tan, S., Tu, J., Wang, P., Wang, S., Wang, W., Wu, S., Xu, B., Xu, J., Yang, A., Yang, H., Yang, J., Yang, S., Yao, Y., Yu, B., Yuan, H., Yuan, Z., Zhang, J., Zhang, X., Zhang, Y., Zhang, Z., Zhou, C., Zhou, J., Zhou, X., Zhu, T.: Qwen technical report (2023), https:\/\/arxiv.org\/abs\/2309.16609, accessed: 26 Jun 2026"},{"key":"29_CR2","unstructured":"Bai, S., Chen, K., Liu, X., Wang, J., Ge, W., Song, S., Dang, K., Wang, P., Wang, S., Tang, J., Zhong, H., Zhu, Y., Yang, M., Li, Z., Wan, J., Wang, P., Ding, W., Fu, Z., Xu, Y., Ye, J., Zhang, X., Xie, T., Cheng, Z., Zhang, H., Yang, Z., Xu, H., Lin, J.: Qwen2.5-vl technical report (2025), https:\/\/arxiv.org\/abs\/2502.13923, accessed: 26 Jun 2026"},{"key":"29_CR3","unstructured":"Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., Joseph, N., Kadavath, S., Kernion, J., Conerly, T., El-Showk, S., Elhage, N., Hatfield-Dodds, Z., Hernandez, D., Hume, T., Johnston, S., Kravec, S., Lovitt, L., Nanda, N., Olsson, C., Amodei, D., Brown, T.B., Clark, J., McCandlish, S., Olah, C., Mann, B., Kaplan, J.: Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint 2204.05862 (2022), https:\/\/arxiv.org\/abs\/2204.05862, accessed: 26 Jun 2026"},{"key":"29_CR4","unstructured":"Chen, X., Li, G., Wang, Z., Jin, B., Qian, C., Wang, Y., Wang, H., Zhang, Y., Zhang, D., Zhang, T., Tong, H., Ji, H.: Rm-r1: Reward modeling as reasoning. arXiv preprint arXiv:2505.02387 (2025), https:\/\/arxiv.org\/abs\/2505.02387, accessed: 26 Jun 2026"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Z., Wu, J., Wang, W., Su, W., Chen, G., Xing, S., Zhong, M., Zhang, Q., Zhu, X., Lu, L., et\u00a0al.: Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 24185\u201324198 (2024)","DOI":"10.1109\/CVPR52733.2024.02283"},{"key":"29_CR6","unstructured":"Dai, J., Pan, X., Sun, R., Ji, J., Xu, X., Liu, M., Wang, Y., Yang, Y.: Safe rlhf: Safe reinforcement learning from human feedback (2023), https:\/\/arxiv.org\/abs\/2310.12773, accessed: 26 Jun 2026"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Feng, K., Gong, K., Li, B., Guo, Z., Wang, Y., Peng, T., Wu, J., Zhang, X., Wang, B., Yue, X.: Video-r1: Reinforcing video reasoning in mllms (2025), https:\/\/arxiv.org\/abs\/2503.21776, accessed: 26 Jun 2026","DOI":"10.52202\/085713-3310"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Fu, C., Dai, Y., Luo, Y., Li, L., Ren, S., Zhang, R., Wang, Z., Zhou, C., Shen, Y., Zhang, M., et\u00a0al.: Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 24108\u201324118 (2025)","DOI":"10.1109\/CVPR52734.2025.02245"},{"key":"29_CR9","unstructured":"Gemini Team: Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv preprint arXiv:2403.05530 (2024), https:\/\/arxiv.org\/abs\/2403.05530, accessed: 26 Jun 2026"},{"key":"29_CR10","unstructured":"Google DeepMind: Introducing gemini 2.0: Our new ai model for the agentic era. https:\/\/blog.google\/technology\/google-deepmind\/google-gemini-ai-update-december-2024\/ (Dec 2024), accessed: 26 Jun 2026"},{"key":"29_CR11","unstructured":"Gunjal, A., Wang, A., Lau, E., Nath, V., He, Y., Liu, B., Hendryx, S.: Rubrics as rewards: Reinforcement learning beyond