244 lines
12 KiB
JSON
244 lines
12 KiB
JSON
{
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"model_id": "qpi",
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"model_name": "QPI 三元定性模型",
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"model_type": "routing_model",
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"pipeline_position": "pre_analysis",
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"one_sentence_definition": "QPI 用于判断某个认知主体在特定上下文中，如何将期望—现实落差框定为 Question、Problem、Issue、mixed 或 no-call，并识别其中的上下文缺口、动态演化和误框定风险。",
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"core_question": "谁在什么场景下，把什么期望—现实落差框定成了哪类问题；这个框定是否缺上下文、正在演化或存在误框定风险？",
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"core_mechanism": "先执行 no-call gate，再检查主体位置、场景上下文、责任范围、经验水平、时间尺度、成功标准和硬反馈可用性；在上下文不足时输出 provisional classification 和 missing_context，不做高置信定性；随后抽取期望—现实落差、三类匮乏物、动态阶段、治理负荷和误框定风险，区分同一框架内的 mixed 与跨主体视角分歧。",
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"status": "draft",
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"source_articles": [
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"article_qpi_primary_001",
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"article_qpi_contextual_2025_001",
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"article_cognitive_os_synthesis_001"
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],
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"source_evidence": [
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"excerpt_qpi_primary_spectrum_001",
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"excerpt_qpi_primary_question_001",
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"excerpt_qpi_primary_problem_001",
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"excerpt_qpi_primary_issue_001",
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"excerpt_qpi_primary_pathology_reduction_001",
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"excerpt_qpi_primary_pathology_inflation_001",
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"excerpt_qpi_contextual_subjectivity_001",
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"excerpt_qpi_contextual_gap_001",
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"excerpt_qpi_contextual_dynamic_001",
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"excerpt_qpi_cognitive_os_application_001"
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],
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"input_types": [
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"模糊问题",
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"复杂问题",
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"业务问题描述",
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"组织冲突叙述",
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"需求或故障初始表述",
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"需要进入深度分析前的问题定义"
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],
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"output_types": [
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"问题类型判断",
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"分类作用域",
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"是否临时判断",
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"主体位置",
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"场景上下文",
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"责任范围",
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"经验水平",
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"问题拥有者",
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"问题来源",
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"时间尺度",
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"期望现实落差",
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"上下文充足度",
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"缺失上下文",
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"三类匮乏物强度",
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"主导匮乏物",
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"分类理由",
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"分类置信度",
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"多视角分类",
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"动态阶段",
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"可能演化轨迹",
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"成功标准稳定性",
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"硬反馈可用性",
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"治理负荷",
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"澄清问题",
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"证据缺口",
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"核心匮乏物判断",
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"误判风险",
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"推荐处理路径",
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"后续模型调用建议"
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],
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"call_when": [
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"用户输入的问题定义不清。",
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"同一问题被不同主体以不同方式理解。",
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"用户急于求解但尚未判断问题性质。",
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"问题中混杂事实、路径、利益、共识和系统结构。",
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"需要决定后续调用哪些认知模型。",
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"怀疑存在暴力降维、恶意升维或手段与目标错配。"
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],
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"do_not_call_when": [
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"用户只是查询明确事实。",
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"用户已经给出明确执行任务。",
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"输入是纯创意写作且不需要问题定性。",
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"问题已经被清楚分类且只需要执行后续步骤。",
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"输入不是问题定义，而是纯粹的润色或格式转换请求。"
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],
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"trigger_keywords": [
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"问题到底是什么",
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"为什么解决不了",
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"卡在哪里",
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"技术问题还是组织问题",
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"方向是不是错了",
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"为什么反复出现",
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"该怎么定义这个问题",
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"这个问题怎么拆",
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"缺数据",
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"缺路径",
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"缺共识"
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],
