@inproceedings{jia-etal-2026-one,
    title = "One Battle After Another: Probing {LLM}s' Limits on Multi-Turn Instruction Following with a Benchmark Evolving Framework",
    author = "Jia, Qi  and
      Shen, Ye  and
      Song, Xiujie  and
      Zhang, Kaiwei  and
      Wang, Shibo  and
      Pei, Dun  and
      Zhu, Xiangyang  and
      Zhai, Guangtao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.433/",
    pages = "9574--9590",
    ISBN = "979-8-89176-390-6",
    abstract = "Evaluating LLMs' instruction-following ability in multi-topic dialogues is essential yet challenging. Existing benchmarks are limited to a fixed number of turns, susceptible to saturation and failing to account for users' interactive experience. In this work, we propose a novel framework featuring a three-layer tracking mechanism and a query synthesis agent to mimic sequential user behaviors. Grounded in Flow Theory, we introduce process-centric metrics and terminate a conversational evaluation only upon exhausting user patience. Leveraging this framework, we present EvolIF, an evolving benchmark covering 12 constraint groups. Our analysis reveals deficiencies in failure recovery and fine-grained instruction following, with performance stratification becoming evident as conversational depth increases. GPT-5 demonstrates the most sustained resilience, maintaining a 66.40{\%} stability score, outperforming Gemini-3-Pro by 5.59{\%}, while other models lag behind."
}