FrankenReport: Early Exiting in Long-Form Generation Using Expected Value of Computation
Zhengping Jiang, Gonzalo Ramos, Jina Suh, Shiqian Rachel Ng, Elias Stengel-Eskin, Justin Svegliato, Benjamin Van Durme, Andy Huntington, Sam Thomson
Abstract
While deep research systems address interactive information-seeking needs impressively, their real-world deployments face latency and resource-consumption challenges. We present FrankenReport, an interface for long-form knowledge-seeking report generation that supports adaptive early exiting per section: it evaluates intermediate outputs during generation and predicts whether further targeted computation will yield significant quality gains. In a simulation study, FrankenReport outperforms random allocation baselines by a large margin (up to 4x) under low budgets and smoothly recovers full-pipeline quality as the budget grows, showing that future quality gains are predictable from intermediate drafts. Through experiments and user studies, we further show that despite varying preferences across users and topics, FrankenReport adapts to simple, natural user feedback as efficiently as methods requiring much costlier supervision such as generated drafts and explicit rationales.