# Investigating the Role of Reasoning-Language Alignment in Monolingual Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2610.03136 · HTML: https://mlsanity.com/paper/2610.03136/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)
- Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/)

## Builds on (most cited first, 14 of 14)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge](https://mlsanity.com/paper/2411.16594/) (2024)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting](https://mlsanity.com/paper/2305.07004/) (2023)
- [Reassessing Extractive QA Datasets at Scale: LLM-as-a-Judge and In-Depth Analyses](https://mlsanity.com/paper/2504.11972/) (2025)
- [When Models Reason in Your Language: Controlling Thinking Language Comes at the Cost of Accuracy](https://mlsanity.com/paper/2505.22888/) (2025)
- [HiChunk: Evaluating and Enhancing Retrieval-Augmented Generation with Hierarchical Chunking](https://mlsanity.com/paper/2509.11552/) (2025)
- [Enhancing Retrieval Augmented Generation with Hierarchical Text Segmentation Chunking](https://mlsanity.com/paper/2507.09935/) (2025)
- [Automatic Dataset Generation for Knowledge Intensive Question Answering Tasks](https://mlsanity.com/paper/2505.14212/) (2025)
- [Large Language Models Are Overconfident in Their Own Responses](https://mlsanity.com/paper/2606.03437/) (2026)
- [Improving Multilingual Retrieval-Augmented Language Models through Dialectic Reasoning Argumentations](https://mlsanity.com/paper/2504.04771/) (2025)
- [SemRAG: Semantic Knowledge-Augmented RAG for Improved Question-Answering](https://mlsanity.com/paper/2507.21110/) (2025)
- [Dating the Model: Hidden Dates in System Prompts Affect LLM Evaluation](https://mlsanity.com/paper/2609.36931/) (2026)

## Abstract (arXiv)

Reasoning traces improve large language models (LLMs), but current models are trained to reason mostly in English. It has been shown that forcing a model to reason in another language degrades accuracy, even when the reasoning language matches the language of the prompt -- but only for a setting where the model reasons over a short prompt. Here, we ask whether the same holds for retrieval-augmented generation (RAG), where the model must read and integrate a large amount of retrieved evidence in the target language. To study this, we build a fully monolingual German RAG question-answering testbed over the fictional world of the tabletop role-playing game The Dark Eye, a domain that is richly documented in German but too niche for the model to answer from memory, so that it has to rely on retrieval. Varying the forced reasoning language of an agentic RAG system on this testbed, we find that aligning the reasoning language with the language of the query and the retrieved documents helps. Forced German reasoning outperforms forced French, although the model benchmarks higher in French, so the benefit comes from alignment and not from language proficiency. The advantage grows when the retrieved context is richer and structure-aware. However, forced German only reaches the level of the model's native, unconstrained English reasoning without surpassing it, showing that native multilingual reasoning is needed. We publicly release the testbed and QA benchmark.