# CORAL: Adaptive Retrieval Loop for Culturally-Aligned Multilingual RAG

arXiv: https://arxiv.org/abs/2604.25676 · HTML: https://mlsanity.com/paper/2604.25676/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation.
- Builds on 27 of its 43 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Placed in

- Retrieval-augmented generation / [Generation and context integration](https://mlsanity.com/rag/generation-and-context-integration/)

## Builds on (most cited first, 27 of 27)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs](https://mlsanity.com/paper/2312.05934/) (2023)
- [BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages](https://mlsanity.com/paper/2406.09948/) (2024)
- [MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering](https://mlsanity.com/paper/2007.15207/) (2020)
- [RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation](https://mlsanity.com/paper/2404.00610/) (2024)
- [CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean](https://mlsanity.com/paper/2403.06412/) (2024)
- [PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers](https://mlsanity.com/paper/2406.12430/) (2024)
- [RATT: A Thought Structure for Coherent and Correct LLM Reasoning](https://mlsanity.com/paper/2406.02746/) (2024)
- [Investigating Language Preference of Multilingual RAG Systems](https://mlsanity.com/paper/2502.11175/) (2025)
- [IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance](https://mlsanity.com/paper/2311.09820/) (2023)
- [Multilingual Retrieval Augmented Generation for Culturally-Sensitive Tasks: A Benchmark for Cross-lingual Robustness](https://mlsanity.com/paper/2410.01171/) (2024)
- [MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2410.13716/) (2024)
- [On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.00597/) (2025)
- [SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment](https://mlsanity.com/paper/2509.03934/) (2025)
- [A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning](https://mlsanity.com/paper/2408.05141/) (2024)
- [Language Drift in Multilingual Retrieval-Augmented Generation: Characterization and Decoding-Time Mitigation](https://mlsanity.com/paper/2511.09984/) (2025)
- [MEMERAG: A Multilingual End-to-End Meta-Evaluation Benchmark for Retrieval Augmented Generation](https://mlsanity.com/paper/2502.17163/) (2025)
- [BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression](https://mlsanity.com/paper/2410.15277/) (2024)
- [Towards Geo-Culturally Grounded LLM Generations](https://mlsanity.com/paper/2502.13497/) (2025)
- [Relevance Isn't All You Need: Scaling RAG Systems With Inference-Time Compute Via Multi-Criteria Reranking](https://mlsanity.com/paper/2504.07104/) (2025)
- [Quality-Aware Translation Tagging in Multilingual RAG system](https://mlsanity.com/paper/2510.23070/) (2025)

## Abstract (arXiv)

Multilingual retrieval-augmented generation (mRAG) is often implemented within a fixed retrieval space, typically via query or document translation or multilingual embedding vector representations. However, this approach may be inadequate for culturally grounded queries, in which retrieval-condition misalignment may occur. Even strong retrievers and generators may struggle to produce culturally relevant answers when sourcing evidence from inappropriate linguistic or regional contexts. To this end, we introduce CORAL (COntext-aware Retrieval with Agentic Loop, an adaptive retrieval methodology for mRAG that enables iterative refinement of both the retrieval space (corpora) and the retrieval probe (query) based on the quality of the evidence. The overall process includes: (1) selecting corpora, (2) retrieving documents, (3) critiquing evidence for relevance and cultural alignment, and (4) checking sufficiency. If the retrieved documents are insufficient to answer the query correctly, the system (5) reselects corpora and rewrites the query. Across two cultural QA benchmarks, CORAL achieves up to a 3.58%p accuracy improvement on low-resource languages relative to the strongest baselines.