chain_summary dict | source_record dict | evidence_chain list | retrieval_evaluation_target dict |
|---|---|---|---|
{
"step_count": 4,
"unique_required_evidence_count": 5,
"dependency_depth": 4,
"chain_complete": true,
"evidence_sufficiency_rule": "必须覆盖线段相加、斜边高对应的两条直角边关系、正平方根取值以及三角形周长公式；只检索到斜边高定理但没有线段分割或周长公式时，不能支持完整解题链。"
} | {
"question_text": "在直角三角形ABC中，∠C=90°。从C向斜边AB作高CD，D在线段AB上。已知AD=9，DB=16。三角形ABC的周长是多少？",
"problem_category": "直角三角形、斜边高与线段关系",
"given_conditions": [
"三角形ABC为直角三角形，∠C=90°。",
"CD垂直于AB，且D在线段AB上。",
"AD=9。",
"DB=16。",
"AC和BC均为正长度。"
],
"answer_choices": [
"A. 54",
"B. 60",
"C. 64",
... | [
{
"step_key": "geo_s0",
"step_order": 0,
"step_goal": "由AD和DB求斜边AB。",
"prerequisite_step_keys": [],
"required_evidence_keys": [
"GEO-E1"
],
"required_evidence_roles": [
"线段分割相加规则"
],
"expected_conclusion": "AB=25。"
},
{
"step_key": "geo_s1",
"step_order": ... | {
"must_retrieve_all_required": true,
"relevant_evidence_keys": [
"GEO-E1",
"GEO-E2",
"GEO-E3",
"GEO-E4",
"GEO-E5"
],
"distractor_evidence_keys": [
"GEO-D1",
"GEO-D2",
"GEO-D3"
],
"coverage_unit": "步骤级必要证据和依赖分支的证据链级完整覆盖"
} |
{
"step_count": 4,
"unique_required_evidence_count": 6,
"dependency_depth": 4,
"chain_complete": true,
"evidence_sufficiency_rule": "只有同时覆盖LIN-E1至LIN-E6，并且证据适用于对应步骤，才能完成建模、代入、求解和验证；只检索到线性方程公式而缺少变量定义、代入规则或验证规则时，证据链不完整。"
} | {
"question_text": "某学校售出成人票和学生票。购买2张成人票和3张学生票共需33美元，购买4张成人票和1张学生票共需31美元。若成人票价格为a美元、学生票价格为s美元，成人票价格是多少？",
"problem_category": "线性方程组与实际情境建模",
"given_conditions": [
"成人票价格为a美元，学生票价格为s美元。",
"2张成人票和3张学生票的总价为33美元。",
"4张成人票和1张学生票的总价为31美元。",
"a和s均为正数。"
],
"answer_choices": [
"A. 6美元",
"B. 7美... | [
{
"step_key": "lin_s0",
"step_order": 0,
"step_goal": "定义未知价格并建立两元一次方程组。",
"prerequisite_step_keys": [],
"required_evidence_keys": [
"LIN-E1",
"LIN-E2"
],
"required_evidence_roles": [
"未知量定义",
"数量关系建模"
],
"expected_conclusion": "得到2a+3s=33和4a+s=31。"
},
... | {
"must_retrieve_all_required": true,
"relevant_evidence_keys": [
"LIN-E1",
"LIN-E2",
"LIN-E3",
"LIN-E4",
"LIN-E5",
"LIN-E6"
],
"distractor_evidence_keys": [
"LIN-D1",
"LIN-D2",
"LIN-D3"
],
"coverage_unit": "步骤级必要证据以及整条证据链的完整覆盖"
} |
{
"step_count": 5,
"unique_required_evidence_count": 5,
"dependency_depth": 5,
"chain_complete": true,
"evidence_sufficiency_rule": "必须覆盖指数模型、同底幂相除、正数约束下的平方根、系数反解和目标函数求值五类证据；仅检索到指数函数定义或幂运算公式，不能支持从两个已知函数值确定参数并完成最终求值。"
} | {
"question_text": "函数f(x)=a·b^x，其中a和b均为正数。已知f(1)=12且f(3)=48。f(5)的值是多少？",
"problem_category": "指数函数与指数运算",
"given_conditions": [
"f(x)=a·b^x。",
"a>0且b>0。",
"f(1)=12。",
"f(3)=48。"
],
"answer_choices": [
"A. 96",
"B. 144",
"C. 192",
"D. 384"
],
"correct_answer": "C",
"candi... | [
{
"step_key": "exp_s0",
"step_order": 0,
"step_goal": "根据指数函数模型展开两个已知函数值。",
"prerequisite_step_keys": [],
"required_evidence_keys": [
"EXP-E1"
],
"required_evidence_roles": [
"指数函数模型与参数条件"
],
"expected_conclusion": "得到a·b=12和a·b^3=48。"
},
{
"step_key": "exp_s1... | {
"must_retrieve_all_required": true,
"relevant_evidence_keys": [
"EXP-E1",
"EXP-E2",
"EXP-E3",
"EXP-E4",
"EXP-E5"
],
"distractor_evidence_keys": [
"EXP-D1",
"EXP-D2",
"EXP-D3"
],
"coverage_unit": "按推理步骤评估必要证据覆盖，并要求整条参数识别到目标求值的证据链完整"
} |
SAT Multi-Step Math Evidence Retrieval Benchmark
This benchmark contains SAT math problems requiring multi-step derivations, a candidate evidence library of concepts and formulas, and annotations identifying the evidence needed at each step along with complete evidence chains. It assesses whether retrieval-augmented generation systems retrieve knowledge that is both complete across the solution steps and applicable to the problem, rather than merely returning one related formula. It supports SAT math knowledge retrieval, evidence-chain evaluation, and the development of retrieval-augmented generation systems.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| chain_summary | object | Calculated summary statistics for the multi-step evidence chain, including its size, dependency depth, and completeness. |
| source_record | object | The original record supplied during dataset creation, containing the problem, candidate evidence library, and complete step-by-step annotations. |
| evidence_chain | array | An ordered evidence chain derived from the original step annotations, representing the complete and applicable knowledge required at each reasoning step. |
| retrieval_evaluation_target | object | A RAG retrieval evaluation target derived from the reference evidence chain, emphasizing evidence-set completeness and applicability. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Source & Contact
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