arXiv:2601.19839v1 Announce Type: cross Abstract: Existing human-robot interaction systems often lack mechanisms for sustained personalization and dynamic adaptation in multi-user environments, limiting their effectiveness in real-world deployments. We present HARMONI, a multimodal personalization framework that leverages large language models to enable socially assistive robots to manage long-term multi-user interactions. The framework integrates four key modules: […]
EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Models
arXiv:2502.04424v3 Announce Type: replace-cross Abstract: With the integration of Multimodal large language models (MLLMs) into robotic systems and various AI applications, embedding emotional intelligence (EI) capabilities into these models is essential for enabling robots to effectively address human emotional needs and interact seamlessly in real-world scenarios. Existing static, text-based, or text-image benchmarks overlook the multimodal […]
Meaning Is Not A Metric: Using LLMs to make cultural context legible at scale
arXiv:2505.23785v2 Announce Type: replace-cross Abstract: This position paper argues that large language models (LLMs) can make cultural context, and therefore human meaning, legible at an unprecedented scale in AI-based sociotechnical systems. We argue that such systems have previously been unable to represent human meaning because they rely on thin descriptions (numerical representations that enforce standardization […]
LLM Agents for Knowledge Discovery in Atomic Layer Processing
arXiv:2509.26201v2 Announce Type: replace Abstract: Large Language Models (LLMs) have garnered significant attention for several years now. Recently, their use as independently reasoning agents has been proposed. In this work, we test the potential of such agents for knowledge discovery in materials science. We repurpose LangGraph’s tool functionality to supply agents with a black box […]
Human Simulation Computation: A Human-Inspired Framework for Adaptive AI Systems
arXiv:2601.13887v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated strong capabilities in knowledge representation and reasoning based on textual data. However, their reliance on language material alone limits their ability to adapt, verify reasoning outcomes, and operate effectively in open and dynamic real-world environments. In this paper, we propose Human Simulation Computation (HSC), […]
Causal Pre-training Under the Fairness Lens: An Empirical Study of TabPFN
arXiv:2601.17912v2 Announce Type: replace-cross Abstract: Foundation models for tabular data, such as the Tabular Prior-data Fitted Network (TabPFN), are pre-trained on a massive number of synthetic datasets generated by structural causal models (SCM). They leverage in-context learning to offer high predictive accuracy in real-world tasks. However, the fairness properties of these foundational models, which incorporate […]
Damper-B-PINN: Damper Characteristics-Based Bayesian Physics-Informed Neural Network for Vehicle State Estimation
arXiv:2502.20772v3 Announce Type: replace Abstract: Accurate state estimation is fundamental to intelligent vehicles. Wheel load, one of the most important chassis states, serves as an essential input for advanced driver assistance systems (ADAS) and exerts a direct influence on vehicle stability and safety. However, wheel load estimation remains challenging due to the complexity of chassis […]
A general framework for adaptive nonparametric dimensionality reduction
arXiv:2511.09486v2 Announce Type: replace-cross Abstract: Dimensionality reduction is a fundamental task in modern data science. Several projection methods specifically tailored to take into account the non-linearity of the data via local embeddings have been proposed. Such methods are often based on local neighbourhood structures and require tuning the number of neighbours that define this local […]
Gradient-Direction-Aware Density Control for 3D Gaussian Splatting
arXiv:2508.09239v2 Announce Type: replace-cross Abstract: The emergence of 3D Gaussian Splatting (3DGS) has significantly advanced Novel View Synthesis (NVS) through explicit scene representation, enabling real-time photorealistic rendering. However, existing approaches manifest two critical limitations in complex scenarios: (1) Over-reconstruction occurs when persistent large Gaussians cannot meet adaptive splitting thresholds during density control. This is exacerbated […]
Universal Multi-Domain Translation via Diffusion Routers
arXiv:2510.03252v3 Announce Type: replace-cross Abstract: Multi-domain translation (MDT) aims to learn translations between multiple domains, yet existing approaches either require fully aligned tuples or can only handle domain pairs seen in training, limiting their practicality and excluding many cross-domain mappings. We introduce universal MDT (UMDT), a generalization of MDT that seeks to translate between any […]
Do LLMs Give Good Romantic Relationship Advice? A Study on User Satisfaction and Attitude Change
arXiv:2601.11527v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly being used to provide support and advice in personal domains such as romantic relationships, yet little is known about user perceptions of this type of advice. This study investigated how people evaluate advice on LLM-generated romantic relationships. Participants rated advice satisfaction, model reliability, and […]
When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering
arXiv:2601.19827v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) extends large language models (LLMs) beyond parametric knowledge, yet it is unclear when iterative retrieval-reasoning loops meaningfully outperform static RAG, particularly in scientific domains with multi-hop reasoning, sparse domain knowledge, and heterogeneous evidence. We provide the first controlled, mechanism-level diagnostic study of whether synchronized iterative retrieval and […]