diff --git a/README.md b/README.md index 9539c273b12e916b50f8d0825df0e35f6d3254f1..beb598cde822cfc3ba742edd392c003685dade1f 100644 --- a/README.md +++ b/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//*.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. diff --git a/README.zh-CN.md b/README.zh-CN.md index 817694c2e5a279fe20129710a76fda2f9ab65b6c..a6e396f55a19dfce4b79abf32dfdae88e1627e00 100644 --- a/README.zh-CN.md +++ b/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。 diff --git a/build_provenance.json b/build_provenance.json new file mode 100644 index 0000000000000000000000000000000000000000..510cd11ac699aea1ea942c599800ad11b87482dc --- /dev/null +++ b/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": 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sha256:87ed498b8deec9d14d8249442d7bc0fce6199fa122a6a463d8e09191bbe314b3 -size 127007 diff --git a/corpora/nhtsa_temporal_change_nissan.jsonl.gz b/corpora/nhtsa_temporal_change_nissan.jsonl.gz deleted file mode 100644 index 05b3314b3b2daede4a1701a16917bf1face39b90..0000000000000000000000000000000000000000 --- a/corpora/nhtsa_temporal_change_nissan.jsonl.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:be234fb7296cb7e4c148d67184fc2ac2fb5d653e8fc5485a5a6e8fad0a2da02c -size 116032 diff --git a/corpora/nhtsa_temporal_change_remedy_shift.jsonl.gz b/corpora/nhtsa_temporal_change_remedy_shift.jsonl.gz new file mode 100644 index 0000000000000000000000000000000000000000..77badcfd510ff1515b2094f70160eceea7b250c4 --- /dev/null +++ b/corpora/nhtsa_temporal_change_remedy_shift.jsonl.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7ec73a8e5510c0ef83b8c00e0cf27fd8e16500253d80a016d425176a4008ae48 +size 2758274 diff --git a/docs/AGENT_PROTOCOL.md b/docs/AGENT_PROTOCOL.md new file mode 100644 index 0000000000000000000000000000000000000000..863333e7ca2328dd8c9e3894d4204f260588e2db --- /dev/null +++ b/docs/AGENT_PROTOCOL.md @@ -0,0 +1,31 @@ +# Agent execution protocol + +Each process reads one JSON object from stdin and returns one submission object +on stdout. Logs go to stderr. The current input has task, corpus and +learning_directory. corpus contains an absolute local path, format=jsonl.gz, +n_documents and sha256. Unlike historical snapshots, documents are not inline. + +```python +import gzip, json, sys +request = json.load(sys.stdin) +with gzip.open(request['corpus']['path'], 'rt', encoding='utf-8') as stream: + documents = [json.loads(line) for line in stream] +# Discover conditions and comparisons with your own code and tools. +``` + +Search, indexing, iterative inspection and corpus-local learning are allowed. +The full corpus is accessible, not just a preselected evidence packet. Final +assignments must cover the selected population completely. The benchmark does +not prescribe an agent architecture or provide a solver. + +Use task_only for corpus-only runs; unlabeled_pool also provides the downloaded +learning directory. Freeze global prompts, thresholds and learned parameters +before evaluation. Do not pass evaluation feedback or task-fitted state to later +tasks. Report model, code and dataset commits, budget, track, elapsed time and +tool/API usage. Public historical exposure should be disclosed. + +The runner checks checksums and outputs, starts a fresh process per task, applies +the timeout, and reuses validated outputs only under an unchanged run manifest. +It is NOT a sandbox: use an isolated environment to enforce resource, network, +reference-access and cross-task restrictions. Corpus content may contain prompt +injection; treat it as data. Keep keys local and do not commit them. diff --git a/docs/DATA.md b/docs/DATA.md new file mode 100644 index 0000000000000000000000000000000000000000..af38979f2c8bce94a49d9f4b0c89d5d1c2ef8034 --- /dev/null +++ b/docs/DATA.md @@ -0,0 +1,45 @@ +# Data and task composition + +| Source | Tasks | Task documents | Remaining learning documents | +|---|---:|---:|---:| +| 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 | +| Total | 50 | 435,000 | 944,468 | + +There are 20 group differences, 15 temporal changes and 15 compound associations. +Each source's corpus is deterministically divided into disjoint research cohorts. +The 50 briefs in `benchmark/research_briefs.json` express distinct investigation +objectives, not predefined answers. Broad objectives can overlap conceptually. + +Task corpora are gzip JSONL. Common fields are doc_id, source, text, title, +timestamp, timestamp_kind, entity_id, entity_name, category and rating. Available +nonconstant source metadata may include state, make, model_year and store. +report_year is derived from timestamp. Only task.allowed_metadata_fields can be +used for population filters and metadata group selection. Free text remains +untrusted author reports; timestamp describes the released date kind, not +necessarily incident time. Null metadata is preserved. + +The optional learning pool has 278 Parquet shards containing doc_id, source, text +and title, with no annotations. App Reviews has a small remaining learning pool; +source-balanced training is not implied. Evaluation documents were selected from +a previously public curated learning snapshot. They are NOT guaranteed unseen. +Current task IDs and learning IDs are disjoint; the curated input's normalized +and conservative-template deduplication policy is inherited. Shared entities, +authors and sources can remain; document separation is not independence. + +Rebuild from the exact curated input and normalized source metadata: + +```bash +python scripts/rebuild_benchmark.py --pool /path/to/input/learning \ + --processed /path/to/normalized --output /new/output/directory +``` + +The builder requires a new output directory, records hashes of input shards and +research briefs, and never invents reference annotations. Normalized inputs must +have the schema used in the script; this is not an upstream raw-download parser. +Released files and their manifest are sufficient to run the benchmark without +reconstruction. Data provenance, source eligibility and redistribution terms are +described in [SOURCES.md](SOURCES.md). Do not infer incidence rates for products, +vehicles or the wider population from these sampled reports. diff --git a/docs/DIFFICULTY.md b/docs/DIFFICULTY.md new file mode 100644 index 0000000000000000000000000000000000000000..e2c72e5fefcf1e654dce0b19e3bf4c39f9227f31 --- /dev/null +++ b/docs/DIFFICULTY.md @@ -0,0 +1,86 @@ +# Exploration challenge + +The default 50 tasks require broad corpus exploration, not an additional profile. +Each contains 5,000 or 10,000 documents. The agent selects its text conditions, +population, group values or time boundary. Research objectives cover practical +tradeoffs, process breakdowns, adaptation burdens, recurrence and consequences +without prescribing which pattern exists. + +The difficulty is in finding a substantive relationship, operationalizing it +across the analysis population, and explaining its scope and competing accounts. +Corpus size alone is not evidence of a difficult or valid evaluation. + +A selected population must contain at least 500 documents for App Reviews and +1,000 otherwise. Each metadata/time comparison arm needs at least 50 or 100 +documents respectively. These thresholds refer to total documents, not positive +cases. Unknown labels must be retained and interpreted. At most three +nonredundant findings are accepted. Tiny handpicked groups and ID/text-based +population filters are prohibited. + +The fixed audit axes are entity_id, rating and report_year. Missing or degenerate +axes remain unavailable, never silently replaced with a favorable axis. In +particular, stratifying on the same metadata as the comparison may leave no +comparable strata; report that limitation. A correctly established composition +effect or reversal can earn full credit. Stability in every axis is not required. + +## Exact audit calculations + +All base statistics remain required. Each finding additionally reports the +`robustness_*` numeric/null fields returned by `expected`: + +```python +from textinsightbench.validation import expected +from textinsightbench.difficulty import audit + +# The runner's task already includes the discovery protocol. The agent supplies +# definitions, all document assignments, evidence and interpretation itself. +finding['statistics'] = expected(finding, task, documents) +statistics, stratum_details = audit(finding, task, documents) +``` + +For group/time tasks, arm 0 and arm 1 are the agent's declared comparison groups, +excluding unknown condition assignments. For compound tasks, arm 0 is known +not-A and arm 1 is known A, restricted to known B; the outcome is B. Thus the +effect always matches the sign convention of the declared comparison. + +For each axis: + +1. A comparable stratum has at least 5 known documents in **each** arm. Its effect + is `100 × (positive_rate_arm1 − positive_rate_arm0)`. +2. The standardized effect is the mean of eligible stratum effects, weighted by + their pooled known-arm counts. `covered_crude_delta_pp` recomputes the crude + contrast on those same eligible rows, so differences in covered populations + are not mistaken for reversals. Report eligible/excluded known counts and the + minimum and maximum stratum effects. +3. Select the largest nonmissing stratum by its full document count, independently + of labels; ties use lexical order of its string value. Remove it and missing + metadata rows. The retained effect is null unless each arm still has at least + 5 known documents. Report removed and retained-arm counts. +4. `max_support_share` is the largest stratum's share of positive outcomes among + known-arm documents with nonmissing metadata. Report positive outcomes with + missing metadata separately. For compound tasks this is B concentration in the + jointly known population, not the proportion of A-and-B quotes. + +All expected fields must be present, no extra statistics are accepted, and values +must match recomputation within 1e-6. The three supporting quotes must reference +positive documents (joint positives for compound findings). A counterexample must +reference a known negative or, for compound tasks, a known discordant case. The +semantic judge still checks whether assignments and quotes are actually correct. + +The audit does not create independent samples, establish causal identification, +eliminate multiple-testing risk, or act as unseen holdout confirmation. Unknown +assignment bounds remain mandatory for group/time findings. Compound findings +must interpret the jointly unknown population and avoid unsupported extrapolation. + + +## Evaluation + +Quality is support × (15 + 25S + 20E + 30D + 10C). Depth requires a useful finding +and a supported discussion of alternatives and search/selection bias. See +[scoring](SCORING.md). Numeric audit correctness does not establish semantic +label correctness: the current judge audits a document sample, with uncertainty +preserved. No independent validation phase or causal identification is implied. + +Current tasks automatically select this protocol. Do not use --difficulty hard. +The CLI retains historical profile support solely for historical task snapshots. +Runs, reviews and scores are bound to the current task hashes. diff --git a/docs/ORGANIZER.md b/docs/ORGANIZER.md new file mode 100644 index 0000000000000000000000000000000000000000..5af63c32f86e9344865ad45afe04fc7f1a780ba8 --- /dev/null +++ b/docs/ORGANIZER.md @@ -0,0 +1,33 @@ +# Evaluation operations + +Use the commit-pinned participant data and the same code revision as participants. +Validate all submissions before semantic scoring. Current scoring works without +a reference file; optional public evaluation assets record reference availability +and the matching task hash, not 50 new answers. + +```bash +tib download --organizer --output evaluation +tib judge --data data/participant --submissions runs/agent/submissions \ + --base-url "$JUDGE_BASE_URL" --model "$JUDGE_MODEL" \ + --audit-documents 160 --output runs/agent/reviews +tib evaluate --data data/participant --submissions runs/agent/submissions \ + --reviews runs/agent/reviews --output runs/agent/report.json +``` + +Set JUDGE_API_KEY locally. Nonempty tasks incur one quality API request each; +abstentions make no API calls. No reference-matching requests occur for current +tasks. Configure model, timeout, max input characters and output tokens; an +oversized packet fails explicitly without silent truncation. Use a sufficiently +large model context or adjust the documented audit budget (60–2000 documents). +Many quotations may require raising a very small audit budget. + +Persist judge_config.json for the audit seed and configuration. Completed valid +reviews are reused. Changed tasks, corpora, submissions or judge configuration +require fresh output directories. Do not tune an agent