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Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Distribution Shift

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arXiv:2605. 31250v1 Announce Type: new Abstract: We propose a unified framework for addressing three key challenges of distribution shift: (1) estimating a model's performance on an unlabeled target domain, (2) explaining the shift by identifying the features responsible, and (3) improving the target domain performance.

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Authors: Salim I. Amoukou, Emanuele Albini, Tom Bewley, Saumitra Mishra, Manuela Veloso
Released: 2026-06-01 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Distribution Shift overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, under, alignment, source, line, rate, project, control. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.31250

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