ARGUS is a new agent-based framework for persuasive argument generation that incorporates a Theory-of-Mind Reasoner to model audience beliefs and values. It uses a component-aware planner to break arguments into subtopics, assign rhetorical functions (logos, pathos, ethos, kairos), and guide evidence retrieval during planning. A refinement module iteratively addresses multi-dimensional weaknesses, and evaluations on three benchmarks show ARGUS outperforming strong baselines and effectively shifting resistant audience stances.
By Zhe Hu
The paper introduces AXLE, a pre‑annotation stage that uses a scenario vignette and guided conversation to generate a first‑person micro‑narrative for annotators. In a 2x2 study with 104 annotators on ambiguous hate‑speech items, AXLE increased identity resonance, sensitivity to harm, label entropy, and reduced inter‑annotator agreement, suggesting it surfaces plural perspectives rather than forcing consensus.
By Sheza Munir, Ramaravind Kommiya Mothilal, Syed Ishtiaque Ahmed
The study evaluates how well language‑model agents can simulate individual social media reactions by comparing predictions under different prompt conditions. Eight Serbian participants’ reactions to 68 posts were recorded, and four language models were asked to predict these reactions using prompts that varied in profile content and instruction style. The results show that prompts emphasizing attitudinal content and intuitive, immediate responses yield the highest fidelity, outperforming demographic backstories and a crowd baseline, and suggesting that such agents could act as general‑purpose simulated users.
By Ljubisa Bojic, Tijana Stanic, Joerg Matthes, Agariadne Dwinggo Samala, Bojana Dinic, Jue Wang
arXiv:2609.17496v1 Announce Type: new
Abstract: LLM assistants are widely used for daily social advice, yet evaluating their social reasoning in such consultation settings remains challenging since (...
By Amir Taubenfeld, Zorik Gekhman, Avigail Grinstein-Dabush, Itay Laish, Ariel Goldstein, Marian Croak, Avinatan Hassidim, Yossi Matias, Amir Feder
arXiv:2507. 01548v3 Announce Type: replace-cross Abstract: This paper explores how older migrants in urban China can record stories that everyday language and design often miss.
By Yunfei Chen, Wen Zhan, Peiyue Lin, Ziqun Hua, Ying Hu
arXiv:2606. 05330v1 Announce Type: cross Abstract: Large language models can shift human beliefs across high-stakes domains, but most persuasion studies rely on pre/post belief change.
By Jared Moore, Noah Goodman, Nick Haber, Max Kleiman-Weiner
arXiv:2606. 11835v1 Announce Type: cross Abstract: Collecting participants' lived experiences is central to design research.
By Zhiqing Wang, Steven Dow
arXiv:2604. 22770v2 Announce Type: replace-cross Abstract: Most digital language learning curricula rely on discrete-item quizzes that test recall rather than applied conversational proficiency.
By Nicy Scaria, Silvester John Joseph Kennedy, Deepak Subramani
arXiv:2609.39333v1 Announce Type: cross
Abstract: Autonomous AI agents can turn authors' goals into interactive narratives by independently organizing and carrying out generation and revision. As age...
By Wenjin Wang, Jiazhen Lei, Yuxin Sha, Nuwa Xi, Meng Zhao, Xingxi Yin, Qi Liu, Yuliang Shen, Zixun Sun
The House with a Million Windows (HWAMW) is an LLM-based interactive fiction system that lets users narrate a story and then view it through a series of AI-generated "windows" that reframe the narrative in various literary styles. The system is grounded in the psychological restorying intervention, aiming to deepen users' exploration of meaning in their personal stories. Empirical results indicate that HWAMW enhances users' sense of narrative identity, and expert reviews suggest it achieves this by facilitating restorying rather than simply generating new content.
By Cody Kommers, Sarah G Immel, Drew Hemment, Mina Lee
Theory of Mind (ToM) benchmarks for Large Language Models (LLMs) typically rely on passive question-answering formats, but the deployment of LLMs in increasingly agentic and autonomous forms demands new evaluations. In this paper we evaluate an agent's ability to induce specific belief states in other agents by taking actions rather than using conversational persuasion, a capability we call Non-Conversational Planning ToM (NCP-ToM).
arXiv:2608.22566v1 Announce Type: new
Abstract: Multi-agent large language model (LLM) systems are designed to improve reasoning by decomposing tasks across multiple agents with specialized functions...
By Vedant Khatri, Anthony Cusimano, Zachari Swiecki, Zhen Xu, Xiner Liu, Renzhe Yu