on judge feedback and +present that score as a frozen run. Publish all coverage and uncertainty metrics, +not only a favorable subset. + +The structural verifier is exhaustive; semantic review is sampled and fallible. +This release does not require independent verification or add an unseen holdout. +For enforceable comparisons, run participants in an isolated environment without +evaluation access; the process adapter alone cannot enforce this. diff --git a/docs/SCORING.md b/docs/SCORING.md new file mode 100644 index 0000000000000000000000000000000000000000..75769c7cb49d844734090b04f4ae9737efb88b25 --- /dev/null +++ b/docs/SCORING.md @@ -0,0 +1,65 @@ +# Scoring + +The current scoring protocol is finding-quality-discovery. Commit-pinned current +scores must not be compared directly with historical narrow-task scores. + +## Local gates + +Each finding needs a permitted population, valid agent-selected comparison, +minimum population/arm sizes, complete condition partitions, exact text offsets, +and all recomputed statistics. Invalid submissions do not receive invented +quality scores. See [submission contract](SUBMISSIONS.md). + +## Evidence-based quality + +A semantic assessment supplies support, task_fulfilled, duplicate_of, rationale, +and four dimensions from 0 to 1: S statistical validity, E evidence entailment, +D analytical depth and C calibration. Dimension anchors are 1 fully justified, +0.75 minor gaps, 0.5 material limitations, 0.25 weak and 0 absent/wrong. + +```text +finding quality = support × (15 + 25S + 20E + 30D + 10C) +``` + +Support factors: supported=1, partial=0.5, unsupported=0. Uncertain stays null. +An unfulfilled task or duplicate gets 0 regardless of dimensions. Generic +sentiment, metadata frequencies or an unexamined aggregate contrast do not +fulfill the task. Depth requires a substantive discovery, a defensible choice +of scope/comparison, competing explanations, and correct interpretation of +robustness checks. Material audit omissions cap D at 0.5; merely restating +numbers caps it at 0.25. These semantic caps are judge instructions, not +deterministically proven properties. + +Example: a supported, nonduplicate finding with S=.8, E=.9, D=.75, C=.8 earns +15+20+18+22.5+8 = 83.5. Partial support halves it to 41.75. This is an illustrative +calculation, not an observed agent result. + +## Semantic audit + +All selected-population arithmetic and partitions are checked. The model then +sees at most 160 documents by default: submitted quotations, samples from each +nonempty positive/negative/unknown assignment cell and a corpus-wide remainder. +The seed is created after submission and saved with judge configuration for +reproducibility; sampled IDs are retained in each review. This is a bounded +audit of submitted labels, not a new independently labeled or held-out dataset. + +Sampled label mistakes undermine semantic support even if the counts add up. +Absence of sampled errors does not prove all labels correct. The judge must use +uncertain when the packet cannot resolve a claim. No full-corpus semantic +guarantee or unbiased estimator of label accuracy is claimed. Report judge +model, input budget and audit size; model-based scores have evaluator error. + +## Aggregation and reference availability + +Task quality averages all submitted finding scores; any unresolved finding makes +task quality unavailable. An abstention is valid but unscored, not a verified +absence of useful findings. A full quality_mean is available only when every +task has a score. conditional_quality_mean covers only scored tasks and must be +reported alongside scored_tasks, abstention_rate, valid_submission_rate, missing, +invalid and pending counts. Source and family breakdowns are included. + +Current redesigned tasks have no fixed reference conclusions. reference_coverage +is null. Earlier 50 conclusions are retained only in history, not reused against +new questions. Novel supported findings are not penalized for lacking a fixed +match. Reviews bind task, corpus, submission, reference configuration and scoring +hashes; stale reviews must not be reused. diff --git a/docs/SOURCES.md b/docs/SOURCES.md new file mode 100644 index 0000000000000000000000000000000000000000..8f7047c962a6ac260ec5976d7633c188378531d5 --- /dev/null +++ b/docs/SOURCES.md @@ -0,0 +1,19 @@ +# Source attribution and data terms + +TextInsightBench is a derived research collection of third-party text. It does not grant a new license to source reviews or complaints. This distribution is public. Redistribution authorization for this release was confirmed before publication. The data card uses `license: other` because upstream terms differ; this is not a blanket open-source license for third-party text. The recorded upstream source status below is preserved. + +| Source | Upstream location | Recorded source status | +|---|---|---| +| Amazon Reviews'23, All Beauty | https://amazon-reviews-2023.github.io/main.html | Research release; source text redistribution terms require upstream verification | +| Android App Reviews | https://huggingface.co/datasets/sealuzh/app_reviews | Downloaded dataset card lists the license as unknown; snapshot `9eaa95f66364367e8752b0f34c00f67aafa95d15` | +| CFPB Consumer Complaint Database | https://www.consumerfinance.gov/data-research/consumer-complaints/ | Public complaint data; retain source attribution and original publisher context | +| NHTSA complaints | https://www.nhtsa.gov/nhtsa-datasets-and-apis | Public government release; source is the 2020–2024 complaints archive | + +Raw download endpoints recorded for reproducibility: + +- Amazon: `https://mcauleylab.ucsd.edu/public_datasets/data/amazon_2023/raw/review_categories/All_Beauty.jsonl.gz` +- CFPB: `https://files.consumerfinance.gov/ccdb/complaints.csv.zip` +- NHTSA: `https://static.nhtsa.gov/odi/ffdd/cmpl/COMPLAINTS_RECEIVED_2020-2024.zip` +- App Reviews: `https://huggingface.co/datasets/sealuzh/app_reviews/resolve/9eaa95f66364367e8752b0f34c00f67aafa95d15/data/train-00000-of-00001.parquet` + +Released hashes identify the frozen derived files even when upstream downloads change. Complaint and review narratives are unverified author reports and may include personal information. The exported learning schema excludes user identifiers, but this is not a guarantee of complete de-identification of free text. Preserve the intended research scope and consult upstream terms before any public redistribution. Public repository access does not itself grant a permissive license for this code, compilation or third-party text. diff --git a/docs/SUBMISSIONS.md b/docs/SUBMISSIONS.md new file mode 100644 index 0000000000000000000000000000000000000000..a6dddd6192f9180fe7fb557a2a91350030a10e98 --- /dev/null +++ b/docs/SUBMISSIONS.md @@ -0,0 +1,63 @@ +# Submission contract + +Write one UTF-8 JSON file per task, named `.json`. The agent process protocol writes that same object to stdout. Do not wrap it in Markdown. + +```json +{ + "task_id": "the task ID", + "findings": [], + "abstention_reason": "No sufficiently supported finding was identified." +} +``` + +A nonempty answer contains up to three findings. Every finding has: + +| Field | Required content | +|---|---| +| `finding_id` | Unique identifier within the answer | +| `claim` | Specific downstream conclusion, including direction and scope | +| `kind` | Exactly the task's kind | +| `scope` | Corpus, group, time and entity scope | +| `population` | Object with `filters`: zero to three conjunctive metadata rules | +| `comparison` | Agent-selected groups/date boundary, or null for association | +| `definitions` | One observable condition for group/time tasks; two for association tasks. Each has `condition_id`, `inclusion`, `exclusion` | +| `assignments` | One complete document partition per condition: `condition_id`, `positive_doc_ids`, `negative_doc_ids`, `unknown_doc_ids` | +| `statistics` | Exactly the recomputed fields below | +| `evidence` | Original quotation records: `doc_id`, `start`, `end`, `quote`, `role` (`supporting`, `counterexample`, `context`) | +| `limitations` | Nonempty list describing uncertainty, confounding and inference limits | + +At least three distinct supporting documents and a known negative/discordant counterexample (if assigned cases exist) are required, with at most 15 spans per finding. Every selected-population document belongs to exactly one state per condition. Positive means the stated report is present; negative means it is not reported under the definition; unknown preserves unresolved judgments. Quotations refer to the `text` field. Python `text[start:end]` must equal `quote`, using Unicode characters, not UTF-8 bytes or JavaScript UTF-16 code units. Do not normalize or edit evidence text before computing offsets. + +Population example: `{"filters":[{"field":"rating","op":"gte","value":2}]}`. +Use `{"filters":[]}` for all documents. Allowed fields are listed in each task; +operators are eq, in, gte and lte. An in list has 1–20 scalar values. Missing +metadata never matches a filter. Document IDs, text and post-hoc condition labels +cannot filter the population. Explain scope choices in scope and limitations. + +Group example: `{"field":"rating","groups":[[1,2],[4,5]]}`. Each group has +1–20 distinct values, and the groups must be disjoint. This example is a syntax +illustration, not a sufficient research finding. Group values omitted from both +arms remain in the selected population and require assignments; statistics +report their exclusion. Temporal example: +`{"field":"timestamp","cutoff":"2023-06-01"}`. Compound association uses null. +Minimum population and arm sizes are specified in each task. + +Use the implementation to compute statistics from your assignments: + +```python +from textinsightbench.validation import expected +finding["statistics"] = expected(finding, task, documents) +``` + +For group/time tasks, required statistics are `group0_total_n`, `group0_known_n`, `group0_positive_n`, `group0_unknown_n`, `group0_rate_known` and the corresponding five `group1_*` fields, plus `excluded_metadata_n`, `delta_known_pp`, `delta_identification_lower_pp`, `delta_identification_upper_pp`. + +Group order follows `comparison.groups`; for time tasks, group 0 is before the cutoff and group 1 is on or after. The known rate is positive / (total − unknown). The reported difference is **group 1 minus group 0**, in percentage points. The lower/upper identification bounds allocate unknowns to all compatible states within the finite corpus. These bounds are not confidence intervals. Missing comparison metadata is excluded from named denominators but still needs a condition judgment. + +For association tasks, required statistics are `known_joint_n`, `unknown_joint_n`, `n11`, `n10`, `n01`, `n00`, `p_b_given_a`, `p_b_given_not_a`, `conditional_difference_pp`, `lift`. Condition A is the first definition and B the second. Joint cells count documents with known judgments for both conditions; unknowns are counted separately. Zero denominators produce JSON `null`, never NaN or infinity. Additional unsupported statistics are not accepted by this contract. + +Current findings also require corpus_total_n, population_total_n, +population_coverage and all robustness_* fields returned by expected. Pass the +entire task corpus to expected; it applies the declared population itself. Do not +pre-filter twice. See [audit formulas](DIFFICULTY.md). The schema is bundled in +textinsightbench/output.schema.json and the dataset root. Synthetic discovery +fixtures appear in tests/test_discovery.py; they are not released-task answers. diff --git a/docs/VERIFICATION.md b/docs/VERIFICATION.md new file mode 100644 index 0000000000000000000000000000000000000000..9745f2dd6b871311adecd14a10fddb3669c43eb9 --- /dev/null +++ b/docs/VERIFICATION.md @@ -0,0 +1,23 @@ +# Verification scope + +The release was checked with local automated tests and an all-50-task abstaining +agent smoke run. This verifies loading, corpus hashes, task dispatch, output +contracts and reporting, not real-agent mining quality or empirical difficulty. + +Synthetic tests exercise agent-selected group/date comparisons, forbidden +filters, overlapping groups, minimum population sizes, exact partitions, +Simpson-style reversals, missing metadata, counterexamples, quotation offsets, +score bindings, null reference coverage and bounded reproducible semantic packets. +No paid agent benchmark or independent validation set was added. + +```bash +python -m unittest discover -s tests -v +tib verify --data data/participant --with-learning +``` + +The data builder deterministically selects disjoint IDs from an already curated +pool, preserves original text, enriches released metadata and filters selected +documents out of the remaining pool. It records exact input shard hashes. +Current-snapshot disjointness does not erase historical public exposure. +The design increases the required exploration and analysis workload; actual +difficulty and discriminative power still require empirical agent results. diff --git a/learning/amazon_beauty/part-00000.parquet b/learning/amazon_beauty/part-00000.parquet index 15e7b6ccd5861d7e76b3c991e04a988da428c413..f9fb48503c0a7b0c044ca1b20f905cb1e959b9e0 100644 --- a/learning/amazon_beauty/part-00000.parquet +++ b/learning/amazon_beauty/part-00000.