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Math for AI safety: an invitation for mathematicians — arXiv:2609.15289

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.15289
License (as archived): CC0-1.0
Archived: 2026-10-15T15:05:03.398Z
SHA-256 of the archived text: 20c9e1334afb305c15fdede8faa19eda390920044a7c94cb900ac5d922f70af3
Stable URL of this page: https://metraipolis.com/documento/dc_65dfd2e79597

How to cite

Math for AI safety: an invitation for mathematicians — arXiv:2609.15289 — arXiv (en), https://arxiv.org/abs/2609.15289 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_65dfd2e79597 (Metraipolis archive dc_65dfd2e79597, sha256 20c9e1334afb305c15fdede8faa19eda390920044a7c94cb900ac5d922f70af3)

BibTeX

@misc{dc65dfd2e79597,
  title = {Math for AI safety: an invitation for mathematicians — arXiv:2609.15289},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.15289},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_65dfd2e79597; sha256 20c9e1334afb305c15fdede8faa19eda390920044a7c94cb900ac5d922f70af3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.15289. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Artificial intelligence threatens to outrun human understanding and control. New mathematics is needed to design AI that is legible, steerable, and cooperative with humanity. I organize this invitation by mathematical field, so you can turn straight to your own: logic and game theory for cooperation; probability for agency and world-models; algebra and representation theory for learned features; analysis and geometry for generalization and training dynamics. Each section ends with an open problem that is accessible to a working mathematician with no prior experience in AI safety.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-17 in cs.LG).
Categorie arXiv: History and Overview (math.HO) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.15289 — 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 2026-10-15T14:08:19.184Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).