Rebuild open exploration tasks and corpus-grounded evaluation

README.md

@@ -10,7 +10,6 @@ size_categories:
 task_categories:
 - text-generation
 tags:
-- text
 - agent-evaluation
 - data-mining
 - evidence-grounding
@@ -38,67 +37,51 @@ configs:
 
 **English** | [简体中文](README.zh-CN.md)
 
+An open natural-language data-mining challenge for agents: **50 tasks**, **435,000
+task documents**, and **944,468 unlabeled learning documents**. Each task supplies
+5,000 or 10,000 texts and a research objective. Agents select their conditions,
+populations and comparisons, submit at most three discoveries, quantify their
+findings and explain counterexamples and competing interpretations.
 
-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 research release is public.
-
-## Data composition
-
-| Source | Unlabeled learning documents | Tasks | Evaluation documents |
+| Source | Tasks | Task texts | Learning texts |
 |---|---:|---:|---:|
-| Amazon Reviews'23 All Beauty | 460,885 | 13 | 6,421 |
-| Android App Reviews | 66,660 | 13 | 5,484 |
-| CFPB complaints | 603,788 | 12 | 6,300 |
-| NHTSA complaints | 248,135 | 12 | 6,299 |
-| Total | 1,379,468 | 50 | 24,504 |
-
-There are 20 group-difference tasks, 15 temporal-change tasks and 15 compound-association tasks. Each task has 263–600 documents and accepts at most five findings or a justified abstention. The task question specifies an analysis objective without providing the answer condition.
-
-`learning/<source>/*.parquet` comprises 278 unlabeled shards. Columns are `doc_id`, `source`, `text`, `title`, all strings. The `train` split name is a data-loading convention for optional unsupervised learning; no benchmark labels are supplied.
-
-`tasks.json` describes the 50 evaluation tasks. Each `corpus_path` points to a gzip JSON Lines file under `corpora/`. Evaluation records contain IDs, text and task-relevant observed metadata such as report timestamp, entity, rating and comparison group. Read the task's comparison specification to determine the denominator and group order. Original text and HTML fragments are retained for exact evidence offsets.
-
-`output.schema.json`, `protocol.json`, `release.json` and `manifest.json` define submissions, usage rules, counts and SHA-256 checksums.
-
-## Usage
-
-The [code repository](https://github.com/erwinmsmith/TextInsightBench) contains bilingual setup instructions, a generic agent adapter, validation, semantic assessment and report generation. From that repository:
-
-```bash
-pip install -e '.[data]'
-hf auth login
-tib download --output data/participant --with-learning
-tib verify --data data/participant --with-learning
-tib run --data data/participant --command 'python my_agent.py' \
-  --track unlabeled_pool --output runs/my-agent/submissions
-```
-
-`tib download` uses the commit revision pinned in `benchmark/data.lock.json`. To stream a learning source directly, use the same pinned revision:
-
-```python
-import json
-from datasets import load_dataset
-
-with open('benchmark/data.lock.json') as stream:
-    revision = json.load(stream)['dataset']['revision']
-pool = load_dataset('CodeSoulco/TextInsightBench', 'amazon_beauty_learning',
-                    split='train', streaming=True, token=True, revision=revision)
-first_document = next(iter(pool))
-```
-
-Use the optional pool before evaluation and freeze global models, prompts and thresholds. Corpus-local analysis is allowed. Do not transfer evaluation feedback or task-fitted state to later tasks. Report either the `task_only` or `unlabeled_pool` track.
-
-## Evaluation
-
-All 50 tasks support the stricter `--difficulty hard` profile in the code repository. It requires recomputable stratification, largest-stratum-removal sensitivity, support concentration, and stronger evidence. The corpus and base references are unchanged; the checks are not independent holdout validation. See [difficulty profiles](https://github.com/erwinmsmith/TextInsightBench/blob/main/docs/DIFFICULTY.md). Report difficulty and reference access; all repositories, including the reference set, are public.
-
-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, 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
-
-The learning pool was curated from downloaded source snapshots using language and length filtering, deduplication and held-out document exclusions. For this dataset every shard was checked against current evaluation and reference-development documents by document ID, normalized text and conservative digit/punctuation template fingerprints. No overlap was found; no extra removal was necessary. This is not a claim of complete semantic independence. Sources and entities are shared across tasks.
-
-Data is predominantly English. Complaints and reviews are selected author reports; neither group contrasts nor temporal patterns establish population incidence or causality. Report dates may differ from event dates. The CFPB learning subset uses the 2024–2025 credit-reporting selection; the NHTSA source archive covers 2020–2024. Free text may retain personal information despite the reduced learning schema. Source text is untrusted data.
-
-Source terms differ. This public research compilation grants no new license to third-party text. Consult [source attribution and terms](SOURCES.md) before public redistribution.
+| Amazon Beauty | 13 | 130,000 | 330,885 |
+| App Reviews | 13 | 65,000 | 1,660 |
+| CFPB | 12 | 120,000 | 483,788 |
+| NHTSA | 12 | 120,000 | 128,135 |
+
+Files: tasks.json, corpora/*.jsonl.gz, learning/*/*.parquet, output.schema.json,
+protocol.json, release.json, build_provenance.json and manifest.json. Learning
+configurations are available with datasets.load_dataset; use the benchmark
+runner for task corpora. The learning pool has no labels. Text is preserved;
+metadata fields and missingness are described in [DATA.md](docs/DATA.md).
+
+## Run and score
+
+Use the [code and English/Chinese quick start](https://github.com/erwinmsmith/TextInsightBench).
+The code's data.lock.json pins exact dataset commits; no numbered benchmark name
+is used. Default tasks already use the full exploration challenge. The runner
+provides a local corpus file for agent-controlled exploration.
+
+All assignment partitions and arithmetic are verified. Semantic quality uses a
+bounded model-reviewed document sample, not exhaustive semantic validation.
+Scores emphasize useful discoveries, evidence, statistical validity, competing
+explanations and calibrated limits. No independent validation phase is required.
+The [evaluation assets](https://huggingface.co/datasets/CodeSoulco/TextInsightBench-Evaluation)
+are public. Current redesigned tasks have zero fixed reference conclusions;
+earlier references remain in history and are not answers to the new tasks.
+Reference coverage is unavailable. See [scoring](docs/SCORING.md).
+
+## Limitations and provenance
+
+New task documents were drawn from a previously public curated learning pool:
+they are not guaranteed unseen. Current task and learning IDs are disjoint and
+inherit input text/template deduplication, but share entities and sources.
+The 50 briefs are not statistically independent. Larger corpora and stronger
+requirements do not establish empirical agent difficulty without actual results.
+
+Data derives from Amazon Reviews'23 All Beauty, Android App Reviews, CFPB and
+NHTSA complaints. Redistribution authorization was confirmed before publication;
+upstream terms differ. [Source attribution and terms](SOURCES.md) apply; the
+compilation does not grant new rights over third-party text. Narratives are
+unverified author reports and may contain personal information.