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"negative_triggers": [
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"请直接查询",
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"请直接改写",
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"只要清单",
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"不要分析原因",
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"不要展开",
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"只改错别字"
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],
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"related_models": [
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"intellectual_archaeology"
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],
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"conflicting_models": [],
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"disciplinary_anchors": [
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"问题定义理论",
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"决策科学",
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"系统论",
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"复杂系统",
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"组织行为",
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"工程问题分解"
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],
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"common_misuses": [
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"把 QPI 当作完整解决方案，而不是问题定义和路由模型。",
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"把所有复杂叙事都归为课题，逃避可执行的难题拆解。",
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"把系统性课题暴力压缩成个人执行难题。",
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"把具体可解决的难题恶意升维成不可处理的课题。",
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"在事实缺口上过度建模，拖慢本应直接检索的提问。",
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"忽略问题拥有者，直接给出单一视角的分类。"
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,
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"把缺少主体和场景的复杂短句直接判成高置信 Q/P/I。",
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"把跨主体视角分歧误写成同一主体内的 mixed。"
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],
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"failure_modes": [
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"核心匮乏物识别错误，导致后续资源配置错位。",
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"未识别权力叙事，接受了伪问题或甩锅框架。",
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"把动态平衡型课题误判为可终结的难题。",
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"把明确工程障碍误判为需要无限讨论的课题。",
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"分类没有给出理由。",
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"分类之后没有给出后续处理路径。"
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,
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"缺少 subject_position 或 scenario_context 时仍输出 high confidence。",
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"只按表层关键词定性，忽略责任范围、成功标准、硬反馈和治理负荷。"
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],
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"selection_priority": 9,
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"confidence_level": "medium",
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"stability_profile": {
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"stability_level": "B",
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"needs_stabilization": true,
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"main_risks": [
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"Q/P/I 在真实复杂案例中可能同时存在，需要先区分同一主体场景内的 mixed 与不同主体视角导致的 inter_viewpoint_divergence。",
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"权力博弈判断依赖上下文，证据不足时不能断言动机。",
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"边界案例仍需用真实业务输入扩展。",
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"主体性、上下文充足度、动态演化和治理负荷需要继续通过 owner 原始案例校准。"
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],
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"reason": "三分结构清晰，适合作为入口路由模型，但需要补充大量边界案例，防止过度升维或降维。",
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"next_stabilization_action": "补充主体-上下文-动态案例、混合问题、误用问题和轻量问题测试，并记录主导匮乏物与多视角分歧判断标准。",
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"stabilization_notes": "当前已具备 v0.1 调用基础，但仍保持 draft 状态。"
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},
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"regression_status": "pending",
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"example_inputs": [
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"我们团队每次都说要长期主义，但一到季度 KPI 就回到短期动作，这到底是什么问题？",
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"我知道要做模型库，但不知道先用数据库还是文件系统，这属于什么问题？"
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],
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"example_outputs": [
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"第一个输入更接近课题，因为它涉及激励结构、时间尺度冲突和组织共识。",
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"第二个输入更接近难题，因为目标明确，主要缺的是实现路径和技术取舍。"
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],
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"output_contract": [
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"classification_scope: subject_contextual | multi_perspective | insufficient_context | no_call。",
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"is_provisional: true | false；缺少主体或场景时必须为 true。",
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"subject_position: 当前认知主体的位置；未知时写 unknown。",
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"scenario_context: 问题发生的场景；未知时写 unknown。",
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"responsibility_scope: individual_learning | team_execution | cross_function_governance | owner_governance | unknown。",
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"context_sufficiency: high | medium | low。",
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"missing_context: 缺少哪些主体、场景、目标、资源、责任或时间信息。",
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"problem_owner: 谁的问题；未知时写 unknown。",
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"problem_source: 问题从哪里被提出，是否可能带有立场、恐惧或甩锅框架。",
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"time_scale: short_term | mid_term | long_term | mixed | unknown。",
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"expectation_reality_gap: expected、reality、gap_summary；未知时写 unknown。",
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"scarcity_profile: 分别标出 data_scarcity、path_or_resource_scarcity、consensus_or_order_scarcity 的 high | medium | low | unknown。",
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"dominant_scarcity: data | path_resource | consensus_order | mixed | unknown。",
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"classification: question | problem | issue | mixed | no_call。",
