# SearchJev Training Data

**The official training data of [SearchJev](https://github.com/EvoScientist/SearchJev): A Fast and Calibrated System-1 Model for Search Agents.**

[Paper](https://arxiv.org/abs/2610.05107) · [Code](https://github.com/EvoScientist/SearchJev) · [Models](https://huggingface.co/collections/SearchJev/searchjev) · [Citation](#citation)

**Language / 语言：** [English](#english) | [中文](#中文)

---

<a id="english"></a>

## English

### Overview

This dataset is the training data used to build **SearchJev**, a discriminative System-1 model that makes fast, calibrated judgments for search agents. Instead of generating free text, SearchJev reads a *state* (a query, a search-results page, retrieved evidence, or a partial agent trajectory) together with a set of *typed questions*, and returns a structured answer with a probability distribution for each question. Typical questions include whether to keep searching or answer now, which result is worth opening, whether the evidence is sufficient, which follow-up query to issue, and whether a document is relevant, timely and authoritative.

The dataset unifies 73 Chinese and English sources (public benchmarks, search-agent trajectories, mined hard negatives and purpose-built synthetic scenarios) into a single format, with group-aware splits and per-question loss weights for multi-task training.

- **Paper:** https://arxiv.org/abs/2610.05107
- **Code:** https://github.com/EvoScientist/SearchJev
- **Models:** https://huggingface.co/collections/SearchJev/searchjev
- **Release version:** v3.0.0 (2026-10-02). The directory name `search_jev_unified_v2_release_20260928` is kept for compatibility; the authoritative version is `release_version` in `manifest.json`.

### Highlights

- **Search-decision focused.** 38 capabilities organised into 8 decision buckets, from next-action and stop-search decisions to query–document relevance, evidence verification and query understanding.
- **One schema for every task.** Each row is a `state` plus one or more typed questions (`noul` / `choice` / `score`) with reference answers, so heterogeneous tasks can be trained and evaluated jointly.
- **Leakage-controlled splits.** Rows that share a mother sample, a translation or mining parent, or an identical input are grouped and assigned to a single split (test > val > train). Cross-split groups and exact duplicate inputs are both zero.
- **Training-ready metadata.** A line-aligned index file carries per-question loss weights, evaluation eligibility, label provenance, decision bucket and a full-state token pre-check (every retained question fits in 6,144 tokens with the Qwen3 tokenizer).
- **Bilingual.** About 64.5% of training rows are Chinese-dominant and 35.4% are Latin-script-dominant.

### Statistics

| Split | Rows | Questions |
|---|---:|---:|
| train | 467,368 | 646,664 |
| val | 138,321 | 172,221 |
| test | 189,708 | 229,084 |
| **Total** | **795,397** | **1,047,969** |

Additional evaluation resources:

| Resource | Size | Purpose |
|---|---:|---|
| Model-selection set | 5,000 rows | Checkpoint selection; covers 66 validation source identities and all 38 capabilities, one row per mother group |
| Calibration set | 2,000 rows | Reserved for probability calibration (temperature / thresholds); disjoint from the model-selection set and test groups |

### Task Taxonomy

Every row belongs to one decision bucket. The loss share is the recommended share of total training loss, implemented by the per-bucket multipliers in `training_config.json`.

| Bucket | Description | Train rows | Loss share |
|---|---|---:|---:|
| A | Search actions: next action, stop vs. continue, marginal gain, next-hop query selection | 120,025 | 40% |
| B1 | Query–document quality: timeliness, authority, user satisfaction | 61,221 | 10% |
| B2 | Query–document relevance and ranking | 55,376 | 12% |
| C | Evidence and fact verification | 41,288 | 12% |
| D1 | Sentence-pair and rewrite equivalence | 65,465 | 8% |
| D2 | Query assessment: search need, information content, noise | 42,729 | 8% |
| D3 | Query intent classification | 29,737 | 4% |
| E | General understanding: sentiment, NLI, topic, preference | 51,527 | 6% |

The 38 capabilities are: `action_imitation`, `clarify_decision`, `contextual_rewrite`, `coreference`, `emotion`, `evidence`, `evidence_sufficiency`, `evidence_sufficiency_state`, `failure_recovery`, `fresh_serp`, `intent`, `intent_equivalence`, `keyword_match`, `logic_mc`, `marginal_gain`, `next_query`, `next_query_zh`, `nli`, `preference_pair`, `qd_quality`, `query_classification`, `query_fit`, `query_info`, `query_need`, `query_noise`, `reading_mc`, `relevance`, `response_quality`, `retrieval_gain`, `review_rating`, `rewrite`, `satisfaction`, `sentiment_binary`, `serp_select`, `source_time`, `state_action`, `stop_boundary`, `topic_classification`.

