To what extent does zero-shot reasoning accuracy degrade in Llama3, Codestral, and Deepseek R1 when forecastin

Abstract

Abstract: The field of artificial intelligence has undergone a revolution from foundational Transformer architectures to reasoning-capable systems approaching human-level performance. We present LLMOrbit, a comprehensive circular taxonomy navigating the landscape of large language models spanning 2019-2025. This survey examines over 50 models across 15 organizations through eight interconnected orbital dimensions, documenting architectural innovations, training methodologies, and efficiency patterns defining modern LLMs, generative AI, and agentic systems. We identify three critical crises: (1) data sca

Research Question

To what extent does zero-shot reasoning accuracy degrade in Llama3, Codestral, and Deepseek R1 when forecasting time-series anomalies in domain-shifted datasets compared to in-domain benchmarks?

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