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"classification_confidence: high | medium | low。",
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"classification_by_viewpoint: 多视角分歧时列出不同主体下的分类和理由。",
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"dynamic_stage: initial | evolving | recurring | stabilized | unknown。",
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"possible_trajectory: question_to_problem | problem_to_issue | issue_to_problem | stabilized | unknown。",
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"success_criteria_stability: stable | unstable | contested | unknown。",
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"hard_feedback_availability: high | medium | low | unknown。",
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"governance_load: high | medium | low | unknown。",
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"evidence_gap: 列出还缺哪些信息才能稳定判断；mixed 或 low confidence 时必须填写。",
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"misclassification_risk: 标出暴力降维、恶意升维或手段错配风险。",
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"recommended_clarifying_questions: 上下文不足时必须给出需要补问的问题。",
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"recommended_next_step: 检索 | 工程拆解 | 共识协调 | 补充上下文 | 不调用。",
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"next_model_candidates: 后续可调用模型 ID；无则为空数组。"
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],
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"structured_output_contract": {
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"classification_scope": ["subject_contextual", "multi_perspective", "insufficient_context", "no_call"],
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"is_provisional": "boolean",
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"subject_position": "string | unknown",
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"scenario_context": "string | unknown",
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"responsibility_scope": ["individual_learning", "team_execution", "cross_function_governance", "owner_governance", "unknown"],
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"context_sufficiency": ["high", "medium", "low"],
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"missing_context": "array<string>",
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"problem_owner": "string | unknown",
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"problem_source": "string | unknown",
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"time_scale": ["short_term", "mid_term", "long_term", "mixed", "unknown"],
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"experience_level": ["novice", "experienced", "expert", "cross_domain", "unknown"],
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"expectation_reality_gap": {
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"expected": "string | unknown",
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"reality": "string | unknown",
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"gap_summary": "string | unknown"
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},
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"scarcity_profile": {
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"data_scarcity": ["high", "medium", "low", "unknown"],
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"path_or_resource_scarcity": ["high", "medium", "low", "unknown"],
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"consensus_or_order_scarcity": ["high", "medium", "low", "unknown"]
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},
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"dominant_scarcity": ["data", "path_resource", "consensus_order", "mixed", "unknown"],
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"classification": ["question", "problem", "issue", "mixed", "no_call"],
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"classification_confidence": ["high", "medium", "low"],
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"classification_by_viewpoint": "array<object>",
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"dynamic_stage": ["initial", "evolving", "recurring", "stabilized", "unknown"],
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"possible_trajectory": "array<string>",
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"success_criteria_stability": ["stable", "unstable", "contested", "unknown"],
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"hard_feedback_availability": ["high", "medium", "low", "unknown"],
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"governance_load": ["high", "medium", "low", "unknown"],
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"evidence_gap": "array<string>",
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"misclassification_risk": "array<string>",
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"recommended_clarifying_questions": "array<string>",
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"recommended_next_step": ["检索", "工程拆解", "共识协调", "补充上下文", "不调用"],
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"next_model_candidates": "array<model_id>"
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},
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"depth_control": {
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"principle": "先定性，再决定是否进入深层模型。",
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"stop_conditions": [
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"如果只是明确事实查询，停止在提问层并建议检索。",
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"如果已明确是执行任务，停止继续路由。",
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"如果分类证据不足，先输出 evidence_gap 并要求补充上下文。",
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"如果 negative_triggers 命中且没有复杂问题信号，输出 classification=no_call。"
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],
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"overuse_warning": "QPI 是前置路由模型，不应替代后续具体分析模型。"
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},
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"stabilization_path": [
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"补充真实业务混合案例。",
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"补充暴力降维和恶意升维案例。",
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"形成主导匮乏物判定示例库。"
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],
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"productization_notes": "QPI 应作为问题回答系统的前置路由模型，用于防止系统在问题类型错误的情况下直接给答案。",
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"version": "0.1",
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"last_updated": "2026-06-16"
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}
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