Read next because Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions overlaps with clean result "EOS-in-loss was the confound: masking the recipient's EOS from cross-entropy revives within-marker chunk-binding from 1.3% to 23.5% (MODERATE confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", clean result "Longer persona system prompts pull a [ZLT] marker toward the source persona — stronger source rate and less bystander leakage across an N=48 LoRA panel on Qwen2.5-7B-Instruct (MODERATE confidence)". Matching terms: training, line, rate, does, model. Source: arxiv cs.LG (Machine Learning).