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Targeted search shows that random-device testing underestimates worst-case error in a simulated wave-based neural operator — arXiv:2610.07529

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.07529
License (as archived): CC0-1.0
Archived: 2026-10-07T11:41:14.372Z
SHA-256 of the archived text: 2e2f09c2e4412602e14e5a7d420857e475f834c0e22580ab76515f4ed8a561c9
Stable URL of this page: https://metraipolis.com/documento/dc_b09f98aaa6c4

How to cite

Targeted search shows that random-device testing underestimates worst-case error in a simulated wave-based neural operator — arXiv:2610.07529 — arXiv (en), https://arxiv.org/abs/2610.07529 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_b09f98aaa6c4 (Metraipolis archive dc_b09f98aaa6c4, sha256 2e2f09c2e4412602e14e5a7d420857e475f834c0e22580ab76515f4ed8a561c9)

BibTeX

@misc{dcb09f98aaa6c4,
  title = {Targeted search shows that random-device testing underestimates worst-case error in a simulated wave-based neural operator — arXiv:2610.07529},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.07529},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_b09f98aaa6c4; sha256 2e2f09c2e4412602e14e5a7d420857e475f834c0e22580ab76515f4ed8a561c9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.07529. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Wave-based processors promise fast, energy-efficient Fourier layers for neural operators. They are usually validated on randomly sampled devices, but using them requires knowing how large their error can become under fabrication and alignment variation. In a stylised numerical case study, a hybrid Fourier neural operator runs its four spectral layers on simulated coherent 4f processors with 32 toleranced knobs, whose half-widths are representative rather than calibrated. For 120 models (four tasks, six training methods, five seeds), we compared the worst of N random in-spec devices with a searched one. On a deterministic simulator with one frozen draw of the random static errors, the searched device's held-out error was 1.08-3.10 times the maximum over 200 Monte Carlo devices and 1.06-2.71 times that over 1000. With 20 fresh static draws, it still exceeded the maximum over 200 random devices in 116 of 120 models. Under uniform sampling, the probability of drawing such a device is at most 0.37% per model (two-sided 95% Clopper-Pearson), which says nothing about how large its error is. The gap persisted with uniform or Sobol' sampling at the search's budget, shared knobs, a second crosstalk model, box scales of 0.25-2 and a pixel-level device model. Models trained only with random static errors reached 3.7-39.9 times their nominal error on searched devices, and fine-tuning on random and gradient-searched devices gave the lowest searched error of the six in all 20 task-seed pairs. For two heat-exchanger quantities, a search targeted at each exceeded the worst of 1000 random devices in all 39 models, and hence the Wilks 95/95 limit (worst of 59). For the mean pressure of 11 models, no random device exceeded a 1% error threshold, but the searched device did. Random testing estimates how often errors exceed a threshold; worst-device search gives a lower bound on how large they can be.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Emerging Technologies (cs.ET); Optics (physics.optics).
Pagina del lavoro: https://arxiv.org/abs/2610.07529 — 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 2026-10-07T11:19:24.770Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).