@InProceedings{lee:2026:wmt1,
  author    = {Lee, Soyoung},
  title     = {AdaptiveMT at WMT26: A Controller–Executor–Reducer Agent Harness for Document-Level Translation},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {1325--1333},
  abstract  = {We describe AdaptiveMT, our submission to the English–Korean condition of the WMT26 General Machine Translation task in the unconstrained track. AdaptiveMT is a language-general adaptive translation harness with target-language-specific profiles; this run uses the Korean profile. The system uses a controller–executor–reducer agent harness. For each document, the harness analyzes the source, generates independent candidate translations from two heterogeneous models, compares them with token-level and semantic diffs, and synthesizes a final translation with a judge model. Mandatory QA hooks and deterministic Korean-specific guards run before finalization; failures trigger bounded local repair. Multimodal documents use structured visual context extracted from images or sampled video frames before translation. We processed all 2,078 distributed blindset records under two Korean target tags; the subsequently released General MT evaluation set contains 198 English-to-Korean documents tagged kor\_Hang. On this official subset, the harness made 1,441 successful LLM calls at an estimated list-price inference cost of \$12.03, and all 198 documents finished with passing hooks. In blindset-wide diagnostics, synthesis eliminated observed candidate-level mechanical errors, and a GPT-5 judge preferred the final output over a bare GPT-4.1 baseline on 600 sampled documents (44.5\% vs. 30.0\%, 25.5\% ties).},
  url       = {https://aclanthology.org/2026.wmt-1.71}
}