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:3fb6dbc04aa6f7e3622a26007f92b61b7cebdab2d0c48f0c36001c828b99b6a7 -size 940430 +oid sha256:5af03f81247461ab9d8516e1e790d39d11c8c071166bdbd43dcc2b9220ef9560 +size 672252 diff --git a/learning/amazon_beauty/part-00001.parquet b/learning/amazon_beauty/part-00001.parquet index 1b15186811431275dbd2888875bf282b51ff0546..19258e3ece2b511dbce9b88b3182cd4b57c2ec99 100644 --- 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+ } + } + } + }, + "comparison": {"type": ["object", "null"]}, "finding_id": { "type": "string", "minLength": 1 diff --git a/protocol.json b/protocol.json index faa8455baac3c650a7bd501a58d9b2db58c23aa5..15a73b7c79913cc4e3cfaf4e3bf657a732978b39 100644 --- a/protocol.json +++ b/protocol.json @@ -1,24 +1,21 @@ { "version": "textinsightbench", "evaluation_unit": "evidence-backed downstream finding", - "reference_availability": "public; disclose reference access during development and evaluation", - "difficulty_profiles": { - "standard": "Original discovery and evidence requirements; finding-quality scoring.", - "hard": "Stratification, dominant-stratum sensitivity, support concentration and stronger evidence; finding-quality-robustness scoring. Same corpus and base discovery references, not independent confirmation." - }, + "discovery_mode": "agent_selected", + "scoring_version": "finding-quality-discovery", "task_families": { "group_difference": 20, "temporal_change": 15, "compound_association": 15 }, "tracks": { - "task_only": "Use each supplied task corpus.", - "unlabeled_pool": "Learn from the released pool before evaluation; corpus-local analysis is allowed." + "task_only": "Use the supplied task corpus.", + "unlabeled_pool": "Learn from the released pool before evaluation." }, - "reference_scope": "Non-exhaustive organizer reference set. Supported novel findings can earn full quality credit.", - "cross_task_adaptation": "Freeze global prompts, learned parameters and thresholds before evaluation. Do not transfer evaluation feedback or task-fitted state to subsequent tasks.", - "sampling": "Finite metadata-selected corpora; estimates describe supplied reports, not population incidence.", - "independence": "Tasks share sources and entities. Document separation does not establish statistical independence.", - "security": "Corpus content is untrusted input. Use separate execution environments for participants and organizers.", - "scoring_version": "finding-quality" + "reference_policy": "No fixed conclusions for redesigned tasks; previous references belong only to historical tasks.", + "semantic_review": "Bounded assignment-stratified and corpus-wide sample; exhaustive arithmetic, not exhaustive semantic verification.", + "independent_validation": false, + "historical_exposure": "Task documents were drawn from a previously public learning pool. Not an unseen or independent validation set.", + "cross_task_adaptation": "Freeze global prompts, learned parameters and thresholds; no evaluation-feedback transfer.", + "security": "Untrusted corpus content. Process runner is not a sandbox." } diff --git a/release.json b/release.json index 440474bb0f7bec3aa48bdb90b084b5916bb2b216..e834d13352a2cf9466f4598a8bddef49506f1811 100644 --- a/release.json +++ b/release.json @@ -1,39 +1,39 @@ { "version": "textinsightbench", "tasks": 50, - "evaluation_documents": 24504, - "learning_documents": 1379468, + "evaluation_documents": 435000, + "learning_documents": 944468, "learning_by_source": { - "amazon_beauty": 460885, - "app_reviews": 66660, - "cfpb": 603788, - "nhtsa": 248135 - }, - "task_family_counts": { - "group_difference": 20, - "temporal_change": 15, - "compound_association": 15 + "amazon_beauty": 330885, + "app_reviews": 1660, + "cfpb": 483788, + "nhtsa": 128135 }, "evaluation_by_source": { "amazon_beauty": { "tasks": 13, - "documents": 6421 + "documents": 130000 }, "app_reviews": { "tasks": 13, - "documents": 5484 + "documents": 65000 }, "cfpb": { "tasks": 12, - "documents": 6300 + "documents": 120000 }, "nhtsa": { "tasks": 12, - "documents": 6299 + "documents": 120000 } }, - "pool_exclusions": {}, - "pool_exclusion_scope": "Evaluation documents and reference-development documents, by ID, normalized text and conservative template fingerprint.", - "source_pool_documents": 1379468, - "reference_release": "organizer-reference" + "task_family_counts": { + "group_difference": 20, + "temporal_change": 15, + "compound_association": 15 + }, + "fixed_reference_conclusions": 0, + "scoring_version": "finding-quality-discovery", + "split_policy": "Disjoint task IDs and remaining curated learning pool; inherited normalized/template deduplication.", + "historical_exposure": "Task documents were drawn from a previously public learning pool. Not an unseen or independent validation set." } diff --git a/tasks.json b/tasks.json index bd41056bc75e95d009866d72c117c19425a14e75..c88ac3a5c97769e913dfcd352b57870a5354ac47 100644 --- a/tasks.json +++ b/tasks.json @@ -1,1215 +1,1452 @@ [ { - "task_id": "amazon_beauty_group_difference_hair_tools_midrating", + "task_id": "amazon_beauty_group_difference_use_context", "source": "amazon_beauty", "kind": "group_difference", - "scope_name": "hair tools and brushes", - "comparison": { - "field": "comparison_group", - "groups": [ - "rating_4_5", - "rating_3" - ] - }, - "question": "Within hair tools and brushes, investigate substantive differences in reported experiences between rating_4_5 and rating_3. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 373, - "max_findings": 5, - "corpus_path": "corpora/amazon_beauty_group_difference_hair_tools_midrating.jsonl.gz", - "corpus_sha256": "7bf3a1c9537c78e0b6cf1beee22908d19fcef3ed815f79857a8df0494adb9910", - "output_contract": "output.schema.json", - "selection_scope": { - "product_context": "hair_tools" - }, - "dependence_block": "amazon_beauty", - "split": "evaluation" + "question": "Discover a non-obvious difference in how use context changes reported product performance; distinguish context from overall satisfaction. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "category", + "store", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/amazon_beauty_group_difference_use_context.jsonl.gz", + "corpus_sha256": "6785025adfdb510a0bf69a5845e0f9d8cb9a452f673fa3b71a90e1e21d417d2e", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "amazon_beauty_group_difference_hair_tools", + "task_id": "amazon_beauty_group_difference_expectation_gaps", "source": "amazon_beauty", "kind": "group_difference", - "scope_name": "hair tools and brushes", - "comparison": { - "field": "comparison_group", - "groups": [ - "rating_4_5", - "rating_1_2" - ] - }, - "question": "Within hair tools and brushes, investigate substantive differences in reported experiences between rating_4_5 and rating_1_2. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/amazon_beauty_group_difference_hair_tools.jsonl.gz", - "corpus_sha256": "0603e2eb2807b292830d973eb385dcd0ba95a390af6f5dd0f7415efe73732db3", - "output_contract": "output.schema.json", - "selection_scope": { - "product_context": "hair_tools" - }, - "dependence_block": "amazon_beauty", - "split": "evaluation" + "question": "Discover where expectations and reported experience diverge differently across defensible populations; explain the practical consequence. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "category", + "store", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/amazon_beauty_group_difference_expectation_gaps.jsonl.gz", + "corpus_sha256": "3440fd78c7249b974cd529a533c6944de8844ff508210e88e25cb4941ca12001", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "amazon_beauty_group_difference_nails", + "task_id": "amazon_beauty_group_difference_failure_concentration", "source": "amazon_beauty", "kind": "group_difference", - "scope_name": "nail-care products", - "comparison": { - "field": "comparison_group", - "groups": [ - "rating_4_5", - "rating_1_2" - ] - }, - "question": "Within nail-care products, investigate substantive differences in reported experiences between rating_4_5 and rating_1_2. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/amazon_beauty_group_difference_nails.jsonl.gz", - "corpus_sha256": "04ffe62594b4d0abe15710fd717ebeefac4e45e6f445bde27bfb1059bc8293ae", - "output_contract": "output.schema.json", - "selection_scope": { - "product_context": "nails" - }, - "dependence_block": "amazon_beauty", - "split": "evaluation" + "question": "Find a specific failure pattern whose concentration across populations is obscured by overall review volume. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "category", + "store", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/amazon_beauty_group_difference_failure_concentration.jsonl.gz", + "corpus_sha256": "a175d794cdb8b49efff320ec98eacead5ccbd46312086577e1c3b3c47ef825dc", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "amazon_beauty_group_difference_skin", + "task_id": "amazon_beauty_group_difference_adaptation", "source": "amazon_beauty", "kind": "group_difference", - "scope_name": "skin-care and cleansing products", - "comparison": { - "field": "comparison_group", - "groups": [ - "rating_4_5", - "rating_1_2" - ] - }, - "question": "Within skin-care and cleansing products, investigate substantive differences in reported experiences between rating_4_5 and rating_1_2. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/amazon_beauty_group_difference_skin.jsonl.gz", - "corpus_sha256": "e1443c24e6fce8201361c340d320ea4042617f4382ab253a9a8d207c4d45d029", - "output_contract": "output.schema.json", - "selection_scope": { - "product_context": "skin" - }, - "dependence_block": "amazon_beauty", - "split": "evaluation" + "question": "Discover a difference in consumer adaptation or workarounds and examine whether apparent success masks recurring limitations. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "category", + "store", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/amazon_beauty_group_difference_adaptation.jsonl.gz", + "corpus_sha256": "716a311328ed1ddde2499e631e049f4cf6bbdc250cdd6ab56a0b87ab13b3e1c0", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "amazon_beauty_group_difference_makeup", + "task_id": "amazon_beauty_group_difference_durability_tradeoffs", "source": "amazon_beauty", "kind": "group_difference", - "scope_name": "makeup products", - "comparison": { - "field": "comparison_group", - "groups": [ - "rating_4_5", - "rating_1_2" - ] - }, - "question": "Within makeup products, investigate substantive differences in reported experiences between rating_4_5 and rating_1_2. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/amazon_beauty_group_difference_makeup.jsonl.gz", - "corpus_sha256": "25e5a34896f27ed3a86e054808c1ac3823d24dc0815f915e37d2c358a94932e5", - "output_contract": "output.schema.json", - "selection_scope": { - "product_context": "makeup" - }, - "dependence_block": "amazon_beauty", - "split": "evaluation" + "question": "Find a population-dependent tradeoff between immediate experience and continued usefulness without equating star ratings with the mechanism. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "category", + "store", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/amazon_beauty_group_difference_durability_tradeoffs.jsonl.gz", + "corpus_sha256": "b0a90336494c58026737a6ecb734e3cd9418d39deda7e1169647def0fff21a34", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "amazon_beauty_temporal_change_grooming", + "task_id": "amazon_beauty_temporal_change_experience_shift", "source": "amazon_beauty", "kind": "temporal_change", - "scope_name": "shaving and grooming products", - "comparison": { - "field": "timestamp", - "cutoff": "2019-03-04", - "groups": [ - "before", - "on_or_after" - ] - }, - "question": "Within shaving and grooming products, investigate changes in reported experiences before versus on/after 2019-03-04. Identify a specific changing pattern, quantify both period denominators, and examine whether entity, product, rating or reporting composition could explain it. Dates are reporting/review dates, not necessarily event or software-release dates. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/amazon_beauty_temporal_change_grooming.jsonl.gz", - "corpus_sha256": "fc74044946083aeaa6d18a49b2174dafeb55ac72d264db09698c778db3226b9c", - "output_contract": "output.schema.json", - "selection_scope": { - "product_context": "grooming" - }, - "dependence_block": "amazon_beauty", - "split": "evaluation" + "question": "Discover a meaningful shift in the substance of reported experiences and locate a defensible time boundary; test a composition explanation. