mlsanityData to 7 Oct 2026

ChronoPlay: A Framework for Modeling Dual Dynamics and Authenticity in Game RAG Benchmarks

arXiv 2510.18455 ↗First posted 21 Oct 2025cs.CLcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Retrieval Augmented Generation (RAG) systems are increasingly vital in dynamic domains like online gaming, yet the lack of a dedicated benchmark has impeded standardized evaluation in this area. The core difficulty lies in Dual Dynamics: the constant interplay between game content updates and the shifting focus of the player community. Furthermore, the necessity of automating such a benchmark introduces a critical requirement for player-centric authenticity to ensure generated questions are realistic. To address this integrated challenge, we introduce ChronoPlay, a novel framework for the automated and continuous generation of game RAG benchmarks. ChronoPlay utilizes a dual-dynamic update mechanism to track both forms of change, and a dual-source synthesis engine that draws from official sources and player community to ensure both factual correctness and authentic query patterns. We instantiate our framework on three distinct games to create the first dynamic RAG benchmark for the gaming domain, offering new insights into model performance under these complex and realistic conditions. Code is avaliable at: https://github.com/hly1998/ChronoPlay.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 24 of its 45 references that are in the map. First posted on arXiv 21 Oct 2025.

Builds on

24 of 45 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025DRAGOn: Designing RAG On Periodically Updated Corpus8 Jul
  2. 2025HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation3 Mar
  3. 2024CRAG -- Comprehensive RAG Benchmark7 Jun
  4. 2024Evaluation of Retrieval-Augmented Generation: A Survey13 May

Other references · 20

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  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  5. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
  6. 2025Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Modelscited by 1,175
  7. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  8. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,050
  9. 2024Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluatorscited by 865
  10. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  11. 2022RealTime QA: What's the Answer Right Now?cited by 239
  12. 2023Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrationscited by 42
8 more
  1. 2024OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domaincited by 28
  2. 2025LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversationcited by 19
  3. 2025Benchmarking Retrieval-Augmented Generation for Chemistrycited by 19
  4. 2023Carpe Diem: On the Evaluation of World Knowledge in Lifelong Language Modelscited by 17
  5. 2024LiveXiv -- A Multi-Modal Live Benchmark Based on Arxiv Papers Contentcited by 17
  6. 2022A Theory of Dynamic Benchmarkscited by 15
  7. 2024GrowOVER: How Can LLMs Adapt to Growing Real-World Knowledge?cited by 11
  8. 2014Evolution of Reddit: From the Front Page of the Internet to a Self-referential Community?cited by 5

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