### Synthetic Search-Decision Scenarios

To cover decisions that public datasets rarely label, the dataset includes 27,183 rows of purpose-built synthetic scenarios (`input_origin = synth_v1`) and 10,109 rows of multi-dimensional quality data (`synth_tas`).

| Track | Scenario | train | val | test |
|---|---|---:|---:|---:|
| T1 | State-conditioned action: evidence sufficiency, conflict, budget, next step | 2,710 | 165 | 185 |
| T2 | SERP selection: which results are worth opening | 494 | 28 | 32 |
| T3 | Marginal gain: new information, redundant evidence, conflict, freshness | 1,802 | 117 | 124 |
| T4 | Next-hop query selection (Chinese candidates) | 3,663 | 440 | 366 |
| T5 | Source and time: authority and timeliness | 3,762 | 265 | 293 |
| T6 | Failure recovery: failed searches or page reads, unusable results, injected content | 5,297 | 313 | 366 |
| T7 | Contextual rewrite: intent preservation in multi-turn dialogue | 1,320 | 317 | 271 |
| T10 | Stop boundary: sufficient, outdated, conflicting and redundant evidence | 378 | 28 | 20 |
| T11 | Clarification: when to ask the user and what to do next | 3,744 | 374 | 309 |

See `SYNTHETIC_SCENARIOS.md` for details.

### Data Sources

| Family | Representative sources |
|---|---|
| Retrieval and relevance | T2Ranking, DuRetrieval, Multi-CPR (e-commerce / medical / video), QBQTC, KUAKE-QQR, LCQMC, BQ Corpus, ATEC, AFQMC, CHIP-STS, PAWS-X (zh), QReCC, dialogue rewrite equivalence, query–document quality annotations |
| Evidence and verification | VitaminC, CFEVER, TrendFact, RAGTruth |
| Search decisions | Search-agent trajectories, KUAKE-QIC, Search Arena, AgentRewardBench |
| Search hard negatives | Mined hard negatives for gain, next hop, query fit and sufficiency, including Chinese translations and a harder subset |
| Query understanding | CLINC150, Banking77, MASSIVE (zh), query information / search-need / noise assessment |
| General understanding | CLUE (CMNLI, OCNLI, TNEWS, CSL, WSC, C3), LogiQA (zh), ChnSentiCorp, Weibo Senti 100k, Online Shopping 10 Cats, THUCNews titles, Amazon Reviews Multi (zh), Douban and JD reviews, multi-turn emotion dialogue, COIG-P, CValues, HelpSteer2 / HelpSteer3 |
| Synthetic | Search-decision scenarios T1–T11 and multi-dimensional quality data (see above) |

Some sources (e.g. Search Arena, AgentRewardBench, RAGTruth, the QReCC test split) appear only in the test split and serve as held-out evaluation.

### Data Format

Each line of `{train,val,test}.jsonl` is one sample: a `state`, a dictionary of `questions`, the reference `answers`, and a `difficulty` level.

```json
{
  "state": {
    "premise": "我走到近前,见是一位工人模样的中年妇女",
    "hypothesis": "我看见了一位妇女。"
  },
  "questions": {
    "nli": {
      "type": "choice",
      "instructions": "前提能不能撑起假设？还是矛盾或无关？",
      "criteria": {
        "entailment": "蕴含（可由前提推出）",
        "neutral": "无关（前提不能确定）",
        "contradiction": "矛盾（与前提冲突）"
      }
    }
  },
  "answers": {
    "nli": {
      "type": "choice",
      "choice": "entailment",
      "confidence": 1.0,
      "probabilities": {"entailment": 1.0, "neutral": 0.0, "contradiction": 0.0}
    }
  },
  "difficulty": 3
}
```

**Question types**

| Type | Meaning | `criteria` | Answer |
|---|---|---|---|
| `noul` | Is the proposition true? | optional `{"true": ..., "false": ...}` | `noul`: probability of true |
| `choice` | Pick one option from a fixed set | `{option: description}` | `choice`, `confidence`, `probabilities` over all options |
| `score` | Ordinal rating with 2–10 levels | list of level descriptions, low to high | `score` (expected level), `confidence`, `probabilities` keyed by level |

`state` may be a string, a JSON object or a list. A row may contain several questions about the same state; each question is answered independently.

