Multi$^2$: Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments
arXiv:2606.03698v1 Announce Type: new Abstract: A central goal of large language model (LLM) research is to build agentic systems that can plan, act, and adapt through sustained interaction with dynamic environments. While recent LLM-based agents exhibit impressive contextual reasoning, their long-horizon decision-making remains fragile, often suffering from objective drift, where goals and plans drift over extended interactions. We introduce Multi$^2$, a hierarchical multi-agent decision-making framework that explicitly decomposes agent behavior into complementary roles. A high-level agent (S
The continuous development and scaling of large language models have pushed the boundaries of their autonomous decision-making capabilities, necessitating frameworks to address current limitations.
Improving long-horizon decision-making and reducing objective drift in LLM-based agents is critical for their real-world deployment across various industries and applications.
The introduction of hierarchical multi-agent frameworks like Multi^2 indicates a new architectural approach to building more robust and adaptable AI agents, moving beyond monolithic LLM applications.
- · AI software developers
- · Automation industries
- · SaaS platforms leveraging AI agents
- · Monolithic LLM approaches
- · Tasks requiring sustained, complex reasoning by single agents
More reliable and capable AI agents will emerge for complex, multi-step tasks.
This framework could accelerate the automation of white-collar workflows, as agents become more resilient to objective drift.
Hierarchical agentic systems may lead to new forms of organizational structures within companies, with agent teams performing specialized functions.
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