Empirical Jacobian analysis reveals that token influence in trained language models decays as a power law with distance (exponent ~0.8), a learned property not present in random models.
SAIA: a seamless Slurm-native solution for HPC-based services.The Journal of Supercomputing, 82(7):403, May 2026
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How Token Influence Decays with Distance: A Green-Function View of Trained Language Models
Empirical Jacobian analysis reveals that token influence in trained language models decays as a power law with distance (exponent ~0.8), a learned property not present in random models.