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "category", + "store", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/amazon_beauty_temporal_change_experience_shift.jsonl.gz", + "corpus_sha256": "3acea0c3c632ae53a0b8216303f5e762e5c6c4d875cae0cd41ad37e5f5d6f279", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "amazon_beauty_temporal_change_fragrance", + "task_id": "amazon_beauty_temporal_change_emerging_friction", "source": "amazon_beauty", "kind": "temporal_change", - "scope_name": "fragrances", - "comparison": { - "field": "timestamp", - "cutoff": "2020-04-03", - "groups": [ - "before", - "on_or_after" - ] - }, - "question": "Within fragrances, investigate changes in reported experiences before versus on/after 2020-04-03. Identify a specific changing pattern, quantify both period denominators, and examine whether entity, product, rating or reporting composition could explain it. Dates are reporting/review dates, not necessarily event or software-release dates. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 332, - "max_findings": 5, - "corpus_path": "corpora/amazon_beauty_temporal_change_fragrance.jsonl.gz", - "corpus_sha256": "f0b8ef66e8475ee334f68cef89c225aba9bc27d25a23b68a704e67c42bea8be4", - "output_contract": "output.schema.json", - "selection_scope": { - "product_context": "fragrance" - }, - "dependence_block": "amazon_beauty", - "split": "evaluation" + "question": "Find an emerging or receding usage friction, distinguishing a change in its prevalence from changes in review volume. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "category", + "store", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/amazon_beauty_temporal_change_emerging_friction.jsonl.gz", + "corpus_sha256": "0fdad3186e8869d20df3ba9bd4c8e20b107a7738b3bc0bc26e051047f1342445", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "amazon_beauty_temporal_change_skin", + "task_id": "amazon_beauty_temporal_change_expectation_evolution", "source": "amazon_beauty", "kind": "temporal_change", - "scope_name": "skin-care and cleansing products", - "comparison": { - "field": "timestamp", - "cutoff": "2019-01-08", - "groups": [ - "before", - "on_or_after" - ] - }, - "question": "Within skin-care and cleansing products, investigate changes in reported experiences before versus on/after 2019-01-08. Identify a specific changing pattern, quantify both period denominators, and examine whether entity, product, rating or reporting composition could explain it. Dates are reporting/review dates, not necessarily event or software-release dates. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/amazon_beauty_temporal_change_skin.jsonl.gz", - "corpus_sha256": "a599b62a475936e0a2dd9463750221c26e0d93ebad93007ddb0eee4c44e1c0a9", - "output_contract": "output.schema.json", - "selection_scope": { - "product_context": "skin" - }, - "dependence_block": "amazon_beauty", - "split": "evaluation" + "question": "Investigate how an observable expectation-experience mismatch changes over time; explain alternative reasons for the apparent shift. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "category", + "store", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/amazon_beauty_temporal_change_expectation_evolution.jsonl.gz", + "corpus_sha256": "5c7a2e89ef64887d6c595c9c934ebcf74f49443cd668e6a24f81e63d4af90ba2", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "amazon_beauty_temporal_change_hair_tools", + "task_id": "amazon_beauty_temporal_change_persistence", "source": "amazon_beauty", "kind": "temporal_change", - "scope_name": "hair tools and brushes", - "comparison": { - "field": "timestamp", - "cutoff": "2019-09-07", - "groups": [ - "before", - "on_or_after" - ] - }, - "question": "Within hair tools and brushes, investigate changes in reported experiences before versus on/after 2019-09-07. Identify a specific changing pattern, quantify both period denominators, and examine whether entity, product, rating or reporting composition could explain it. Dates are reporting/review dates, not necessarily event or software-release dates. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/amazon_beauty_temporal_change_hair_tools.jsonl.gz", - "corpus_sha256": "97692f42b1bd3360e0dc566f43bdbd98eb32eff99ad31ac3176bda62c72083b6", - "output_contract": "output.schema.json", - "selection_scope": { - "product_context": "hair_tools" - }, - "dependence_block": "amazon_beauty", - "split": "evaluation" + "question": "Discover a change in a recurring product limitation and assess whether it is broad or driven by a concentrated product mix. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "category", + "store", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/amazon_beauty_temporal_change_persistence.jsonl.gz", + "corpus_sha256": "dc970fad8c52af79bdeeae49c5de9ad9f584df1df305d5e6f9d1e516208f18fe", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "amazon_beauty_compound_association_hair_care", + "task_id": "amazon_beauty_compound_association_tradeoff_coupling", "source": "amazon_beauty", "kind": "compound_association", - "scope_name": "hair-care products", - "comparison": null, - "question": "Within hair-care products, discover a recurring association between two distinct reported experiences or events. Explain the context, quantify the joint table and conditional difference, inspect cases where only one occurs, and distinguish an informative association from a definition that makes co-occurrence inevitable. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 416, - "max_findings": 5, - "corpus_path": "corpora/amazon_beauty_compound_association_hair_care.jsonl.gz", - "corpus_sha256": "e4eddaf50a700ed6094f04ddc99568ab903792ac52c5e08a55e4f6cbf778336d", - "output_contract": "output.schema.json", - "selection_scope": { - "product_context": "hair_care" - }, - "dependence_block": "amazon_beauty", - "split": "evaluation" + "question": "Discover a useful association between two distinct reported experiences that reveals a practical product tradeoff. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "category", + "store", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/amazon_beauty_compound_association_tradeoff_coupling.jsonl.gz", + "corpus_sha256": "c04589fdea1fa2c7d149c79fdfcd067279ed7e00035e97281deaa34b0055ac21", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "amazon_beauty_compound_association_nails", + "task_id": "amazon_beauty_compound_association_context_response", "source": "amazon_beauty", "kind": "compound_association", - "scope_name": "nail-care products", - "comparison": null, - "question": "Within nail-care products, discover a recurring association between two distinct reported experiences or events. Explain the context, quantify the joint table and conditional difference, inspect cases where only one occurs, and distinguish an informative association from a definition that makes co-occurrence inevitable. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 600, - "max_findings": 5, - "corpus_path": "corpora/amazon_beauty_compound_association_nails.jsonl.gz", - "corpus_sha256": "d63dab5181db0e3247ff20c5b7081a15f55e892e19447efb79ff1c45f84173aa", - "output_contract": "output.schema.json", - "selection_scope": { - "product_context": "nails" - }, - "dependence_block": "amazon_beauty", - "split": "evaluation" + "question": "Find a context-response relationship in the text and distinguish it from two descriptions of the same event. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "category", + "store", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/amazon_beauty_compound_association_context_response.jsonl.gz", + "corpus_sha256": "43f4593b06a95535aa2f31217c779cf79556329bbf95d0f0a438d18da56b5bba", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "amazon_beauty_compound_association_makeup", + "task_id": "amazon_beauty_compound_association_failure_recovery", "source": "amazon_beauty", "kind": "compound_association", - "scope_name": "makeup products", - "comparison": null, - "question": "Within makeup products, discover a recurring association between two distinct reported experiences or events. Explain the context, quantify the joint table and conditional difference, inspect cases where only one occurs, and distinguish an informative association from a definition that makes co-occurrence inevitable. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 600, - "max_findings": 5, - "corpus_path": "corpora/amazon_beauty_compound_association_makeup.jsonl.gz", - "corpus_sha256": "418d2433feed78d3894cebcb8d857f7befb6bd341713af57a167133922904583", - "output_contract": "output.schema.json", - "selection_scope": { - "product_context": "makeup" - }, - "dependence_block": "amazon_beauty", - "split": "evaluation" + "question": "Discover a relationship between an observed difficulty and a distinct response or recovery experience; inspect discordant cases. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "category", + "store", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/amazon_beauty_compound_association_failure_recovery.jsonl.gz", + "corpus_sha256": "14e775677d297df136104b3d1976a0ac2e7377555d9d184550ccba1d2ef60657", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "amazon_beauty_compound_association_skin", + "task_id": "amazon_beauty_compound_association_expectation_behavior", "source": "amazon_beauty", "kind": "compound_association", - "scope_name": "skin-care and cleansing products", - "comparison": null, - "question": "Within skin-care and cleansing products, discover a recurring association between two distinct reported experiences or events. Explain the context, quantify the joint table and conditional difference, inspect cases where only one occurs, and distinguish an informative association from a definition that makes co-occurrence inevitable. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 600, - "max_findings": 5, - "corpus_path": "corpora/amazon_beauty_compound_association_skin.jsonl.gz", - "corpus_sha256": "301b4dd5f7e96f47abb8c03f2cb70147ed12c4a975fb62fa8423746f286dd1b4", - "output_contract": "output.schema.json", - "selection_scope": { - "product_context": "skin" - }, - "dependence_block": "amazon_beauty", - "split": "evaluation" + "question": "Find a nontrivial link between an observable expectation and a subsequent reported behavior without treating co-occurrence as causation. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "category", + "store", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/amazon_beauty_compound_association_expectation_behavior.jsonl.gz", + "corpus_sha256": "c497a23a66defb3a05936efade05dcce4e85759a672430a73912d88670848c69", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "app_reviews_group_difference_services", + "task_id": "app_reviews_group_difference_workflow_disruption", "source": "app_reviews", "kind": "group_difference", - "scope_name": "Google Play Services and Google Authenticator", - "comparison": { - "field": "comparison_group", - "groups": [ - "com.google.android.gms", - "com.google.android.apps.authenticator2" - ] - }, - "question": "Within Google Play Services and Google Authenticator, investigate substantive differences in reported experiences between com.google.android.gms and com.google.android.apps.authenticator2. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/app_reviews_group_difference_services.jsonl.gz", - "corpus_sha256": "6eeb2a116c21363507b9350e202b0625655610a374745e7fb8ced83a1c166d42", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "com.google.android.gms", - "com.google.android.apps.authenticator2" - ] - }, - "dependence_block": "app_reviews", - "split": "evaluation" + "question": "Discover how a specific workflow disruption differs across defensible user or application populations. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "timestamp", + "report_year" + ], + "min_population_n": 500, + "min_group_n": 50, + "n_documents": 5000, + "corpus_path": "corpora/app_reviews_group_difference_workflow_disruption.jsonl.gz", + "corpus_sha256": "6d29658f83b46fc9847979547322109fea46cda30fb962731496a34e648ab6ea", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "app_reviews_group_difference_messaging", + "task_id": "app_reviews_group_difference_access_barriers", "source": "app_reviews", "kind": "group_difference", - "scope_name": "Telegram and SMS Backup+", - "comparison": { - "field": "comparison_group", - "groups": [ - "org.telegram.messenger", - "com.zegoggles.smssync" - ] - }, - "question": "Within Telegram and SMS Backup+, investigate substantive differences in reported experiences between org.telegram.messenger and com.zegoggles.smssync. