SOURCE-LINKED INTELLIGENCE
LatentMD: Benchmarking Markdown Boundary Failures in LLM-Generated Text
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 LatentMD, a benchmark and evaluation protocol for diagnosing CommonMark-level fence-boundary failures in LLM-generated Markdown. LatentMD separates content correctness from boundary correctness, enabling detection of outputs that are content-correct but boundary-broken. The benchmark contains 4,179 prom
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-07T03:35:26.000Z
First collected: 2026-09-20T20:52:10.320Z. This is not the publication date.