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Rethinking Layer Relevance in Large Language Models Beyond Cosine Similarity

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arXiv:2605. 14075v1 Announce Type: new Abstract: Large language models (LLMs) have revolutionized natural language processing.

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Authors: Cristian Hinostroza, Rodrigo Toro Icarte, Christ Devia, Andres Carvallo De Ferari, Eugenio Herrera-Berg, Denis Parra, Jorge F Silva
Released: 2026-05-15 (3mo ago)
Topic: Related to current project
Relation to my work: Read next because Rethinking Layer Relevance in Large Language Models Beyond Cosine Similarity 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 "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation". Matching terms: under, rate, language, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.14075

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