AnyEdit++ proposes Bayes-Chunk, an adaptive segmentation method based on Bayesian Surprise, with theoretical claims of structural independence and causal locality, reporting superior results over baselines on math, code, and narrative tasks.
Beyond task vectors: Selective task arithmetic based on importance metrics
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Empirical scaling laws for LLM merging show a size-dependent floor and 1/k-like tail in cross-entropy loss that holds across architectures and merging methods.
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AnyEdit++: Adaptive Long-Form Knowledge Editing via Bayesian Surprise
AnyEdit++ proposes Bayes-Chunk, an adaptive segmentation method based on Bayesian Surprise, with theoretical claims of structural independence and causal locality, reporting superior results over baselines on math, code, and narrative tasks.
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Model Merging Scaling Laws in Large Language Models
Empirical scaling laws for LLM merging show a size-dependent floor and 1/k-like tail in cross-entropy loss that holds across architectures and merging methods.