Modified CL loss achieves IIA of 0.9988±0.0005 on synthetic 10-class dataset training/test sets

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• DAS behavioral loss achieves IIA of 0.997±0.001 on synthetic 10-class dataset training/test setsfinding0.832
  IIA baseline for DAS behavioral loss on synthetic dataset
• Modified CL loss produces EMD along feature dimensions of 0.007±0.001 on synthetic 10-class datasetfinding0.823
  Quantitative improvement in divergence reduction using the modified CL loss on synthetic dataset
• Modified CL loss outperforms behavioral DAS loss in OOD transfer from dense to sparse class partitionfinding0.782
  Key practical utility result: CL loss improves generalization of alignment to out-of-distribution settings
• For small CL loss weights epsilon, IIA is maintained (potentially improved) while EMD decreases in Boundless DAS on a 7B LLMfinding0.780
  Empirical result showing the CL loss can reduce divergence without sacrificing interpretability accuracy
• Modified CL Lossframework0.758
  Novel variant of CL loss introduced in this paper targeting only causal subspace dimensions to improve OOD performance
• The modified CL loss is confined to a narrow set of simplistic settings and is not specific to pernicious divergenceconcept0.755
  Explicitly identified limitation of the proposed mitigation method
• The CL auxiliary loss can directly reduce representational divergence in practical interpretability settings without sacrificing interpretability method accuracyclaim0.742
  Central practical contribution: the CL loss offers a viable mitigation strategy
• Current training methods rely on loss minimization, meaning the experiential profile of training is predominantly negative across billions of parameter updatesclaim0.712
  Ethical implication about the nature of AI training experience if the thesis holds