Synthetic Reader Panels: Tournament-Based Ideation with LLM Personas for Autonomous Publishing

TL;DR

This paper addresses the ideation bottleneck in high-volume publishing by proposing Synthetic Reader Panels, which replace traditional human focus groups with diverse LLM-instantiated reader personas evaluated through tournament-style competitions. The method grounds each persona in demographic, behavioral, and psychographic attributes and uses multiple tournament formats with a weighted rubric and anti-slop checks to filter concepts. Deployments in a multi-imprint pipeline ($6$ imprints, $609$ titles) show that synthetic panels can reveal demographic segmentation, flag structural issues unseen by homogeneous reviewers, and boost the proportion of high-quality survivors from $15\%$ to $62\%$ in selected samples. The work discusses biases, diversity limitations, and human-in-the-loop integration, and outlines future directions including longitudinal calibration and adaptive panel composition.

Abstract

We present a system for autonomous book ideation that replaces human focus groups with synthetic reader panels -- diverse collections of LLM-instantiated reader personas that evaluate book concepts through structured tournament competitions. Each persona is defined by demographic attributes (age group, gender, income, education, reading level), behavioral patterns (books per year, genre preferences, discovery methods, price sensitivity), and consistency parameters. Panels are composed per imprint to reflect target demographics, with diversity constraints ensuring representation across age, reading level, and genre affinity. Book concepts compete in single-elimination, double-elimination, round-robin, or Swiss-system tournaments, judged against weighted criteria including market appeal, originality, and execution potential. To reject low-quality LLM evaluations, we implement five automated anti-slop checks (repetitive phrasing, generic framing, circular reasoning, score clustering, audience mismatch). We report results from deployment within a multi-imprint publishing operation managing 6 active imprints and 609 titles in distribution. Three case studies -- a 270-evaluator panel for a children's literacy novel, and two 5-person expert panels for a military memoir and a naval strategy monograph -- demonstrate that synthetic panels produce actionable demographic segmentation, identify structural content issues invisible to homogeneous reviewers, and enable tournament filtering that eliminates low-quality concepts while enriching high-quality survivors from 15% to 62% of the evaluated pool.

Figures (1)

• Figure 1: Score distributions across panel segments and evaluation criteria for three case studies. Color encodes score magnitude (red = low, green = high). Demographic segmentation reveals systematic score variation: specialist audiences consistently rate higher than general audiences within each case.