用大模型角色模拟信息检索实验，支持可复现的多场景研究。
OpenIIR: An Open Simulation Platform for Information Retrieval Research
- 构建四类多智能体实验框架，配置角色预算与检索策略。
- 每轮实验生成结构化输出，支持结果横向对比。
- 开源核心平台与插件接口，适合信息检索方向研究者使用。
OpenIIR 通过数百个由大模型驱动的智能体，在参数化、可复现的 IR 研究实验中运行。研究人员可在四种多智能体研究范式（思辨小组、社交平台、精选推荐流、内容生成者与可信度检测器的演化共进化）下，配置角色预算、检索策略、排名器选择、干预时机与突变率等参数，并在不同设置下重复运行同一研究以对比结果。每次运行生成结构化输出（论证图、曝光日志、适应度轨迹、对话记录），供下游评估直接使用。新研究仅需编写 200–400 行插件代码，基于共享核心组件（智能体运行时、世界模型存储、检索原语、声明提取器、角色本体）。主要贡献包括：(i) 共享核心；(ii) 可插拔场景类型接口；(iii) 四种已发布类型及参考运行（Panel、Social-Media、Curated-Feed、Multi-Generational）；(iv) 针对开放信息检索问题的六项模块化扩展设计。
原文摘要 · Abstract (English)
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.
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