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This paper introduces ZGCM-1, a highly efficient foundation model for math and agentic search that combines internal thinking with external tool use, and shows it can perform well on various benchmarks despite its compact size. Practitioners may care about the efficiency improvements and scalable architecture of ZGCM-1.
This paper explores how AI can be applied across different stages of game development, from playing games to designing and testing them, and how to reuse capabilities across these stages. Practitioners might care about how to apply AI to improve game development efficiency and effectiveness.
This paper introduces IQuest-Q1, a large and flexible AI model designed to handle complex decision-making tasks, which could be useful for developers building agentic systems that require flexible and adaptive behavior.
This paper develops a framework for understanding and optimizing work agents, which are systems that use foundation models and runtime harnesses to complete complex tasks. Practitioners might care about this research if they're building systems that need to reliably execute tasks across multiple tools and workflows.