How to Steer Your Multi-Agent System: Human-LLM Collaborative Planning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917522726453248 |
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| author | He, Zeyu Kim, Hannah Zhang, Dan Hruschka, Estevam |
| author_facet | He, Zeyu Kim, Hannah Zhang, Dan Hruschka, Estevam |
| contents | In orchestrated multi-agent systems, humans often struggle to manage plans due to their complexity and limited transparency. Existing approaches rely on outcome-level supervision, where users verify only final outputs without visibility into intermediate reasoning. We formalize a design space for human-LLM co-planning interactions along three axes: mode (semantic vs. structural), scope (global vs. targeted), and level (low vs. high-level edits). We realize it in AMBIPOM, a prototype supporting process-level supervision through both semantic and structural interactions. Through a user study, we characterize how users navigate this space, revealing hybrid workflows and effort-control-risk trade-offs; through a controlled benchmark, we analyze how LLMs revise plans under varying scope and revision strategies. Our findings yield design insights for more transparent, controllable, and effective human-AI co-planning. We release code and data at https://github.com/megagonlabs/ambipom. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23023 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | How to Steer Your Multi-Agent System: Human-LLM Collaborative Planning He, Zeyu Kim, Hannah Zhang, Dan Hruschka, Estevam Multiagent Systems Human-Computer Interaction In orchestrated multi-agent systems, humans often struggle to manage plans due to their complexity and limited transparency. Existing approaches rely on outcome-level supervision, where users verify only final outputs without visibility into intermediate reasoning. We formalize a design space for human-LLM co-planning interactions along three axes: mode (semantic vs. structural), scope (global vs. targeted), and level (low vs. high-level edits). We realize it in AMBIPOM, a prototype supporting process-level supervision through both semantic and structural interactions. Through a user study, we characterize how users navigate this space, revealing hybrid workflows and effort-control-risk trade-offs; through a controlled benchmark, we analyze how LLMs revise plans under varying scope and revision strategies. Our findings yield design insights for more transparent, controllable, and effective human-AI co-planning. We release code and data at https://github.com/megagonlabs/ambipom. |
| title | How to Steer Your Multi-Agent System: Human-LLM Collaborative Planning |
| topic | Multiagent Systems Human-Computer Interaction |
| url | https://arxiv.org/abs/2605.23023 |