It is an interesting, reasoned and promising approach. But there a few issues which I would like to have clarified in the rebuttal to accept the paper. The idea of the paper seems to strongly rely on the paper "Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms" by Blondel et al., where ANOVA kernels have already been used. Can you explain in more detail the difference and contributions in comparison to this paper? I'm wondering why such approaches cannot be applied to the given problem or why it is not better to adapt them to HOFMs.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. The Doctoral Consortium provides an opportunity for a group of PhD students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. In the first of our interviews with the 2025 cohort, we meet Kunpeng (Chris) Xu and find out more about his research and future plans. I am a final-year Ph.D. student at the ProspectUs-Lab, Université de Sherbrooke, Canada, where I have been working with Professor Shengrui Wang and Professor Lifei Chen since 2021. I explore data-driven kernel representation learning to develop more adaptive and expressive models for complex time series, while also investigating subspace learning and its applications in AI.

Data models are necessary for the birth of data and of any data-driven system. Indeed, every algorithm, every machine learning model, every statistical model, and every database has an underlying data model without which the system would not be usable. Hence, data models are excellent sites for interrogating the (material, social, political, ...) conditions giving rise to a data system. Towards this, drawing inspiration from literary criticism, we propose to closely read data models in the same spirit as we closely read literary artifacts. Close readings of data models reconnect us with, among other things, the materiality, the genealogies, the techne, the closed nature, and the design of technical systems. While recognizing from literary theory that there is no one correct way to read, it is nonetheless critical to have systematic guidance for those unfamiliar with close readings. This is especially true for those trained in the computing and data sciences, who too often are enculturated to set aside the socio-political aspects of data work. A systematic methodology for reading data models currently does not exist. To fill this gap, we present the CREDAL methodology for close readings of data models. We detail our iterative development process and present results of a qualitative evaluation of CREDAL demonstrating its usability, usefulness, and effectiveness in the critical study of data.

Recent advancements in large language models (LLMs) revolutionize the field of intelligent agents, enabling collaborative multi-agent systems capable of tackling complex problems across various domains. However, the potential of conformity within these systems, analogous to phenomena like conformity bias and groupthink in human group dynamics, remains largely unexplored, raising concerns about their collective problem-solving capabilities and possible ethical implications. This paper presents a comprehensive study on conformity in LLM-driven multi-agent systems, focusing on three aspects: the existence of conformity, the factors influencing conformity, and potential mitigation strategies. In particular, we introduce BenchForm, a new conformity-oriented benchmark, featuring reasoning-intensive tasks and five distinct interaction protocols designed to probe LLMs' behavior in collaborative scenarios. Several representative LLMs are evaluated on BenchForm, using metrics such as conformity rate and independence rate to quantify conformity's impact. Our analysis delves into factors influencing conformity, including interaction time and majority size, and examines how the subject agent rationalizes its conforming behavior. Furthermore, we explore two strategies to mitigate conformity effects, i.e., developing enhanced personas and implementing a reflection mechanism. Several interesting findings regarding LLMs' conformity are derived from empirical results and case studies. We hope that these insights can pave the way for more robust and ethically-aligned collaborative AI systems. Our benchmark and code are available at BenchForm.

Although Large Language Models (LLMs) succeed in human-guided conversations such as instruction following and question answering, the potential of LLM-guided conversations-where LLMs direct the discourse and steer the conversation's objectives-remains under-explored. In this study, we first characterize LLM-guided conversation into three fundamental components: (i) Goal Navigation; (ii) Context Management; (iii) Empathetic Engagement, and propose GuideLLM as an installation. We then implement an interviewing environment for the evaluation of LLM-guided conversation. Specifically, various topics are involved in this environment for comprehensive interviewing evaluation, resulting in around 1.4k turns of utterances, 184k tokens, and over 200 events mentioned during the interviewing for each chatbot evaluation. We compare GuideLLM with 6 state-of-the-art LLMs such as GPT-4o and Llama-3-70b-Instruct, from the perspective of interviewing quality, and autobiography generation quality. For automatic evaluation, we derive user proxies from multiple autobiographies and employ LLM-as-a-judge to score LLM behaviors. We further conduct a human-involved experiment by employing 45 human participants to chat with GuideLLM and baselines. We then collect human feedback, preferences, and ratings regarding the qualities of conversation and autobiography. Experimental results indicate that GuideLLM significantly outperforms baseline LLMs in automatic evaluation and achieves consistent leading performances in human ratings.

