arxiv
PublishedSeptember 3, 2026 at 4:00 AM
When Prompts Interact: Assessing Prompt Arithmetic for Deconfounding under Distribution Shift
Publisher summary· verbatim
arXiv:2605.03096v2 Announce Type: replace-cross Abstract: In classification tasks, models may rely on confounding variables to achieve strong in-distribution performance, capturing spurious features that fail under distribution shift. This shortcut behavior leads to substantial degradation in out-of
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivAutomatic Sentence Completion Using Bengali Audio Speech with a Transformer-Based Early Speech Recognition System2harxivWhat Should We Remove? Evaluating the Reliability and Costs of RLVR Data Decontamination2harxivFGSS-BO: Feedback-Guided Hypothesis Management for LLM-Based Bayesian Optimization2harxivLLM-Based Weak Annotation for Cybersecurity Tweet Classification: A Teacher-Student Framework2hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