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    <abstract>Existing text-to-video retrieval benchmarks often permit matching from static appearance, making it difficult to determine whether models capture state changes and final outcomes. We introduce GenState-AI, a benchmark comprising 956 AI-generated videos and 195 verified state-change queries across Toy, Cartoon, Home, and Manipulation domains. Its construction uses prompt-controlled variants that target either conflicting temporal/state outcomes or semantic substitutions. Following human verification, all valid videos contribute to subset-level retrieval pools, while complete query-specific triplets support diagnostic hard-negative ordering. We evaluate VCF-Lik, Qwen3-VL, and LanguageBind using both standard Recall@K and triplet-based consistency metrics. Qwen3-VL (8B) achieves the best macro-average R@1 and triplet consistency, while LanguageBind obtains the highest semantic discrimination accuracy. However, stronger retrieval does not consistently yield correct hard-negative ordering, and Qwen3-VL and LanguageBind remain weak on Manipulation triplets, where VCF-Lik performs substantially better. GenState-AI therefore provides a controlled diagnostic testbed for distinguishing global retrieval quality from fine-grained temporal and semantic grounding in AI-generated videos. Dataset: https://huggingface.co/datasets/Mh-Li/genstate-ai</abstract>
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%0 Conference Proceedings
%T GenState-AI: State-Aware Dataset for Text-to-Video Retrieval on AI-Generated Videos
%A Li, Minghan
%A Chen, Tongna
%A Lv, Tianrui
%A Zhang, Yishuai
%A An, Suchao
%A Zhou, Guodong
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F li-etal-2026-genstate
%X Existing text-to-video retrieval benchmarks often permit matching from static appearance, making it difficult to determine whether models capture state changes and final outcomes. We introduce GenState-AI, a benchmark comprising 956 AI-generated videos and 195 verified state-change queries across Toy, Cartoon, Home, and Manipulation domains. Its construction uses prompt-controlled variants that target either conflicting temporal/state outcomes or semantic substitutions. Following human verification, all valid videos contribute to subset-level retrieval pools, while complete query-specific triplets support diagnostic hard-negative ordering. We evaluate VCF-Lik, Qwen3-VL, and LanguageBind using both standard Recall@K and triplet-based consistency metrics. Qwen3-VL (8B) achieves the best macro-average R@1 and triplet consistency, while LanguageBind obtains the highest semantic discrimination accuracy. However, stronger retrieval does not consistently yield correct hard-negative ordering, and Qwen3-VL and LanguageBind remain weak on Manipulation triplets, where VCF-Lik performs substantially better. GenState-AI therefore provides a controlled diagnostic testbed for distinguishing global retrieval quality from fine-grained temporal and semantic grounding in AI-generated videos. Dataset: https://huggingface.co/datasets/Mh-Li/genstate-ai
%U https://aclanthology.org/2026.findings-aacl.124/
%P 2435-2448

Markdown (Informal)

[GenState-AI: State-Aware Dataset for Text-to-Video Retrieval on AI-Generated Videos](https://aclanthology.org/2026.findings-aacl.124/) (Li et al., Findings 2026)

• GenState-AI: State-Aware Dataset for Text-to-Video Retrieval on AI-Generated Videos (Li et al., Findings 2026)

ACL

• Minghan Li, Tongna Chen, Tianrui Lv, Yishuai Zhang, Suchao An, and Guodong Zhou. 2026. GenState-AI: State-Aware Dataset for Text-to-Video Retrieval on AI-Generated Videos. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026, pages 2435–2448, Hengqin, China. Association for Computational Linguistics.