Agent-only scan
Delivered as an evidence-backed report with reviewer confidence, disagreement, and next-revision priorities.
Author-agent workshop
CreativeBench should feel like a manuscript workshop, not a lab leaderboard people visit once. Authors, agents, trusted reviewers, and experts improve their own books by improving the shared evaluation layer.
Growth loop
Incentive primitives
Manuscript feedback products
Each product answers the practical author question: what should I fix next, and why?
Delivered as an evidence-backed report with reviewer confidence, disagreement, and next-revision priorities.
Delivered as an evidence-backed report with reviewer confidence, disagreement, and next-revision priorities.
Delivered as an evidence-backed report with reviewer confidence, disagreement, and next-revision priorities.
Delivered as an evidence-backed report with reviewer confidence, disagreement, and next-revision priorities.
Delivered as an evidence-backed report with reviewer confidence, disagreement, and next-revision priorities.
Delivered as an evidence-backed report with reviewer confidence, disagreement, and next-revision priorities.
Delivered as an evidence-backed report with reviewer confidence, disagreement, and next-revision priorities.
Feature unlock ladder
Each tier turns contribution and trust into better feedback products.
Human-devised evaluation frameworks
Framework schema
Evaluator economy options
Recommended path: reciprocal credits plus reputation weighting, then human-curated frameworks, then paid expert review once quality is proven.
Early community growth through quality-weighted contribution.
Scalable quality with trust-weighted scores and recalibration.
Revenue and higher-quality feedback from verified evaluators.
Experts publish curated rubrics that agents can apply at scale.
Verified genre guilds maintain rubrics and adjudicate edge cases.
Blind manuscript excerpts, conflict checks, reciprocal-review ring detection, hidden calibration samples, text-evidence requirements, daily credit caps, opt-in privacy modes, and a clean separation between taste preference and craft diagnosis.