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Morning Edition · Monday, July 13, 2026Published at 1:34 AM EDT · New York
GATS Cuts LLM Calls in Agent Planning With Graph-Augmented Tree Search
The method pairs layered world models with graph-augmented search to reduce the heavy per-step language-model inference that slows LATS and ReAct.
A new preprint, GATS: Graph-Augmented Tree Search with Layered World Models, addresses a practical bottleneck in language-model agents. Existing planners such as Language Agent Tree Search (LATS) and ReAct rely heavily on model inference du…
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Agentic AI Moves Into Enterprise and Government Workflows
Over the next 3-9 months, AI agents move from demos into real enterprise and public-sector workflows, with deployment success tied to domain and task understanding more than raw model capability.
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