Illusion of Alignment: Detecting Hidden Disagreement in Collaborative Dialogue

arXiv:2608.08210 · cs.AI · Submitted 2026-08-08 · Read on arXiv

cs.AI

Submitted: 2026-08-08

Updated: 2026-10-13

Code: https://github.com/THUNLP-MT/IoA

License: http://creativecommons.org/licenses/by/4.0/

The gist: Collaborative dialogue can end with apparent agreement while participants still differ on goals, assumptions, or execution plans, creating an illusion of alignment (IoA).

Terminology

Abstract

Collaborative dialogue can end with apparent agreement while participants still differ on goals, assumptions, or execution plans, creating an illusion of alignment (IoA). A real-user study across 18 meetings confirms that IoA arises routinely in human collaboration. Yet IoA poses a paradox: if participants were aware of such disagreements, they would already be explicit; if not, they cannot articulate them when asked, leaving IoA invisible to both participants and observers. In this work, we make IoA detectable by generating diagnostic multiple-choice questions whose divergent answers across participants provide direct behavioral evidence of hidden disagreement. We construct IoA-Suite, a dataset and evaluation protocol for detecting hidden disagreement, spanning five task types and six domains. We find that even the best model attains only 49.5% F1, with the bottleneck traced to private context that the dialogue does not surface. We then train IoA-Prober-8B based on IoA-Suite, reaching 51.8% F1 on IoA-Suite. Across the aforementioned 18 real meetings, it surfaces 2.89 hidden disagreements per meeting that participants confirm they had not voiced, transferring to live human dialogue. Further, in multi-agent collaboration, pairing IoA-Prober-8B with LLM agents improves downstream task performance on BigCodeBench-Hard and HiddenBench.

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