mlsanityData to 14 Oct 2026

QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback-based Self-Correction

arXiv 2403.11886 ↗First posted 18 Mar 2024cs.CL cs.AIcited by 27

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

Abstract (arXiv)

Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success. However, we find existing methods fall short in terms of reliability and efficiency when hallucinations are encountered. In this paper, we address these challenges with a framework called QueryAgent, which solves a question step-by-step and performs step-wise self-correction. We introduce an environmental feedback-based self-correction method called ERASER. Unlike traditional approaches, ERASER leverages rich environmental feedback in the intermediate steps to perform selective and differentiated self-correction only when necessary. Experimental results demonstrate that QueryAgent notably outperforms all previous few-shot methods using only one example on GrailQA and GraphQ by 7.0 and 15.0 F1. Moreover, our approach exhibits superiority in terms of efficiency, including runtime, query overhead, and API invocation costs. By leveraging ERASER, we further improve another baseline (i.e., AgentBench) by approximately 10 points, revealing the strong transferability of our approach.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 13 of its 29 references that are in the map. First posted on arXiv 18 Mar 2024.

Builds on

13 of 29 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments13 Mar

Other references · 12

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  4. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,407
  5. 2023AgentBench: Evaluating LLMs as Agentscited by 1,267
  6. 2023Teaching Large Language Models to Self-Debugcited by 988
  7. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  8. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
  9. 2023Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategiescited by 238
  10. 2022DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Basescited by 84
  11. 2023Code-Style In-Context Learning for Knowledge-Based Question Answeringcited by 13
  12. 2023Make a Choice! Knowledge Base Question Answering with In-Context Learningcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 20; 2026: 3.

27 papers.

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  1. 2024From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactionscited by 46
  2. 2025A Survey on the Optimization of Large Language Model-based Agentscited by 46
  3. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 33
  4. 2025KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Searchcited by 22
  5. 2025Coarse-to-Fine Grounded Memory for LLM Agent Planningcited by 18
  6. 2025CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent Systemcited by 15
  7. 2025AgentMaster: A Multi-Agent Conversational Framework Using A2A and MCP Protocols for Multimodal Information Retrieval and Analysiscited by 13
  8. 2024ProductAgent: Benchmarking Conversational Product Search Agent with Asking Clarification Questionscited by 10
  9. 2025Generalizability of Large Language Model-Based Agents: A Comprehensive Surveycited by 10
  10. 2025Alita-G: Self-Evolving Generative Agent for Agent Generationcited by 10
  11. 2025The Power of Personality: A Human Simulation Perspective to Investigate Large Language Model Agentscited by 8
  12. 2025Breaking the Frozen Subspace: Importance Sampling for Low-Rank Optimization in LLM Pretrainingcited by 7
15 more
  1. 2025Reason-Align-Respond: Aligning LLM Reasoning with Knowledge Graphs for KGQAcited by 7
  2. 2025Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Modelscited by 7
  3. 2025Chain-of-Query: Unleashing the Power of LLMs in SQL-Aided Table Understanding via Multi-Agent Collaborationcited by 6
  4. 2024TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Datacited by 5
  5. 2025KBQA-R1: Reinforcing Large Language Models for Knowledge Base Question Answeringcited by 3
  6. 2025Self-Correction Distillation for Structured Data Question Answeringcited by 2
  7. 2025CIMR: Contextualized Iterative Multimodal Reasoning for Robust Instruction Following in LVLMscited by 1
  8. 2025Dual-Evolving Reinforcement Learning for Automated Reward Designcited by 1
  9. 2024Molly: Making Large Language Model Agents Solve Python Problem More Logicallycited by 0
  10. 2025TimeHC-RL: Temporal-aware Hierarchical Cognitive Reinforcement Learning for Enhancing LLMs' Social Intelligencecited by 0
  11. 2025Text-to-SPARQL Goes Beyond English: Multilingual Question Answering Over Knowledge Graphs through Human-Inspired Reasoningcited by 0
  12. 2025USB-Rec: An Effective Framework for Improving Conversational Recommendation Capability of Large Language Modelcited by 0
  13. 2026HIERA: Hierarchical Multi-Agent Relevance Assessment for Content Discovery Systemscited by 0
  14. 2026Towards Robust Tool Use in Agents via Experience-Driven Adaptive Guidancecited by 0
  15. 2026X-Tree: Tokenizing Reusable Experience for Efficient Agent Generalizationcited by 0

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  1. 2019Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study
  2. 2026Retrieval as Reasoning: Self-Evolving Agent-Native Retrieval via LLM-Wiki
  3. 2026ROZA Graphs: Self-Improving Near-Deterministic RAG through Evidence-Centric Feedback
  4. 2025RAGCap-Bench: Benchmarking Capabilities of LLMs in Agentic Retrieval Augmented Generation Systems
  5. 2026AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG
  6. 2025Improving LLM Reasoning via Dependency-Aware Query Decomposition and Logic-Parallel Content Expansion
  7. 2025AGENTIQL: An Agent-Inspired Multi-Expert Framework for Text-to-SQL Generation
  8. 2025Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs