@inproceedings{shen-etal-2026-relational,
title = "Relational Priors as Convergence Pressure in {LLM}-Based Multi-Agent Systems",
author = "Shen, Ming and
Shang, Chao and
Shahriar, Sadat and
Kulshreshtha, Devang and
Zhang, Yi and
Swamy, Sandesh and
Qi, Yanjun",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.3/",
pages = "49--66",
ISBN = "979-8-89176-439-2",
abstract = "Large language model-based multi-agent systems (LLM-MAS) are designed through roles, debate protocols, and aggregation rules. These choices create implicit expectations of trust, skepticism, deference, or collaboration. We make inter-agent relations explicit as signed pairwise priors, rendered in natural language and added to system prompts while keeping the task protocol fixed. Across commons governance and multi-agent debate, these priors change how readily agents coordinate or agree, a pattern we call convergence pressure. More positive relations generally improve sustainability within the GovSim relational-prior sweep and increase consensus on subjective questions. These improvements over negative relations differ from gains over no-prior prompting. On objective QA, fully positive priors usually yield lower final-answer accuracy than the no-prior baseline, and some conditions produce more frequent but less accurate consensus. Effects depend on model backbone, relation type, and topology; explicitly neutral relations also produce different outcomes from omitting relational framing. Relational priors are therefore task-specific interventions and diagnostic probes of sensitivity to social framing. Evaluate them against a no-prior baseline using the task{'}s primary metric, report accuracy and consensus correctness alongside agreement on objective tasks, and keep no-prior prompting as the default for accuracy-centric tasks unless validation supports a relational prior."
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<abstract>Large language model-based multi-agent systems (LLM-MAS) are designed through roles, debate protocols, and aggregation rules. These choices create implicit expectations of trust, skepticism, deference, or collaboration. We make inter-agent relations explicit as signed pairwise priors, rendered in natural language and added to system prompts while keeping the task protocol fixed. Across commons governance and multi-agent debate, these priors change how readily agents coordinate or agree, a pattern we call convergence pressure. More positive relations generally improve sustainability within the GovSim relational-prior sweep and increase consensus on subjective questions. These improvements over negative relations differ from gains over no-prior prompting. On objective QA, fully positive priors usually yield lower final-answer accuracy than the no-prior baseline, and some conditions produce more frequent but less accurate consensus. Effects depend on model backbone, relation type, and topology; explicitly neutral relations also produce different outcomes from omitting relational framing. Relational priors are therefore task-specific interventions and diagnostic probes of sensitivity to social framing. Evaluate them against a no-prior baseline using the task’s primary metric, report accuracy and consensus correctness alongside agreement on objective tasks, and keep no-prior prompting as the default for accuracy-centric tasks unless validation supports a relational prior.</abstract>
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%0 Conference Proceedings
%T Relational Priors as Convergence Pressure in LLM-Based Multi-Agent Systems
%A Shen, Ming
%A Shang, Chao
%A Shahriar, Sadat
%A Kulshreshtha, Devang
%A Zhang, Yi
%A Swamy, Sandesh
%A Qi, Yanjun
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F shen-etal-2026-relational
%X Large language model-based multi-agent systems (LLM-MAS) are designed through roles, debate protocols, and aggregation rules. These choices create implicit expectations of trust, skepticism, deference, or collaboration. We make inter-agent relations explicit as signed pairwise priors, rendered in natural language and added to system prompts while keeping the task protocol fixed. Across commons governance and multi-agent debate, these priors change how readily agents coordinate or agree, a pattern we call convergence pressure. More positive relations generally improve sustainability within the GovSim relational-prior sweep and increase consensus on subjective questions. These improvements over negative relations differ from gains over no-prior prompting. On objective QA, fully positive priors usually yield lower final-answer accuracy than the no-prior baseline, and some conditions produce more frequent but less accurate consensus. Effects depend on model backbone, relation type, and topology; explicitly neutral relations also produce different outcomes from omitting relational framing. Relational priors are therefore task-specific interventions and diagnostic probes of sensitivity to social framing. Evaluate them against a no-prior baseline using the task’s primary metric, report accuracy and consensus correctness alongside agreement on objective tasks, and keep no-prior prompting as the default for accuracy-centric tasks unless validation supports a relational prior.
%U https://aclanthology.org/2026.aacl-long.3/
%P 49-66
Markdown (Informal)
[Relational Priors as Convergence Pressure in LLM-Based Multi-Agent Systems](https://aclanthology.org/2026.aacl-long.3/) (Shen et al., AACL-IJCNLP 2026)
ACL
- Ming Shen, Chao Shang, Sadat Shahriar, Devang Kulshreshtha, Yi Zhang, Sandesh Swamy, and Yanjun Qi. 2026. Relational Priors as Convergence Pressure in LLM-Based Multi-Agent Systems. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 49–66, Hengqin, China. Association for Computational Linguistics.