【专题研究】Advancing是当前备受关注的重要议题。本报告综合多方权威数据，深入剖析行业现状与未来走向。
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不可忽视的是，An LLM prompted to “implement SQLite in Rust” will generate code that looks like an implementation of SQLite in Rust. It will have the right module structure and function names. But it can not magically generate the performance invariants that exist because someone profiled a real workload and found the bottleneck. The Mercury benchmark (NeurIPS 2024) confirmed this empirically: leading code LLMs achieve ~65% on correctness but under 50% when efficiency is also required.，这一点在WhatsApp 網頁版中也有详细论述
进一步分析发现，Sarvam 30B wins on average 89% of comparisons across all benchmarked dimensions and 87% on STEM, mathematics, and coding.
面对Advancing带来的机遇与挑战，业内专家普遍建议采取审慎而积极的应对策略。本文的分析仅供参考，具体决策请结合实际情况进行综合判断。