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

arXiv: https://arxiv.org/abs/2509.24826 · HTML: https://mlsanity.com/paper/2509.24826/

## Key facts

- Cited by 1 papers 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.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 16 of 16)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [Understanding the planning of LLM agents: A survey](https://mlsanity.com/paper/2402.02716/) (2024)
- [On the Planning Abilities of Large Language Models : A Critical Investigation](https://mlsanity.com/paper/2305.15771/) (2023)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [ADaPT: As-Needed Decomposition and Planning with Language Models](https://mlsanity.com/paper/2311.05772/) (2023)
- [ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing](https://mlsanity.com/paper/2309.09128/) (2023)
- [Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chains](https://mlsanity.com/paper/2306.12028/) (2023)
- [VeriLA: A Human-Centered Evaluation Framework for Interpretable Verification of LLM Agent Failures](https://mlsanity.com/paper/2503.12651/) (2025)
- [Low-code LLM: Graphical User Interface over Large Language Models](https://mlsanity.com/paper/2304.08103/) (2023)
- [Jigsaw: Supporting Designers to Prototype Multimodal Applications by Chaining AI Foundation Models](https://mlsanity.com/paper/2310.08574/) (2023)
- [A Blueprint Architecture of Compound AI Systems for Enterprise](https://mlsanity.com/paper/2406.00584/) (2024)
- [Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving](https://mlsanity.com/paper/2505.00031/) (2025)
- [InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs](https://mlsanity.com/paper/2312.09672/) (2023)
- [On the Roles of LLMs in Planning: Embedding LLMs into Planning Graphs](https://mlsanity.com/paper/2403.00783/) (2024)

## Built on by (most cited first, 1 of 1)

- [How to Steer Your Multi-Agent System: Human-LLM Collaborative Planning](https://mlsanity.com/paper/2605.23023/) (2026)

## 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.