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From Representation to Readout: Mechanistic Analysis of Membership Signals in Language Models — arXiv:2610.41445

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41445
License (as archived): CC0-1.0
Archived: 2026-11-03T07:43:19.022Z
SHA-256 of the archived text: 8df466aa4005b58e24b081e322d022b7caee0a48bc29c3447c8801ceb6caf03a
Stable URL of this page: https://metraipolis.com/documento/dc_f87bf535eb54

How to cite

From Representation to Readout: Mechanistic Analysis of Membership Signals in Language Models — arXiv:2610.41445 — arXiv (en), https://arxiv.org/abs/2610.41445 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f87bf535eb54 (Metraipolis archive dc_f87bf535eb54, sha256 8df466aa4005b58e24b081e322d022b7caee0a48bc29c3447c8801ceb6caf03a)

BibTeX

@misc{dcf87bf535eb54,
  title = {From Representation to Readout: Mechanistic Analysis of Membership Signals in Language Models — arXiv:2610.41445},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41445},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f87bf535eb54; sha256 8df466aa4005b58e24b081e322d022b7caee0a48bc29c3447c8801ceb6caf03a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41445. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Membership inference aims to determine whether a particular example was included in a model's training data. Such membership signals are often broadly described as memorization, while the underlying mechanisms can involve both how information is represented internally and how it is accessed by the model to produce behaviors. We therefore present a mechanistic study of membership signals in large language models (LLMs). We introduce Decodable Information Score, Used Information Score, and Readout Accessibility Gap (DUG), a set of complementary measures that separately quantify membership information decodable from internal representations and the extent to which it is exposed through the model's readout. Across supervised fine-tuning (SFT) and reinforcement learning (RL)-based membership inference methods, membership-specific effects appear in both the amount of decodable information and readout under SFT, while under RL-based membership inference methods they appear primarily in readout. Activation patching further localizes this membership-specific effect to intermediate layers. To examine whether these findings extend to a more general RL setting, we further study reinforcement learning with verifiable rewards (RLVR) contamination, where the training reward is independent of the reference solution. In this setting, we find that behavioral changes can emerge without a corresponding increase in likelihood-based membership inference signals.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.41445 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T07:39:37.384Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).