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LLM DeepSWE Pareto Frontier
多模型在DeepSWE基准上的帕累托前沿，揭示代码生成效率与成本平衡。
Article URL: https://deepswe.datacurve.ai/ Comments URL: https://news.ycombinator.com/item?id=49200656 Points: 1 # Comments: 0
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Dense2MoE: Pushing the Pareto Frontier of On-Device LLMs via Unified Pruning and Upcycling
提出Dense2MoE统一剪枝与升级方法，将稠密LLM转化为MoE架构，显著提升设备端大模型的效率与性能，推动帕累托前沿。
arXiv:2605.26496v1 Announce Type: cross Abstract: The Mixture of Experts MoE architecture is highly promising for resource constrained on device deplo…
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BigMac: Breaking the Pareto Frontier of Compute and Memory in Multimodal LLM Training
突破多模态LLM训练中计算与内存的帕累托前沿，BigMac提出高效优化方案。
arXiv:2605.25451v1 Announce Type: new Abstract: Training multimodal large language models (MLLMs) is challenged by both model and data heterogeneity. …