This paper presents HamRaz, a novel Persian-language mental health dataset designed for Person-Centered Therapy (PCT) using Large Language Models (LLMs). Despite the growing application of LLMs in AI-driven psychological counseling, existing datasets predominantly focus on Western and East Asian contexts, overlooking cultural and linguistic nuances essential for effective Persian-language therapy. To address this gap, HamRaz combines script-based dialogues with adaptive LLM role-playing, ensuring coherent and dynamic therapy interactions. We also introduce HamRazEval, a dual evaluation framework that measures conversational quality and therapeutic effectiveness using General Dialogue Metrics and the Barrett-Lennard Relationship Inventory (BLRI). Experimental results show HamRaz outperforms conventional Script Mode and Two-Agent Mode, producing more empathetic, context-aware, and realistic therapy sessions. By releasing HamRaz, we contribute a culturally adapted, LLM-driven resource to advance AI-powered psychotherapy research in diverse communities.

Mental-illness stigma is a persistent social problem, hampering both treatment-seeking and recovery. Accordingly, there is a pressing need to understand it more clearly, but analyzing the relevant data is highly labor-intensive. Therefore, we designed a chatbot to engage participants in conversations; coded those conversations qualitatively with AI assistance; and, based on those coding results, built causal knowledge graphs to decode stigma. The results we obtained from 1,002 participants demonstrate that conversation with our chatbot can elicit rich information about people's attitudes toward depression, while our AI-assisted coding was strongly consistent with human-expert coding. Our novel approach combining large language models (LLMs) and causal knowledge graphs uncovered patterns in individual responses and illustrated the interrelationships of psychological constructs in the dataset as a whole. The paper also discusses these findings' implications for HCI researchers in developing digital interventions, decomposing human psychological constructs, and fostering inclusive attitudes.

This is a relevant and timely paper that has been reviewed by four knowledgeable referees, who also thoroughly considered the author's response to their initial reviews. Three of these reviewers recommend acceptance, providing detailed suggestions on how to improve this work before its final submission. This dissenting opinion was upheld by R3 after discussion with other referees. R3 in my opinion correctly brings up that if the proposed approach aims to improve runtime with an approximate algorithm, this must be sufficiently demonstrated in experiments vs. straightforward alternatives (such as retraining-based methods). That has not been done in the original submission neither in the rebuttal.

Weaknesses: - The authors compared their method to the baseline approach only. However, there are plenty of curriculum learning methods that could have been used as relevant state-of-the-art competing methods to compare with, e.g. Comparison with such competing methods is mandatory, in my opinion. I believe that the non-linearity is typically applied before the pooling operation. Even so, it is not clear why it works so well.

Cancer surgery is a key treatment for gastrointestinal (GI) cancers, a group of cancers that account for more than 35% of cancer-related deaths worldwide, but postoperative complications are unpredictable and can be life-threatening. In this paper, we investigate how recent advancements in large language models (LLMs) can benefit remote patient monitoring (RPM) systems through clinical integration by designing RECOVER, an LLM-powered RPM system for postoperative GI cancer care. To closely engage stakeholders in the design process, we first conducted seven participatory design sessions with five clinical staff and interviewed five cancer patients to derive six major design strategies for integrating clinical guidelines and information needs into LLM-based RPM systems. We then designed and implemented RECOVER, which features an LLM-powered conversational agent for cancer patients and an interactive dashboard for clinical staff to enable efficient postoperative RPM. Finally, we used RECOVER as a pilot system to assess the implementation of our design strategies with four clinical staff and five patients, providing design implications by identifying crucial design elements, offering insights on responsible AI, and outlining opportunities for future LLM-powered RPM systems.