Decodable But Not Detachable: Training Data Granularity Determines Parametric Modularity in Large Language Models
Do large language models contain domain-specific parametric shells: concentrated, causally necessary neuron populations whose removal selectively degrades a target domain while sparing others? We apply a uniform causal methodology across two domain granularities, three model families (1.5B to 7B parameters), and eight domains. At the academic subject level, zero neurons exceed 60\% domain selectivity across 939,008 combined FFN neurons and causal damage matrices are flat, despite domain identity being linearly decodable above 85\% accuracy. At the language and modality level, 0.65--1.14\% of neurons exceed 60\% selectivity, damage matrices are near-perfectly diagonal (ratios up to 595:1),...
| Authors | Marcus Armstrong, Navid Ayoobi, Arjun Mukherjee |
|---|---|
| Primary category | cs.AI |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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