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

TL;DR

This paper introduces a system that uses diverse synthetic reader panels of LLM personas to evaluate and select book concepts through structured tournaments, replacing traditional focus groups and improving content quality and demographic targeting.

Contribution

The paper presents a novel autonomous book ideation system utilizing LLM-generated reader personas and tournament-based evaluation, enhancing diversity, quality control, and actionable insights in publishing.

Findings

Synthetic panels produce meaningful demographic segmentation.

Tournament filtering increases high-quality concept retention from 15% to 62%.

Automated anti-slop checks improve evaluation reliability.

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.