@conference {2025,
	title = {Measuring and Mitigating Rapport Bias of Large Language Models under Multi-Agent Social Interactions},
	booktitle = {Proceedings of ICLR},
	year = {2026},
	address = {Rio de Janeiro, Brazil},
	abstract = {Large language models (LLMs) are increasingly deployed in multi-agent systems (MAS) as components of collaborative intelligence, where peer interactions dynamically shape individual decision-making. Although prior work has focused on conformity bias, we extend the analysis to examine how LLMs form trust from previous impressions, resist misinformation, and integrate peer input during interaction, key factors for achieving collective intelligence under complex social dynamics. We present KAIROS, a benchmark simulating quiz contests with peer agents of varying reliability, offering fine-grained control over conditions such as expert-novice roles, noisy crowds, and adversarial peers. LLMs receive both historical interactions and current peer responses, allowing systematic investigation into how trust, peer action, and self-confidence influence decisions. As for mitigation strategies, we evaluate prompting, supervised fine-tuning, and reinforcement learning, Group Relative Policy Optimisation (GRPO), across multiple models. Our results reveal that GRPO with multi-agent context combined with outcome-based rewards and unconstrained reasoning achieves the best overall performance, but also decreases the robustness to social influence compared to Base models. The code and datasets are available at: https://github.com/declare-lab/KAIROS},
	url = {https://arxiv.org/abs/2508.18321},
	author = {M. Song and T. D. Pala and W. Jin and A. Zadeh and C. Li and D. Herremans and S. Poria}
}
@conference {2026,
	title = {PromptDistill: Query-based Selective Token Retention in Intermediate Layers for Efficient Large Language Model Inference},
	booktitle = {19th International Natural Language Generation Conference (INLG)},
	year = {2026},
	month = {10/2026},
	abstract = {As large language models (LLMs) tackle increasingly complex tasks and longer documents, their inference cost becomes a major bottleneck. To address this, we propose PromptDistill, a novel, training-free method that improves inference efficiency while preserving generation quality. PromptDistill identifies and retains the most informative tokens by leveraging early-layer attention, preserving their hidden states while reducing computation in later layers. Unlike previous methods such as H2O, SnapKV, and GemFilter, which perform compression only after full input processing, or ignore contextual dependencies, PromptDistill dynamically allocates computation to relevant tokens while maintaining global context. Experiments on LLaMA 3.1 8B, Phi 3.5 Mini, Qwen2 7B/14B, Phi 3 128k across LongBench, InfBench, and Needle in a Haystack demonstrate that PromptDistill achieves substantial efficiency gains with minimal quality loss. With a single-stage selection strategy, it outperforms GemFilter, H2O, and SnapKV, achieving an overall 1\% to 5\% performance improvement with faster inference. Additionally, we explore multi-stage selection, which further improves efficiency while maintaining strong generation performance. These results demonstrate the adaptability of LLM hidden representations and provide valuable insights into optimizing inference for long-context scenarios. Link to codes will be added after anonymous review.},
	url = {https://arxiv.org/abs/2503.23274},
	author = {W. Jin and M. Song and T. D. Pala and C.Y. Ken and D. Herremans and S. Poria}
}
@conference {2026,
	title = {Scaffolded Vulnerability: Chatbot-Mediated Reciprocal Self-Disclosure and Need-Supportive Interaction in Couples},
	booktitle = {Proceedings of CHI},
	year = {2026},
	publisher = {ACM},
	organization = {ACM},
	address = {Barcelona, Spain},
	abstract = {Reciprocal self-disclosure and need-supportive behavior are essential for close relationships, yet prior systems rarely engage the motivational underpinnings, autonomy, competence, and relatedness, that help partners internalize supportive behaviors. We introduce a Self-Determination Theory{\textendash}guided chatbot that mediates self-disclosure between romantic partners by scaffolding these needs through structured questions and reflection follow-ups. In a randomized study (N=72; 36 couples), we compared three conditions: Partner Support (PS: chatbot support + partner-reflection scaffolds), Direct Support (DS: chatbot support only), and Basic Prompt (BP: questions only). PS conversations were longest and most engaged; PS and DS elicited deeper disclosures and stronger relatedness support than BP. Within PS, reflection phases concentrated partner-provided need support. Controlled motivation decreased across conditions, closeness increased only in PS, and vitality declined in BP. We contribute empirical evidence that SDT-guided mediation amplifies support and closeness, a design blueprint for relatedness technologies, and an SDT framework for advancing AI-mediated conversation design.},
