mlsanityData to 14 Oct 2026

Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario

arXiv 2406.12429 ↗First posted 18 Jun 2024cs.AIcited by 10

On the map: LLM agents and tool use → Tool learning and function calling

Abstract (arXiv)

Current research on tool learning primarily focuses on selecting the most effective tool from a wide array of options, often overlooking cost-effectiveness, a crucial factor in human problem-solving. In this paper, we address the selection of homogeneous tools by predicting both their performance and the associated cost required to accomplish a given task. We then assign queries to the optimal tools in a cost-effective manner. Our experimental results demonstrate that our method achieves higher performance at a lower cost compared to strong baseline approaches.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 17 of its 24 references that are in the map. First posted on arXiv 18 Jun 2024.

Builds on

17 of 24 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Tool Learning with Large Language Models: A Survey28 May
  2. 2024AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls6 Feb
  3. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs31 Jul
  4. 2023ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases8 Jun
  5. 2023Toolformer: Language Models Can Teach Themselves to Use Tools9 Feb

Other references · 12

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2023Qwen Technical Reportcited by 3,344
  6. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  7. 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 853
  8. 2023LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusioncited by 564
  9. 2023Large Language Model Routing with Benchmark Datasetscited by 172
  10. 2023Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-Modelingcited by 95
  11. 2016Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answeringcited by 35
  12. 2024Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmarkcited by 22

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 3.

10 papers.

  1. 2025LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directionscited by 50
  2. 2025MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrieverscited by 7
  3. 2025LTRR: Learning To Rank Retrievers for LLMscited by 4
  4. 2025RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentationcited by 3
  5. 2025LoSemB: Logic-Guided Semantic Bridging for Inductive Tool Retrievalcited by 3
  6. 2026MemFlow: Intent-Driven Memory Orchestration for Small Language Model Agentscited by 3
  7. 2025Unsupervised Query Routing for Retrieval Augmented Generationcited by 0
  8. 2025TelcoAI: Advancing 3GPP Technical Specification Search through Agentic Multi-Modal Retrieval-Augmented Generationcited by 0
  9. 2026RealRoute: Dynamic Query Routing System via Retrieve-then-Verify Paradigmcited by 0
  10. 2026A Unified Structured Query Understanding Framework for Industrial Semantic Searchcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Beyond Single-Shot: Multi-step Tool Retrieval via Query Planning
  2. 2025Online-Optimized RAG for Tool Use and Function Calling
  3. 2025Graph RAG-Tool Fusion
  4. 2025Query Decomposition for RAG: Balancing Exploration-Exploitation
  5. 2024Data-Efficient Massive Tool Retrieval: A Reinforcement Learning Approach for Query-Tool Alignment with Language Models
  6. 2024Planning and Editing What You Retrieve for Enhanced Tool Learning
  7. 2026Tools Are Not Islands: Set-Level Tool Retrieval for LLM Agents via Query-Conditioned Hyperedge Prediction
  8. 2026Lightweight Query Routing for Adaptive RAG: A Baseline Study on RAGRouter-Bench