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LatentMD: Benchmarking Markdown Boundary Failures in LLM-Generated Text

Lee, Sungjune et al.
arXiv (OAI Expanded) · Papers
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computation-and-languagemachine-learningsoftware-engineering
Large language models (LLMs) increasingly generate Markdown that is consumed by renderers, agents, code extractors, and structured downstream pipelines. Yet existing evaluations often conflate content quality with format adherence, leaving Markdown boundary failures under-measured. We introduce Late
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