Structure and Diversity Aware Context Bubble Construction Achieves Top-k Retrieval for Enterprises

The effective use of information within large language models often relies on Retrieval Augmented Generation (RAG) systems, yet current methods frequently struggle with fragmented information and insufficient contextual understanding. Amir Khurshid from the Bravada Group and Abhishek Sehgal from Eye Dream Pty Ltd, along with their colleagues, address these limitations in their new research on context bubble construction for enterprise retrieval systems. Their work proposes a framework that builds coherent and informative context sets by carefully considering both document structure and the diversity of information included. This approach moves beyond simple top-k passage ranking, instead assembling ‘bubbles’ of relevant spans while adhering to strict token limits and providing a traceable record of its selections. Through experiments on real-world enterprise data, the researchers demonstrate that this method significantly reduces redundancy, improves coverage of complex queries, and enhances the overall quality and faithfulness of generated answers.

The authors argue that building a good context pack is crucial for LLM performance and propose Context Bubbles, a method that prioritises document structure and diversity alongside relevance. Context Bubbles reframes retrieval as an assembly problem, aiming to create a compact, auditable context pack. The proposed method leverages document structure , such as sections, sheets, and rows , and diversity to build context packs under strict token limits.

Research demonstrates that document structure and diversity are vital signals for efficient context construction, outperforming flat top-K retrieval. The system provides a transparent and auditable approach to context building, crucial for enterprise applications where trust and control are paramount. Experiments show that Context Bubbles achieves better coverage, reduced redundancy, and improved answer correctness compared to traditional methods. Limitations include non-deterministic chunk identifiers, lexical candidate generation susceptible to paraphrasing, and lexical diversity thresholds that may not fully capture semantic overlap. Future research will focus on addressing these limitations by exploring deterministic chunk identities, multimodal chunking policies, hybrid lexical-semantic retrieval, and improved provenance models. This paper advocates for a more intelligent and structured approach to retrieval in RAG systems, emphasising the importance of context assembly for optimal LLM performance and trustworthy applications.

Context Bubble Construction for Structured Documents

The study addresses limitations in retrieval-augmented generation (RAG) systems, specifically the fragmentation of information and redundancy issues encountered when processing structured, enterprise documents. Researchers pioneered a novel framework termed ‘context bubble’ construction, designed to assemble coherent and citable bundles of text spans within a strict token budget. This method moves beyond traditional top-k retrieval by actively preserving and exploiting inherent document structure, organising content at multiple granularities, including sections and rows, to improve information coherence. The core of the technique lies in a constrained selection process initiated from high-relevance anchor spans, balancing query relevance with marginal coverage and redundancy penalties.

Unlike standard top-k approaches, the context bubble explicitly constrains diversity and adheres to a predefined token budget, resulting in compact context sets that maximise the utility of the limited context window available to large language models. A key methodological innovation is the emission of a full retrieval trace, meticulously documenting the scoring and selection choices made during context bubble construction, providing complete auditability and enabling deterministic tuning. Experiments using enterprise documents demonstrate that the context bubble significantly reduces redundant context while simultaneously improving coverage of secondary facets within queries. Ablation studies confirmed the necessity of both structural priors and diversity constraint selection, with their synergistic effect enhancing methodological elements. This research demonstrates a more robust and informative context assembly, ultimately enhancing the performance of large language models in complex, document-intensive tasks.

Context Bubble Improves RAG Performance and Accuracy

Scientists have developed a novel framework, termed ‘context bubble’, designed to improve the performance of large language models (LLMs) when utilising retrieval-augmented generation (RAG) techniques. The research addresses limitations in traditional RAG systems, specifically fragmentation, over-representation, and insufficient context, particularly when dealing with complex, structured documents. Experiments demonstrate that the context bubble significantly reduces redundant context within the information provided to the LLM, enhancing both answer quality and citation faithfulness. The team measured the efficiency of their approach on enterprise documents, achieving a substantial reduction in duplicated content compared to standard top-k retrieval methods.

Results demonstrate improved coverage of secondary facets of a query, meaning the system is better able to incorporate related but less directly relevant information. Crucially, the work emits a full audit trail, detailing the scoring and selection choices made during context construction, enabling deterministic tuning and increased transparency. This detailed record allows researchers to understand precisely why certain information was included or excluded, facilitating refinement of the system. The framework preserves and exploits inherent document structure by organising information at multiple granularities, such as sections and rows, and utilising task-conditioned structural priors to guide the selection process.

Starting with highly relevant anchor spans, the system builds a context bubble through constrained selection, balancing query relevance with coverage and redundancy penalties. Ablation studies confirmed that both the incorporation of structural priors and the diversity constraint selection are essential components, with removal of either leading to decreased coverage and increased redundancy. The study’s findings suggest a pathway towards more reliable and accurate LLM applications in complex domains, where comprehensive and well-structured information is critical for generating trustworthy outputs, with the framework’s auditable nature offering a significant advantage for applications requiring accountability and explainability.

Structure and Diversity Enhance Contextual Recall

This research introduces a novel framework for constructing context bubbles for large language models, addressing limitations found in traditional retrieval-augmented generation methods. The approach moves beyond simple top-k passage selection by explicitly incorporating document structure and diversity constraints, assembling coherent and citable bundles of text within a defined token limit. By leveraging multi-granular spans and task-conditioned structural priors, the method prioritises both query relevance and comprehensive coverage of information, including secondary facets often missed by standard techniques. Experiments utilising enterprise documents demonstrate that this structure-informed approach significantly reduces redundancy in retrieved context, improves answer quality, and enhances citation faithfulness.

Ablation studies confirm the importance of both structural priors and diversity constraints, indicating that their combined effect is crucial for achieving optimal performance. The authors acknowledge limitations related to the specific document types used in evaluation, and suggest future work could explore the application of this framework to a wider range of corpora and tasks. Further research may also focus on refining the structural priors to better capture complex document relationships.