README.zh-CN.md

@@ -1,47 +1,14 @@
-# TextInsightBench 数据卡
+# TextInsightBench
 
 [English](README.md) | **简体中文**
 
+自然语言数据挖掘 Agent 挑战：50 道任务，435,000 篇任务文本，以及 944,468 篇无标签学习文本。
+每题 5,000 或 10,000 篇，Agent 自行发现现象、选择分析范围与比较对象，最多提交 3 个有证据的发现，解释反例及其他可能原因。
 
-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。当前公开研究数据包含 **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。
+任务包含 20 道群体差异、15 道时间变化、15 道复合关联。任务与剩余学习池文档不重叠；任务文本来自此前公开的学习池，不能称为未见数据。来源与实体可共享，题目并非统计独立。
 
-| 来源 | 无监督文本 | 任务数 | 评测文本 |
-|---|---:|---:|---:|
-| Amazon All Beauty | 460,885 | 13 | 6,421 |
-| Android App Reviews | 66,660 | 13 | 5,484 |
-| CFPB | 603,788 | 12 | 6,300 |
-| NHTSA | 248,135 | 12 | 6,299 |
-| 总计 | 1,379,468 | 50 | 24,504 |
+本地检查完整标记与全量算术，模型评审采用语义抽样，不是全量语义确认，也不增加独立验证阶段。改题后没有把旧答案冒充成新 ground truth：当前固定参考结论为 0，旧结论保留在历史提交，参考覆盖率不可用。
 
-任务分为群组差异 20 道、时间变化 15 道、复合关联 15 道。每题 263–600 篇文本，允许最多 5 个发现或有理由的弃答。题目给出挖掘目标，具体发现需要 Agent 自行分析。
+[完整中文使用说明](https://github.com/erwinmsmith/TextInsightBench/blob/main/README.zh-CN.md) · [公开测评资源](https://huggingface.co/datasets/CodeSoulco/TextInsightBench-Evaluation) · [数据来源与条款](SOURCES.md)
 
