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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 develops a new approach to guiding corpus interaction in agentic search, which uses relevance to improve the accuracy and efficiency of search agents in complex question answering and reasoning tasks. Practitioners may care about this research if they want to build more effective search systems that can quickly and reliably retrieve relevant information.
This paper proposes a new method to improve the performance of large language models on knowledge-intensive tasks by distilling knowledge from proprietary models and using reinforcement learning. Practitioners may care about this approach because it can help bridge the gap between proprietary and open-source models, leading to more effective and robust AI systems.