CogRAG: Tackling Heterogeneous Cognitive Demands in RAG via Stratified Retrieval and Reasoning
On the map: LLM reasoning → Knowledge- and tool-augmented reasoning
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Abstract (arXiv)
Retrieval-Augmented Generation (RAG) frameworks typically process all queries through a one-size-fits-all pipeline, ignoring the heterogeneous cognitive demands of different tasks. This cognitive-blind approach causes two failure modes: cascading errors when low-level factual gaps trigger hallucinated reasoning, and reasoning-answer inconsistency in higher-order analytical tasks. We introduce CogRAG, a training-free, domain-agnostic framework that tackles these heterogeneous cognitive demands via stratified retrieval and reasoning. Inspired by Bloom's Taxonomy, CogRAG uses the predicted cognitive load of a query as a central control signal that coordinates two modules: Cognition-Adaptive Evidence Refinement supplements missing context via fact-centric or option-centric paths, and Cognition-Stratified Structured Reasoning replaces unconstrained chain-of-thought with cognition-aligned reasoning templates. We evaluate CogRAG on a demanding professional testbed, the Registered Dietitian qualification examination. CogRAG effectively reduces early-stage factual errors and eliminates reasoning-answer inconsistency, raising Qwen3-8B accuracy from 73.4% to 85.8% in single-choice mode and from 63.3% to 80.5% in scenario mode. These results highlight cognitive-stratified control as an effective, generalizable paradigm for reliable complex reasoning in large language models.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 15 of its 24 references that are in the map. First posted on arXiv 1 Apr 2026.
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References · 15
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
- 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
- 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
- 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 519
- 2023Active Retrieval Augmented Generationcited by 506
- 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 464
- 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
- 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
- 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 191
- 2024Corrective Retrieval Augmented Generationcited by 187
- 2024FinBen: A Holistic Financial Benchmark for Large Language Modelscited by 140
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- 2025RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning
- 2025PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning
- 2026Beyond Factual Accuracy: Evaluating Global Reasoning Integrity in RAG Systems with LogicScore
- 2025Human Cognition Inspired RAG with Knowledge Graph for Complex Problem Solving
- 2026Skill-RAG: Failure-State-Aware Retrieval Augmentation via Hidden-State Probing and Skill Routing
- 2025From Facts to Conclusions : Integrating Deductive Reasoning in Retrieval-Augmented LLMs
- 2025Retrieval as a Decision: Training-Free Adaptive Gating for Efficient RAG
- 2026The Magnitude Mirage: Rethinking Confidence for Reasoning-Intensive Retrieval