**Index files.** `{split}_index.jsonl` is aligned line by line with `{split}.jsonl` and holds metadata. Key fields:

| Field | Description |
|---|---|
| `sample_id`, `source`, `input_origin` | Sample identifier and provenance |
| `capability`, `task_family`, `decision_bucket` | Task taxonomy |
| `training_lane`, `supervision`, `weak`, `label_tier`, `target_provenance` | Label provenance and supervision strength |
| `loss_weight`, `row_loss_scale` | Per-question loss weights and row-level scale |
| `evaluation_eligible`, `eval_track` | Whether each question is used in evaluation, and its evaluation track |
| `gold`, `confidence`, `teacher_reference_probability` | Reference answers and auxiliary teacher probabilities |
| `canonical_group`, `group_keys` | Grouping keys used for leakage-free splitting |
| `language_heuristic`, `difficulty`, `token_preflight` | Language, difficulty and token-length statistics |

### Files

| File | Description |
|---|---|
| `train.jsonl`, `val.jsonl`, `test.jsonl` | Main data |
| `train_index.jsonl`, `val_index.jsonl`, `test_index.jsonl` | Line-aligned metadata |
| `model_selection.jsonl`, `model_selection_index.jsonl` | Fixed checkpoint-selection set |
| `calibration_blind.jsonl`, `calibration_reference.jsonl` | Calibration set: questions without answers, and the corresponding reference labels |
| `training_config.json` | Reference training configuration (Qwen3-1.7B, LoRA, bucket multipliers) |
| `budget.json` | Loss budget by bucket |
| `manifest.json` | Release metadata and SHA-256 checksums |
| `data_report.json`, `DATA_REPORT.md` | Statistics and detailed construction report |
| `SYNTHETIC_SCENARIOS.md` | Synthetic scenario documentation |
| `VERSION_HISTORY.md`, `VERSION_HISTORY.json` | Version history |
| `changes.jsonl` | Per-row change log relative to the previous release |

### Usage

```python
import json

with open("train.jsonl", encoding="utf-8") as f_data, \
     open("train_index.jsonl", encoding="utf-8") as f_index:
    for line, meta_line in zip(f_data, f_index):
        sample, meta = json.loads(line), json.loads(meta_line)
        for qid, question in sample["questions"].items():
            answer = sample["answers"][qid]
            weight = meta["loss_weight"][qid] * meta["row_loss_scale"]
            ...
```

For training, we recommend one question per forward pass (independent context), weighting each question by `loss_weight × row_loss_scale × bucket multiplier`. The SearchJev repository provides a reference training and evaluation pipeline that reads this directory directly.

### Construction and Quality Control

- **Unified schema.** All sources are mapped to the state / typed-question / answer format; instructions are diversified to prevent template overfitting.
- **Label cleaning.** Questions with suspected or disputed labels, unadjudicated negatives, unresolved rubric mappings, or shortcut features (e.g. future actions visible in the input) are removed from supervision rather than relabelled automatically.
- **Leakage control.** Group-aware splitting over mother samples, translation and mining parents, and identical inputs; exact duplicate inputs are removed.
- **Length control.** Every retained question is checked against a 6,144-token budget with its full state. Over-length questions are removed as a whole instead of being truncated.
- **Loss budgeting.** Bucket multipliers are computed so the realised loss shares match the target A/B1/B2/C/D1/D2/D3/E = 40/10/12/12/8/8/4/6%.

