[Endorsement Request] cs.LG — When does a predictive system learn it's the one predicting? (40 experiments, 12 falsified hypotheses)

Ketney Otto, a researcher and academic staff member at Lucian Blaga University of Sibiu in Romania, is requesting a cs.LG arXiv endorsement for a manuscript titled "Leakage-Aware Benchmarking of Machine Learning for Food Spectroscopy: How Validation Design Changes Reported Predictive Performance." The study compares random splitting, group-aware validation, batch-aware validation, and external/domain-shift validation to assess how validation design affects reported ML performance in food spectroscopy. Otto supplied the official arXiv endorsement code 3MLSXP and offered an institutional profile, ORCID, and publication record for verification.

I am a researcher and academic staff member at Lucian Blaga University of Sibiu, Romania, preparing my first arXiv submission in cs.LG Machine Learning . Manuscript: Leakage-Aware Benchmarking of Machine Learning for Food Spectroscopy: How Validation Design Changes Reported Predictive Performance The study investigates how validation design affects reported ML performance in food spectroscopy, comparing random splitting, group-aware validation, batch-aware validation, and external/domain-shift validation. I am looking for someone who is currently eligible to endorse submissions in cs.LG and is willing to inspect the manuscript before deciding. To avoid asking anyone to follow an unfamiliar authentication link, here is only the official arXiv endorsement code: Subject: cs.LG Endorsement Code: 3MLSXP The code can be entered directly after logging into arXiv through the official endorsement interface. I can also provide my institutional profile, ORCID, publication record, or any additional information required. Thank you for considering the request. Ketney Otto