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arXiv:2603. 03305v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to generate executable outputs, JSON objects, and API calls, where a single syntax error can make the output unusable.
arXiv:2610.33949v1 Announce Type: cross Abstract: Pipelines that require structured outputs from hosted language models often specify a Pydantic schema in the prompt, validate the response, and retry...
The paper investigates the so‑called ‘structure tax’ that is often attributed to constraining large language model outputs to structured formats like JSON or XML. By evaluating various models, datasets, and schema designs, the authors find that the accuracy loss depends more on how the schema is organized than on the presence of structure itself. Specifically, reasoning‑first field ordering can match or surpass free‑form accuracy, whereas answer‑first ordering leads to significant drops, especially in smaller models. The study also shows that well‑designed schemas improve confidence calibration and the separability of correct versus incorrect representations in transformer layers.
arXiv:2609.06993v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly generate Markdown that is consumed by renderers, agents, code extractors, and structured downstream pipelin...
arXiv:2609.21509v1 Announce Type: new Abstract: When language models reason in chain-of-thought or exchange free-text intermediates, they serialize structured information into natural language. How m...
SERIAL-7 is a Korean‑English benchmark that evaluates large language models on their ability to generate machine‑verifiable structured outputs across seven representations. It introduces an executable verification framework that separates semantic derivation (Derive), constrained artifact generation (Realize), and cross‑representation preservation (Preserve), using 177 validated artifacts and 43 atomic representation constraints. The benchmark shows that reliable structured generation requires explicit, executable verification of how artifacts are realized and preserved, as evidenced by varying compliance rates across ten models.