OpenIIR runs hundreds of LLM-driven personas as parameterised, reproducible IR research experiments. Researchers configure agents across four kinds of multi-agent study (deliberative panels, social platforms, curated recommender feeds, and evolutionary co-evolution between content producers and credibility detectors) under many priors, rounds, and constraints. Persona budgets, retrieval policies, ranker choices, intervention timings, and mutation rates are declared up front, and the same study can be re-run under different settings to compare outcomes side by side. Every run produces structured outputs (argument graphs, exposure logs, fitness traces, transcripts) that a downstream evaluator can consume directly, and a new study is a 200--400 line plug-in over a shared core (agent runtime, world-model store, retrieval primitives, claim extractor, persona ontology). The contributions are: (i) the shared core; (ii) a type interface for pluggable scenarios; (iii) four released types with reference runs (Panel, Social-Media, Curated-Feed, Multi-Generational); and (iv) six modular extensions sketched against open IR research questions.

翻译：OpenIIR以参数化、可复现的信息检索研究实验形式，运行数百个由大语言模型驱动的人物角色。研究者可在多种先验条件、轮次和约束下，跨四种多智能体研究类型（ deliberative panels、social platforms、curated recommender feeds，以及内容生产者与可信度检测器之间的进化协同演化）配置智能体。角色预算、检索策略、排序器选择、干预时机和变异率均预先声明，同一研究可在不同设置下重复运行以并排比较结果。每次运行生成结构化输出（论点图、曝光日志、适应性轨迹、对话记录），下游评估器可直接消费，新研究仅需在共享核心（智能体运行时、世界模型存储、检索原语、主张提取器、角色本体）上插入200–400行代码。本文贡献包括：(i) 共享核心；(ii) 可插拔场景的类型接口；(iii) 四种已发布类型及其参考运行（Panel、Social-Media、Curated-Feed、Multi-Generational）；(iv) 针对开放信息检索研究问题勾勒的六个模块化扩展。