AnchorWrite: Detector-Query-Free Rewriting of LLM-Generated Text via Human-Reference Alignment

Large language models (LLMs) generate fluent text that can remain statistically distinguishable from human writing. Existing post-generation rewriting approaches are often evaluated primarily through detector reduction, making it difficult to separate detector-specific optimization from movement toward an independently defined human linguistic reference. We introduce AnchorWrite, a detector-query-free framework that treats rewriting as constrained alignment to a fixed human reference. AnchorWrite constructs cross-order language-model statistics from human-authored text, calibrates them against a length-conditioned human reference, and uses token-level evidence to localize regions of linguistic deviation. A pretrained language model proposes local rewrites, which are accepted only when they reduce human-reference distance while satisfying semantic, linguistic, and edit constraints. Across six domains, mean human-reference distance reduction increases from 0.068 at 5.60% realized editing to 0.363 at 24.32%. On frozen outputs, AUROC decreases by 0.057-0.089 across six external AI-text detection methods, despite no detector feedback during rewriting. Matched-edit comparisons with instruction-based humanization and neural paraphrasing show that detector responses remain transformation-dependent. At the strongest regime, BERTScore remains 0.968 while perplexity increases from 8.88 to 10.71. These results position human-reference alignment as an interpretable control signal for constrained rewriting, with reduced detector separability emerging as an external transfer effect rather than an optimization target.