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Separating Oblivious and Adaptive Models of Variable Selection

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arXiv:2602. 16568v2 Announce Type: replace-cross Abstract: Sparse recovery is among the most well-studied problems in learning theory and high-dimensional statistics.

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Authors: Ziyun Chen, Jerry Li, Kevin Tian, Yusong Zhu
Released: 2026-06-24 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Separating Oblivious and Adaptive Models of Variable Selection overlaps with clean result "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)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", 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: under, line, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2602.16568

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