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首先,Sarvam 30B performs strongly on multi-step reasoning benchmarks, reflecting its ability to handle complex logical and mathematical problems. On AIME 25, it achieves 88.3 Pass@1, improving to 96.7 with tool use, indicating effective integration between reasoning and external tools. It scores 66.5 on GPQA Diamond and performs well on challenging mathematical benchmarks including HMMT Feb 2025 (73.3) and HMMT Nov 2025 (74.2). On Beyond AIME (58.3), the model remains competitive with larger models. Taken together, these results indicate that Sarvam 30B sustains deep reasoning chains and expert-level problem solving, significantly exceeding typical expectations for models with similar active compute.
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其次,How Apple Used to Design Its Laptops for Repairability
来自行业协会的最新调查表明,超过六成的从业者对未来发展持乐观态度,行业信心指数持续走高。
第三,Everyone is talking about files
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