### Limitations

- Targets are hard reference labels derived from the original annotations, demonstrations or construction rules. The `confidence = 1.0` in answers encodes the target and is not an empirical accuracy; calibrated probabilities should be fitted on the calibration set.
- Labels in the action-imitation track describe what a demonstrator did, not necessarily the optimal policy.
- Bucket A is still largely built from derived English multi-hop data; Chinese rows make up about 19% of bucket A in train.
- The model-selection set is stratified and should not be read as an estimate of accuracy on the natural distribution.

### License

This dataset is derived from multiple public datasets and synthetic data. Each subset remains subject to the license and terms of use of its original source. Please consult the upstream licenses before use. The dataset is intended for research purposes.

<a id="citation"></a>

### Citation

If you use this dataset, please cite:

```bibtex
@misc{cao2026searchjev,
  title  = {{SearchJev}: A Fast and Calibrated System-1 Model for Search Agents},
  author = {Cao, Congfeng and Zuo, Lipeng and Papakostas, Konstantinos and Xu, Qiwei and Xu, Songwei and
            Zhou, Lun and Ren, Zhaochun and Lyu, Yougang and Yan, Xiaohui},
  year   = {2026}
}
```

---

<a id="中文"></a>

## 中文

### 概述

本数据集是 **[SearchJev](https://github.com/EvoScientist/SearchJev)** 的官方训练数据。SearchJev 是面向搜索智能体的快速、可校准的 System-1 判别模型：它不生成自由文本，而是读取一份*状态*（查询、搜索结果页、检索证据或智能体的部分轨迹）和一组*类型化问题*，为每个问题输出结构化答案及概率分布。典型问题包括：继续搜索还是直接作答、哪条结果值得打开、证据是否充分、下一跳应发起什么查询、文档是否相关、及时和权威等。

数据集将 73 个中英文来源（公开基准、搜索智能体轨迹、挖掘的难负例以及专门构造的合成场景）统一为同一格式，采用按组切分，并提供题级损失权重，可直接用于多任务训练。

- **论文：** https://arxiv.org/abs/2610.05107
- **代码：** https://github.com/EvoScientist/SearchJev
- **模型：** https://huggingface.co/collections/SearchJev/searchjev
- **发布版本：** v3.0.0（2026-10-02）。目录名 `search_jev_unified_v2_release_20260928` 为兼容而保留，版本以 `manifest.json` 中的 `release_version` 为准。

### 主要特点

- **聚焦搜索决策。** 38 项能力归入 8 个决策桶，覆盖下一步动作与停止搜索决策、查询–文档相关性、证据核查和查询理解等。
- **统一格式。** 每行由一个 `state` 和一道或多道类型化问题（`noul` / `choice` / `score`）及参考答案组成，异构任务可联合训练与评测。
- **防泄漏切分。** 共享母本、翻译或挖掘父本、相同输入的样本归为一组，整组划入同一切分（优先级 test > val > train）。跨切分组与完全重复输入均为 0。
- **开箱即用的元数据。** 逐行对齐的索引文件提供题级损失权重、评测资格、标签来源、决策桶及完整状态长度预检（全部保留题在 Qwen3 分词器下不超过 6,144 token）。
- **中英双语。** 训练集中约 64.5% 的行以中文为主，35.4% 以拉丁字母文本为主。

### 数据规模

| 切分 | 行数 | 题数 |
|---|---:|---:|
| train | 467,368 | 646,664 |
| val | 138,321 | 172,221 |
| test | 189,708 | 229,084 |
| **合计** | **795,397** | **1,047,969** |

附加评测资源：

| 资源 | 规模 | 用途 |
|---|---:|---|
| 模型选择集 | 5,000 行 | 用于选择 checkpoint；覆盖验证集 66 个来源身份及全部 38 项能力，每个母本组 1 行 |
| 校准集 | 2,000 行 | 预留用于概率校准（temperature / 阈值）；与模型选择集和测试集母本组不重叠 |