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/app_reviews_group_difference_messaging.jsonl.gz", - "corpus_sha256": "9ece304ebe773d0d3f163f83509250cba9edbfad02c77b04b6180afacc055e23", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "org.telegram.messenger", - "com.zegoggles.smssync" - ] - }, - "dependence_block": "app_reviews", - "split": "evaluation" + "question": "Find a non-obvious disparity in a concrete barrier to successful use; distinguish the barrier from generic dissatisfaction. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "timestamp", + "report_year" + ], + "min_population_n": 500, + "min_group_n": 50, + "n_documents": 5000, + "corpus_path": "corpora/app_reviews_group_difference_access_barriers.jsonl.gz", + "corpus_sha256": "04d0d03bd172f6cc3c97e1a0fadd754d04f0a1c0fb9f87d6256d33810aa7f832", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "app_reviews_group_difference_emulators", + "task_id": "app_reviews_group_difference_adaptation_cost", "source": "app_reviews", "kind": "group_difference", - "scope_name": "PPSSPP and Reicast", - "comparison": { - "field": "comparison_group", - "groups": [ - "org.ppsspp.ppsspp", - "com.reicast.emulator" - ] - }, - "question": "Within PPSSPP and Reicast, investigate substantive differences in reported experiences between org.ppsspp.ppsspp and com.reicast.emulator. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/app_reviews_group_difference_emulators.jsonl.gz", - "corpus_sha256": "3f5c9c619d7f67699ef85e4dc1fa680909c3be82d46029b7b3469f1b31674c63", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "org.ppsspp.ppsspp", - "com.reicast.emulator" - ] - }, - "dependence_block": "app_reviews", - "split": "evaluation" + "question": "Discover a difference in workarounds or adaptation burdens and investigate whether application mix explains it. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "timestamp", + "report_year" + ], + "min_population_n": 500, + "min_group_n": 50, + "n_documents": 5000, + "corpus_path": "corpora/app_reviews_group_difference_adaptation_cost.jsonl.gz", + "corpus_sha256": "4301009e1eea520b8a264beca45ca1a7c174053602a4dd4f65c039e68a18f1fd", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "app_reviews_group_difference_display", + "task_id": "app_reviews_group_difference_promise_experience", "source": "app_reviews", "kind": "group_difference", - "scope_name": "AcDisplay and Muzei", - "comparison": { - "field": "comparison_group", - "groups": [ - "com.achep.acdisplay", - "net.nurik.roman.muzei" - ] - }, - "question": "Within AcDisplay and Muzei, investigate substantive differences in reported experiences between com.achep.acdisplay and net.nurik.roman.muzei. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/app_reviews_group_difference_display.jsonl.gz", - "corpus_sha256": "bcd975abf7668f97da3fdc890d0b9aa22c354b30f4a72405be977f0f91ca28df", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "com.achep.acdisplay", - "net.nurik.roman.muzei" - ] - }, - "dependence_block": "app_reviews", - "split": "evaluation" + "question": "Find where promised or expected utility diverges from reported practical utility across populations. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "timestamp", + "report_year" + ], + "min_population_n": 500, + "min_group_n": 50, + "n_documents": 5000, + "corpus_path": "corpora/app_reviews_group_difference_promise_experience.jsonl.gz", + "corpus_sha256": "c6773ed652dad7f7a97aa11e9c67f121bd36b90342421ebb347399e84500c5dc", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "app_reviews_group_difference_files", + "task_id": "app_reviews_group_difference_failure_impact", "source": "app_reviews", "kind": "group_difference", - "scope_name": "FrostWire and DiskUsage", - "comparison": { - "field": "comparison_group", - "groups": [ - "com.frostwire.android", - "com.google.android.diskusage" - ] - }, - "question": "Within FrostWire and DiskUsage, investigate substantive differences in reported experiences between com.frostwire.android and com.google.android.diskusage. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/app_reviews_group_difference_files.jsonl.gz", - "corpus_sha256": "3e368b42a24419a7048cd324ac589efdaf9370d85f9e28d69d53e20b6db93ac1", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "com.frostwire.android", - "com.google.android.diskusage" - ] - }, - "dependence_block": "app_reviews", - "split": "evaluation" + "question": "Discover a difference in the consequences of a recurring failure pattern, not merely which population gives lower ratings. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "timestamp", + "report_year" + ], + "min_population_n": 500, + "min_group_n": 50, + "n_documents": 5000, + "corpus_path": "corpora/app_reviews_group_difference_failure_impact.jsonl.gz", + "corpus_sha256": "b0d947e90b21b9ff9f11ef662f288fcf6ac4a09b0e087c57b5461f357f48e438", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "app_reviews_temporal_change_sky_map", + "task_id": "app_reviews_temporal_change_workflow_evolution", "source": "app_reviews", "kind": "temporal_change", - "scope_name": "com.google.android.stardroid", - "comparison": { - "field": "timestamp", - "cutoff": "2016-09-16", - "groups": [ - "before", - "on_or_after" - ] - }, - "question": "Within com.google.android.stardroid, investigate changes in reported experiences before versus on/after 2016-09-16. Identify a specific changing pattern, quantify both period denominators, and examine whether entity, product, rating or reporting composition could explain it. Dates are reporting/review dates, not necessarily event or software-release dates. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 281, - "max_findings": 5, - "corpus_path": "corpora/app_reviews_temporal_change_sky_map.jsonl.gz", - "corpus_sha256": "e1ff80a1fbc71daea8ef2c82461e29d6a7d8258948acf236742f665dc81ad528", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "com.google.android.stardroid" - ] - }, - "dependence_block": "app_reviews", - "split": "evaluation" + "question": "Discover a time-local change in workflow experience; choose and justify the boundary and investigate application composition. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "timestamp", + "report_year" + ], + "min_population_n": 500, + "min_group_n": 50, + "n_documents": 5000, + "corpus_path": "corpora/app_reviews_temporal_change_workflow_evolution.jsonl.gz", + "corpus_sha256": "c6eec878b2d6f0d2a644abf456cfcef9eec0055a2c52903cf2c90fda29dd149b", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "app_reviews_temporal_change_publishing", + "task_id": "app_reviews_temporal_change_regression_pattern", "source": "app_reviews", "kind": "temporal_change", - "scope_name": "org.wordpress.android", - "comparison": { - "field": "timestamp", - "cutoff": "2016-08-14", - "groups": [ - "before", - "on_or_after" - ] - }, - "question": "Within org.wordpress.android, investigate changes in reported experiences before versus on/after 2016-08-14. Identify a specific changing pattern, quantify both period denominators, and examine whether entity, product, rating or reporting composition could explain it. Dates are reporting/review dates, not necessarily event or software-release dates. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 330, - "max_findings": 5, - "corpus_path": "corpora/app_reviews_temporal_change_publishing.jsonl.gz", - "corpus_sha256": "d1c32acf084891b7ec1a4cf3a445da01ebd96ad87585426b955094318511b402", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "org.wordpress.android" - ] - }, - "dependence_block": "app_reviews", - "split": "evaluation" + "question": "Find a substantive emerging or receding failure pattern without assuming any release date or assigning an unsupported cause. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "timestamp", + "report_year" + ], + "min_population_n": 500, + "min_group_n": 50, + "n_documents": 5000, + "corpus_path": "corpora/app_reviews_temporal_change_regression_pattern.jsonl.gz", + "corpus_sha256": "4e6c919a7274a5a75e7ea74baf229baf8d7f89a6e809b89627d8f49e7086d76e", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "app_reviews_temporal_change_accessibility", + "task_id": "app_reviews_temporal_change_utility_shift", "source": "app_reviews", "kind": "temporal_change", - "scope_name": "com.google.android.marvin.talkback", - "comparison": { - "field": "timestamp", - "cutoff": "2016-12-27", - "groups": [ - "before", - "on_or_after" - ] - }, - "question": "Within com.google.android.marvin.talkback, investigate changes in reported experiences before versus on/after 2016-12-27. Identify a specific changing pattern, quantify both period denominators, and examine whether entity, product, rating or reporting composition could explain it. Dates are reporting/review dates, not necessarily event or software-release dates. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 312, - "max_findings": 5, - "corpus_path": "corpora/app_reviews_temporal_change_accessibility.jsonl.gz", - "corpus_sha256": "065dfd4e00d14f32bb8a9b3b42951f1bb5ea3e6228bbb187a430f80e142a78e7", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "com.google.android.marvin.talkback" - ] - }, - "dependence_block": "app_reviews", - "split": "evaluation" + "question": "Discover a change in how people describe realized utility, separating prevalence from changing document volume. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "timestamp", + "report_year" + ], + "min_population_n": 500, + "min_group_n": 50, + "n_documents": 5000, + "corpus_path": "corpora/app_reviews_temporal_change_utility_shift.jsonl.gz", + "corpus_sha256": "4001efee0dd9dd4eccb78d24f19205e670f1b0a948c442f6150b629a6bf7842e", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "app_reviews_temporal_change_game", + "task_id": "app_reviews_temporal_change_recovery_shift", "source": "app_reviews", "kind": "temporal_change", - "scope_name": "com.watabou.pixeldungeon", - "comparison": { - "field": "timestamp", - "cutoff": "2016-04-02", - "groups": [ - "before", - "on_or_after" - ] - }, - "question": "Within com.watabou.pixeldungeon, investigate changes in reported experiences before versus on/after 2016-04-02. Identify a specific changing pattern, quantify both period denominators, and examine whether entity, product, rating or reporting composition could explain it. Dates are reporting/review dates, not necessarily event or software-release dates. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 301, - "max_findings": 5, - "corpus_path": "corpora/app_reviews_temporal_change_game.jsonl.gz", - "corpus_sha256": "604c99cfd524ae0c5fd54ed560ad9f30bdc14caaec030552ff1b3277777aabc4", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "com.watabou.pixeldungeon" - ] - }, - "dependence_block": "app_reviews", - "split": "evaluation" + "question": "Investigate a temporal change in reported recovery or adaptation; establish the scope of the observed change. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "timestamp", + "report_year" + ], + "min_population_n": 500, + "min_group_n": 50, + "n_documents": 5000, + "corpus_path": "corpora/app_reviews_temporal_change_recovery_shift.jsonl.gz", + "corpus_sha256": "4e83517b29353d38333c02a64a9c79d7e447dff5774c136b1d8666189faed7d2", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "app_reviews_compound_association_services", + "task_id": "app_reviews_compound_association_friction_response", "source": "app_reviews", "kind": "compound_association", - "scope_name": "com.google.android.gms", - "comparison": null, - "question": "Within com.google.android.gms, discover a recurring association between two distinct reported experiences or events. Explain the context, quantify the joint table and conditional difference, inspect cases where only one occurs, and distinguish an informative association from a definition that makes co-occurrence inevitable. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 600, - "max_findings": 5, - "corpus_path": "corpora/app_reviews_compound_association_services.jsonl.gz", - "corpus_sha256": "88f6ff1feca6108610ad1cc9d8c285e64370b8fad404c84283d032aa393450e5", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "com.google.android.gms" - ] - }, - "dependence_block": "app_reviews", - "split": "evaluation" + "question": "Discover a relationship between a concrete usage friction and a distinct user response; analyze counterexamples. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "timestamp", + "report_year" + ], + "min_population_n": 500, + "min_group_n": 50, + "n_documents": 5000, + "corpus_path": "corpora/app_reviews_compound_association_friction_response.jsonl.gz", + "corpus_sha256": "e7fdbe896c544a28d2432beb0cd8ea540dd066995052fdd0422529fcc3a45c79", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "app_reviews_compound_association_messaging", + "task_id": "app_reviews_compound_association_utility_tradeoff", "source": "app_reviews", "kind": "compound_association", - "scope_name": "org.telegram.messenger", - "comparison": null, - "question": "Within org.telegram.messenger, discover a recurring association between two distinct reported experiences or events. Explain the context, quantify the joint table and conditional difference, inspect cases where only one occurs, and distinguish an informative association from a definition that makes co-occurrence inevitable. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 600, - "max_findings": 5, - "corpus_path": "corpora/app_reviews_compound_association_messaging.jsonl.gz", - "corpus_sha256": "df146622c29b7f150f10d17b39aa147f047cde596c16344e25f613abbe379dff", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "org.telegram.messenger" - ] - }, - "dependence_block": "app_reviews", - "split": "evaluation" + "question": "Find two distinct experiences whose association reveals a non-obvious utility tradeoff. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "timestamp", + "report_year" + ], + "min_population_n": 500, + "min_group_n": 50, + "n_documents": 5000, + "corpus_path": "corpora/app_reviews_compound_association_utility_tradeoff.jsonl.gz", + "corpus_sha256": "5a821458938fa073211797c3d25cd555cf6cb3e38ceb78d1a218f6ae737474f3", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "app_reviews_compound_association_emulator", + "task_id": "app_reviews_compound_association_context_failure", "source": "app_reviews", "kind": "compound_association", - "scope_name": "com.opendoorstudios.ds4droid", - "comparison": null, - "question": "Within com.opendoorstudios.ds4droid, discover a recurring association between two distinct reported experiences or events. Explain the context, quantify the joint table and conditional difference, inspect cases where only one occurs, and distinguish an informative association from a definition that makes co-occurrence inevitable. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 297, - "max_findings": 5, - "corpus_path": "corpora/app_reviews_compound_association_emulator.jsonl.gz", - "corpus_sha256": "a7655a57e93d61d8e6fcbc74323e6c7cb3551edb44ae1f8120cba7d8663d7d18", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "com.opendoorstudios.ds4droid" - ] - }, - "dependence_block": "app_reviews", - "split": "evaluation" + "question": "Discover an association between a stated usage context and a specific failure or success experience. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "timestamp", + "report_year" + ], + "min_population_n": 500, + "min_group_n": 50, + "n_documents": 5000, + "corpus_path": "corpora/app_reviews_compound_association_context_failure.jsonl.gz", + "corpus_sha256": "c3f58c81c9a0b78f6d2b8364a345b290fc0b06d01d88f9d0cc0eded1a6effd72", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "app_reviews_compound_association_news", + "task_id": "app_reviews_compound_association_recovery_limit", "source": "app_reviews", "kind": "compound_association", - "scope_name": "org.npr.android.news", - "comparison": null, - "question": "Within org.npr.android.news, discover a recurring association between two distinct reported experiences or events. Explain the context, quantify the joint table and conditional difference, inspect cases where only one occurs, and distinguish an informative association from a definition that makes co-occurrence inevitable. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 263, - "max_findings": 5, - "corpus_path": "corpora/app_reviews_compound_association_news.jsonl.gz", - "corpus_sha256": "a541586e7409cd922902b8dda3721eeea7d4f8e3afc2ecdf38d1dbbe1b6c4d8b", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "org.npr.android.news" - ] - }, - "dependence_block": "app_reviews", - "split": "evaluation" + "question": "Find a relationship between attempted recovery and a separately defined limitation; avoid defining one condition by the other. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "rating", + "timestamp", + "report_year" + ], + "min_population_n": 500, + "min_group_n": 50, + "n_documents": 5000, + "corpus_path": "corpora/app_reviews_compound_association_recovery_limit.jsonl.gz", + "corpus_sha256": "c1d1817ca8e28f367481be1cc06dd65cf1981aefc2186d111bb36e72ff1822af", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "cfpb_group_difference_tx", + "task_id": "cfpb_group_difference_resolution_burden", "source": "cfpb", "kind": "group_difference", - "scope_name": "TX credit-reporting complaints", - "comparison": { - "field": "comparison_group", - "groups": [ - "Experian Information Solutions Inc.", - "TRANSUNION INTERMEDIATE HOLDINGS, INC." - ] - }, - "question": "Within TX credit-reporting complaints, investigate substantive differences in reported experiences between Experian Information Solutions Inc. and TRANSUNION INTERMEDIATE HOLDINGS, INC.. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/cfpb_group_difference_tx.jsonl.gz", - "corpus_sha256": "342389725fd318329c262304dd9f4f7e2d23784f11a89d5b6e37eb587315d505", - "output_contract": "output.schema.json", - "selection_scope": { - "state": "TX", - "entity_ids": [ - "Experian Information Solutions Inc.", - "TRANSUNION INTERMEDIATE HOLDINGS, INC." - ] - }, - "dependence_block": "cfpb", - "split": "evaluation" + "question": "Discover how a specific burden in pursuing resolution differs across defensible complaint populations. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/cfpb_group_difference_resolution_burden.jsonl.gz", + "corpus_sha256": "c4e3babbc5fc8ef08f5af60773228147293dc6702a35e5608e7e3627ab0daeb9", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "cfpb_group_difference_fl", + "task_id": "cfpb_group_difference_process_breakdown", "source": "cfpb", "kind": "group_difference", - "scope_name": "FL credit-reporting complaints", - "comparison": { - "field": "comparison_group", - "groups": [ - "EQUIFAX, INC.", - "Experian Information Solutions Inc." - ] - }, - "question": "Within FL credit-reporting complaints, investigate substantive differences in reported experiences between EQUIFAX, INC. and Experian Information Solutions Inc.. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/cfpb_group_difference_fl.jsonl.gz", - "corpus_sha256": "956ca973b1c716fd47d47c4c783c6103b009971327c6601ea8089b012dabe1f5", - "output_contract": "output.schema.json", - "selection_scope": { - "state": "FL", - "entity_ids": [ - "EQUIFAX, INC.", - "Experian Information Solutions Inc." - ] - }, - "dependence_block": "cfpb", - "split": "evaluation" + "question": "Find a disparity in an observable procedural breakdown; distinguish process from generic negative sentiment. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/cfpb_group_difference_process_breakdown.jsonl.gz", + "corpus_sha256": "99076217188f9ba014ccac3e579437f0ed65f281cb1f545bbb85b26bb33a25f1", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "cfpb_group_difference_ca", + "task_id": "cfpb_group_difference_downstream_impact", "source": "cfpb", "kind": "group_difference", - "scope_name": "CA credit-reporting complaints", - "comparison": { - "field": "comparison_group", - "groups": [ - "TRANSUNION INTERMEDIATE HOLDINGS, INC.", - "EQUIFAX, INC." - ] - }, - "question": "Within CA credit-reporting complaints, investigate substantive differences in reported experiences between TRANSUNION INTERMEDIATE HOLDINGS, INC. and EQUIFAX, INC.. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/cfpb_group_difference_ca.jsonl.gz", - "corpus_sha256": "a71889fd0d80351cca9cbe3037ff8f78ffd28df901b523934f47052c4ab8cfa7", - "output_contract": "output.schema.json", - "selection_scope": { - "state": "CA", - "entity_ids": [ - "TRANSUNION INTERMEDIATE HOLDINGS, INC.", - "EQUIFAX, INC." - ] - }, - "dependence_block": "cfpb", - "split": "evaluation" + "question": "Discover a population-dependent difference in a concrete downstream consequence described in narratives. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/cfpb_group_difference_downstream_impact.jsonl.gz", + "corpus_sha256": "e710a403889b7767436cd527b1298a746b190923170b6b8bd4ccb8f2b09fd176", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "cfpb_group_difference_ny", + "task_id": "cfpb_group_difference_recurrence", "source": "cfpb", "kind": "group_difference", - "scope_name": "NY credit-reporting complaints", - "comparison": { - "field": "comparison_group", - "groups": [ - "Experian Information Solutions Inc.", - "EQUIFAX, INC." - ] - }, - "question": "Within NY credit-reporting complaints, investigate substantive differences in reported experiences between Experian Information Solutions Inc. and EQUIFAX, INC.. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/cfpb_group_difference_ny.jsonl.gz", - "corpus_sha256": "4c67f818b625c97fef339d9fae2cb2c6da7f3954be67b4c89a21cf712f384932", - "output_contract": "output.schema.json", - "selection_scope": { - "state": "NY", - "entity_ids": [ - "Experian Information Solutions Inc.", - "EQUIFAX, INC." - ] - }, - "dependence_block": "cfpb", - "split": "evaluation" + "question": "Find where a recurring problem differs across populations and examine whether reporting composition explains the contrast. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/cfpb_group_difference_recurrence.jsonl.gz", + "corpus_sha256": "4ecec6553ad61e0263b914d27727845acf5832ea3f5792d62829dbb303f43fa9", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "cfpb_group_difference_ga", + "task_id": "cfpb_group_difference_information_asymmetry", "source": "cfpb", "kind": "group_difference", - "scope_name": "GA credit-reporting complaints", - "comparison": { - "field": "comparison_group", - "groups": [ - "TRANSUNION INTERMEDIATE HOLDINGS, INC.", - "Experian Information Solutions Inc." - ] - }, - "question": "Within GA credit-reporting complaints, investigate substantive differences in reported experiences between TRANSUNION INTERMEDIATE HOLDINGS, INC. and Experian Information Solutions Inc.. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/cfpb_group_difference_ga.jsonl.gz", - "corpus_sha256": "dd34175d1223d074acd10185663b02a355438119618716f87019185269c170ab", - "output_contract": "output.schema.json", - "selection_scope": { - "state": "GA", - "entity_ids": [ - "TRANSUNION INTERMEDIATE HOLDINGS, INC.", - "Experian Information Solutions Inc." - ] - }, - "dependence_block": "cfpb", - "split": "evaluation" + "question": "Discover a difference in an observable information or communication barrier and explain its practical implications. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/cfpb_group_difference_information_asymmetry.jsonl.gz", + "corpus_sha256": "4625c7fe3e2077a1470798b78be0bec4ad4b5ed71ce990a977ca5f5fcd9416ad", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "cfpb_temporal_change_experian_refreshed", + "task_id": "cfpb_temporal_change_process_shift", "source": "cfpb", "kind": "temporal_change", - "scope_name": "experian credit-reporting complaints", - "comparison": { - "field": "timestamp", - "cutoff": "2025-01-16", - "groups": [ - "before", - "on_or_after" - ] - }, - "question": "Within experian credit-reporting complaints, investigate changes in reported experiences before versus on/after 2025-01-16. Identify a specific changing pattern, quantify both period denominators, and examine whether entity, product, rating or reporting composition could explain it. Dates are reporting/review dates, not necessarily event or software-release dates. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/cfpb_temporal_change_experian_refreshed.jsonl.gz", - "corpus_sha256": "a34018f6a8138a27f97ba8517d1914678a7b10baae54468349e0f6405d29b5a6", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "Experian Information Solutions Inc." - ] - }, - "dependence_block": "cfpb", - "split": "evaluation" + "question": "Discover a shift in an observable complaint-handling experience, selecting a defensible time boundary without assuming policy causation. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/cfpb_temporal_change_process_shift.jsonl.gz", + "corpus_sha256": "d2e12302d7cc8d254ac7d56b84c732e3f1316ffb49c586257e2c538eedb6a1cc", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "cfpb_temporal_change_transunion", + "task_id": "cfpb_temporal_change_burden_shift", "source": "cfpb", "kind": "temporal_change", - "scope_name": "transunion credit-reporting complaints", - "comparison": { - "field": "timestamp", - "cutoff": "2025-01-17", - "groups": [ - "before", - "on_or_after" - ] - }, - "question": "Within transunion credit-reporting complaints, investigate changes in reported experiences before versus on/after 2025-01-17. Identify a specific changing pattern, quantify both period denominators, and examine whether entity, product, rating or reporting composition could explain it. Dates are reporting/review dates, not necessarily event or software-release dates. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/cfpb_temporal_change_transunion.jsonl.gz", - "corpus_sha256": "15e0b19d239fd5fcac6886022061ee804759170ad51a9314c760a57746cfecaf", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "TRANSUNION INTERMEDIATE HOLDINGS, INC." - ] - }, - "dependence_block": "cfpb", - "split": "evaluation" + "question": "Find an emerging or declining resolution burden and assess whether company composition explains the trend. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/cfpb_temporal_change_burden_shift.jsonl.gz", + "corpus_sha256": "2683e4387f7510a03def31373ea2a163bf5b19d4cfc4e85a52a3f8c8ab1152dd", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "cfpb_temporal_change_equifax", + "task_id": "cfpb_temporal_change_impact_shift", "source": "cfpb", "kind": "temporal_change", - "scope_name": "equifax credit-reporting complaints", - "comparison": { - "field": "timestamp", - "cutoff": "2025-01-25", - "groups": [ - "before", - "on_or_after" - ] - }, - "question": "Within equifax credit-reporting complaints, investigate changes in reported experiences before versus on/after 2025-01-25. Identify a specific changing pattern, quantify both period denominators, and examine whether entity, product, rating or reporting composition could explain it. Dates are reporting/review dates, not necessarily event or software-release dates. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/cfpb_temporal_change_equifax.jsonl.gz", - "corpus_sha256": "6f4e061f5ad4d2dd8c4b9f1216945b58a1943b382eadc23310fbb68f7d7748ed", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "EQUIFAX, INC." - ] - }, - "dependence_block": "cfpb", - "split": "evaluation" + "question": "Discover a change in a concrete reported consequence, separating narrative prevalence from complaint volume. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/cfpb_temporal_change_impact_shift.jsonl.gz", + "corpus_sha256": "9f7c7548a64e35c45297f3cde83d7f8038033138b792439e5515728207f79177", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "cfpb_temporal_change_other_companies", + "task_id": "cfpb_temporal_change_recurrence_shift", "source": "cfpb", "kind": "temporal_change", - "scope_name": "other companies credit-reporting complaints", - "comparison": { - "field": "timestamp", - "cutoff": "2025-01-31", - "groups": [ - "before", - "on_or_after" - ] - }, - "question": "Within other companies credit-reporting complaints, investigate changes in reported experiences before versus on/after 2025-01-31. Identify a specific changing pattern, quantify both period denominators, and examine whether entity, product, rating or reporting composition could explain it. Dates are reporting/review dates, not necessarily event or software-release dates. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/cfpb_temporal_change_other_companies.jsonl.gz", - "corpus_sha256": "f95d4cb507b9f9abeb319d89a9c2da0fad9d15a73dfe03566faf21c34c128361", - "output_contract": "output.schema.json", - "selection_scope": { - "exclude_entity_ids": [ - "Experian Information Solutions Inc.", - "TRANSUNION INTERMEDIATE HOLDINGS, INC.", - "EQUIFAX, INC." - ] - }, - "dependence_block": "cfpb", - "split": "evaluation" + "question": "Investigate a temporal change in repeated or unresolved experiences and identify limits on its interpretation. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/cfpb_temporal_change_recurrence_shift.jsonl.gz", + "corpus_sha256": "cfbf6cbafd7e6f546f6a57f915414b39e7d9f014dc5b0420555204bc1dcf45e9", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "cfpb_compound_association_experian", + "task_id": "cfpb_compound_association_process_consequence", "source": "cfpb", "kind": "compound_association", - "scope_name": "Experian Information Solutions Inc.", - "comparison": null, - "question": "Within Experian Information Solutions Inc., discover a recurring association between two distinct reported experiences or events. Explain the context, quantify the joint table and conditional difference, inspect cases where only one occurs, and distinguish an informative association from a definition that makes co-occurrence inevitable. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 600, - "max_findings": 5, - "corpus_path": "corpora/cfpb_compound_association_experian.jsonl.gz", - "corpus_sha256": "9794bdd6c496d54cb1e6c39a27d32b761fd97c215a3caca016224058c5545475", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "Experian Information Solutions Inc." - ] - }, - "dependence_block": "cfpb", - "split": "evaluation" + "question": "Discover an association between a specific procedural experience and a distinct consequence in complaint narratives. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/cfpb_compound_association_process_consequence.jsonl.gz", + "corpus_sha256": "1613cd6fffb0a6e1e4afa19daf0d0b2d93ef9f4804be64b842357f1cd90c29b1", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "cfpb_compound_association_transunion", + "task_id": "cfpb_compound_association_response_recurrence", "source": "cfpb", "kind": "compound_association", - "scope_name": "TRANSUNION INTERMEDIATE HOLDINGS, INC.", - "comparison": null, - "question": "Within TRANSUNION INTERMEDIATE HOLDINGS, INC., discover a recurring association between two distinct reported experiences or events. Explain the context, quantify the joint table and conditional difference, inspect cases where only one occurs, and distinguish an informative association from a definition that makes co-occurrence inevitable. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 600, - "max_findings": 5, - "corpus_path": "corpora/cfpb_compound_association_transunion.jsonl.gz", - "corpus_sha256": "3ca730750df35e081f6dc9b10313f1403b735bcfed0b2acb57b7e6d3c774b67b", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "TRANSUNION INTERMEDIATE HOLDINGS, INC." - ] - }, - "dependence_block": "cfpb", - "split": "evaluation" + "question": "Find a nontrivial relationship between an observable response and recurrence or persistence, inspecting discordant narratives. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/cfpb_compound_association_response_recurrence.jsonl.gz", + "corpus_sha256": "ec2d781f6f88f9c60cb938271b449f055cd05ca9423f475f3cbcfd13d1cb312a", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "cfpb_compound_association_equifax", + "task_id": "cfpb_compound_association_barrier_behavior", "source": "cfpb", "kind": "compound_association", - "scope_name": "EQUIFAX, INC.", - "comparison": null, - "question": "Within EQUIFAX, INC., discover a recurring association between two distinct reported experiences or events. Explain the context, quantify the joint table and conditional difference, inspect cases where only one occurs, and distinguish an informative association from a definition that makes co-occurrence inevitable. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 600, - "max_findings": 5, - "corpus_path": "corpora/cfpb_compound_association_equifax.jsonl.gz", - "corpus_sha256": "e69f5827c448b4d06a946b12693498e24396bedb0c51cadde571fbf1332852be", - "output_contract": "output.schema.json", - "selection_scope": { - "entity_ids": [ - "EQUIFAX, INC." - ] - }, - "dependence_block": "cfpb", - "split": "evaluation" + "question": "Discover a relationship between an information barrier and a distinct consumer action; separate association from explanation. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/cfpb_compound_association_barrier_behavior.jsonl.gz", + "corpus_sha256": "6391e2cd6c52c404541bedd2ec00529ddad030de537884b1eb626d7ccfa7d916", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "nhtsa_group_difference_ford_suvs", + "task_id": "nhtsa_group_difference_operating_context", "source": "nhtsa", "kind": "group_difference", - "scope_name": "FORD|ESCAPE versus FORD|EXPLORER", - "comparison": { - "field": "comparison_group", - "groups": [ - "FORD|ESCAPE", - "FORD|EXPLORER" - ] - }, - "question": "Within FORD|ESCAPE versus FORD|EXPLORER, investigate substantive differences in reported experiences between FORD|ESCAPE and FORD|EXPLORER. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/nhtsa_group_difference_ford_suvs.jsonl.gz", - "corpus_sha256": "f7cccc93c1b8860d9d024a25d85bbb5e6750341277778dae75775116b763e82b", - "output_contract": "output.schema.json", - "selection_scope": { - "vehicle_models": [ - "FORD|ESCAPE", - "FORD|EXPLORER" - ] - }, - "dependence_block": "nhtsa", - "split": "evaluation" + "question": "Discover a population-dependent difference in a failure experience under a concrete operating context; do not infer vehicle incidence rates. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "make", + "model_year", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/nhtsa_group_difference_operating_context.jsonl.gz", + "corpus_sha256": "ede22b73f2267d0c26ddf9cddad9258e7789cbfb0ea8e021c493a6db0c91eee9", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "nhtsa_group_difference_pickups_refreshed", + "task_id": "nhtsa_group_difference_warning_gap", "source": "nhtsa", "kind": "group_difference", - "scope_name": "FORD|F-150 versus RAM|1500", - "comparison": { - "field": "comparison_group", - "groups": [ - "FORD|F-150", - "RAM|1500" - ] - }, - "question": "Within FORD|F-150 versus RAM|1500, investigate substantive differences in reported experiences between FORD|F-150 and RAM|1500. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/nhtsa_group_difference_pickups_refreshed.jsonl.gz", - "corpus_sha256": "b6adb92bd8b62d86128c3ec0a4b9ccce05d2fecb552e932d6d7ab19bcddc067e", - "output_contract": "output.schema.json", - "selection_scope": { - "vehicle_models": [ - "FORD|F-150", - "RAM|1500" - ] - }, - "dependence_block": "nhtsa", - "split": "evaluation" + "question": "Find a disparity in observable warning or detectability experiences and assess vehicle-composition explanations. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "make", + "model_year", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/nhtsa_group_difference_warning_gap.jsonl.gz", + "corpus_sha256": "716425316e216574113b2d3d1b896ef07226ea57c42d23cd06039e619a3ad413", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "nhtsa_group_difference_sedans", + "task_id": "nhtsa_group_difference_repair_persistence", "source": "nhtsa", "kind": "group_difference", - "scope_name": "HYUNDAI|SONATA versus KIA|OPTIMA", - "comparison": { - "field": "comparison_group", - "groups": [ - "HYUNDAI|SONATA", - "KIA|OPTIMA" - ] - }, - "question": "Within HYUNDAI|SONATA versus KIA|OPTIMA, investigate substantive differences in reported experiences between HYUNDAI|SONATA and KIA|OPTIMA. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 453, - "max_findings": 5, - "corpus_path": "corpora/nhtsa_group_difference_sedans.jsonl.gz", - "corpus_sha256": "e5d1a5f8804ccccbb3b1d12b379721fd011df906cfa7bf5b33a925057092609b", - "output_contract": "output.schema.json", - "selection_scope": { - "vehicle_models": [ - "HYUNDAI|SONATA", - "KIA|OPTIMA" - ] - }, - "dependence_block": "nhtsa", - "split": "evaluation" + "question": "Discover a difference in recurrence or persistence following attempted remedy, supported by narrative evidence. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "make", + "model_year", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/nhtsa_group_difference_repair_persistence.jsonl.gz", + "corpus_sha256": "ac1dac2f19742f6fc514d43896e671cad3164506f93cbf0938a9ef5e12836383", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "nhtsa_group_difference_honda_cars", + "task_id": "nhtsa_group_difference_functional_impact", "source": "nhtsa", "kind": "group_difference", - "scope_name": "HONDA|ACCORD versus HONDA|CIVIC", - "comparison": { - "field": "comparison_group", - "groups": [ - "HONDA|ACCORD", - "HONDA|CIVIC" - ] - }, - "question": "Within HONDA|ACCORD versus HONDA|CIVIC, investigate substantive differences in reported experiences between HONDA|ACCORD and HONDA|CIVIC. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/nhtsa_group_difference_honda_cars.jsonl.gz", - "corpus_sha256": "353e7a9d740023b84c30f64d2e698023c1b406300f665991b38c567e0d3bbf72", - "output_contract": "output.schema.json", - "selection_scope": { - "vehicle_models": [ - "HONDA|ACCORD", - "HONDA|CIVIC" - ] - }, - "dependence_block": "nhtsa", - "split": "evaluation" + "question": "Find a non-obvious population difference in functional consequences rather than merely counting complaints. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "make", + "model_year", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/nhtsa_group_difference_functional_impact.jsonl.gz", + "corpus_sha256": "946156eadb1bb69752ff39405b27fe3f0c7e82e9aee3a7f9767b4bd872246d40", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "nhtsa_group_difference_subaru", + "task_id": "nhtsa_group_difference_adaptation_burden", "source": "nhtsa", "kind": "group_difference", - "scope_name": "SUBARU|OUTBACK versus SUBARU|FORESTER", - "comparison": { - "field": "comparison_group", - "groups": [ - "SUBARU|OUTBACK", - "SUBARU|FORESTER" - ] - }, - "question": "Within SUBARU|OUTBACK versus SUBARU|FORESTER, investigate substantive differences in reported experiences between SUBARU|OUTBACK and SUBARU|FORESTER. Discover which concrete situations distinguish them, quantify the contrast with both denominators, and examine counterexamples and differences in group composition. