LLM推理并非万能，主观任务中会失灵？这篇论文剖析失败模式并给出动态路由方案。
arXiv:2608.08889v1 Announce Type: new Abstract: Recommendation systems thrive on personalization, where ''correctness'' is rarely a binary truth but a…
提出动态logit级门控机制，优化LLM专家路由效率，大幅降低推理成本。
arXiv:2606.04378v1 Announce Type: new Abstract: Leveraging multiple specialized LLMs can combine complementary strengths, but existing approaches trad…
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针对异构LLM池的混合动态路由架构，无需因模型目录变更而重训练，解决现有二元路由的耦合问题。
arXiv:2605.17106v1 Announce Type: cross Abstract: Production LLM deployments increasingly maintain heterogeneous model pools spanning order-of-magnitu…
一种新型动态路由方法通过二进制专家激活掩码减少MoE冗余计算，无需重训即可加速推理。
arXiv:2605.14438v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enhance the efficiency of large language models by activating o…