Archived
2026-09-10Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning
Mehrnaz Mofakhami, Ananya Sahu, Alejandro R. Salamanca, Daniel D'souza, Alexandre Berard, Thomas Euyang, et al.
arXiv:2609.10445v1Today’s pick is a paper on making language models reason in the same language they’re asked in. Most strong reasoning models still default to English internally, even when the user prompts them in Spanish, Arabic, or Hindi. That can blur meaning, reduce accessibility, and miss culturally specific knowledge that lives best in the original language. The authors call this in-language reasoning, or L2 reasoning, and study how to make it reliable through data mixing during fine-tuning. Their key idea is surprisingly practical: instead of adding reasoning supervision in every language, they combine broader multilingual coverage, non-reasoning text, and a strong English reasoning backbone. With that recipe, they train a 3.35-billion-parameter model that reasons in-language across 60 languages and multiple benchmarks, while keeping performance strong. The result matters because it points to a scalable path for multilingual AI that feels native to users, not translated after the fact.
Also spotted that day
IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea SpecificationsLikelihood-free inference with nuisance parameters through normalizing flowsA positive resolution of the gap-entropy conjectureCharacterizing Language Generation in the Limit: Finite Witnesses and a Separation-Width HierarchShow-Harness: Just a VLM Agent Can Play RobotsOptimal Low-Rank Quantum State Tomography with Bounded-Sample Joint MeasurementsQuantum Feature Engineering for Credit Default Prediction: When and Why IQP Circuits Help Linear ClassifiersCross-Model Agreement as a Deployment-Time Reliability Signal for Automatic Polyp SegmentationIBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model IdentifierLearning with Covariance Matrices: Principal Component Analysis Meets Learning with GraphsNonmaximal sums of maximally monotone operators under Rockafellar's constraint qualificationDeep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power SystemsSemigroup-JEPA: Latent Dynamics Consistency for Zero-Shot Physics GeneralizationJarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition
Previous daily papers
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