Large language models produce fluent fiction, yet their creative output is widely seen as flat. We ask where this quality originates in the training and whether it affects different domains of human fiction equally. We construct a matched story-continuation paradigm across StoryStar (public-platform), TMAS (prompt-guided), and The New Yorker (professional literary)—and compare continuations from four OLMo 32B checkpoints (Base, SFT, DPO, RLVR) against matched human text. Because these checkpoints share architecture, scale, tokenizer, and pretraining, the design isolates the post-training effect. We measure each continuation along three sentence-level dimensions: thematic motion, affective prevalence, and linguistic diversity. Across all three, post-training compresses dynamic variation: thematic transitions become more uniform, high-intensity emotions give way to neutrality, and stylistic diversity across stories shrinks. We term this progressive loss narrative flattening. The effect is directionally stable across story domains but gap size depends on the human baseline: professional literary fiction is compressed most, while public-platform and prompt-guided stories show smaller gaps, consistent with their human baselines sitting closer to the model’s default rhythm. Post-trained endpoints converge across domains, suggesting alignment produces a continuation regime largely insensitive to the source domain’s narrative texture.

This position paper argues that researchers should not only study how human biases enter large language models (LLMs), but actively leverage those biases as diagnostic tools to surface society’s "unwritten code"—the tacit norms and hidden heuristics that govern high-stakes decisions yet resist explicit articulation. We present a case study in peer review: a framework that iteratively pushes LLMs to generate self-consistent hypotheses explaining score differences across 26,731 submissions to 46 conferences. LLMs’ normative priors (cues extracted by self-talk) emphasizing theoretical rigor are systematically updated toward posteriors (cues actually used in the judgment) emphasizing storytelling and contextualization—a finding cross-validated by independent human survey evidence. Human reviewers explicitly reward prior-aligned aspects (r=0.49) but rarely articulate the storytelling posteriors (r=-0.14), despite implicitly rewarding them. These findings generalize to out-of-sample judgment and different models. We argue this framework applies to hiring, admissions, and criminal justice, offering a scalable path to making hidden evaluative codes visible for public scrutiny and equitable reform.