@inproceedings{smirnov-etal-2026-thinkbooster,
    title = "{T}hink{B}ooster: A Unified Framework for Seamless Test-Time Scaling of {LLM} Reasoning",
    author = "Smirnov, Vladislav  and
      Nguyen, Quang-Chieu  and
      Senichev, Sergey  and
      Ta, Minh Ngoc  and
      Fadeeva, Ekaterina  and
      Vazhentsev, Artem  and
      Galimzianova, Daria  and
      Rozanov, Nikolai  and
      Mazanov, Viktor  and
      Ni, Jingwei  and
      Wu, Tianyi  and
      Kiselev, Igor  and
      Sachan, Mrinmaya  and
      Gurevych, Iryna  and
      Nakov, Preslav  and
      Baldwin, Timothy  and
      Shelmanov, Artem",
    editor = "Durrett, Greg  and
      Jian, Ping",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-demo.70/",
    pages = "715--727",
    ISBN = "979-8-89176-392-0",
    abstract = "Test-time compute (TTC) scaling has emerged as a powerful paradigm for improving large language model (LLM) reasoning by allocating additional compute during inference, e.g., via multi-sample generation and verifier-based reranking. Existing TTC scaling strategies and reasoning scorers remain fragmented, evaluated under inconsistent protocols, and are rarely analyzed through the lens of quality-cost trade-offs. We introduce ThinkBooster, a unified framework for seamless test-time compute scaling of LLM reasoning, which consists of (i) a modular Python library implementing state-of-the-art TTC scaling strategy and scorer families, (ii) a benchmark that jointly evaluates performance and computational efficiency, and (iii) a deployable OpenAI-compatible proxy service that enables drop-in integration of adaptive reasoning into real-world applications. We further provide a demo visual debugger for inspecting the reasoning trajectories, intermediate selection decisions, and alternative reasoning paths. Empirical results on mathematical and coding tasks reveal the performance-compute trade-offs of TTC scaling strategies and scoring methods and demonstrate that ThinkBooster provides practical gains in real-world tasks. The code is available online under an MIT license."
}