mlsanityData to 7 Oct 2026

GuideBench: Benchmarking Domain-Oriented Guideline Following for LLM Agents

arXiv 2505.11368 ↗First posted 16 May 2025cs.CLcited by 14

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Large language models (LLMs) have been widely deployed as autonomous agents capable of following user instructions and making decisions in real-world applications. Previous studies have made notable progress in benchmarking the instruction following capabilities of LLMs in general domains, with a primary focus on their inherent commonsense knowledge. Recently, LLMs have been increasingly deployed as domain-oriented agents, which rely on domain-oriented guidelines that may conflict with their commonsense knowledge. These guidelines exhibit two key characteristics: they consist of a wide range of domain-oriented rules and are subject to frequent updates. Despite these challenges, the absence of comprehensive benchmarks for evaluating the domain-oriented guideline following capabilities of LLMs presents a significant obstacle to their effective assessment and further development. In this paper, we introduce GuideBench, a comprehensive benchmark designed to evaluate guideline following performance of LLMs. GuideBench evaluates LLMs on three critical aspects: (i) adherence to diverse rules, (ii) robustness to rule updates, and (iii) alignment with human preferences. Experimental results on a range of LLMs indicate substantial opportunities for improving their ability to follow domain-oriented guidelines.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 26 of its 47 references that are in the map. First posted on arXiv 16 May 2025.

Builds on

26 of 47 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Revolutionizing Bridge Operation and Maintenance with LLM-based Agents: An Overview of Applications and Insights14 Jul
  2. 2023Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents20 Nov
  3. 2023Agents: An Open-source Framework for Autonomous Language Agents14 Sep
  4. 2023The Rise and Potential of Large Language Model Based Agents: A Survey14 Sep

Other references · 22

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2024The Llama 3 Herd of Modelscited by 14,192
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  5. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  6. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
  7. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,057
  8. 2024Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMscited by 564
  9. 2022Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studiescited by 506
  10. 2023Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflictscited by 333
  11. 2024Knowledge Conflicts for LLMs: A Surveycited by 274
  12. 2024R-Judge: Benchmarking Safety Risk Awareness for LLM Agentscited by 268
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  1. 2023Playing repeated games with Large Language Modelscited by 213
  2. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 204
  3. 2025Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMscited by 162
  4. 2024Benchmarking Complex Instruction-Following with Multiple Constraints Compositioncited by 147
  5. 2023EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Educationcited by 80
  6. 2024FOFO: A Benchmark to Evaluate LLMs' Format-Following Capabilitycited by 79
  7. 2023Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Supportcited by 75
  8. 2023Can Large Language Models Understand Real-World Complex Instructions?cited by 69
  9. 2023Can LLMs Follow Simple Rules?cited by 45
  10. 2023Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedbackcited by 31

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 9.

14 papers.

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  1. 2025Routine: A Structural Planning Framework for LLM Agent System in Enterprisecited by 9
  2. 2025Efficient Context Scaling with LongCat ZigZag Attentioncited by 6
  3. 2025Reasoning over Boundaries: Enhancing Specification Alignment via Test-time Deliberationcited by 3
  4. 2025SOP-Maze: Evaluating Large Language Models on Complicated Business Standard Operating Procedurescited by 3
  5. 2026Agents at Risk: How Users Unwittingly Undermine LLM Safetycited by 3
  6. 2026SAGE: A Service Agent Graph-guided Evaluation Benchmarkcited by 2
  7. 2026SLBench: Evaluating How LLM Agents Follow Logical Relations in Skillscited by 2
  8. 2025FreshTab: Sourcing Fresh Data for Table-to-Text Generation Evaluationcited by 1
  9. 2026Multi-Objective and Mixed-Reward Reinforcement Learning via Reward-Decorrelated Policy Optimizationcited by 1
  10. 2026FM SO.P: A Progressive Task Mixture Framework with Automatic Evaluation for Cross-Domain SOP Understandingcited by 0
  11. 2026DiagFlowBench: Evaluating How Language Models Handle Off-Procedure Inputs in Grounded Diagnostic Dialoguecited by 0
  12. 2026RecurSE: Bounded Recursive Self-Evaluation for LLM Rubric Judgescited by 0
2 more
  1. 2026Policy Loopholes in Agent Evaluation: When Policy Ambiguity Masquerades as Agent Errorcited by 0
  2. 2026Who Holds the Pen? Let Specifications, Not Agents, Sign Offcited by 0

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  2. 2025LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling Research
  3. 2025EDGE: Efficient Data Selection for LLM Agents via Guideline Effectiveness
  4. 2023Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub
  5. 2025Domain Specific Benchmarks for Evaluating Multimodal Large Language Models
  6. 2024A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback Learning
  7. 2025AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios
  8. 2026UniACE: A Unified Framework for Evaluating LLM Agentic Capabilities