### 任务体系

每行归属一个决策桶。损失份额为各桶在总训练损失中的推荐占比，由 `training_config.json` 中的桶乘子实现。

| 桶 | 说明 | 训练行数 | 损失份额 |
|---|---|---:|---:|
| A | 搜索动作：下一步动作、停止/继续搜索、边际增益、下一跳查询选择 | 120,025 | 40% |
| B1 | 查询–文档质量：时效性、权威性、用户满意度 | 61,221 | 10% |
| B2 | 查询–文档相关性与排序 | 55,376 | 12% |
| C | 证据与事实核查 | 41,288 | 12% |
| D1 | 句对与改写等价 | 65,465 | 8% |
| D2 | 查询评估：检索需求、信息量、噪声 | 42,729 | 8% |
| D3 | 查询意图分类 | 29,737 | 4% |
| E | 通用理解：情感、自然语言推理、主题、偏好 | 51,527 | 6% |

38 项能力的完整列表见英文部分。

### 搜索决策合成场景

为覆盖公开数据集中少有标注的决策环节，数据集包含 27,183 行专门构造的合成场景（`input_origin = synth_v1`），以及 10,109 行多维质量数据（`synth_tas`）。

| 轨道 | 场景 | train | val | test |
|---|---|---:|---:|---:|
| T1 | 状态条件化动作：证据充分性、冲突、预算与下一步行动 | 2,710 | 165 | 185 |
| T2 | SERP 选读：选择值得打开的搜索结果 | 494 | 28 | 32 |
| T3 | 边际增益：新增信息、重复证据、冲突与时效变化 | 1,802 | 117 | 124 |
| T4 | 下一跳查询：中文候选查询选择 | 3,663 | 440 | 366 |
| T5 | 来源与时间：权威性与时效性判断 | 3,762 | 265 | 293 |
| T6 | 失败恢复：搜索或读页失败、结果不可用、注入内容 | 5,297 | 313 | 366 |
| T7 | 上下文改写：多轮对话中的意图保持 | 1,320 | 317 | 271 |
| T10 | 停止边界：充分、过时、冲突与冗余证据 | 378 | 28 | 20 |
| T11 | 澄清决策：何时向用户澄清及下一步行动 | 3,744 | 374 | 309 |

详见 `SYNTHETIC_SCENARIOS.md`。

### 数据来源

| 类别 | 代表性来源 |
|---|---|
| 检索与相关性 | T2Ranking、DuRetrieval、Multi-CPR（电商/医疗/视频）、QBQTC、KUAKE-QQR、LCQMC、BQ Corpus、ATEC、AFQMC、CHIP-STS、PAWS-X（中文）、QReCC、对话改写等价、查询–文档质量标注 |
| 证据与核查 | VitaminC、CFEVER、TrendFact、RAGTruth |
| 搜索决策 | 搜索智能体轨迹、KUAKE-QIC、Search Arena、AgentRewardBench |
| 搜索难负例 | 面向增益、下一跳、查询匹配与充分性的挖掘难负例，含中文翻译版与高难度子集 |
| 查询理解 | CLINC150、Banking77、MASSIVE（中文）、查询信息量/检索需求/噪声评估 |
| 通用理解 | CLUE（CMNLI、OCNLI、TNEWS、CSL、WSC、C3）、LogiQA（中文）、ChnSentiCorp、Weibo Senti 100k、Online Shopping 10 Cats、THUCNews 标题、Amazon Reviews Multi（中文）、豆瓣与京东评论、多轮情感对话、COIG-P、CValues、HelpSteer2 / HelpSteer3 |
| 合成数据 | 搜索决策场景 T1–T11 及多维质量数据（见上） |

部分来源（如 Search Arena、AgentRewardBench、RAGTruth、QReCC 测试集）仅出现在 test 中，作为留出评测使用。

### 数据格式

`{train,val,test}.jsonl` 每行一个样本，包含 `state`、`questions`（问题字典）、`answers`（参考答案）和 `difficulty`（难度等级），示例见英文部分。

**题型**

| 类型 | 含义 | `criteria` | 答案 |
|---|---|---|---|
| `noul` | 命题是否为真 | 可选 `{"true": ..., "false": ...}` | `noul`：为真的概率 |
| `choice` | 从固定选项中单选 | `{选项: 描述}` | `choice`、`confidence`、全部选项上的 `probabilities` |
| `score` | 2–10 档有序打分 | 档位描述列表（低 → 高） | `score`（期望档位）、`confidence`、按档位的 `probabilities` |

