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

Authors: Youngkyung Seo , MiHyeon Kim , Jisoo Lee , Hwijung Ryu

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Abstract: 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.

Comments:

NeurIPS 2026 Workshop on AI for Verifiable Coding

Subjects:

Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL)

Cite as:

arXiv:2610.41308 [cs.AI]

(or

arXiv:2610.41308v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.41308

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Mihyeon Kim [ view email ]

[v1]

Fri, 30 Oct 2026 06:15:59 UTC (4,645 KB)

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