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    <abstract>Multilingual LLMs answer the same multi-hop reasoning question across languages, but we lack a mechanistic account of whether they share an internal circuit. We identify Bridge Routing Heads (BRH) in two large multilingual LLMs through a three-stage pipeline. The resulting language-specific head sets exhibit near-complete mutual exclusivity across the five languages, with a mean Jaccard similarity of only 0.017 for Llama 3.1 70B and 0.057 for Qwen 2.5 72B, revealing language-idiosyncratic circuits. Ablating general BRH increases two-hop Negative Log-Likelihood (NLL) by 39-89\times the random-head baseline, providing direct causal evidence of their role. Amplifying these heads in a failing target-language pass rescues up to 51.7% of cross-lingual failures, with no training. The two models share this dual-circuit pattern but allocate heads differently: Llama concentrates chaining in a large general pool, while Qwen leans on larger language-specific pools. Together these results show that activation-level intervention alone can recover correct answers from cross-lingual reasoning failures.</abstract>
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%0 Conference Proceedings
%T Bridge Routing Heads: Where Multilingual Multi-hop Reasoning Lives in LLMs
%A Kim, Seunghan
%A Choe, Minyeong
%A Kim, Hyunil
%A Cho, Haehyun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F kim-etal-2026-bridge
%X Multilingual LLMs answer the same multi-hop reasoning question across languages, but we lack a mechanistic account of whether they share an internal circuit. We identify Bridge Routing Heads (BRH) in two large multilingual LLMs through a three-stage pipeline. The resulting language-specific head sets exhibit near-complete mutual exclusivity across the five languages, with a mean Jaccard similarity of only 0.017 for Llama 3.1 70B and 0.057 for Qwen 2.5 72B, revealing language-idiosyncratic circuits. Ablating general BRH increases two-hop Negative Log-Likelihood (NLL) by 39-89\times the random-head baseline, providing direct causal evidence of their role. Amplifying these heads in a failing target-language pass rescues up to 51.7% of cross-lingual failures, with no training. The two models share this dual-circuit pattern but allocate heads differently: Llama concentrates chaining in a large general pool, while Qwen leans on larger language-specific pools. Together these results show that activation-level intervention alone can recover correct answers from cross-lingual reasoning failures.
%U https://aclanthology.org/2026.emnlp-main.2605/
%P 59179-59195

Markdown (Informal)

[Bridge Routing Heads: Where Multilingual Multi-hop Reasoning Lives in LLMs](https://aclanthology.org/2026.emnlp-main.2605/) (Kim et al., EMNLP 2026)

• Bridge Routing Heads: Where Multilingual Multi-hop Reasoning Lives in LLMs (Kim et al., EMNLP 2026)

ACL

• Seunghan Kim, Minyeong Choe, Hyunil Kim, and Haehyun Cho. 2026. Bridge Routing Heads: Where Multilingual Multi-hop Reasoning Lives in LLMs. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 59179–59195, Budapest, Hungary. Association for Computational Linguistics.