FrankenReport: Early Exiting in Long-Form Generation Using Expected Value of Computation

2026-09-06

AI Summary

研究解决了长篇报告生成中的延迟和资源消耗问题，通过FrankenReport接口实现自适应提前退出机制，评估中间输出并预测后续计算的价值。

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.

Problem

• long-form generation
• early exiting
• resource-consumption

Innovation

• Early Exiting
• Adaptive Computation
• Long-Form Generation
• Quality Prediction