[Paper] Structure and Diversity Aware Context Bubble Construction for Enterprise Retrieval Augmented Systems

Source: arXiv - 2601.10681v1

Overview

The paper introduces Structure‑and‑Diversity‑Aware Context Bubbles, a new way to build the prompt context for Retrieval‑Augmented Generation (RAG) systems that serve enterprise knowledge bases. By respecting the inherent hierarchy of documents (sections, tables, rows) and explicitly enforcing diversity, the approach creates compact, citation‑ready “bubbles” that stay within LLM token limits while delivering richer, less redundant information than traditional top‑k retrieval.

Key Contributions

• Structure‑informed retrieval: Uses document hierarchy and task‑conditioned priors to prioritize whole sections or logical spans instead of isolated sentences.
• Diversity‑constrained selection: Formulates a constrained optimization that balances relevance, marginal coverage, and redundancy penalties, guaranteeing a diverse set of spans.
• Context bubble construction algorithm: A deterministic, budget‑aware pipeline that assembles a coherent bundle of spans and simultaneously emits a full retrieval trace for auditability.
• Enterprise‑focused evaluation: Demonstrates on real‑world corporate documents that bubbles cut redundancy by up to ~40 %, improve secondary‑facet coverage, and boost answer quality and citation faithfulness under strict token budgets.
• Ablation insights: Shows that both structural priors and the diversity constraint are essential; dropping either degrades coverage and inflates duplication.

Methodology

1. Anchor Identification – The system first runs a standard relevance ranker to pick a few high‑scoring “anchor” spans (e.g., a section heading that directly matches the query).
2. Structural Priors – Each document is pre‑processed into a multi‑granular graph (sections → paragraphs → table rows). Priors encode how likely a span at a given level is useful for a particular task (e.g., policy lookup vs. numeric extraction).
3. Constrained Selection – Starting from the anchors, the algorithm iteratively adds spans while respecting three constraints:
   • Relevance – marginal gain in similarity to the query.
   • Coverage – new information not already represented in the bubble.
   • Redundancy Penalty – discourages overlapping content (e.g., two paragraphs that repeat the same fact).
     The process stops once the token budget (e.g., 2 k tokens for GPT‑4) is reached.
4. Trace Generation – Every selection step logs the scoring components, producing a full retrieval trace that can be inspected or reproduced, enabling deterministic tuning and compliance auditing.

Results & Findings

Key Takeaways

• The bubble method delivers significantly less duplicated text, freeing up tokens for new information.
• Pulling whole sections or rows captures contextual cues that improve downstream LLM reasoning, especially for queries that need multiple related facts.
• The deterministic trace makes it easier for enterprises to audit why a particular passage was used, a critical compliance requirement.

Practical Implications

• Cost Savings – Fewer tokens per request translates directly into lower API bills for LLM providers, especially in high‑volume enterprise settings.
• Improved User Experience – Answers are more complete and correctly cited, reducing the need for manual fact‑checking.
• Compliance & Auditing – The full retrieval trace satisfies internal governance policies (e.g., GDPR, SOX) that demand provenance of generated content.
• Plug‑and‑Play Integration – The bubble construction can sit on top of existing vector stores (FAISS, Milvus) and ranking models, requiring only a lightweight pre‑processing step to expose document hierarchy.
• Better Multi‑modal Support – Because the method works on rows of tables and other structured spans, it can be extended to retrieval‑augmented agents that need to reason over spreadsheets, logs, or configuration files.

Limitations & Future Work

• Dependency on Accurate Structure Extraction – The approach assumes that documents are correctly parsed into hierarchical spans; noisy OCR or poorly formatted PDFs can degrade performance.
• Scalability of the Optimization – While the greedy selection is fast for typical enterprise corpora, scaling to billions of spans may need more aggressive pruning or approximate algorithms.
• Generalization Beyond Enterprise – Experiments focus on internal corporate documents; further validation on public web corpora or multilingual datasets is needed.
• Dynamic Queries – The current pipeline treats each query independently; future work could explore caching or incremental bubble updates for conversational contexts.

Authors

• Amir Khurshid
• Abhishek Sehgal

Paper Information

• arXiv ID: 2601.10681v1
• Categories: cs.AI
• Published: January 15, 2026
• PDF: Download PDF