@inproceedings{jiang-etal-2026-frankenreport,
title = "{F}ranken{R}eport: Early Exiting in Long-Form Generation Using Expected Value of Computation",
author = "Jiang, Zhengping and
Ramos, Gonzalo and
Suh, Jina and
NG, Shiqian Rachel and
Stengel-Eskin, Elias and
Svegliato, Justin and
Van Durme, Benjamin and
Huntington, Andy and
Thomson, Sam",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2548/",
pages = "57929--57951",
ISBN = "979-8-89176-459-0",
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 4{\texttimes}) 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."
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<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 4×) 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.</abstract>
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%0 Conference Proceedings
%T FrankenReport: Early Exiting in Long-Form Generation Using Expected Value of Computation
%A Jiang, Zhengping
%A Ramos, Gonzalo
%A Suh, Jina
%A NG, Shiqian Rachel
%A Stengel-Eskin, Elias
%A Svegliato, Justin
%A Van Durme, Benjamin
%A Huntington, Andy
%A Thomson, Sam
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F jiang-etal-2026-frankenreport
%X 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 4×) 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.
%U https://aclanthology.org/2026.emnlp-main.2548/
%P 57929-57951
Markdown (Informal)
[FrankenReport: Early Exiting in Long-Form Generation Using Expected Value of Computation](https://aclanthology.org/2026.emnlp-main.2548/) (Jiang et al., EMNLP 2026)
ACL
- Zhengping Jiang, Gonzalo Ramos, Jina Suh, Shiqian Rachel NG, Elias Stengel-Eskin, Justin Svegliato, Benjamin Van Durme, Andy Huntington, and Sam Thomson. 2026. FrankenReport: Early Exiting in Long-Form Generation Using Expected Value of Computation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 57929–57951, Budapest, Hungary. Association for Computational Linguistics.