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This work presents a diagnostic and optimization framework grounded in a key empirical finding: the value of tokens within a CoT reasoning sequence is highly non-uniform, and this non-uniformity can be effectively characterized by token-level log probability signals.
Equivariant graph neural networks encode the E(3) symmetries that molecular force fields must obey, yet they are often trained against geometric objectives whose own symmetry is rarely examined. We develop a symmetry-sensitive diagnostic protocol that combines rigid-transform invariance and equivariance metrics, a pari...
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