HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures
Most data-mixing methods assume the corpus has already been partitioned into groups, and the choice of those groups determines what a mixer can express. Existing labels, including provenance, topic or format taxonomies, and flat embedding clusters, commit to one semantic axis at one granularity; changing the resolution rebuilds the labels. We argue the bottleneck is the label system, not the mixer, and provide a hierarchical one. HERMES is a data-derived labeling substrate: a Learned Semantic Transform followed by 3-stage residual vector quantization annotates each document once into a coarse-
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- LinkedLinked via arxiv author · 85%Ziyun Qiao →
“HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures”
- LinkedLinked via arxiv author · 85%Yue Min →
“HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures”
- LinkedLinked via arxiv author · 85%Ruining Chen →
“HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures”
- LinkedLinked via arxiv author · 85%Yujun Li →
“HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures”
- FuzzySimilar title/name (fuzzy) · 87%CVHub520/X-AnyLabeling →
“Fuzzy title match (0.94): “HERMES: A Multi-Granularity Labeling Substrate for Pre-train” ≈ “CVHub520/X-AnyLabeling””
- FuzzySimilar title/name (fuzzy) · 59%NousResearch/hermes-agent →
“Fuzzy title match (0.73): “HERMES: A Multi-Granularity Labeling Substrate for Pre-train” ≈ “NousResearch/hermes-agent””
- FuzzySimilar title/name (fuzzy) · 59%EKKOLearnAI/hermes-studio →
“Fuzzy title match (0.73): “HERMES: A Multi-Granularity Labeling Substrate for Pre-train” ≈ “EKKOLearnAI/hermes-studio””
- FuzzySimilar title/name (fuzzy) · 59%fathah/hermes-desktop →
“Fuzzy title match (0.73): “HERMES: A Multi-Granularity Labeling Substrate for Pre-train” ≈ “fathah/hermes-desktop””