`state` 可以是字符串、JSON 对象或列表。一行可包含针对同一状态的多道问题，每道题独立作答。

**索引文件。** `{split}_index.jsonl` 与 `{split}.jsonl` 逐行对齐，保存元数据。主要字段：

| 字段 | 说明 |
|---|---|
| `sample_id`、`source`、`input_origin` | 样本标识与来源 |
| `capability`、`task_family`、`decision_bucket` | 任务分类 |
| `training_lane`、`supervision`、`weak`、`label_tier`、`target_provenance` | 标签来源与监督强度 |
| `loss_weight`、`row_loss_scale` | 题级损失权重与行级缩放 |
| `evaluation_eligible`、`eval_track` | 各题是否参与评测及所属评测轨道 |
| `gold`、`confidence`、`teacher_reference_probability` | 参考答案与辅助教师概率 |
| `canonical_group`、`group_keys` | 防泄漏切分所用的分组键 |
| `language_heuristic`、`difficulty`、`token_preflight` | 语言、难度与长度统计 |

### 文件说明

| 文件 | 说明 |
|---|---|
| `train.jsonl`、`val.jsonl`、`test.jsonl` | 主数据 |
| `train_index.jsonl`、`val_index.jsonl`、`test_index.jsonl` | 逐行对齐的元数据 |
| `model_selection.jsonl`、`model_selection_index.jsonl` | 固定的 checkpoint 选择集 |
| `calibration_blind.jsonl`、`calibration_reference.jsonl` | 校准集：不含答案的题目及对应参考标签 |
| `training_config.json` | 参考训练配置（Qwen3-1.7B、LoRA、桶乘子） |
| `budget.json` | 各桶损失预算 |
| `manifest.json` | 发布元数据与 SHA-256 校验值 |
| `data_report.json`、`DATA_REPORT.md` | 统计数据与详细构建报告 |
| `SYNTHETIC_SCENARIOS.md` | 合成场景说明 |
| `VERSION_HISTORY.md`、`VERSION_HISTORY.json` | 版本记录 |
| `changes.jsonl` | 相对上一版本的逐行变更记录 |

### 使用方法

读取方式见英文部分的 Python 示例。训练时建议每次前向只放一道题（独立上下文），每题损失权重为 `loss_weight × row_loss_scale × 桶乘子`。SearchJev 代码仓库提供了可直接读取本目录的参考训练与评测流程。

### 构建与质量控制

- **统一格式。** 所有来源映射为「状态 / 类型化问题 / 答案」格式，并对题目指令做多样化改写，避免模板过拟合。
- **标签清洗。** 疑似错误或存在争议的标签、未经判定的负例、口径未明的评分映射以及存在捷径特征（如输入中可见未来动作）的题目一律移出监督，不依据模型投票自动改标。
- **防泄漏。** 基于母本、翻译与挖掘父本及相同输入做分组切分，并去除完全重复的输入。
- **长度控制。** 每道保留题均以完整状态做 6,144 token 预检，超长题整题移除而非截断证据。
- **损失预算。** 按桶计算乘子，使实际损失份额与目标 A/B1/B2/C/D1/D2/D3/E = 40/10/12/12/8/8/4/6% 一致。

### 局限性

- 训练目标为源自原始标注、示范行为或构造规则的硬参考标签。答案中的 `confidence = 1.0` 仅表示目标编码，不代表实际准确率；校准概率应在校准集上拟合。
- 动作模仿类题目的标签表示示范者的实际行为，不一定是最优策略。
- A 桶仍主要来自派生的英文多跳数据，训练集中 A 桶的中文行约占 19%。
- 模型选择集为分层抽样，不能作为自然分布下准确率的估计。

### 许可

本数据集由多个公开数据集及合成数据加工而成，各子集仍受其原始来源的许可与使用条款约束，使用前请查阅相应上游许可。本数据集仅供研究使用。

### 引用

如使用本数据集，请引用：

```bibtex
@misc{cao2026searchjev,
  title  = {{SearchJev}: A Fast and Calibrated System-1 Model for Search Agents},
  author = {Cao, Congfeng and Zuo, Lipeng and Papakostas, Konstantinos and Xu, Qiwei and Xu, Songwei and
            Zhou, Lun and Ren, Zhaochun and Lyu, Yougang and Yan, Xiaohui},
  year   = {2026}
}
```