arXiv:2601.19479v1 Announce Type: cross Abstract: Injury occurrence in football poses significant challenges for athletes and teams, carrying personal, competitive, and financial consequences. While machine learning has been applied to injury prediction before, existing approaches often rely on static pre-season data and binary outcomes, limiting their real-world utility. This study investigates the feasibility of using a […]
AROMMA: Unifying Olfactory Embeddings for Single Molecules and Mixtures
arXiv:2601.19561v1 Announce Type: cross Abstract: Public olfaction datasets are small and fragmented across single molecules and mixtures, limiting learning of generalizable odor representations. Recent works either learn single-molecule embeddings or address mixtures via similarity or pairwise label prediction, leaving representations separate and unaligned. In this work, we propose AROMMA, a framework that learns a unified […]
A Unifying View of Coverage in Linear Off-Policy Evaluation
arXiv:2601.19030v1 Announce Type: cross Abstract: Off-policy evaluation (OPE) is a fundamental task in reinforcement learning (RL). In the classic setting of linear OPE, finite-sample guarantees often take the form $$ textrmEvaluation error le textrmpoly(C^pi, d, 1/n,log(1/delta)), $$ where $d$ is the dimension of the features and $C^pi$ is a coverage parameter that characterizes the degree […]
Pixel-Grounded Retrieval for Knowledgeable Large Multimodal Models
arXiv:2601.19060v1 Announce Type: cross Abstract: Visual Question Answering (VQA) often requires coupling fine-grained perception with factual knowledge beyond the input image. Prior multimodal Retrieval-Augmented Generation (MM-RAG) systems improve factual grounding but lack an internal policy for when and how to retrieve. We propose PixSearch, the first end-to-end Segmenting Large Multimodal Model (LMM) that unifies region-level […]
Privacy-Preserving Model Transcription with Differentially Private Synthetic Distillation
arXiv:2601.19090v1 Announce Type: cross Abstract: While many deep learning models trained on private datasets have been deployed in various practical tasks, they may pose a privacy leakage risk as attackers could recover informative data or label knowledge from models. In this work, we present emphprivacy-preserving model transcription, a data-free model-to-model conversion solution to facilitate model […]
m2sv: A Scalable Benchmark for Map-to-Street-View Spatial Reasoning
arXiv:2601.19099v1 Announce Type: cross Abstract: Vision–language models (VLMs) achieve strong performance on many multimodal benchmarks but remain brittle on spatial reasoning tasks that require aligning abstract overhead representations with egocentric views. We introduce m2sv, a scalable benchmark for map-to-street-view spatial reasoning that asks models to infer camera viewing direction by aligning a north-up overhead map […]
Bridging Gulfs in UI Generation through Semantic Guidance
arXiv:2601.19171v1 Announce Type: cross Abstract: While generative AI enables high-fidelity UI generation from text prompts, users struggle to articulate design intent and evaluate or refine results-creating gulfs of execution and evaluation. To understand the information needed for UI generation, we conducted a thematic analysis of UI prompting guidelines, identifying key design semantics and discovering that […]
HELM: A Human-Centered Evaluation Framework for LLM-Powered Recommender Systems
arXiv:2601.19197v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into recommendation systems has introduced unprecedented capabilities for natural language understanding, explanation generation, and conversational interactions. However, existing evaluation methodologies focus predominantly on traditional accuracy metrics, failing to capture the multifaceted human-centered qualities that determine the real-world user experience. We introduce framework (textbfHuman-centered […]
Causal Graph Neural Networks for Healthcare
arXiv:2511.02531v4 Announce Type: replace-cross Abstract: Healthcare artificial intelligence systems routinely fail when deployed across institutions, with documented performance drops and perpetuation of discriminatory patterns embedded in historical data. This brittleness stems, in part, from learning statistical associations rather than causal mechanisms. Causal graph neural networks address this triple crisis of distribution shift, discrimination, and inscrutability […]
Adaptive Test-Time Training for Predicting Need for Invasive Mechanical Ventilation in Multi-Center Cohorts
arXiv:2512.06652v2 Announce Type: replace-cross Abstract: Accurate prediction of the need for invasive mechanical ventilation (IMV) in intensive care units (ICUs) patients is crucial for timely interventions and resource allocation. However, variability in patient populations, clinical practices, and electronic health record (EHR) systems across institutions introduces domain shifts that degrade the generalization performance of predictive models […]
Stream-Voice-Anon: Enhancing Utility of Real-Time Speaker Anonymization via Neural Audio Codec and Language Models
arXiv:2601.13948v2 Announce Type: replace-cross Abstract: Protecting speaker identity is crucial for online voice applications, yet streaming speaker anonymization (SA) remains underexplored. Recent research has demonstrated that neural audio codec (NAC) provides superior speaker feature disentanglement and linguistic fidelity. NAC can also be used with causal language models (LM) to enhance linguistic fidelity and prompt control […]
More at Stake: How Payoff and Language Shape LLM Agent Strategies in Cooperation Dilemmas
arXiv:2601.19082v1 Announce Type: new Abstract: As LLMs increasingly act as autonomous agents in interactive and multi-agent settings, understanding their strategic behavior is critical for safety, coordination, and AI-driven social and economic systems. We investigate how payoff magnitude and linguistic context shape LLM strategies in repeated social dilemmas, using a payoff-scaled Prisoner’s Dilemma to isolate sensitivity […]