@inproceedings{singh-etal-2026-mt,
    title = "{MT}-{OSC}: Path for {LLM}s that Get Lost in Multi-Turn Conversation",
    author = "Singh, Jyotika  and
      Tu, Fang  and
      Ballesteros, Miguel  and
      Sun, Weiyi  and
      Ghoshal, Sandip  and
      Yuan, Michelle  and
      Benajiba, Yassine  and
      Ravi, Sujith  and
      Roth, Dan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1354/",
    pages = "27137--27160",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs) suffer significant performance degradation when user instructions and context are distributed over multiple conversational turns, yet multi-turn (MT) interactions dominate chat interfaces. The routine approach of appending full chat history to prompts rapidly exhausts context windows, leading to increased latency, higher computational costs, and diminishing returns as conversations extend. We introduce **MT-OSC**, a **O**ne-off **S**equential **C**ondensation framework that efficiently and automatically condenses chat history in the background without disrupting the user experience. MT-OSC employs a Condenser Agent that uses a few-shot inference-based Condenser and a lightweight Decider to selectively retain essential information, reducing token counts by up to 72{\%} in 10-turn dialogues. Evaluated across 13 state-of-the-art LLMs and diverse multi-turn benchmarks, MT-OSC consistently narrows the multi-turn performance gap{---}yielding improved or preserved accuracy across datasets while remaining robust to distractors and irrelevant turns. Our results establish MT-OSC as a scalable solution for multi-turn chats, enabling richer context within constrained input spaces, reducing latency and operational cost, while balancing performance."
}