@article{zhao-etal-2026-bridging-auxiliary,
title = "Bridging Auxiliary Constraints to Resolve Instruction Following in Large Reasoning Models",
author = "Zhao, Zhengyi and
Zhang, Shubo and
Wang, Huimin and
Wang, Zezhong and
Zhao, Yutian and
Zheng, Yefeng and
Li, Binyang and
He, Yulan and
Wong, Kam-Fai and
Wu, Xian",
journal = "Transactions of the Association for Computational Linguistics",
volume = "14",
year = "2026",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2026.tacl-1.60/",
doi = "10.1162/tacl.a.730",
pages = "1329--1347",
abstract = "Large Reasoning Models (LRMs) have demonstrated impressive capabilities in many tasks, yet they struggle with reliably following multiple instructions, either by failing to satisfy individual constraints or by struggling to balance competing constraints simultaneously. We formalize this challenge as the Constraint Adherence Problem (CAP). This paper introduces a novel framework that addresses CAP by representing instructions as a structured knowledge graph of constraints. Our approach, Constraint Relationship Graph Completion (CRGC), explicitly models relationships between constraints, identifies adherence challenges, and discovers ``bridge constraints'' that help the model better focus on and reconcile requirements. Bridge constraints act as auxiliary instructions that make primary constraints more salient and compatible. Unlike existing approaches that enhance instruction following through general training methods, CRGC specifically improves constraint satisfaction by leveraging the model{'}s own knowledge to create better pathways for generation. Experiments across three popular instruction following datasets demonstrate that our approach reduces constraint violations by 39{\%} compared to standard prompting while maintaining reasoning abilities of large reasoning models."
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<abstract>Large Reasoning Models (LRMs) have demonstrated impressive capabilities in many tasks, yet they struggle with reliably following multiple instructions, either by failing to satisfy individual constraints or by struggling to balance competing constraints simultaneously. We formalize this challenge as the Constraint Adherence Problem (CAP). This paper introduces a novel framework that addresses CAP by representing instructions as a structured knowledge graph of constraints. Our approach, Constraint Relationship Graph Completion (CRGC), explicitly models relationships between constraints, identifies adherence challenges, and discovers “bridge constraints” that help the model better focus on and reconcile requirements. Bridge constraints act as auxiliary instructions that make primary constraints more salient and compatible. Unlike existing approaches that enhance instruction following through general training methods, CRGC specifically improves constraint satisfaction by leveraging the model’s own knowledge to create better pathways for generation. Experiments across three popular instruction following datasets demonstrate that our approach reduces constraint violations by 39% compared to standard prompting while maintaining reasoning abilities of large reasoning models.</abstract>
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%0 Journal Article
%T Bridging Auxiliary Constraints to Resolve Instruction Following in Large Reasoning Models
%A Zhao, Zhengyi
%A Zhang, Shubo
%A Wang, Huimin
%A Wang, Zezhong
%A Zhao, Yutian
%A Zheng, Yefeng
%A Li, Binyang
%A He, Yulan
%A Wong, Kam-Fai
%A Wu, Xian
%J Transactions of the Association for Computational Linguistics
%D 2026
%V 14
%I MIT Press
%C Cambridge, MA
%F zhao-etal-2026-bridging-auxiliary
%X Large Reasoning Models (LRMs) have demonstrated impressive capabilities in many tasks, yet they struggle with reliably following multiple instructions, either by failing to satisfy individual constraints or by struggling to balance competing constraints simultaneously. We formalize this challenge as the Constraint Adherence Problem (CAP). This paper introduces a novel framework that addresses CAP by representing instructions as a structured knowledge graph of constraints. Our approach, Constraint Relationship Graph Completion (CRGC), explicitly models relationships between constraints, identifies adherence challenges, and discovers “bridge constraints” that help the model better focus on and reconcile requirements. Bridge constraints act as auxiliary instructions that make primary constraints more salient and compatible. Unlike existing approaches that enhance instruction following through general training methods, CRGC specifically improves constraint satisfaction by leveraging the model’s own knowledge to create better pathways for generation. Experiments across three popular instruction following datasets demonstrate that our approach reduces constraint violations by 39% compared to standard prompting while maintaining reasoning abilities of large reasoning models.
%R 10.1162/tacl.a.730
%U https://aclanthology.org/2026.tacl-1.60/
%U https://doi.org/10.1162/tacl.a.730
%P 1329-1347
Markdown (Informal)
[Bridging Auxiliary Constraints to Resolve Instruction Following in Large Reasoning Models](https://aclanthology.org/2026.tacl-1.60/) (Zhao et al., TACL 2026)
ACL
- Zhengyi Zhao, Shubo Zhang, Huimin Wang, Zezhong Wang, Yutian Zhao, Yefeng Zheng, Binyang Li, Yulan He, Kam-Fai Wong, and Xian Wu. 2026. Bridging Auxiliary Constraints to Resolve Instruction Following in Large Reasoning Models. Transactions of the Association for Computational Linguistics, 14:1329–1347.