arXiv:2606.29932v1 Announce Type: new Abstract: Long-horizon strategic planning in complex strategy games demands concurrent reasoning across multiple decision domains under imperfect information and sparse reward. Existing LLM-based agents suffer from three systematic failures: scene blindness from raw tile coordinates, context overflow and domain coupling from monolithic state dumps, and shallow cross-game learning that treats each episode in isolation. We present SAGA, an LLM multi-agent framework with three mechanisms each directly targeting one class of failure: (i) a Map-Semantic Scene G
The continuous advancements in AI research, particularly in addressing complex long-horizon planning and reasoning, make this development timely as AI agents become more sophisticated.
Sophisticated AI agents capable of long-horizon strategy planning under imperfect information will significantly impact various industries requiring complex decision-making and automation.
The explicit addressing of scene blindness, context overflow, and monolithic state management marks a qualitative leap in LLM-based agent capabilities for strategic, complex environments.
- · AI research institutions
- · Gaming industry
- · Logistics and supply chain management
- · Autonomous systems developers
- · Traditional algorithmic planning methods
- · Human strategic planners in certain domains
- · Companies reliant on less advanced AI agents
More robust and adaptable AI agents will emerge for complex, dynamic environments.
These agents could automate strategic decision-making in sectors like defense, finance, and urban planning.
The development of highly autonomous and adaptable AI entities accelerates, potentially leading to new forms of human-AI collaboration or competition.
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Read at arXiv cs.AI