· ai
[Paper] Scale-Agnostic Kolmogorov-Arnold Geometry in Neural Networks
Recent work by Freedman and Mulligan demonstrated that shallow multilayer perceptrons spontaneously develop Kolmogorov-Arnold geometric (KAG) structure during t...
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- · ai
[Paper] TAGFN: A Text-Attributed Graph Dataset for Fake News Detection in the Age of LLMs
Large Language Models (LLMs) have recently revolutionized machine learning on text-attributed graphs, but the application of LLMs to graph outlier detection, pa...
- · ai
[Paper] On the Origin of Algorithmic Progress in AI
Algorithms have been estimated to increase AI training FLOP efficiency by a factor of 22,000 between 2012 and 2023 [Ho et al., 2024]. Running small-scale ablati...
- · ai
[Paper] ReSAM: Refine, Requery, and Reinforce: Self-Prompting Point-Supervised Segmentation for Remote Sensing Images
Interactive segmentation models such as the Segment Anything Model (SAM) have demonstrated remarkable generalization on natural images, but perform suboptimally...
- · ai
[Paper] TAB-DRW: A DFT-based Robust Watermark for Generative Tabular Data
The rise of generative AI has enabled the production of high-fidelity synthetic tabular data across fields such as healthcare, finance, and public policy, raisi...
- · ai
[Paper] Visualizing LLM Latent Space Geometry Through Dimensionality Reduction
Large language models (LLMs) achieve state-of-the-art results across many natural language tasks, but their internal mechanisms remain difficult to interpret. I...
- · ai
[Paper] MoGAN: Improving Motion Quality in Video Diffusion via Few-Step Motion Adversarial Post-Training
Video diffusion models achieve strong frame-level fidelity but still struggle with motion coherence, dynamics and realism, often producing jitter, ghosting, or ...
- · ai
[Paper] On the Limits of Innate Planning in Large Language Models
Large language models (LLMs) achieve impressive results on many benchmarks, yet their capacity for planning and stateful reasoning remains unclear. We study the...
- · ai
[Paper] Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving
End-to-end (E2E) autonomous driving models have demonstrated strong performance in open-loop evaluations but often suffer from cascading errors and poor general...
- · ai
[Paper] Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning
Latent reasoning represents a new development in Transformer language models that has shown potential in compressing reasoning lengths compared to chain-of-thou...
- · ai
[Paper] Multimodal Robust Prompt Distillation for 3D Point Cloud Models
Adversarial attacks pose a significant threat to learning-based 3D point cloud models, critically undermining their reliability in security-sensitive applicatio...
- · ai
[Paper] Self-Transparency Failures in Expert-Persona LLMs: A Large-Scale Behavioral Audit
If a language model cannot reliably disclose its AI identity in expert contexts, users cannot trust its competence boundaries. This study examines self-transpar...