verifiable domains (2025), https:\/\/arxiv.org\/abs\/2507.17746, accessed: 26 Jun 2026"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Guo, D., Yang, D., Zhang, H., Song, J., Wang, P., Zhu, Q., Xu, R., Zhang, R., Ma, S., Bi, X., et\u00a0al.: DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning. Nature 645(8081), 633\u2013638 (September 2025). https:\/\/doi.org\/10.1038\/s41586-025-09422-z accessed: 26 Jun 2026","DOI":"10.1038\/s41586-025-09422-z"},{"key":"29_CR13","unstructured":"He, Y., Li, W., Zhang, H., Li, S., Mandyam, K., Khosla, S., Xiong, Y., Wang, N., Peng, X., Li, B., Bi, S., Patil, S.G., Qi, Q., Feng, S., Katz-Samuels, J., Pang, R.Y., Gonugondla, S., Lang, H., Yu, Y., Qian, Y., Fazel-Zarandi, M., Yu, L., Benhalloum, A., Awadalla, H., Faruqui, M.: Advancedif: Rubric-based benchmarking and reinforcement learning for advancing llm instruction following (2025), https:\/\/arxiv.org\/abs\/2511.10507, accessed: 26 Jun 2026"},{"key":"29_CR14","unstructured":"Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models (2021), https:\/\/arxiv.org\/abs\/2106.09685, accessed: 26 Jun 2026"},{"key":"29_CR15","unstructured":"Jia, R., Yang, Y., Wu, Y., Gai, Y., Tao, S., Zhou, M., Lin, J., Jiang, X., Jiang, G.: Open rubric system: Scaling reinforcement learning with pairwise adaptive rubric (2026), https:\/\/arxiv.org\/abs\/2602.14069, accessed: 26 Jun 2026"},{"key":"29_CR16","unstructured":"Jin, Z., Yuan, H., Zhu, K., Li, J., Cao, P., Chen, Y., Liu, K., Zhao, J.: Omni-reward: Towards generalist omni-modal reward modeling with free-form preferences (2025), https:\/\/arxiv.org\/abs\/2510.23451, accessed: 26 Jun 2026"},{"key":"29_CR17","unstructured":"Kaufmann, T., Weng, P., Bengs, V., H\u00fcllermeier, E.: A survey of reinforcement learning from human feedback. arXiv preprint arXiv:2312.14925 (2023), https:\/\/arxiv.org\/abs\/2312.14925, accessed: 26 Jun 2026"},{"key":"29_CR18","doi-asserted-by":"publisher","first-page":"36652","DOI":"10.52202\/075280-1594","volume":"36","author":"Y Kirstain","year":"2023","unstructured":"Kirstain, Y., Polyak, A., Singer, U., Matiana, S., Penna, J., Levy, O.: Pick-a-pic: An open dataset of user preferences for text-to-image generation. Adv. Neural. Inf. Process. Syst. 36, 36652\u201336663 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"29_CR19","unstructured":"Lee, H., Phatale, S., Mansoor, H., Mesnard, T., Ferret, J., Lu, K., Bishop, C., Hall, E., Carbune, V., Rastogi, A., Prakash, S.: Rlaif vs. rlhf: Scaling reinforcement learning from human feedback with ai feedback (2024), https:\/\/arxiv.org\/abs\/2309.00267, accessed: 26 Jun 2026"},{"key":"29_CR20","unstructured":"Li, B., Zhang, Y., Guo, D., Zhang, R., Li, F., Zhang, H., Zhang, K., Zhang, P., Li, Y., Liu, Z., Li, C.: Llava-onevision: Easy visual task transfer (2024), https:\/\/arxiv.org\/abs\/2408.03326, accessed: 26 Jun 2026"},{"key":"29_CR21","doi-asserted-by":"crossref","unstructured":"Li, L., Wei, Y., Xie, Z., Yang, X., Song, Y., Wang, P., An, C., Liu, T., Li, S., Lin, B.Y., Kong, L., Liu, Q.: Vl-rewardbench: A challenging benchmark for vision-language generative reward