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SERIAL-7: Deriving, Realizing, and Preserving Machine-Verifiable Outputs Across Representations — arXiv:2610.41308

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41308
License (as archived): CC0-1.0
Archived: 2026-11-02T07:38:52.256Z
SHA-256 of the archived text: c0f9a619b37cb2510e24cb45a2e624542d056f96a3246d708dd47347513128ee
Stable URL of this page: https://metraipolis.com/documento/dc_02c91e2dd554

How to cite

SERIAL-7: Deriving, Realizing, and Preserving Machine-Verifiable Outputs Across Representations — arXiv:2610.41308 — arXiv (en), https://arxiv.org/abs/2610.41308 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_02c91e2dd554 (Metraipolis archive dc_02c91e2dd554, sha256 c0f9a619b37cb2510e24cb45a2e624542d056f96a3246d708dd47347513128ee)

BibTeX

@misc{dc02c91e2dd554,
  title = {SERIAL-7: Deriving, Realizing, and Preserving Machine-Verifiable Outputs Across Representations — arXiv:2610.41308},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41308},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_02c91e2dd554; sha256 c0f9a619b37cb2510e24cb45a2e624542d056f96a3246d708dd47347513128ee},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41308. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language models increasingly generate structured outputs consumed by programs and downstream systems, where semantic correctness alone does not guarantee reliable execution. We introduce SERIAL-7, a Korean-English benchmark built around an executable verification framework for machine-verifiable generation across seven representations. Using 177 validated canonical artifacts, SERIAL-7 separates semantic derivation (Derive), constrained artifact generation (Realize), and cross-representation preservation (Preserve). Its 43 atomic representation constraints are paired with executable checks over format, structure, and task-specific semantics, enabling progressively stricter verification from representation compliance to full artifact validity. Across ten models, REALIZE-L3 HSR ranges from 42.0% to 93.9%, while Full Compliance exposes additional artifact-level failures beyond correct final answers and valid representations. These results show that reliable structured generation requires explicit, executable verification of how artifacts are realized and preserved across representations.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.41308 — 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 2026-11-02T07:37:00.479Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).