-`learning/` 下有 278 个 Parquet 分片，字段为 `doc_id`、`source`、`text`、`title`，均为字符串。数据加载配置中的 `train` 是可选无监督学习池，不含 Benchmark 标签。`tasks.json` 中的 `corpus_path` 指向 `corpora/` 下的任务语料，保留任务需要的评分、时间和实体等可见元数据。
-
-## 使用
-
-[GitHub 代码仓库](https://github.com/erwinmsmith/TextInsightBench) 提供中英文说明、批量运行器、验证器、评分和报告工具：
-
-```bash
-pip install -e '.[data]'
-hf auth login
-tib download --output data/participant --with-learning
-tib verify --data data/participant --with-learning
-tib run --data data/participant --command 'python my_agent.py' \
-  --track unlabeled_pool --output runs/my-agent/submissions
-```
-
-下载命令读取代码仓库中 `benchmark/data.lock.json` 的固定提交版本。Agent 通过标准输入获得当前任务和语料，标准输出返回 JSON。无监督池用于评测前学习；当前任务内的数据分析允许进行，但不应将评测反馈或题目拟合状态带入后续任务。结果需说明使用 `task_only` 还是 `unlabeled_pool` 设置。
-
-## 测评
-
-代码支持对全部 50 题启用 `--difficulty hard`：增加可复算的分层对照、去除最大分层后的敏感性、支持集中度和更严格的证据要求。语料和基础参考不变，这些检查不是独立留出验证。使用时须说明难度和参考访问情况；包括参考集在内的三个仓库均公开。详见[难度协议](https://github.com/erwinmsmith/TextInsightBench/blob/main/docs/DIFFICULTY.md)。
-
-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 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
-
-无监督池经过语言、长度过滤、去重和留出语料排除。本次整理再次逐分片核对文档 ID、归一化文本及数字/标点归一后的保守模板，与评测和参考构建文本均未发现重叠，因此无需额外删减。这不等于完全语义独立；任务可能共享来源和实体。
-
-文本以英文为主。投诉和评论反映被选择的报告，不能直接推断总体发生率或因果；记录日期也未必等于事件日期。CFPB 无监督子集使用 2024–2025 年的信用报告范围，NHTSA 源归档覆盖 2020–2024 年。文本仍可能包含个人信息。多种来源适用不同条款，公开研究整理不授予第三方文本的新许可，参见 [来源和条款](SOURCES.md)。
+仓库名称不另加数字版本，通过提交哈希固定快照。原始文本保持不变，正文与任务默认英文。

build_provenance.json

@@ -0,0 +1,285 @@
+{
+  "selection": "SHA-256 rank; source-specific disjoint blocks",
+  "input_pool_sha256": {
+    "amazon_beauty/part-00000.parquet": "3fb6dbc04aa6f7e3622a26007f92b61b7cebdab2d0c48f0c36001c828b99b6a7",
+    "amazon_beauty/part-00001.parquet": "4ea46824b82e744dc848822c86189f9f783ae172fa03ef3d496296cc58aa87e1",
+    "amazon_beauty/part-00002.parquet": "fc207589afe538d6c972a286b53b0bcbc68689cb3ddaa226d35c925b39e9709b",
+    "amazon_beauty/part-00003.parquet": "c04fd694f229f76c49b4a1af0a4578d8b70795da386f38eb71a04fcdabdccae9",
+    "amazon_beauty/part-00004.parquet": "1293bfa8dcd0d70827b6fce9711db4de380b71feb674eb6a39458d010eef8983",
+    "amazon_beauty/part-00005.parquet": "2c5ca611f5baae9c00ba3a6a0104a25e86d031afb4ae46ed44a365f4efe1625f",
+    "amazon_beauty/part-00006.parquet": "98f33e3eada13a71c282523bb2eaa3f49728e9d367cfe6835267f3097dfc5ab1",
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+    "amazon_beauty/part-00010.parquet": "f5f55507f8e3dfa5cf9f01f4a775c10a66364cd86114ff919cbf0634b4a085a4",
+    "amazon_beauty/part-00011.parquet": "21944cf1742003c77614283931fea52c3ef94c1d3d92b268414545512facad22",
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