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Evolving Scientific Intelligence: From Fragmented Evidence to Distributed Experimental Loops — arXiv:2611.00580

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00580
License (as archived): CC0-1.0
Archived: 2026-11-03T10:41:57.945Z
SHA-256 of the archived text: 9debd0989e4e54c087d123c90a1bc95f88152fbcf10412aa292ae9dc5ac0b42a
Stable URL of this page: https://metraipolis.com/documento/dc_a1986dc17ffb

How to cite

Evolving Scientific Intelligence: From Fragmented Evidence to Distributed Experimental Loops — arXiv:2611.00580 — arXiv (en), https://arxiv.org/abs/2611.00580 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a1986dc17ffb (Metraipolis archive dc_a1986dc17ffb, sha256 9debd0989e4e54c087d123c90a1bc95f88152fbcf10412aa292ae9dc5ac0b42a)

BibTeX

@misc{dca1986dc17ffb,
  title = {Evolving Scientific Intelligence: From Fragmented Evidence to Distributed Experimental Loops — arXiv:2611.00580},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00580},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a1986dc17ffb; sha256 9debd0989e4e54c087d123c90a1bc95f88152fbcf10412aa292ae9dc5ac0b42a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00580. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Scientific discovery advances when evidence from different investigations is brought into contact. Yet paper-centric communication leaves experimental results fragmented, delayed, or unreported, creating a "scientific memory wall" between growing computational capabilities and the evidence accessible to AI systems. We envision a different dynamic: in a network of intelligent laboratories, a failed or overlooked experiment in one domain could trigger a hypothesis in another, whose testing generates evidence that stimulates further investigations. Such chains of information collision and empirical validation could transform isolated discovery loops into a cumulative scientific process. We propose Evolving Scientific Intelligence (ESI), an architectural blueprint for enabling these evidence-driven chain reactions. ESI preserves experimental records in shared, computable scientific memory, where evidence from different domains can be recombined into testable hypotheses. It jointly selects candidates and evaluation methods according to scientific value, reliability, and cost, then coordinates physical tests across distributed experimental nodes. Their feedback revises hypotheses and evaluators, while slower meta-control reassesses research objectives and decision criteria to guard against self-reinforcing errors and misguided optimization. We call for an Information Manhattan Project to build the infrastructure for scientific intelligence that evolves through experimentally grounded chains of discovery.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.00580 — 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 2026-11-03T10:39:50.178Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).