Clarify reference documentation without maintainer attribution

README.md

@@ -38,6 +38,7 @@ configs:
 
 **English** | [简体中文](README.zh-CN.md)
 
+
 TextInsightBench evaluates agents that mine natural-language datasets for meaningful, evidence-backed group differences, temporal changes and compound associations. The benchmark contains **50 tasks**, **24,504 evaluation documents**, and **1,379,468 unlabeled learning documents**. This initial release is private.
 
 ## Data composition
@@ -90,7 +91,7 @@ Use the optional pool before evaluation and freeze global models, prompts and th
 
 Submissions include downstream claims, observable definitions, full positive/negative/unknown document assignments, statistics, exact quotations and limitations. Structural and arithmetic checks precede semantic assessment. Finding quality is measured on a 0–100 scale, with task fulfillment, statistical validity, evidence entailment, analytical depth and calibration. Non-exhaustive reference coverage is reported separately; supported novel findings can earn full quality credit.
 
-The Organizer Reference Set is stored separately for organizers. It contains 50 AI-generated reference conclusions frozen by the maintainer, not independently validated facts. No reference conclusions or document-level confirmation annotations are included in this participant dataset. See the [scoring standard](https://github.com/erwinmsmith/TextInsightBench/blob/main/docs/SCORING.md).
+The Organizer Reference Set is stored separately for organizers. It contains 50 AI-generated reference conclusions, not independently validated facts. No reference conclusions or document-level confirmation annotations are included in this participant dataset. See the [scoring standard](https://github.com/erwinmsmith/TextInsightBench/blob/main/docs/SCORING.md).
 
 ## Curation, limitations and terms
 

README.zh-CN.md

@@ -2,6 +2,7 @@
 
 [English](README.md) | **简体中文**
 
+
 TextInsightBench 评估 Agent 从自然语言数据中挖掘具体、有原文证据支持的结论。当前私密研究数据包含 **50 道任务、24,504 篇评测文本，以及 1,379,468 篇可选无监督学习文本**。任务和主要文档使用英文，原始语料保留原文。
 
 | 来源 | 无监督文本 | 任务数 | 评测文本 |
@@ -35,7 +36,7 @@ tib run --data data/participant --command 'python my_agent.py' \
 
 提交需包含结论、可观察定义、全部文档的 positive/negative/unknown 划分、统计、精确引用和局限。发现质量满分 100，衡量任务满足、统计有效性、证据支持、分析深度和校准。参考覆盖率单独报告，新发现即使没有匹配参考，也可根据证据得到完整质量分。
 
-组织者参考集独立存放，当前包含维护者冻结的 50 个 AI 生成参考结论，未经独立事实验证。参与者数据中不提供参考结论，也不包含文档级确认标注。完整规则见代码仓库。
+组织者参考集独立存放。当前包含 50 个 AI 生成参考结论，未经独立事实验证。参与者数据中不提供参考结论，也不包含文档级确认标注。完整规则见代码仓库。
 
 ## 构建与局限
 

manifest.json

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