SEEK: Steering LLM Reasoning for RAG via Internal Reasoning Sketches

2026-06-08 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, long

Summary

PAGER is a novel page-driven autonomous knowledge representation framework designed to enhance Retrieval-Augmented Generation (RAG) models by addressing the lack of coherent structure in iterative knowledge accumulation. For a given question, PAGER first prompts a Large Language Model (LLM) to create a structured cognitive outline, comprising distinct knowledge aspect slots. It then iteratively retrieves and refines relevant documents to populate each slot, ultimately forming a cohesive "page" that serves as structured contextual input for guiding answer generation. Experiments across multiple knowledge-intensive benchmarks and backbone models demonstrate PAGER's consistent outperformance of RAG baselines. The framework yields higher-quality, information-dense knowledge representations, effectively mitigates knowledge conflicts, and improves LLM utilization of external knowledge.

Key takeaway

For Machine Learning Engineers optimizing Retrieval-Augmented Generation (RAG) systems for knowledge-intensive tasks, PAGER offers a compelling strategy to overcome limitations in unstructured knowledge accumulation. By leveraging LLMs to construct structured cognitive outlines and iteratively populate them with refined evidence, your RAG models can achieve higher-quality knowledge representations, mitigate conflicts, and improve overall answer generation accuracy. Consider exploring this page-driven framework to enhance your LLM's external knowledge utilization.

Key insights

PAGER structures RAG knowledge into cognitive "pages" via iterative retrieval and refinement, improving LLM performance and mitigating conflicts.

Principles

• Structured knowledge representations enhance RAG effectiveness.
• Iterative knowledge completion improves comprehensiveness.
• Cognitive outlines guide targeted information acquisition.

Method

PAGER initializes a structured cognitive outline with blank slots. It iteratively generates sub-queries, retrieves documents, refines them into evidence, and fills each slot to construct a complete contextual page.

In practice

• Generate structured outlines using LLMs for complex queries.
• Implement iterative retrieval to fill knowledge gaps in structured slots.
• Organize retrieved facts into "pages" to reduce knowledge conflicts.

Topics

• Retrieval-Augmented Generation
• Large Language Models
• Knowledge Representation
• Iterative Retrieval
• Structured Knowledge
• Question Answering

Code references

• OpenBMB/PAGER

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.