@inproceedings{wei-etal-2026-causal,
title = "Causal Abstention for Cost-Aware Language-Agent Collaboration",
author = "Wei, Lei and
Huang, Ruiyang and
Kang, Zhaolu and
Liu, Qi and
Peng, Xiao and
Liu, Haoran and
Ying, Zonghao and
Wang, Haoyu and
Shen, Ye and
Dong, Xu and
Li, Hongyu and
Wang, Kesu and
Lian, Yukun and
Xie, Sicong",
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.2214/",
pages = "50362--50381",
ISBN = "979-8-89176-459-0",
abstract = "Multi-agent language systems are often deployed under the assumption that collaboration improves reasoning quality, but this assumption breaks down once token cost, latency, and collaboration-induced errors enter the picture. We study protocol selection under a cost-aware utility objective, where the system decides on each input whether to run a collaborative protocol or fall back to solo reasoning. We propose Causal Advantage Routing for Language agents (CARL), a contextual-bandit framework that estimates the conditional collaboration advantage of each protocol over solo reasoning. CARL combines confounded execution logs with budgeted randomized probes, forms one-vs-solo doubly robust scores and shrunken DR-derived pseudo-outcomes, and at deployment uses a one-sided predictive lower bound for a future realized protocol-solo contrast, otherwise abstaining to solo execution. Across math, code, and multi-hop question-answering benchmarks with an open-weight model, CARL improves cost-aware utility and substantially reduces harmful collaboration over fixed protocols, compute-matched solo strategies, MAS routers, cascade methods, and causal routing baselines, indicating that effective multi-agent systems should estimate when collaboration is causally worth its cost rather than invoking it by default."
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<abstract>Multi-agent language systems are often deployed under the assumption that collaboration improves reasoning quality, but this assumption breaks down once token cost, latency, and collaboration-induced errors enter the picture. We study protocol selection under a cost-aware utility objective, where the system decides on each input whether to run a collaborative protocol or fall back to solo reasoning. We propose Causal Advantage Routing for Language agents (CARL), a contextual-bandit framework that estimates the conditional collaboration advantage of each protocol over solo reasoning. CARL combines confounded execution logs with budgeted randomized probes, forms one-vs-solo doubly robust scores and shrunken DR-derived pseudo-outcomes, and at deployment uses a one-sided predictive lower bound for a future realized protocol-solo contrast, otherwise abstaining to solo execution. Across math, code, and multi-hop question-answering benchmarks with an open-weight model, CARL improves cost-aware utility and substantially reduces harmful collaboration over fixed protocols, compute-matched solo strategies, MAS routers, cascade methods, and causal routing baselines, indicating that effective multi-agent systems should estimate when collaboration is causally worth its cost rather than invoking it by default.</abstract>
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%0 Conference Proceedings
%T Causal Abstention for Cost-Aware Language-Agent Collaboration
%A Wei, Lei
%A Huang, Ruiyang
%A Kang, Zhaolu
%A Liu, Qi
%A Peng, Xiao
%A Liu, Haoran
%A Ying, Zonghao
%A Wang, Haoyu
%A Shen, Ye
%A Dong, Xu
%A Li, Hongyu
%A Wang, Kesu
%A Lian, Yukun
%A Xie, Sicong
%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 wei-etal-2026-causal
%X Multi-agent language systems are often deployed under the assumption that collaboration improves reasoning quality, but this assumption breaks down once token cost, latency, and collaboration-induced errors enter the picture. We study protocol selection under a cost-aware utility objective, where the system decides on each input whether to run a collaborative protocol or fall back to solo reasoning. We propose Causal Advantage Routing for Language agents (CARL), a contextual-bandit framework that estimates the conditional collaboration advantage of each protocol over solo reasoning. CARL combines confounded execution logs with budgeted randomized probes, forms one-vs-solo doubly robust scores and shrunken DR-derived pseudo-outcomes, and at deployment uses a one-sided predictive lower bound for a future realized protocol-solo contrast, otherwise abstaining to solo execution. Across math, code, and multi-hop question-answering benchmarks with an open-weight model, CARL improves cost-aware utility and substantially reduces harmful collaboration over fixed protocols, compute-matched solo strategies, MAS routers, cascade methods, and causal routing baselines, indicating that effective multi-agent systems should estimate when collaboration is causally worth its cost rather than invoking it by default.
%U https://aclanthology.org/2026.emnlp-main.2214/
%P 50362-50381
Markdown (Informal)
[Causal Abstention for Cost-Aware Language-Agent Collaboration](https://aclanthology.org/2026.emnlp-main.2214/) (Wei et al., EMNLP 2026)
ACL
- Lei Wei, Ruiyang Huang, Zhaolu Kang, Qi Liu, Xiao Peng, Haoran Liu, Zonghao Ying, Haoyu Wang, Ye Shen, Xu Dong, Hongyu Li, Kesu Wang, Yukun Lian, and Sicong Xie. 2026. Causal Abstention for Cost-Aware Language-Agent Collaboration. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 50362–50381, Budapest, Hungary. Association for Computational Linguistics.