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Membership inference

3 open-access journal articles on membership inference, published in 2026 in Life of Research. Every record is free to read and download, with a permanent link and a ready-made citation.

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Journal article

Robust Because Coarse, or Robust Because Planned? A Research Agenda for Training-Data Provenance After the Aggregate-Disclosure Turn, in Which Granularity Buys Statistical Power, Prior Randomisation Buys Soundness, and Per-Example Value Has Neither

Regulators have chosen coarse disclosure of training data: the template for the EU AI Act's public summary of training content asks for lists of large datasets, a summary of the most relevant crawled domains and a description of how opt-outs were honoured, and does not require a list of individual…

Cite Related research LOR-00108 · 5 views · 3 downloads
PDF381.3 KB

Journal article

A Contamination Flag Is Not an Inflation Estimate: What Benchmark Contamination Detectors Certify, What Decontamination Is Meant to Buy, and Why the Clean-Subset Comparison That Joins Them Is Biased From Both Sides

Almost every large language model report now includes a contamination analysis: benchmark items that overlap the training corpus are flagged, the model is rescored on the unflagged remainder, and a small gap is read as evidence that the headline numbers stand. Two research literatures sit behind…

Cite Related research LOR-00091 · 8 views · 7 downloads
PDF389.5 KB

Journal article

Out-of-Distribution With Respect to What? Four Referents Behind One Predicate, and Why OOD Detection Results Do Not Transfer to Foundation-Model Reasoning

Out-of-distribution detection was formalised for a setting in which the training distribution is known, the label space is closed, and a designated test set stands in for everything outside it. Foundation models satisfy none of those conditions, and yet a growing body of work reports…

Cite Related research LOR-00033 · 6 views · 12 downloads
PDF509 KB

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