arXiv:2507.06819v3 Announce Type: replace-cross Abstract: Prototype models are an important method for explainable artificial intelligence (XAI) and interpretable machine learning. In this paper, we perform an in-depth analysis of a set of prominent prototype models including ProtoPNet, ProtoPool and PIPNet. For their assessment, we apply a comprehensive set of metrics. In addition to applying standard […]
Is Phase Really Needed for Weakly-Supervised Dereverberation ?
arXiv:2511.17346v1 Announce Type: cross Abstract: In unsupervised or weakly-supervised approaches for speech dereverberation, the target clean (dry) signals are considered to be unknown during training. In that context, evaluating to what extent information can be retrieved from the sole knowledge of reverberant (wet) speech becomes critical. This work investigates the role of the reverberant (wet) […]
CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation
arXiv:2503.09878v4 Announce Type: replace-cross Abstract: Vision foundation models (VFMs) such as DINO have led to a paradigm shift in 2D camera-based perception towards extracting generalized features to support many downstream tasks. Recent works introduce self-supervised cross-modal knowledge distillation (KD) as a way to transfer these powerful generalization capabilities into 3D LiDAR-based models. However, they either […]
SALT: Steering Activations towards Leakage-free Thinking in Chain of Thought
arXiv:2511.07772v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) evolve into personal assistants with access to sensitive user data, they face a critical privacy challenge: while prior work has addressed output-level privacy, recent findings reveal that LLMs often leak private information through their internal reasoning processes, violating contextual privacy expectations. These leaky thoughts occur […]
Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data
arXiv:2507.10998v3 Announce Type: replace-cross Abstract: Adversarial attacks on tabular data present unique challenges due to the heterogeneous nature of mixed categorical and numerical features. Unlike images where pixel perturbations maintain visual similarity, tabular data lacks intuitive similarity metrics, making it difficult to define imperceptible modifications. Additionally, traditional gradient-based methods prioritise $ell_p$-norm constraints, often producing adversarial […]
AI Workers, Geopolitics, and Algorithmic Collective Action
arXiv:2511.17331v1 Announce Type: cross Abstract: According to the theory of International Political Economy (IPE), states are often incentivized to rely on rather than constrain powerful corporations. For this reason, IPE provides a useful lens to explain why efforts to govern Artificial Intelligence (AI) at the international and national levels have thus far been developed, applied, […]
When Bias Pretends to Be Truth: How Spurious Correlations Undermine Hallucination Detection in LLMs
arXiv:2511.07318v2 Announce Type: replace-cross Abstract: Despite substantial advances, large language models (LLMs) continue to exhibit hallucinations, generating plausible yet incorrect responses. In this paper, we highlight a critical yet previously underexplored class of hallucinations driven by spurious correlations — superficial but statistically prominent associations between features (e.g., surnames) and attributes (e.g., nationality) present in the […]
Extending Test-Time Scaling: A 3D Perspective with Context, Batch, and Turn
arXiv:2511.15738v2 Announce Type: replace-cross Abstract: Reasoning reinforcement learning (RL) has recently revealed a new scaling effect: test-time scaling. Thinking models such as R1 and o1 improve their reasoning accuracy at test time as the length of the reasoning context increases. However, compared with training-time scaling, test-time scaling is fundamentally limited by the limited context length […]
MusicAIR: A Multimodal AI Music Generation Framework Powered by an Algorithm-Driven Core
arXiv:2511.17323v1 Announce Type: cross Abstract: Recent advances in generative AI have made music generation a prominent research focus. However, many neural-based models rely on large datasets, raising concerns about copyright infringement and high-performance costs. In contrast, we propose MusicAIR, an innovative multimodal AI music generation framework powered by a novel algorithm-driven symbolic music core, effectively […]
Forecasting Future Anatomies: Longitudinal Brain Mri-to-Mri Prediction
arXiv:2511.02558v2 Announce Type: replace-cross Abstract: Predicting future brain state from a baseline magnetic resonance image (MRI) is a central challenge in neuroimaging and has important implications for studying neurodegenerative diseases such as Alzheimer’s disease (AD). Most existing approaches predict future cognitive scores or clinical outcomes, such as conversion from mild cognitive impairment to dementia. Instead, […]
From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems
arXiv:2503.01424v4 Announce Type: replace Abstract: Research is a fundamental process driving the advancement of human civilization, yet it demands substantial time and effort from researchers. In recent years, the rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. To monitor relevant advancements, this paper presents […]
Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?
arXiv:2508.01109v2 Announce Type: replace Abstract: We investigate whether socio-economic indicators like household wealth leave recoverable imprints in satellite imagery (capturing physical features) and Internet-sourced text (reflecting historical/economic narratives). Using Demographic and Health Survey (DHS) data from African neighborhoods, we pair Landsat images with LLM-generated textual descriptions conditioned on location/year and text retrieved by an AI […]