arXiv Computation and Language October 16, 2026 By Atai Waris Khan, Abdullah Mazhar, Raghu Kumar, Md Shad Akhtar

Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation

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The paper introduces TITAN, a mechanistic framework that uses causal intervention, sparse feature decomposition, and targeted preference optimization to discover and fine‑tune specific translation circuits in multilingual decoder‑only large language models. By restricting updates to less than 5% of the parameters, TITAN achieves significant BLEU score improvements—33.75% for Hindi‑to‑Urdu and 45.86% for Hindi‑to‑Sindhi—while human evaluations confirm gains in adequacy and fluency. The study also reveals that multilingual translation operates through sparse, structured circuits over a shared semantic subspace rather than relying on an English‑centric pivot representation.

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