Title: SchemaRAG: Enhancing Knowledge-Intensive Reasoning of LLMs via Inference-Time Adaptive Schema
Primary area: generative models
Keywords: Knowledge Intensive Reasoning, RAG, LLM

Abstract: Retrieval-Augmented Generation (RAG) often struggles with integrating fragmented knowledge for complex reasoning tasks. Recent efforts introduce structural templates—such as graphs or knowledge-based organizations—to improve multi-document reasoning. However, they are constrained by their rigidity, failing to adapt to diverse, task-specific information structures and often omitting critical dependencies. To address this, we propose SchemaRAG: an adaptive schema-guided RAG framework. Instead of predefined formats like graphs, tables and chunks, SchemaRAG adaptively organize the factual information across documents based on query-specific requirements. Given the input query and documents, it first parses the query into sub-problems and generate strategies for schema constructions, then utilize the organized knowledge to generate final answer. Extensive experiments on real-world benchmarks demonstrate that SchemaRAG consistently outperforms state-of-the-art baselines in knowledge-intensive reasoning and generation quality. Our work highlights the importance of adaptive schema-guided strategies for advancing the capabilities of RAG systems in complex, domain-specific tasks.