arXiv:2601.20518v2 Announce Type: replace-cross Abstract: Topological deep learning has emerged as a powerful paradigm for modeling higher-order relational structures beyond pairwise interactions that standard graph neural networks fail to capture. While combinatorial complexes (CCs) offer a unified topological foundation for the higher-order graph learning, existing topological deep learning methods rely heavily on local message passing […]
Learning from Child-Directed Speech in Two-Language Scenarios: A French-English Case Study
arXiv:2603.12906v1 Announce Type: cross Abstract: Research on developmentally plausible language models has largely focused on English, leaving open questions about multilingual settings. We present a systematic study of compact language models by extending BabyBERTa to English-French scenarios under strictly size-matched data conditions, covering monolingual, bilingual, and cross-lingual settings. Our design contrasts two types of training […]
Aligning Large Language Model Agents with Rational and Moral Preferences: A Supervised Fine-Tuning Approach
arXiv:2507.20796v2 Announce Type: replace-cross Abstract: As large language models (LLMs) increasingly act as autonomous agents in markets and organizations, their behavior in strategic environments becomes economically consequential. We document that off-the-shelf LLM agents exhibit systematic deviations from payoff-sensitive behavior in canonical economic games, including excessive cooperation and limited responsiveness to incentives. We introduce a supervised […]
Auditing Student-AI Collaboration: A Case Study of Online Graduate CS Students
arXiv:2601.08697v4 Announce Type: replace-cross Abstract: As generative AI becomes embedded in higher education, it increasingly shapes how students complete academic tasks. While these systems offer efficiency and support, concerns persist regarding over-automation, diminished student agency, and the potential for unreliable or hallucinated outputs. This study conducts a mixed-methods audit of student-AI collaboration preferences by examining […]
Key-Value Pair-Free Continual Learner via Task-Specific Prompt-Prototype
arXiv:2601.04864v2 Announce Type: replace Abstract: Continual learning aims to enable models to acquire new knowledge while retaining previously learned information. Prompt-based methods have shown remarkable performance in this domain; however, they typically rely on key-value pairing, which can introduce inter-task interference and hinder scalability. To overcome these limitations, we propose a novel approach employing task-specific […]
Computational lexical analysis of Flamenco genres
arXiv:2405.05723v2 Announce Type: replace-cross Abstract: Flamenco, recognized by UNESCO as part of the Intangible Cultural Heritage of Humanity, is a profound expression of cultural identity rooted in Andalusia, Spain. However, there is a lack of quantitative studies that help identify characteristic patterns in this long-lived music tradition. In this work, we present a computational analysis […]
Causal Cellular Context Transfer Learning (C3TL): An Efficient Architecture for Prediction of Unseen Perturbation Effects
arXiv:2603.13051v1 Announce Type: cross Abstract: Predicting the effects of chemical and genetic perturbations on quantitative cell states is a central challenge in computational biology, molecular medicine and drug discovery. Recent work has leveraged large-scale single-cell data and massive foundation models to address this task. However, such computational resources and extensive datasets are not always accessible […]
MalURLBench: A Benchmark Evaluating Agents’ Vulnerabilities When Processing Web URLs
arXiv:2601.18113v3 Announce Type: replace-cross Abstract: LLM-based web agents have become increasingly popular for their utility in daily life and work. However, they exhibit critical vulnerabilities when processing malicious URLs: accepting a disguised malicious URL enables subsequent access to unsafe webpages, which can cause severe damage to service providers and users. Despite this risk, no benchmark […]
SegDAC: Visual Generalization in Reinforcement Learning via Dynamic Object Tokens
arXiv:2508.09325v4 Announce Type: replace-cross Abstract: Visual reinforcement learning policies trained on pixel observations often struggle to generalize when visual conditions change at test time. Object-centric representations are a promising alternative, but most approaches use fixed-size slot representations, require image reconstruction, or need auxiliary losses to learn object decompositions. As a result, it remains unclear how […]
FAPE-IR: Frequency-Aware Planning and Execution Framework for All-in-One Image Restoration
arXiv:2511.14099v3 Announce Type: replace-cross Abstract: All-in-One Image Restoration (AIO-IR) aims to develop a unified model that can handle multiple degradations under complex conditions. However, existing methods often rely on task-specific designs or latent routing strategies, making it hard to adapt to real-world scenarios with various degradations. We propose FAPE-IR, a Frequency-Aware Planning and Execution framework […]
IROSA: Interactive Robot Skill Adaptation using Natural Language
arXiv:2603.03897v2 Announce Type: replace-cross Abstract: Foundation models have demonstrated impressive capabilities across diverse domains, while imitation learning provides principled methods for robot skill adaptation from limited data. Combining these approaches holds significant promise for direct application to robotics, yet this combination has received limited attention, particularly for industrial deployment. We present a novel framework that […]
Geometry-Guided Camera Motion Understanding in VideoLLMs
arXiv:2603.13119v1 Announce Type: cross Abstract: Camera motion is a fundamental geometric signal that shapes visual perception and cinematic style, yet current video-capable vision-language models (VideoLLMs) rarely represent it explicitly and often fail on fine-grained motion primitives. We address this gap with a framework of $textbfbenchmarking$, $textbfdiagnosis$, and $textbfinjection$. We curate $textbfCameraMotionDataset$, a large-scale synthetic dataset […]