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 446, - "max_findings": 5, - "corpus_path": "corpora/nhtsa_group_difference_subaru.jsonl.gz", - "corpus_sha256": "84f69711cba99ed0c2774ee0517ebf1f0bbe01862c99d8df2de2ccb6542d6d92", - "output_contract": "output.schema.json", - "selection_scope": { - "vehicle_models": [ - "SUBARU|OUTBACK", - "SUBARU|FORESTER" - ] - }, - "dependence_block": "nhtsa", - "split": "evaluation" + "question": "Discover a difference in driver adaptation or workaround burdens and identify where the contrast stops generalizing. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "make", + "model_year", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/nhtsa_group_difference_adaptation_burden.jsonl.gz", + "corpus_sha256": "f163516638c93e7f6bc9c8dfd4e958b38549686856bc0b8f2b6f7e519587c42f", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "nhtsa_temporal_change_nissan", + "task_id": "nhtsa_temporal_change_experience_shift", "source": "nhtsa", "kind": "temporal_change", - "scope_name": "nissan vehicle complaints", - "comparison": { - "field": "timestamp", - "cutoff": "2022-03-10", - "groups": [ - "before", - "on_or_after" - ] - }, - "question": "Within nissan vehicle complaints, investigate changes in reported experiences before versus on/after 2022-03-10. Identify a specific changing pattern, quantify both period denominators, and examine whether entity, product, rating or reporting composition could explain it. Dates are reporting/review dates, not necessarily event or software-release dates. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/nhtsa_temporal_change_nissan.jsonl.gz", - "corpus_sha256": "be234fb7296cb7e4c148d67184fc2ac2fb5d653e8fc5485a5a6e8fad0a2da02c", - "output_contract": "output.schema.json", - "selection_scope": { - "make": "NISSAN" - }, - "dependence_block": "nhtsa", - "split": "evaluation" + "question": "Discover a meaningful shift in a specific reported failure experience and test vehicle-age or composition alternatives. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "make", + "model_year", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/nhtsa_temporal_change_experience_shift.jsonl.gz", + "corpus_sha256": "72e6a1fafb0d87ca4e1dee4e4ccaf536d350b0adf4df7aaab9e6b77039571701", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "nhtsa_temporal_change_ford_cars", + "task_id": "nhtsa_temporal_change_remedy_shift", "source": "nhtsa", "kind": "temporal_change", - "scope_name": "ford cars vehicle complaints", - "comparison": { - "field": "timestamp", - "cutoff": "2022-08-22", - "groups": [ - "before", - "on_or_after" - ] - }, - "question": "Within ford cars vehicle complaints, investigate changes in reported experiences before versus on/after 2022-08-22. Identify a specific changing pattern, quantify both period denominators, and examine whether entity, product, rating or reporting composition could explain it. Dates are reporting/review dates, not necessarily event or software-release dates. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/nhtsa_temporal_change_ford_cars.jsonl.gz", - "corpus_sha256": "8ddd37685dd177f0c7688342716f6e70db1e83f9454a4e98a90ce1a722667760", - "output_contract": "output.schema.json", - "selection_scope": { - "vehicle_models": [ - "FORD|FUSION", - "FORD|FOCUS" - ] - }, - "dependence_block": "nhtsa", - "split": "evaluation" + "question": "Find a temporal change in reported remedy or recurrence experiences; select the date boundary and justify its interpretation. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "make", + "model_year", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/nhtsa_temporal_change_remedy_shift.jsonl.gz", + "corpus_sha256": "7ec73a8e5510c0ef83b8c00e0cf27fd8e16500253d80a016d425176a4008ae48", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "nhtsa_temporal_change_honda_suvs", + "task_id": "nhtsa_temporal_change_context_shift", "source": "nhtsa", "kind": "temporal_change", - "scope_name": "honda suvs vehicle complaints", - "comparison": { - "field": "timestamp", - "cutoff": "2022-12-10", - "groups": [ - "before", - "on_or_after" - ] - }, - "question": "Within honda suvs vehicle complaints, investigate changes in reported experiences before versus on/after 2022-12-10. Identify a specific changing pattern, quantify both period denominators, and examine whether entity, product, rating or reporting composition could explain it. Dates are reporting/review dates, not necessarily event or software-release dates. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 500, - "max_findings": 5, - "corpus_path": "corpora/nhtsa_temporal_change_honda_suvs.jsonl.gz", - "corpus_sha256": "87ed498b8deec9d14d8249442d7bc0fce6199fa122a6a463d8e09191bbe314b3", - "output_contract": "output.schema.json", - "selection_scope": { - "vehicle_models": [ - "HONDA|CR-V", - "HONDA|PILOT" - ] - }, - "dependence_block": "nhtsa", - "split": "evaluation" + "question": "Discover a change in context-dependent functional consequences without treating complaint dates as failure incidence. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "make", + "model_year", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/nhtsa_temporal_change_context_shift.jsonl.gz", + "corpus_sha256": "fd27d7c2540e2995397d66cc8a2b0cd7ef183af31c52bc71eb2be8fcdfa146bb", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "nhtsa_compound_association_chevrolet", + "task_id": "nhtsa_compound_association_context_consequence", "source": "nhtsa", "kind": "compound_association", - "scope_name": "CHEVROLET vehicle complaints", - "comparison": null, - "question": "Within CHEVROLET vehicle complaints, discover a recurring association between two distinct reported experiences or events. Explain the context, quantify the joint table and conditional difference, inspect cases where only one occurs, and distinguish an informative association from a definition that makes co-occurrence inevitable. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 600, - "max_findings": 5, - "corpus_path": "corpora/nhtsa_compound_association_chevrolet.jsonl.gz", - "corpus_sha256": "4d6cd70af18f54a25e3dc58b7aaf822cffed08ae28710033820bb6f74c6c764f", - "output_contract": "output.schema.json", - "selection_scope": { - "make": "CHEVROLET" - }, - "dependence_block": "nhtsa", - "split": "evaluation" + "question": "Discover a relationship between an operating context and a distinct functional consequence; examine discordant reports. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "make", + "model_year", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/nhtsa_compound_association_context_consequence.jsonl.gz", + "corpus_sha256": "4fd5b24dadcd6cd471b744226b2e96150b6ceef0fab4efb03f6fbe8dc5f32969", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "nhtsa_compound_association_jeep", + "task_id": "nhtsa_compound_association_warning_response", "source": "nhtsa", "kind": "compound_association", - "scope_name": "JEEP vehicle complaints", - "comparison": null, - "question": "Within JEEP vehicle complaints, discover a recurring association between two distinct reported experiences or events. Explain the context, quantify the joint table and conditional difference, inspect cases where only one occurs, and distinguish an informative association from a definition that makes co-occurrence inevitable. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 600, - "max_findings": 5, - "corpus_path": "corpora/nhtsa_compound_association_jeep.jsonl.gz", - "corpus_sha256": "a4006b33adaa8c42755b801b17c9a054daa5c68570164f923beca76761422586", - "output_contract": "output.schema.json", - "selection_scope": { - "make": "JEEP" - }, - "dependence_block": "nhtsa", - "split": "evaluation" + "question": "Find an association between a warning experience and a separate driver or service response, avoiding circular definitions. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "make", + "model_year", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/nhtsa_compound_association_warning_response.jsonl.gz", + "corpus_sha256": "84088f8a3e51f786c9a9d392a9a0e750ca03c067117d4026385db52d3d0702ef", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "nhtsa_compound_association_toyota_refreshed", + "task_id": "nhtsa_compound_association_remedy_recurrence", "source": "nhtsa", "kind": "compound_association", - "scope_name": "TOYOTA vehicle complaints", - "comparison": null, - "question": "Within TOYOTA vehicle complaints, discover a recurring association between two distinct reported experiences or events. Explain the context, quantify the joint table and conditional difference, inspect cases where only one occurs, and distinguish an informative association from a definition that makes co-occurrence inevitable. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 600, - "max_findings": 5, - "corpus_path": "corpora/nhtsa_compound_association_toyota_refreshed.jsonl.gz", - "corpus_sha256": "77f8ebafd22360c54a0ebc3e7ac4670d18abadb87b3e95693d7a7e7cde14a629", - "output_contract": "output.schema.json", - "selection_scope": { - "make": "TOYOTA" - }, - "dependence_block": "nhtsa", - "split": "evaluation" + "question": "Discover a relationship between an attempted remedy and a separately defined persistence pattern without inferring treatment efficacy. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "make", + "model_year", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/nhtsa_compound_association_remedy_recurrence.jsonl.gz", + "corpus_sha256": "99af91ae51f88b6f536c26c18b3ce1c9b1a5dc4f55feb2c903f083d5f6c05052", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } }, { - "task_id": "nhtsa_compound_association_tesla", + "task_id": "nhtsa_compound_association_coupled_failures", "source": "nhtsa", "kind": "compound_association", - "scope_name": "TESLA vehicle complaints", - "comparison": null, - "question": "Within TESLA vehicle complaints, discover a recurring association between two distinct reported experiences or events. Explain the context, quantify the joint table and conditional difference, inspect cases where only one occurs, and distinguish an informative association from a definition that makes co-occurrence inevitable. Submit at most five evidence-backed findings or a justified abstention. Do not merely list common words, broad categories or generic sentiment. Account explicitly for unknown judgments. Do not infer causality or population incidence. Any analysis method is allowed.", - "n_documents": 600, - "max_findings": 5, - "corpus_path": "corpora/nhtsa_compound_association_tesla.jsonl.gz", - "corpus_sha256": "9ede3f7ba9663dd77b16ddc38488bd79653facdea1fa212c1cff5adf186a1ebe", - "output_contract": "output.schema.json", - "selection_scope": { - "make": "TESLA" - }, - "dependence_block": "nhtsa", - "split": "evaluation" + "question": "Find two distinct reported experiences whose association suggests a useful investigation priority, not a proven mechanical cause. Explore the complete supplied corpus before choosing a scope.\nSelect your own observable text condition(s), analysis population and comparison.\nExplain why the selection addresses the research objective, alternative patterns\nconsidered and search/selection bias. Return at most three nonredundant findings.\nFor each, partition every selected document into positive, negative or unknown\nfor each condition; compute the full statistics with textinsightbench.validation.expected.\nReport metadata-stratified contrasts, largest-stratum removal, support concentration,\nunknown sensitivity and missing-metadata limits. Provide exact quotations from at\nleast three supporting documents and a counterexample if known negative or\ndiscordant cases exist. Discuss competing explanations and where the finding fails.\nGeneric sentiment, metadata counts and restating the brief are insufficient.\nSame-corpus exploration is not independent confirmation or causal evidence.\nAbstain with a reason when no defensible finding meets these requirements.", + "difficulty": "discovery", + "discovery_mode": "agent_selected", + "max_findings": 3, + "allowed_metadata_fields": [ + "entity_id", + "state", + "make", + "model_year", + "timestamp", + "report_year" + ], + "min_population_n": 1000, + "min_group_n": 100, + "n_documents": 10000, + "corpus_path": "corpora/nhtsa_compound_association_coupled_failures.jsonl.gz", + "corpus_sha256": "aad2f74bc7125a4f3055800570c036b565c0e5f4825d0cbd01dfc733b54b32e3", + "robustness_protocol": { + "axes": [ + "entity_id", + "rating", + "report_year" + ], + "min_known_per_arm": 5 + } } ]