Chunked-document retrieval is a common component of retrieval-augmented generation (RAG) systems. Documents are split into overlapping chunks, embedded, and indexed with approximate nearest-neighbor search such as hierarchical navigable small world graphs (HNSW). Overlap improves boundary coverage but induces a practical failure mode: top-k retrieval often returns near-adjacent chunks that repeat evidence and waste prompt budget. We propose Self-Conditioned Positional HNSW (SCP-HNSW), a lightweight modification that appends a low-dimensional positional code to chunk embeddings and uses a two-pass query procedure to estimate and apply a query-specific document-position prior. SCP-HNSW leaves HNSW graph construction and traversal unchanged while adding an auditable minimum-index-gap selector for final context construction. We also integrate industrial review artifacts for generated evidence quality: a 770-review text-evidence audit with 318 fully labeled reviews and a 70-case OCR audit with 350 ratings. The text audit shows that 574 of 770 projected reviews are rated 3/5, only 39 fall in the 1-2 range, and narrative reviewer detail appears much more often than structured issue flags. The OCR audit shows slice-level pass rates from 95% for clean chat screenshots to 45% for handwritten/blurry captures, with moderate to strong agreement. These results motivate overlap-aware, audit-friendly RAG retrieval and identify the remaining controlled retrieval ablations needed for causal performance claims.

翻译：分块文档检索是检索增强生成（RAG）系统的常见组件。文档被分割为重叠的块，经嵌入后通过近似最近邻搜索（如分层可导航小世界图HNSW）进行索引。重叠策略虽能改善边界覆盖，但会诱发一种实际失效模式：top-k检索常返回近邻块，导致证据重复并浪费提示预算。我们提出自条件位置HNSW（SCP-HNSW），这是一种轻量级改进方法，通过在块嵌入后附加低维位置编码，并采用两轮查询流程来估计并应用查询特定的文档位置先验。SCP-HNSW保持HNSW图构建与遍历不变，同时新增可审计的最小索引间隔选择器用于最终上下文构建。我们还整合了面向生成证据质量的工业审查制品：包含318份完全标注的770篇文本证据审计，以及含350项评分的70例OCR审计。文本审计显示，770份预测审查中574份被评为3/5分，仅39份落在1-2分区间，且叙述性审查细节的出现频率远高于结构化问题标记。OCR审计显示切片级通过率从95%（清晰聊天截图）降至45%（手写/模糊截图），评分一致性达中等到强水平。这些结果论证了重叠感知、可审计的RAG检索需求，并识别出需进一步开展控制性检索消融实验才能得出因果性能结论的关键环节。