Paper

Weighted Random Dot Product Graphs

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arXiv:2505. 03649v4 Announce Type: replace Abstract: Modeling of intricate relational patterns has become a cornerstone of contemporary statistical research and related data science fields.

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Authors: Bernardo Marenco, Paola Bermolen, Marcelo Fiori, Federico Larroca, Gonzalo Mateos
Released: 2026-06-11 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Weighted Random Dot Product Graphs overlaps with experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation", experiment "Test FR↔IT bystander-spill symmetry at multi-seed + 5 phrasings — pooled-rate vs per-phrasing asymmetry from #239 fact-check", experiment "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: position, test, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2505.03649

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