{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T11:10:15Z","timestamp":1789038615707,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032374639","type":"print"},{"value":"9783032374646","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\/978-3-032-37464-6_25","type":"book-chapter","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T10:33:55Z","timestamp":1789036435000},"page":"463-480","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reflect-R1: Evidence-Driven Reflection for\u00a0Self-correction in\u00a0Long Video Understanding"],"prefix":"10.1007","author":[{"given":"Shuimu","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuteng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanshen","family":"Guan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zebang","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengqian","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaran","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenming","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"25_CR1","unstructured":"Arumugam, D., Henderson, P., Bacon, P.L.: An information-theoretic perspective on credit assignment in reinforcement learning. arXiv preprint arXiv:2103.06224 (2021)"},{"key":"25_CR2","unstructured":"Bai, S., et al.: Qwen2.5-VL technical report (2025)"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Chen, T.S., et al.: Panda-70m: captioning 70m videos with multiple cross-modality teachers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13320\u201313331 (2024)","DOI":"10.1109\/CVPR52733.2024.01265"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, J., et al.: Stop summation: min-form credit assignment is all process reward model needs for reasoning. arXiv preprint arXiv:2504.15275 (2025)","DOI":"10.52202\/085713-4382"},{"key":"25_CR5","unstructured":"Chenzhaoyu, L., Nie, H., Ma, Y., Xu, F., Yu, X., Long, F.C.: Invert4TVG: a temporal video grounding framework with inversion tasks preserving action understanding ability. In: The Fourteenth International Conference on Learning Representations (2026)"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Feng, K., et al.: Video-R1: reinforcing video reasoning in MLLMs. arXiv preprint arXiv:2503.21776 (2025)","DOI":"10.52202\/085713-3310"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Fu, C., et\u00a0al.: Video-MME: the first-ever comprehensive evaluation benchmark of multi-modal LLMs in video analysis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 24108\u201324118 (2025)","DOI":"10.1109\/CVPR52734.2025.02245"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Godin, F., Kumar, A., Mittal, A.: Learning when not to answer: a ternary reward structure for reinforcement learning based question answering. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers), pp. 122\u2013129 (2019)","DOI":"10.18653\/v1\/N19-2016"},{"key":"25_CR9","unstructured":"Guo, C., He, Y., Nie, Y., Ma, F., Xu, X., Long, C.: T2Sgrid: temporal-to-spatial gridification for video temporal grounding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3443\u20133454 (2026)"},{"issue":"8081","key":"25_CR10","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1038\/s41586-025-09422-z","volume":"645","author":"D Guo","year":"2025","unstructured":"Guo, D., et al.: DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning. Nature 645(8081), 633\u2013638 (2025)","journal-title":"Nature"},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"He, B., et al.: MA-LMM: memory-augmented large multimodal model for long-term video understanding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13504\u201313514 (2024)","DOI":"10.1109\/CVPR52733.2024.01282"},{"key":"25_CR12","unstructured":"Huang, W., et al.: Vision-R1: incentivizing reasoning capability in multimodal large language models. arXiv preprint arXiv:2503.06749 (2025)"},{"key":"25_CR13","unstructured":"Hurst, A., et al.: GPT-4o system card. arXiv preprint arXiv:2410.21276 (2024)"},{"key":"25_CR14","unstructured":"Jaech, A., et al.: Openai o1 system card. arXiv preprint arXiv:2412.16720 (2024)"},{"key":"25_CR15","doi-asserted-by":"publisher","first-page":"22199","DOI":"10.52202\/068431-1613","volume":"35","author":"T Kojima","year":"2022","unstructured":"Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., Iwasawa, Y.: Large language models are zero-shot reasoners. Adv. Neural. Inf. Process. Syst. 35, 22199\u201322213 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR16","unstructured":"Kulkarni, Y., Fazli, P.: Avatar: reinforcement learning to see, hear, and reason over video. arXiv preprint arXiv:2508.03100 (2025)"},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"Liu, S., et al.: EventGPT: event stream understanding with multimodal large language models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 29139\u201329149 (2025)","DOI":"10.1109\/CVPR52734.2025.02713"},{"key":"25_CR18","unstructured":"Liu, Y., Luo, Y., Zhong, Y., Chen, X., Liu, Q., Peng, J.: Sequence modeling of temporal credit assignment for episodic reinforcement learning. arXiv preprint arXiv:1905.13420 (2019)"},{"key":"25_CR19","unstructured":"Madhusudhan, N., Madhusudhan, S.T., Yadav, V., Hashemi, M.: Do LLMs know when to not answer? Investigating abstention abilities of large language models. In: Proceedings of the 31st International Conference on Computational Linguistics, pp. 9329\u20139345 (2025)"},{"key":"25_CR20","doi-asserted-by":"crossref","unstructured":"Nagpal, K., Dong, D., Bouvier, J.B., Mehr, N.: Leveraging large language models for effective and explainable multi-agent credit assignment. arXiv preprint arXiv:2502.16863 (2025)","DOI":"10.65109\/NOKW1037"},{"key":"25_CR21","unstructured":"Pan, J., et al.: Timesearch-R: adaptive temporal search for long-form video understanding via self-verification reinforcement learning. arXiv preprint arXiv:2511.05489 (2025)"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Pan, M., et al.: Ground what you see: hallucination-resistant MLLMs via caption feedback, diversity-aware sampling, and conflict regularization. arXiv preprint arXiv:2601.06224 (2026)","DOI":"10.1609\/aaai.v40i10.37772"},{"key":"25_CR23","doi-asserted-by":"publisher","first-page":"42748","DOI":"10.52202\/075280-1852","volume":"36","author":"V Patraucean","year":"2023","unstructured":"Patraucean, V., et al.: Perception test: a diagnostic benchmark for multimodal video models. Adv. Neural. Inf. Process. Syst. 36, 42748\u201342761 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR24","doi-asserted-by":"crossref","unstructured":"Pereira, J., Lopes, V., Semedo, D., Neves, J.: Self-res: self-reflection in large vision-language models for long video understanding. In: 2025 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20139. IEEE (2025)","DOI":"10.1109\/ICME59968.2025.11209055"},{"key":"25_CR25","unstructured":"Pignatelli, E., et al.: A survey of temporal credit assignment in deep reinforcement learning (2024)"},{"key":"25_CR26","unstructured":"Shao, Z., et\u00a0al.: DeepSeekMath: pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300 (2024)"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Shi, Y., et al.: PVChat: personalized video chat with one-shot learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 23321\u201323331 (2025)","DOI":"10.1109\/ICCV51701.2025.02165"},{"key":"25_CR28","doi-asserted-by":"crossref","unstructured":"Tang, X., Qiu, J., Xie, L., Tian, Y., Jiao, J., Ye, Q.: Adaptive keyframe sampling for long video understanding. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 29118\u201329128 (2025)","DOI":"10.1109\/CVPR52734.2025.02711"},{"key":"25_CR29","unstructured":"Team, G., et al.: Gemini 1.5: unlocking multimodal understanding across millions of tokens of context. arXiv preprint arXiv:2403.05530 (2024)"},{"key":"25_CR30","unstructured":"Tomani, C., Chaudhuri, K., Evtimov, I., Cremers, D., Ibrahim, M.: Uncertainty-based abstention in LLMs improves safety and reduces hallucinations. arXiv preprint arXiv:2404.10960 (2024)"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Wang, H., Qu, C., Huang, Z., Chu, W., Lin, F., Chen, W.: VL-rethinker: incentivizing self-reflection of vision-language models with reinforcement learning. arXiv preprint arXiv:2504.08837 (2025)","DOI":"10.52202\/085713-1037"},{"key":"25_CR32","unstructured":"Wang, R., Ammanabrolu, P.: A practitioner\u2019s guide to multi-turn agentic reinforcement learning. arXiv preprint arXiv:2510.01132 (2025)"},{"key":"25_CR33","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhang, Y., Zohar, O., Yeung-Levy, S.: VideoAgent: long-form video understanding with large language model as agent. In: European Conference on Computer Vision, pp. 58\u201376. Springer (2024)","DOI":"10.1007\/978-3-031-72989-8_4"},{"key":"25_CR34","doi-asserted-by":"publisher","first-page":"24824","DOI":"10.52202\/068431-1800","volume":"35","author":"J Wei","year":"2022","unstructured":"Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. Adv. Neural. Inf. Process. Syst. 35, 24824\u201324837 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR35","unstructured":"Wei, Z., et al.: TruthRL: incentivizing truthful LLMs via reinforcement learning. arXiv preprint arXiv:2509.25760 (2025)"},{"key":"25_CR36","unstructured":"Wu, B., Yu, S., Chen, Z., Tenenbaum, J.B., Gan, C.: STAR: a benchmark for situated reasoning in real-world videos. arXiv preprint (2024). arXiv:2405.09711"},{"key":"25_CR37","doi-asserted-by":"publisher","first-page":"28828","DOI":"10.52202\/079017-0907","volume":"37","author":"H Wu","year":"2024","unstructured":"Wu, H., Li, D., Chen, B., Li, J.: LongVideoBench: a benchmark for long-context interleaved video-language understanding. Adv. Neural. Inf. Process. Syst. 37, 28828\u201328857 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR38","doi-asserted-by":"crossref","unstructured":"Xiao, J., Shang, X., Yao, A., Chua, T.S.: Next-QA: next phase of question-answering to explaining temporal actions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9777\u20139786 (2021)","DOI":"10.1109\/CVPR46437.2021.00965"},{"key":"25_CR39","doi-asserted-by":"crossref","unstructured":"Ye, J., et al.: Re-thinking temporal search for long-form video understanding. In: CVPR, pp. 8579\u20138591 (2025)","DOI":"10.1109\/CVPR52734.2025.00802"},{"key":"25_CR40","unstructured":"Yi, K., et al.: CLEVRER: collision events for video representation and reasoning. arXiv preprint arXiv:1910.01442 (2019)"},{"key":"25_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: ViTCot: video-text interleaved chain-of-thought for boosting video understanding in large language models. In: Proceedings of the 33rd ACM International Conference on Multimedia, MM 2025, pp. 5267\u20135276. Association for Computing Machinery, New York (2025)","DOI":"10.1145\/3746027.3755837"},{"key":"25_CR42","unstructured":"Zhang, Y., et al.: LLaVA-video: video instruction tuning with synthetic data. arXiv preprint arXiv:2410.02713 (2024)"},{"key":"25_CR43","unstructured":"Zhao, F., et al.: FaVChat: hierarchical prompt-query guided facial video understanding with data-efficient GRPO (2026)"},{"key":"25_CR44","doi-asserted-by":"crossref","unstructured":"Zhou, J., et al.: MLVU: benchmarking multi-task long video understanding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13691\u201313701 (2025)","DOI":"10.1109\/CVPR52734.2025.01278"}],"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\/978-3-032-37464-6_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T10:34:16Z","timestamp":1789036456000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37464-6_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032374639","9783032374646"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37464-6_25","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":"11 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"}}]}}