	author = {Z. Jiang and S. Yeo and D. Herremans and S. Perrault}
}
@article {2025,
	title = {Demystifying deep search: a holistic evaluation with hint-free multi-hop questions and factorised metrics},
	journal = {arXiv:2510.05137},
	year = {2025},
	url = {https://arxiv.org/abs/2510.05137},
	author = {M. Song and R. Liu and Wang, Xinyu and Jiang, Yong and Xie, Pengjun and Huang, Fei and Zhou, Jingren and D. Herremans and S. Poria}
}
@conference {2025,
	title = {End-to-End Text-to-SQL with Dataset Selection: Leveraging LLMs for Adaptive Query Generation},
	booktitle = {Proceedings of IJCNN, Rome, Italy},
	year = {2025},
	doi = {10.1109/IJCNN64981.2025.11227747},
	url = {https://arxiv.org/abs/2508.06387},
	author = {A. Tripathi and V. Patle and A. Jain and A. Pundir and S. Menon and A. Kumar Singh and D. Herremans}
}
@article {2025,
	title = {Towards the future of education: cyber-physical learning},
	journal = {Discover Education},
	volume = {4},
	year = {2025},
	pages = {1{\textendash}16},
	abstract = {Singapore University of Technology and Design (SUTD) is embarking on an educational innovation program called SUTD campusX to support its future of education. SUTD campusX aims to innovate new educational models, technology tools, and pedagogies for a new form of learning called {\textquotedblleft}Cyber-Physical Learning{\textquotedblright}, where the concept is that both remote cyber students and face-to-face physical students can learn and interact effectively, seamlessly, and synchronously in the same class. SUTD campusX focuses on various streams of emerging technologies such as learning analytics, immersive technologies (e.g., AR/VR/MR, metaverse learning, gamification), telepresence robotics, and personalized learning. SUTD campusX supports and innovates the signature interdisciplinary, student-centric, and team-based problem-solving active learning model of SUTD to transform it for the cyber-physical learning environment. The various innovation streams of campusX not only generate insightful learning data to create new educational frameworks and practices but also enable a learning analytics and AI-assisted platform to deliver personalized adaptive learning. In this perspective article, we describe and reflect on the forward-looking nature of the various innovation themes and how these innovations act as the front-facing technologies to collect and generate learning insights to power an AI-driven cyber-physical learning platform to realize the future of Cyber-Physical Learning at SUTD.},
	doi = {https://doi.org/10.1007/s44217-025-00474-x},
	url = {https://link.springer.com/article/10.1007/s44217-025-00474-x},
	author = {Sockalingam, N. and Lo, K. and Teo, Ju. and Wei, C.C. and Chow, D. and Herremans, D. and Jun, M.L.M. and Kurniawan, O. and Wang, Y. and Leong, P. K.}
}
@conference {2022,
	title = {HEAR 2021: Holistic Evaluation of Audio Representations},
	booktitle = {Proceedings of Machine Learning Research (PMLR): NeurIPS 2021 Competition Track},
	year = {2022},
	abstract = {What audio embedding approach generalizes best to a wide range of downstream tasks across a variety of everyday domains without fine-tuning? The aim of the HEAR 2021 NeurIPS challenge is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios. HEAR 2021 evaluates audio representations using a benchmark suite across a variety of domains, including speech, environmental sound, and music. In the spirit of shared exchange, each participant submitted an audio embedding model following a common API that is general-purpose, open-source, and freely available to use. Twenty-nine models by thirteen external teams were evaluated on nineteen diverse downstream tasks derived from sixteen datasets. Open evaluation code, submitted models and datasets are key contributions, enabling comprehensive and reproducible evaluation, as well as previously impossible longitudinal studies. It still remains an open question whether one single general-purpose audio representation can perform as holistically as the human ear.},
	url = {https://arxiv.org/abs/2203.03022},
	author = {Joseph Turian and Jordie Shier and Humair Raj Khan and Bhiksha Raj and Bj{\"o}rn W. Schuller and Christian J. Steinmetz and Colin Malloy and George Tzanetakis and Gissel Velarde and Kirk McNally and Max Henry and Nicolas Pinto and Camille Noufi and Christian Clough and D. Herremans and Eduardo Fonseca and Jesse Engel and Justin Salamon and Philippe Esling and Pranay Manocha and Shinji Watanabe and Zeyu Jin and Yonatan Bisk}
}
