BEDA: Belief Estimation as Probabilistic Constraints for Performing Strategic Dialogue Acts

Hengli Li, Zhaoxin Yu, Qi Shen, Chenxi Li, Mengmeng Wang, Tinglang Wu, Yipeng Kang, Yuxuan Wang, Song-Chun Zhu, Zixia Jia, Zilong Zheng

Keywords: theory of mind, belief estimation, dialogue act, pragmatics, dialogue games

TL;DR: We mathematically define two dialogue acts, Adversarial Dialogue Act and Alignment Dialogue Act, which we further incorporate into our model, BEDA to achieve superior performance.

Abstract: Strategic dialogue requires agents to execute distinct dialogue acts, for which belief estimation is essential. While prior work often estimates beliefs accurately, it lacks a principled mechanism to use those beliefs during generation. We bridge this gap by first formalizing two core acts **Adversarial** and **Alignment**---and by operationalizing them via **probabilistic constraints** on what an agent may generate. We instantiate this idea in **BEDA**, a framework that consists of the world set, the belief estimator for belief estimation, and the conditional generator that selects acts and realizes utterances consistent with the inferred beliefs. Across three settings, Conditional Keeper–Burglar (adversarial), Mutual Friends (cooperative), and CaSiNo (negotiation), BEDA consistently outperforms strong baselines: on CKBG it improves success rate by **at least 5.0 points** across backbones and by **20.6 points** with GPT-4.1-nano; on Mutual Friends it achieves an average improvement of **9.3 points**; and on CaSiNo it achieves the optimal deal relative to all baselines. These results indicate that casting belief estimation as constraints provides a simple, general mechanism for reliable strategic dialogue.

Area: Generative and Agentic AI (GAAI)

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Submission Number: 79