Unverified50% confidenceFactExact time
The paper's conclusions are built on the softmax retrieval abstraction and controlled experiments, and actual large model behavior may be more complex.
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9/22/2026
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UnverifiedSoftmax involves exponential operations, row-wise normalization, and global dependencies, making it difficult to parallelize and quantize, and it is especially resource-intensive in long-sequence scenarios73% similarVerifiedSmaller models are more prone to embedding collapse than larger models, due to reasons including limited parameter capacity, a tendency toward optimization shortcuts, and amplified anisotropy issues.73% similarUnverifiedWhen sample sizes are small, deep models or complex ensemble methods often backfire, and a simple baseline should be established first.71% similarUnverifiedFor simple questions, one should proactively switch to a low-consumption mode, reserving the most powerful mode for scenarios that truly require in-depth research in order to conserve credits70% similarUnverifiedSoftmax函数将神经网络logits压缩为概率分布，但其输出值在大规模模型中系统性偏离真实贝叶斯后验概率69% similar
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