Large language model (LLM) contexts are typically constructed using retrieval-augmented generation (RAG), which involves ranking and selecting the top-k passages. The approach causes fragmentation in information graphs in document structures, over-retrieval, and duplication of content alongside insufficient query context, including 2nd and 3rd order facets. In this paper, a structure-informed and diversity-constrained context bubble construction framework is proposed that assembles coherent, citable bundles of spans under a strict token budget. The method preserves and exploits inherent document structure by organising multi-granular spans (e.g., sections and rows) and using task-conditioned structural priors to guide retrieval. Starting from high-relevance anchor spans, a context bubble is constructed through constrained selection that balances query relevance, marginal coverage, and redundancy penalties. It will explicitly constrain diversity and budget, producing compact and informative context sets, unlike top-k retrieval. Moreover, a full retrieval is emitted that traces the scoring and selection choices of the records, thus providing auditability and deterministic tuning. Experiments on enterprise documents demonstrate the efficiency of context bubble as it significantly reduces redundant context, is better able to cover secondary facets and has a better answer quality and citation faithfulness within a limited context window. Ablation studies demonstrate that both structural priors as well as diversity constraint selection are necessary; removing either component results in a decline in coverage and an increase in redundant or incomplete context.

翻译：大型语言模型（LLM）的上下文通常通过检索增强生成（RAG）构建，该方法涉及对段落进行排序并选择前k个。此方法会导致文档结构信息图的碎片化、过度检索、内容重复，以及查询上下文（包括二阶和三阶层面）的不足。本文提出了一种结构感知且多样性约束的上下文气泡构建框架，该框架在严格的令牌预算下，组装连贯、可引用的文本片段束。该方法通过组织多粒度片段（例如章节和行）并利用任务条件化的结构先验来指导检索，从而保留并利用文档的固有结构。从高相关性的锚点片段出发，通过约束选择构建上下文气泡，该选择平衡了查询相关性、边际覆盖度和冗余惩罚。与仅选择前k个的检索方法不同，该方法会明确约束多样性和预算，从而产生紧凑且信息丰富的上下文集合。此外，框架会输出完整的检索过程，记录对候选记录的评分和选择决策，从而提供可审计性和确定性调优能力。在企业文档上的实验表明，上下文气泡方法能有效减少冗余上下文，更好地覆盖次要层面，并在有限的上下文窗口内获得更优的答案质量和引用忠实度。消融研究证明，结构先验和多样性约束选择均不可或缺；移除任一组件都会导致覆盖度下降以及冗余或不完整上下文的增加。