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待翻譯:Test, then Route: How Language Models Execute In-Context Conditional Rules Across Models and Languages

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AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:arXiv:2608.04183v1 Announce Type: new Abstract: When a language model follows an in-context conditional rule such as "if P(x) then A else B," does it assemble a runtime circuit with one module that tests the predicate and another that routes the answer? We probe this with activation patching under a four-donor design whose two swapped-rule donors make the condition and the answer word disagree, so each layer reveals which of the two it carries. Across three open models from two families and six languages sharing one fixed item bank, a mid-stack residual band carries the predicate's truth value: patching it reroutes the answer with predicate-outcome flip near 1.0 and mapping flip near 0.0, meeting a strict pre-specified isolation criterion in 17 of 18 cells, and…

來源arXiv Computational Linguistics作者: Luxshan Thavarasa, Sivasuthan Sukumar
待翻譯:Test, then Route: How Language Models Execute In-Context Conditional Rules Across Models and Languages
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--> [Submitted on 4 Aug 2026] Title:Test, then Route: How Language Models Execute In-Context Conditional Rules Across Models and Languages View a PDF of the paper titled Test, then Route: How Language Models Execute In-Context Conditional Rules Across Models and Languages, by Luxshan Thavarasa and Sivasuthan Sukumar View PDF HTML (experimental) Abstract:When a language model follows an in-context conditional rule such as "if P(x) then A else B," does it assemble a runtime circuit with one module that tests the predicate and another that routes the answer? We probe this with activation patching under a four-donor design whose two swapped-rule donors make the condition and the answer word disagree, so each layer reveals which of the two it carries. Across three open models from two families and six languages sharing one fixed item bank, a mid-stack residual band carries the predicate's truth value: patching it reroutes the answer with predicate-outcome flip near 1.0 and mapping flip near 0.0, meeting a strict pre-specified isolation criterion in 17 of 18 cells, and the same localization holds across five predicate families. The router shows the opposite profile. A learned subspace flips A and B near-perfectly within the trained pair yet transfers to a new pair at approximately 0 in every model, while in Gemma-3-4B (the only model probed cross-lingually) it transfers at approximately 0.98 to the same pair in other languages. Under every probe we ran, the router direction is token-bound and non-transferable (largely answer-readout in Gemma, pair-specific in Qwen) rather than an abstract routing module. Test is modular; under these probes, route is not. Comments: 19 pages, 16 figures. Code and data: this https URL Subjects: Computation and Language (cs.CL) Cite as: arXiv:2608.04183 [cs.CL] (or arXiv:2608.04183v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.04183 arXiv-issued DOI via DataCite (pending registration) Submission history From: Luxshan Thavarasa [view email] [v1] Tue, 4 Aug 2026 19:40:30 UTC (331 KB) Full-text links: Access Paper: View a PDF of the paper titled Test, then Route: How Language Models Execute In-Context Conditional Rules Across Models and Languages, by Luxshan Thavarasa and Sivasuthan Sukumar View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

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