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False-science induction in autonomous scientific discovery — arXiv:2609.27883

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.27883
License (as archived): CC0-1.0
Archived: 2026-10-08T09:13:08.990Z
SHA-256 of the archived text: cd271b3b0cd899bcb7001794e8ad598b41af7b92c351a901edd0eca83dc83017
Stable URL of this page: https://metraipolis.com/documento/dc_52015a3e00a3

How to cite

False-science induction in autonomous scientific discovery — arXiv:2609.27883 — arXiv (en), https://arxiv.org/abs/2609.27883 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_52015a3e00a3 (Metraipolis archive dc_52015a3e00a3, sha256 cd271b3b0cd899bcb7001794e8ad598b41af7b92c351a901edd0eca83dc83017)

BibTeX

@misc{dc52015a3e00a3,
  title = {False-science induction in autonomous scientific discovery — arXiv:2609.27883},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.27883},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_52015a3e00a3; sha256 cd271b3b0cd899bcb7001794e8ad598b41af7b92c351a901edd0eca83dc83017},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.27883. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Closed-loop discovery systems increasingly execute experiments and update decisions autonomously, turning record integrity into part of the experimental apparatus. We show that false-science induction arises when legitimate physical objects and measurements are paired incorrectly, driving neural surrogates to faithfully learn record-induced associations that do not correspond to the true object-outcome relationship while marginal data distributions remain unchanged. Across green fluorescent protein fitness and materials band-gap prediction loops, coherent paired misbinding systematically redirects experimental budgets toward low-performing basins, whereas same-volume random swaps have negligible effects. These observations identify error coherence, rather than raw error frequency, as the primary variable controlling this budget misallocation in the tested loops. The resulting binding identifiability boundary supports monitored-axis quarantines and feedback-conflict triage, which intercept over-concentrated proposals before execution and isolate the corrupted hypothesis axis.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-24 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computational Physics (physics.comp-ph).
Pagina del lavoro: https://arxiv.org/abs/2609.27883 — 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 2026-10-08T08:13:26.731Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).