models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 24657\u201324668 (June 2025)","DOI":"10.1109\/CVPR52734.2025.02296"},{"key":"29_CR22","unstructured":"Li, S., Zhao, J., Wei, M., Ren, H., Zhou, Y., Yang, J., Liu, S., Zhang, K., Chen, W.: Rubrichub: A comprehensive and highly discriminative rubric dataset via automated coarse-to-fine generation (2026), https:\/\/arxiv.org\/abs\/2601.08430, accessed: 26 Jun 2026"},{"key":"29_CR23","unstructured":"Liu, H., Li, C., Li, Y., Li, B., Zhang, Y., Shen, S., Lee, Y.J.: Llava-next: Improved reasoning, ocr, and world knowledge. https:\/\/llava-vl.github.io\/blog\/2024-01-30-llava-next\/ (2024), accessed: 26 Jun 2026"},{"key":"29_CR24","doi-asserted-by":"publisher","first-page":"34892","DOI":"10.52202\/075280-1516","volume":"36","author":"H Liu","year":"2023","unstructured":"Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. Adv. Neural. Inf. Process. Syst. 36, 34892\u201334916 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"29_CR25","unstructured":"Liu, T., Xu, R., Yu, T., Hong, I., Yang, C., Zhao, T., Wang, H.: Openrubrics: Towards scalable synthetic rubric generation for reward modeling and llm alignment (2026), https:\/\/arxiv.org\/abs\/2510.07743, accessed: 26 Jun 2026"},{"key":"29_CR26","unstructured":"Lu, Y., Li, H., Cong, X., Zhang, Z., Wu, Y., Lin, Y., Liu, Z., Liu, F., Sun, M.: Learning to generate structured output with schema reinforcement learning (2025), https:\/\/arxiv.org\/abs\/2502.18878, accessed: 26 Jun 2026"},{"key":"29_CR27","unstructured":"OpenAI: Hello gpt-4o: Introducing our real-time multimodal model. https:\/\/openai.com\/index\/hello-gpt-4o\/ (2024), accessed: 26 Jun 2026"},{"key":"29_CR28","doi-asserted-by":"publisher","first-page":"27730","DOI":"10.52202\/068431-2011","volume":"35","author":"L Ouyang","year":"2022","unstructured":"Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Adv. Neural. Inf. Process. Syst. 35, 27730\u201327744 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"29_CR29","unstructured":"Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C.D., Finn, C.: Direct preference optimization: Your language model is secretly a reward model (2024), https:\/\/arxiv.org\/abs\/2305.18290, accessed: 26 Jun 2026"},{"key":"29_CR30","unstructured":"Shao, Z., Wang, P., Zhu, Q., Xu, R., Song, J., Bi, X., Zhang, H., Zhang, M., Li, Y.K., Wu, Y., Guo, D.: Deepseekmath: Pushing the limits of mathematical reasoning in open language models (2024), https:\/\/arxiv.org\/abs\/2402.03300, accessed: 26 Jun 2026"},{"key":"29_CR31","unstructured":"Srivastava, P., Singh, H., Madhavan, R., Patil, G., Addepalli, S., Suggala, A., Aravamudhan, R., Sharma, S., Laha, A., Raghuveer, A., Shanmugam, K., Precup, D.: Robust reward modeling via causal rubrics (2025), https:\/\/arxiv.org\/abs\/2506.16507, accessed: 26 Jun 2026"},{"key":"29_CR32","unstructured":"Sun, Z., Shen, S., Cao, S., Liu, H., Li, C., Shen, Y., Gan, C., Gui, L.Y., Wang, Y.X., Yang, Y., et\u00a0al.: Aligning large multimodal models with factually augmented rlhf (2024), https:\/\/arxiv.org\/abs\/2309.14525, accessed: 26 Jun 2026"},{"key":"29_CR33","unstructured":"Wang, H., Jin, X., Lu, L., Li, C., Chen, J., Liu, Q., Wang, H.: Earlytom: Early token compression completes fast video understanding (2026), https:\/\/arxiv.org\/abs\/2605.30010, accessed: 26 Jun 2026"},{"key":"29_CR34","unstructured":"Wang, Q., Liu, J., Liang, J., Jiang, Y., Zhang, Y., Chen, J., Zheng, Y., Wang, X., Wan, P., Yue, X., Liu, J., et\u00a0al.: Vr-thinker: Boosting video reward models through thinking-with-image reasoning. arXiv preprint arXiv:2510.10518 (2025), https:\/\/arxiv.org\/abs\/2510.10518, accessed: 26 Jun 2026"},{"key":"29_CR35","unstructured":"Wang, Y., Li, Z., Zang, Y., Wang, C., Lu, Q., Jin, C., Wang, J.: Unified multimodal chain-of-thought reward model through reinforcement fine-tuning. arXiv preprint arXiv:2505.03318 (2025), https:\/\/arxiv.org\/abs\/2505.03318, accessed: 26 Jun 2026"},{"key":"29_CR36","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.52202\/079017-0047","volume":"37","author":"Z Wang","year":"2024","unstructured":"Wang, Z., Dong, Y., Delalleau, O., Zeng, J., Shen, G., Egert, D., Zhang, J., Sreedhar, M.N., Kuchaiev, O.: Helpsteer 2: Open-source dataset for training top-performing reward models. Adv. Neural. Inf. Process. Syst. 37, 1474\u20131501 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"29_CR37","unstructured":"Xie, L., Huang, S., Zhang, Z., Zou, A., Zhai, Y., Ren, D., Zhang, K., Hu, H., Liu, B., Chen, H., Liu, Z., Ding, B.: Auto-rubric: Learning from implicit weights to explicit rubrics for reward modeling (2026), https:\/\/arxiv.org\/abs\/2510.17314, accessed: 26 Jun 2026"},{"key":"29_CR38","doi-asserted-by":"crossref","unstructured":"Xiong, T., Wang, X., Guo, D., Ye, Q., Fan, H., Gu, Q., Huang, H., Li, C.: Llava-critic: Learning to evaluate multimodal models. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 13618\u201313628. (2025)","DOI":"10.1109\/CVPR52734.2025.01271"},{"key":"29_CR39","doi-asserted-by":"publisher","first-page":"15903","DOI":"10.52202\/075280-0700","volume":"36","author":"J Xu","year":"2023","unstructured":"Xu, J., Liu, X., Wu, Y., Tong, Y., Li, Q., Ding, M., Tang, J., Dong, Y.: Imagereward: Learning and evaluating human preferences for text-to-image generation. Adv. Neural. Inf. Process. Syst. 36, 15903\u201315935 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"29_CR40","unstructured":"Xu, J., Guo, Z., He, J., Hu, H., He, T., Bai, S., Chen, K., Wang, J., Fan, Y., Dang, K., Zhang, B., Wang, X., Chu, Y., Lin, J.: Qwen2.5-omni technical report (2025), https:\/\/arxiv.org\/abs\/2503.20215, accessed: 26 Jun 2026"},{"key":"29_CR41","unstructured":"Xu, Y., Hirasawa, T., Kozuno, T., Ushiku, Y.: Am i more pointwise or pairwise? revealing position bias in rubric-based llm-as-a-judge (2026), https:\/\/arxiv.org\/abs\/2602.02219, accessed: 26 Jun 2026"},{"key":"29_CR42","doi-asserted-by":"crossref","unstructured":"Yang, P., Wang, X., Duan, X., Chen, H., Hou, R., Jin, C., Zhu, W.: Avqa: A dataset for audio-visual question answering on videos. In: Proceedings of the 30th ACM international conference on multimedia, pp. 