arXiv:2604.16054v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have achieved impressive progress on vision language benchmarks, yet their capacity for visual cognitive and visuospatial reasoning remains less understood. We introduce “Mind’s Eye”, a multiple-choice benchmark of eight visuo-cognitive tasks inspired by classic human intelligence tests and organized under a novel “A-R-T” taxonomy: Abstraction, […]
Social-JEPA: Emergent Geometric Isomorphism
arXiv:2603.02263v2 Announce Type: replace-cross Abstract: World models compress rich sensory streams into compact latent codes that anticipate future observations. We let separate agents acquire such models from distinct viewpoints of the same environment without any parameter sharing or coordination. After training, their internal representations exhibit a striking emergent property: the two latent spaces are related […]
Temporal Contrastive Decoding: A Training-Free Method for Large Audio-Language Models
arXiv:2604.15383v1 Announce Type: cross Abstract: Large audio-language models (LALMs) generalize across speech, sound, and music, but unified decoders can exhibit a emphtemporal smoothing bias: transient acoustic cues may be underutilized in favor of temporally smooth context that is better supported by language priors, leading to less specific audio-grounded outputs. We propose emphTemporal Contrastive Decoding (TCD), […]
QuantSightBench: Evaluating LLM Quantitative Forecasting with Prediction Intervals
arXiv:2604.15859v1 Announce Type: cross Abstract: Forecasting has become a natural benchmark for reasoning under uncertainty. Yet existing evaluations of large language models remain limited to judgmental tasks in simple formats, such as binary or multiple-choice questions. In practice, however, forecasting spans a far broader scope. Across domains such as economics, public health, and social demographics, […]
Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data
arXiv:2604.15380v1 Announce Type: cross Abstract: We present an exascale workflow for materials discovery using atomistic graph foundation models built on HydraGNN. We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale DeepHyper […]
Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks
arXiv:2604.15390v1 Announce Type: cross Abstract: Code deobfuscation is the task of recovering a readable version of a program while preserving its original behavior. In practice, this often requires days or even months of manual work with complex and expensive analysis tools. In this paper, we explore an alternative approach based on Chain-of-Thought (CoT) prompting, where […]
SocialWise: LLM-Agentic Conversation Therapy for Individuals with Autism Spectrum Disorder to Enhance Communication Skills
arXiv:2604.15347v1 Announce Type: cross Abstract: Autism Spectrum Disorder (ASD) affects more than 75 million people worldwide. However, scalable support for practicing everyday conversation is scarce: Low-cost activities such as story reading yield limited improvement. At the same time, effective role-play therapy demands expensive, in-person sessions with specialists. SocialWise bridges this gap through a browser-based application […]
The Synthetic Media Shift: Tracking the Rise, Virality, and Detectability of AI-Generated Multimodal Misinformation
arXiv:2604.15372v1 Announce Type: cross Abstract: As generative AI advances, the distinction between authentic and synthetic media is increasingly blurred, challenging the integrity of online information. In this study, we present CONVEX, a large-scale dataset of multimodal misinformation involving miscaptioned, edited, and AI-generated visual content, comprising over 150K multimodal posts with associated notes and engagement metrics […]
Technically Love: The Evolution of Human-AI Romance Discourse on Reddit
arXiv:2604.15333v1 Announce Type: cross Abstract: Human-AI romantic relationships are increasingly common, yet little is understood about how public discourse around them emerges and shifts over time. Prior research has examined user experiences and ethical concerns, but lacks longitudinal analyses of user-initiated public discussions. We address this gap by analyzing a high-precision dataset of 3,383 self-disclosed […]
Uncertainty, Vagueness, and Ambiguity in Human-Robot Interaction: Why Conceptualization Matters
arXiv:2604.15339v1 Announce Type: cross Abstract: Uncertainty, vagueness, and ambiguity are closely related and often confused concepts in human-robot interaction (HRI). In earlier studies, these concepts have been defined in contradictory ways and described using inconsistent terminology. This conceptual confusion and lack of terminological consistency undermine empirical comparability, thereby slowing the accumulation of theory. Consequently, consistent […]
LACE: Lattice Attention for Cross-thread Exploration
arXiv:2604.15529v1 Announce Type: new Abstract: Current large language models reason in isolation. Although it is common to sample multiple reasoning paths in parallel, these trajectories do not interact, and often fail in the same redundant ways. We introduce LACE, a framework that transforms reasoning from a collection of independent trials into a coordinated, parallel process. […]
The Crutch or the Ceiling? How Different Generations of LLMs Shape EFL Student Writings
arXiv:2604.15460v1 Announce Type: cross Abstract: The rapid evolution of Large Language Models (LLMs) has made them powerful tools for enhancing student writing. This study explores the extent and limitations of LLMs in assisting secondary-level English as a Foreign Language (EFL) students with their writing tasks. While existing studies focus on output quality, our research examines […]