mlsanityData to 7 Oct 2026

Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking

arXiv 2604.15621 ↗First posted 17 Apr 2026cs.IR cs.AI cs.CLcited by 0

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

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Abstract (arXiv)

Adaptive Retrieval-Augmented Generation aims to mitigate the interference of extraneous noise by dynamically determining the necessity of retrieving supplementary passages. However, as Large Language Models evolve with increasing robustness to noise, the necessity of adaptive retrieval warrants re-evaluation. In this paper, we rethink this necessity and propose AdaRankLLM, a novel adaptive retrieval framework. To effectively verify the necessity of adaptive listwise reranking, we first develop an adaptive ranker employing a zero-shot prompt with a passage dropout mechanism, and compare its generation outcomes against static fixed-depth retrieval strategies. Furthermore, to endow smaller open-source LLMs with this precise listwise ranking and adaptive filtering capability, we introduce a two-stage progressive distillation paradigm enhanced by data sampling and augmentation techniques. Extensive experiments across three datasets and eight LLMs demonstrate that AdaRankLLM consistently achieves optimal performance in most scenarios with significantly reduced context overhead. Crucially, our analysis reveals a role shift in adaptive retrieval: it functions as a critical noise filter for weaker models to overcome their limitations, while serving as a cost-effective efficiency optimizer for stronger reasoning 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 38 of its 55 references that are in the map. First posted on arXiv 17 Apr 2026.

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38 of 55 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Cost-Aware Retrieval-Augmentation Reasoning Models with Adaptive Retrieval Depth17 Oct
  2. 2025Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning8 Mar

Other references · 36

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  10. 2022Improving Passage Retrieval with Zero-Shot Question Generationcited by 164
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  12. 2023RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Modelscited by 69
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  10. 2025CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steeringcited by 12
  11. 2025SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignmentcited by 9
  12. 2026Why Thinking Hurts: Diagnosing and Rectifying Linguistic Inertia in Large Language Models for Recommendationcited by 9
  13. 2026The Next Paradigm Is User-Centric Agent, Not Platform-Centric Servicecited by 8
  14. 2025Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generationcited by 7
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  16. 2025FAIR-RAG: Faithful Adaptive Iterative Refinement for Retrieval-Augmented Generationcited by 7
  17. 2025A Universal Framework for Compressing Embeddings in CTR Predictioncited by 6
  18. 2026SpecSteer: Synergizing Local Context and Global Reasoning for Efficient Personalized Generationcited by 6
  19. 2025Retrieval as a Decision: Training-Free Adaptive Gating for Efficient RAGcited by 5
  20. 2025RAG-IGBench: Innovative Evaluation for RAG-based Interleaved Generation in Open-domain Question Answeringcited by 4
  21. 2026SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utilitycited by 4
  22. 2026Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibrationcited by 4
  23. 2026Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillationcited by 3
  24. 2026Adaptive Retrieval for Reasoning-Intensive Retrievalcited by 1

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