3480\u20133491. (2022)","DOI":"10.1145\/3503161.3548291"},{"key":"29_CR43","doi-asserted-by":"crossref","unstructured":"Yang, Y., He, X., Pan, H., Jiang, X., Deng, Y., Yang, X., Lu, H., Yin, D., Rao, F., Zhu, M., et\u00a0al.: R1-onevision: Advancing generalized multimodal reasoning through cross-modal formalization. arXiv preprint arXiv:2503.10615 (2025)","DOI":"10.1109\/ICCV51701.2025.00229"},{"key":"29_CR44","unstructured":"Yasunaga, M., Zettlemoyer, L., Ghazvininejad, M.: Multimodal rewardbench: Holistic evaluation of reward models for vision language models (2025), https:\/\/arxiv.org\/abs\/2502.14191, accessed: 26 Jun 2026"},{"key":"29_CR45","doi-asserted-by":"crossref","unstructured":"Yue, X., Ni, Y., Zhang, K., Zheng, T., Liu, R., Zhang, G., Stevens, S., Jiang, D., Ren, W., Sun, Y., et\u00a0al.: Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 9556\u20139567 (2024)","DOI":"10.1109\/CVPR52733.2024.00913"},{"key":"29_CR46","unstructured":"Zhang, J., Wang, Z., Gui, L., Sathyendra, S.M., Jeong, J., Veitch, V., Wang, W., He, Y., Liu, B., Jin, L.: Chasing the tail: Effective rubric-based reward modeling for large language model post-training (2026), https:\/\/arxiv.org\/abs\/2509.21500, accessed: 26 Jun 2026"},{"key":"29_CR47","unstructured":"Zhang, R., Gui, L., Sun, Z., Feng, Y., Xu, K., Zhang, Y., Fu, D., Li, C., Hauptmann, A., Bisk, Y., Yang, Y.: Direct preference optimization of video large multimodal models from language model reward. arXiv preprint 2404.01258 (2024), https:\/\/arxiv.org\/abs\/2404.01258, accessed: 26 Jun 2026"},{"key":"29_CR48","unstructured":"Zhang, Y.F., Yang, H., Zhang, H., Shi, Y., Chen, Z., Tian, H., Fu, C., Wang, H., Wu, K., Cui, B., Wang, X., Pan, J., Wang, H., Zhang, Z., Wang, L.: Basereward: A strong baseline for multimodal reward model (2025), https:\/\/arxiv.org\/abs\/2509.16127, accessed: 26 Jun 2026"},{"key":"29_CR49","unstructured":"Zhang, Y., Lu, X., Hu, X., Fu, C., Wen, B., Zhang, T., Liu, C., et\u00a0al.: R1-Reward: Training Multimodal Reward Model through Stable Reinforcement Learning. arXiv preprint 2505.02835 (2025), https:\/\/arxiv.org\/abs\/2505.02835, accessed: 26 Jun 2026"},{"key":"29_CR50","unstructured":"Zhou, Y., Li, S., Liu, S., Fang, W., Zhang, K., Zhao, J., Yang, J., Zhou, Y., Lv, J., Zheng, T., Lu, H., Chen, W., Xie, Y., Song, M.: Breaking the exploration bottleneck: Rubric-scaffolded reinforcement learning for general llm reasoning (2026), https:\/\/arxiv.org\/abs\/2508.16949, accessed: 26 Jun 2026"},{"key":"29_CR51","unstructured":"Zhu, J., Wang, W., Chen, Z., et\u00a0al.: Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models (2025), https:\/\/arxiv.org\/abs\/2504.10479, accessed: 26 Jun 2026"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-37321-2_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T06:45:40Z","timestamp":1789973140000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37321-2_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032373205","9783032373212"],"references-count":51,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37321-2_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"22 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malm\u00f6","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}