Tensor-Coord: Algebraic Decomposition of Joint Plan Tensors for Conflict-Free Multi-Agent LLM Planning
arXiv:2606.16478v1 Announce Type: new Abstract: Large language models (LLMs) remain limited in multi-agent planning because independently generated plans can create coordination failures such as spatial collisions, resource contention, and temporal deadlocks. We introduce Tensor-Coord, a multilinear algebra framework that represents the joint plan of N agents as a third-order tensor \(T \in R^{N \times H \times A}\) over agents, timesteps, and actions. Canonical Polyadic (CP) and Tucker decompositions are used to identify latent coordination structure. The minimal epsilon-approximate CP rank R
The rapid advancement and integration of large language models are exposing critical limitations in multi-agent coordination, making novel planning frameworks essential for complex applications.
Improving multi-agent LLM planning directly addresses current bottlenecks in deploying autonomous AI systems, enabling more reliable and complex automated decision-making and execution.
The introduction of Tensor-Coord provides a structured, algebraic approach to mitigate coordination failures in multi-agent LLM systems, moving beyond ad-hoc solutions to a more formalized planning methodology.
- · AI developers
- · Robotics
- · Logistics
- · Autonomous systems
- · Inefficient multi-agent systems
- · Manual coordination
- · Brute-force planning approaches
Multi-agent LLM systems gain enhanced reliability and efficiency in complex planning scenarios.
Accelerated development and deployment of sophisticated AI agents across various industries, including logistics and manufacturing.
Increased automation leads to significant shifts in labor markets and operational paradigms for complex, multi-stakeholder processes.
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Read at arXiv cs.AI