Auditability of subliminal learning is constrained by channel location, with initialization-dependent body channels allowing pre-training screens while vocabulary geometry and conditional body channels evade them.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
In a solvable attention model, pre-training followed by rank-one LoRA admits sharp asymptotic predictions for test errors and representation alignment via an effective noise term.
Alpha in LoRA outperforms learning-rate scaling, follows a square-root law with rank, and enables a minimalist LoRA-alpha method that improves performance across tasks.
citing papers explorer
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Channel Location Constrains the Auditability of Subliminal Learning
Auditability of subliminal learning is constrained by channel location, with initialization-dependent body channels allowing pre-training screens while vocabulary geometry and conditional body channels evade them.
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High-Dimensional Theory of LoRA Fine-Tuning in a Solvable Attention Model
In a solvable attention model, pre-training followed by rank-one LoRA admits sharp asymptotic predictions for test errors and representation alignment via an effective noise term.
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The Hidden Power of Scaling Factor in LoRA Optimization
Alpha in LoRA outperforms learning-rate scaling, follows a square-root law with rank, and enables a minimalist LoRA-alpha method that improves performance across tasks.