@inproceedings{chen-etal-2026-uncertainty,
    title = "Uncertainty-Aware Routing for Principled Alignment with {M}o{E} Dynamics",
    author = "Chen, Yilong  and
      Shang, Junyuan  and
      Feng, Yuchen  and
      Zhang, Zhenyu  and
      Gu, Naibin  and
      Wang, Ziqi  and
      Liu, Tingwen  and
      Wang, Shuohuan  and
      Sun, Yu  and
      Wu, Hua  and
      Wang, Haifeng",
    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.1801/",
    pages = "38865--38880",
    ISBN = "979-8-89176-390-6",
    abstract = "Mixture-of-Experts (MoE) is a cornerstone for scaling LLMs, yet its training dynamics remain poorly understood, often leading to sub-optimal specialization. Moving beyond static routing, we present a systematic study of the MoE lifecycle using Helmholtz Free Energyand Router Entropy. We identify a universal Three-Stage Phase Transition{---}Exploration, Symmetry Breaking, and Stabilization{---}marked by an Energy Climb and Plateau. This reflects Frustrated Exploration, caused by structural interference between specialization drives and uniformity constraints. To address this, we propose Uncertainty-Aware Routing (UAR), which aligns routing with the model{'}s epistemic state via: (1) Evidence-Triggered Expansion, increasing active experts for high-energy tokens, and (2) Epistemic Masking, applying load-balancing only in high-uncertainty regimes to shield mature experts. Experiments confirm UAR reduces perplexity and improves expert distinctiveness, offering a principled path toward thermodynamically aligned computation."
}