@inproceedings{shen-etal-2026-scaling,
title = "Scaling Collaborative Effort with Agents",
author = "Shen, Shannon Zejiang and
Chen, Valerie and
Gu, Ken and
Ross, Alexis and
Ma, Zixian and
Ross, Jillian and
Gu, Alex and
Si, Chenglei and
Chi, Wayne and
Peng, Andi and
Shen, Jocelyn J and
Talwalkar, Ameet and
Wu, Tongshuang and
Sontag, David",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.595/",
pages = "12254--12271",
ISBN = "979-8-89176-395-1",
abstract = "Current evaluations of agents remain centered around one-shot task completion, failing to account for the inherently iterative and collaborative nature of many real-world problems, where human goals are often underspecified and evolve. We argue for a shift from building and assessing task completion agents to developing collaborative agents, assessed not only by the quality of their final outputs but by how well they engage with and enhance human effort throughout the problem-solving process. To support this shift, we introduce collaborative effort scaling, a framework that captures how an agent{'}s utility grows with increasing user involvement. Through case studies and simulated evaluations, we show that state-of-the-art agents often underperform in multi-turn, real-world scenarios, revealing a missing ingredient in agent design: the ability to sustain engagement and scaffold user understanding. Collaborative effort scaling offers a lens for diagnosing agent behavior and guiding development toward more effective interactions."
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<abstract>Current evaluations of agents remain centered around one-shot task completion, failing to account for the inherently iterative and collaborative nature of many real-world problems, where human goals are often underspecified and evolve. We argue for a shift from building and assessing task completion agents to developing collaborative agents, assessed not only by the quality of their final outputs but by how well they engage with and enhance human effort throughout the problem-solving process. To support this shift, we introduce collaborative effort scaling, a framework that captures how an agent’s utility grows with increasing user involvement. Through case studies and simulated evaluations, we show that state-of-the-art agents often underperform in multi-turn, real-world scenarios, revealing a missing ingredient in agent design: the ability to sustain engagement and scaffold user understanding. Collaborative effort scaling offers a lens for diagnosing agent behavior and guiding development toward more effective interactions.</abstract>
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%0 Conference Proceedings
%T Scaling Collaborative Effort with Agents
%A Shen, Shannon Zejiang
%A Chen, Valerie
%A Gu, Ken
%A Ross, Alexis
%A Ma, Zixian
%A Ross, Jillian
%A Gu, Alex
%A Si, Chenglei
%A Chi, Wayne
%A Peng, Andi
%A Shen, Jocelyn J.
%A Talwalkar, Ameet
%A Wu, Tongshuang
%A Sontag, David
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F shen-etal-2026-scaling
%X Current evaluations of agents remain centered around one-shot task completion, failing to account for the inherently iterative and collaborative nature of many real-world problems, where human goals are often underspecified and evolve. We argue for a shift from building and assessing task completion agents to developing collaborative agents, assessed not only by the quality of their final outputs but by how well they engage with and enhance human effort throughout the problem-solving process. To support this shift, we introduce collaborative effort scaling, a framework that captures how an agent’s utility grows with increasing user involvement. Through case studies and simulated evaluations, we show that state-of-the-art agents often underperform in multi-turn, real-world scenarios, revealing a missing ingredient in agent design: the ability to sustain engagement and scaffold user understanding. Collaborative effort scaling offers a lens for diagnosing agent behavior and guiding development toward more effective interactions.
%U https://aclanthology.org/2026.findings-acl.595/
%P 12254-12271
Markdown (Informal)
[Scaling Collaborative Effort with Agents](https://aclanthology.org/2026.findings-acl.595/) (Shen et al., Findings 2026)
ACL
- Shannon Zejiang Shen, Valerie Chen, Ken Gu, Alexis Ross, Zixian Ma, Jillian Ross, Alex Gu, Chenglei Si, Wayne Chi, Andi Peng, Jocelyn J Shen, Ameet Talwalkar, Tongshuang Wu, and David Sontag. 2026. Scaling Collaborative Effort with Agents. In Findings of the Association for Computational Linguistics: ACL 2026, pages 12254–12271, San Diego, California, United States. Association for Computational Linguistics.