mlsanityData to 14 Oct 2026

No Action Without a NOD: A Heterogeneous Multi-Agent Architecture for Reliable Service Agents

arXiv 2605.12240 ↗First posted 12 May 2026cs.AIcited by 0

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

Abstract (arXiv)

Large language model (LLM) agents have increasingly advanced service applications, such as booking flight tickets. However, these service agents suffer from unreliability in long-horizon tasks, as they often produce policy violations, tool hallucinations, and misaligned actions, which greatly impedes their real-world deployment. To address these challenges, we propose NOD (Navigator-Operator-Director), a heterogeneous multi-agent architecture for service agents. Instead of maintaining task state implicitly in dialogue context as in prior work, we externalize a structured Global State to enable explicit task state tracking and consistent decision-making by the Navigator. Besides, we introduce selective external oversight before critical actions, allowing an independent Director agent to verify execution and intervene when necessary. As such, NOD effectively mitigates error propagation and unsafe behavior in long-horizon tasks. Experiments on τ2-Bench demonstrate that NOD achieves higher task success rates and critical action precision over baselines. More importantly, NOD improves the reliability of service agents by reducing policy violations, tool hallucinations, and user-intent misalignment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 35 of its 69 references that are in the map. First posted on arXiv 12 May 2026.

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35 of 69 references.

Closest ancestors · 5

Same direction, newest first

  1. 2026Beyond IVR: Benchmarking Customer Support LLM Agents for Business-Adherence2 Jan
  2. 2025Survey on Evaluation of LLM-based Agents20 Mar
  3. 2024Preemptive Detection and Correction of Misaligned Actions in LLM Agents16 Jul
  4. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains17 Jun
  5. 2023A Survey on Large Language Model based Autonomous Agents22 Aug

Other references · 30

  1. 2025Qwen3 Technical Reportcited by 8,820
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  5. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  6. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  7. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
  8. 2025gpt-oss-120b & gpt-oss-20b Model Cardcited by 1,386
  9. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
  10. 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,081
  11. 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 1,017
  12. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 1,011
18 more
  1. 2025OpenAI GPT-5 System Cardcited by 989
  2. 2025τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environmentcited by 627
  3. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 324
  4. 2024Know Your Limits: A Survey of Abstention in Large Language Modelscited by 121
  5. 2024Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approachcited by 76
  6. 2026ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedbackcited by 72
  7. 2024LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Modelscited by 70
  8. 2024Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinationscited by 54
  9. 2024Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Modelscited by 47
  10. 2024Understanding the Dark Side of LLMs' Intrinsic Self-Correctioncited by 42
  11. 2024Reducing Tool Hallucination via Reliability Alignmentcited by 39
  12. 2022"Do you follow me?": A Survey of Recent Approaches in Dialogue State Trackingcited by 22
  13. 2024ToolScan: A Benchmark for Characterizing Errors in Tool-Use LLMscited by 18
  14. 2023LLM-State: Open World State Representation for Long-horizon Task Planning with Large Language Modelcited by 15
  15. 2025Unsupervised Hallucination Detection by Inspecting Reasoning Processescited by 5
  16. 2025ToolCritic: Detecting and Correcting Tool-Use Errors in Dialogue Systemscited by 5
  17. 2025How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on τ-benchcited by 3
  18. 2025Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulationcited by 2

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