{"type":"ScholarlyArticle","title":"Tree-of-AdEditor: Heuristic Tree Reasoning for Automated Video Advertisement Editing with Large Language Model","authors":[{"name":"Yuqi Zhang","slug":"yuqi-zhang","given":"Yuqi","family":"Zhang","affiliation":"School of Computer Science , Northwestern Polytechnical University; Department of Computing , The Hong Kong Polytechnic University"},{"name":"Bin Guo","slug":"bin-guo","given":"Bin","family":"Guo","affiliation":"School of Computer Science , Northwestern Polytechnical University"},{"name":"Nuo Li","slug":"nuo-li","given":"Nuo","family":"Li","affiliation":"Computation and Artificial Intelligence Innovative College , Fudan University"},{"name":"Ying Zhang","slug":"ying-zhang","given":"Ying","family":"Zhang","affiliation":"School of Computer Science , Northwestern Polytechnical University"},{"name":"Shijie Wang","slug":"shijie-wang","given":"Shijie","family":"Wang","affiliation":"Department of Computing , The Hong Kong Polytechnic University"},{"name":"Zhiwen Yu","slug":"zhiwen-yu","given":"Zhiwen","family":"Yu","affiliation":"School of Computer Science , Northwestern Polytechnical University"},{"name":"Qing Li","slug":"qing-li","given":"Qing","family":"Li","affiliation":"Department of Computing , The Hong Kong Polytechnic University"}],"abstract":"Video advertising has become a popular marketing strategy on e-commerce platforms, requiring high-level semantic reasoning like selling point discovery, narrative organization. Previous rule-based methods struggle with these complex tasks, and learning-based approaches demand large datasets and high training costs. Recently, Large Language Models have opened incredible opportunities for advancing intelligent video advertisement editing. However, Input-output (IO) prompting and Chain-of-Thought (CoT) struggle to adapt to the nonlinear thinking hierarchy of video editing, where editors iteratively select shots or revert them to explore potential editing solutions. While Tree-of-Thought (ToT) offers a conceptual structure that mirrors this hierarchy, it falls short in aligning with effective video advertising strategies and lacks robust fact-checking mechanisms. To address these, we propose a novel framework, Tree-of-AdEditor (ToAE), which constructs a reasoning tree to mimic human editors, and incorporates domain-specific theories and heuristic fact-checking to identify optimal editing solutions. Specifically, motivated by effective advertisement principles, we develop a \"local-global\" mechanism to guide LLM in both the shot level and sequence level decision-making. We introduce a visual incoherence pruning module to provide external heuristic fact-checking, ensuring visual attractiveness and reducing computation costs. Quantitative experiments and expert evaluation demonstrate the superiority of our method compared to baselines.","doi":"10.24963/ijcai.2025/968","url":"https://ijcai.org/proceedings/2025/968","pdf":"https://ijcai.org/proceedings/2025/0968.pdf","markdown":"https://ijcai.org/proceedings/2025/968.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/968","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Planning and Scheduling","track":"Main Track","number":968,"pages":{"first":8705,"last":8713},"keywords":["Agent-based and Multi-agent Systems: MAS: Coordination and cooperation","Planning and Scheduling: PS: Applications","Humans and AI: HAI: Applications","Multidisciplinary Topics and Applications: MTA: Other"],"videos":[],"references":[{"position":1,"raw":"[Arev et al., 2014] Ido Arev, Hyun Soo Park, Yaser Sheikh, Jessica Hodgins, and Ariel Shamir. 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