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Neural Activation Patterns Across Language Model Architectures: A Comprehensive Analysis of Cognitive Task Performance

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arXiv:2605. 15436v1 Announce Type: new Abstract: This paper presents a comprehensive analysis of neural activation patterns across six distinct large language model (LLM) architectures, examining their performance on twelve cognitive task categories.

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Authors: Mahdi Naser-Moghadasi, Faezeh Ghaderi
Released: 2026-05-18 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Neural Activation Patterns Across Language Model Architectures: A Comprehensive Analysis of Cognitive Task Performance overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", 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)". Matching terms: code, rate, compare, language, model. Source: arxiv cs.CL (NLP).
arXiv: 2605.15436

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