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, […]
Comparative Study of UNet-based Architectures for Liver Tumor Segmentation in Multi-Phase Contrast-Enhanced Computed Tomography
arXiv:2510.25522v3 Announce Type: replace-cross Abstract: Segmentation of liver structures in multi-phase contrast-enhanced computed tomography (CECT) plays a crucial role in computer-aided diagnosis and treatment planning for liver diseases, including tumor detection. In this study, we investigate the performance of UNet-based architectures for liver tumor segmentation, starting from the original UNet and extending to UNet3+ with […]
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 […]
FORWARD: Dataset of a forwarder operating in rough terrain
arXiv:2511.17318v1 Announce Type: cross Abstract: We present FORWARD, a high-resolution multimodal dataset of a cut-to-length forwarder operating in rough terrain on two harvest sites in the middle part of Sweden. The forwarder is a large Komatsu model equipped with a variety of sensors, including RTK-GNSS, 360-camera, operator vibration sensors, internal CAN-bus signal recording, and multiple […]
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 […]
Bootstrap Off-policy with World Model
arXiv:2511.00423v2 Announce Type: replace-cross Abstract: Online planning has proven effective in reinforcement learning (RL) for improving sample efficiency and final performance. However, using planning for environment interaction inevitably introduces a divergence between the collected data and the policy’s actual behaviors, degrading both model learning and policy improvement. To address this, we propose BOOM (Bootstrap Off-policy […]