AdaSTORM: Scaling LLM Reasoning on Dynamic Graphs via Adaptive Spatio-Temporal Multi-Agent Collaboration
arXiv:2606.16328v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate remarkable potential in dynamic graph reasoning, but suffer from a scaling bottleneck: current models can only handle graphs with tens of nodes, constrained by exponential reasoning overhead and finite context windows. While multi-agent systems (MAS) offer collective reasoning and topology-aware orchestration, capabilities naturally suited for graph-structured tasks, their application to dynamic graphs remains unexplored. This paper presents Scaling LLM Reasoning on Dynamic Graphs via Adaptive Spatio-Tempo
The increasing complexity of AI tasks and the limitations of current LLM architectures necessitate innovations in scaling and multi-agent systems.
Overcoming the scaling bottleneck for LLMs on dynamic graphs is crucial for advancing AI's capabilities in complex, real-world scenarios, particularly in areas like logistics, cybersecurity, and resource management.
This research outlines a methodology that moves beyond current LLM constraints in handling dynamic graph data, enabling more sophisticated and scalable AI applications.
- · AI developers
- · LLM providers
- · Data scientists
- · Logistics and supply chain sectors
- · Traditional graph processing methods
- · LLMs without multi-agent capabilities
Improved performance and scalability of AI systems in dynamic and complex environments.
Expansion of LLM applications into domains previously too complex or data-intensive.
Accelerated development of autonomous AI systems capable of real-time adaptation and decision-making in highly dynamic settings.
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Read at arXiv cs.AI