mlsanityData to 14 Oct 2026

AIPOM: Agent-aware Interactive Planning for Multi-Agent Systems

arXiv 2509.24826 ↗First posted 29 Sep 2025cs.HC cs.MAcited by 1

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

Abstract (arXiv)

Large language models (LLMs) are being increasingly used for planning in orchestrated multi-agent systems. However, existing LLM-based approaches often fall short of human expectations and, critically, lack effective mechanisms for users to inspect, understand, and control their behaviors. These limitations call for enhanced transparency, controllability, and human oversight. To address this, we introduce AIPOM, a system supporting human-in-the-loop planning through conversational and graph-based interfaces. AIPOM enables users to transparently inspect, refine, and collaboratively guide LLM-generated plans, significantly enhancing user control and trust in multi-agent workflows. Our code and demo video are available at https://github.com/megagonlabs/aipom.

Key facts. Cited by 1 paper in the map (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 16 of its 29 references that are in the map. First posted on arXiv 29 Sep 2025.

Builds on

16 of 29 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025VeriLA: A Human-Centered Evaluation Framework for Interpretable Verification of LLM Agent Failures16 Mar
  2. 2024Understanding the planning of LLM agents: A survey5 Feb

Other references · 14

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  3. 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,035
  4. 2023On the Planning Abilities of Large Language Models : A Critical Investigationcited by 312
  5. 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Promptscited by 268
  6. 2023ADaPT: As-Needed Decomposition and Planning with Language Modelscited by 177
  7. 2023ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testingcited by 88
  8. 2023Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chainscited by 24
  9. 2023Low-code LLM: Graphical User Interface over Large Language Modelscited by 13
  10. 2023Jigsaw: Supporting Designers to Prototype Multimodal Applications by Chaining AI Foundation Modelscited by 11
  11. 2024A Blueprint Architecture of Compound AI Systems for Enterprisecited by 6
  12. 2025Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solvingcited by 4
2 more
  1. 2023InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMscited by 2
  2. 2024On the Roles of LLMs in Planning: Embedding LLMs into Planning Graphscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026How to Steer Your Multi-Agent System: Human-LLM Collaborative Planningcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challenges
  2. 2025LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey
  3. 2025LLM Reasoner and Automated Planner: A new NPC approach
  4. 2025LLMs Working in Harmony: A Survey on the Technological Aspects of Building Effective LLM-Based Multi Agent Systems
  5. 2024Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interface
  6. 2025Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
  7. 2025Large Language Models for Planning: A Comprehensive and Systematic Survey
  8. 2026AdaPlanBench: Evaluating Adaptive Planning in Large Language Model Agents under World and User Constraints