AI Research

Open and Emergent Problems in Agentic Privacy and Security: A Contextual Angle

Generative AI

Computer Vision: SIFT algorithm (Scale Invariant Feature Transform)

Elegant object matching from several viewpoints The post Computer Vision: SIFT algorithm (Scale Invariant Feature Transform) appeared first on Towards Data Science.

What the Kyoto Vision Means for AI-Powered Science

Seventeen countries have endorsed the Kyoto Vision for AI-powered science. Its push toward autonomous discovery raises questions about computing access and how governments will turn policy ambitions into research…

What If AI Is a Phase in Earth’s Evolution?

Philosopher Benjamin Bratton has a much bigger way of thinking about AI: as the newest step in a billions-of-years process where matter produced life, life produced intelligence, and intelligence learned to build…

How to Build a Cheap, Yet Reliable Model Router With Jev

Model routing can finally be a design choice rather than a system enhancement The post How to Build a Cheap, Yet Reliable Model Router With Jev appeared first on Towards Data Science.

😺 Trump named an AI czar

What Nvidia and AMD’s AI Acquisitions Say About the Next Chip War

Nvidia’s planned Hugging Face acquisition and AMD’s World Labs deal bring both chipmakers closer to the developers and researchers shaping future AI workloads, giving them an earlier view of how compute requirements may…

How Far Does a Shared Linear Map Go? Probing Feature-Space Manipulability for Image Editing

arXiv:2605.11203v2 Announce Type: replace Abstract: Understanding how image-space transformations manifest in a model's internal representations is a longstanding goal in representation analysis. Prior work has shown…

JARQ: Joint Alternating Refinement for Quantization

arXiv:2609.38599v2 Announce Type: replace Abstract: Group-wise post-training quantizers for large language models round weights onto a grid that is not refit to the resulting integer codes. We show that this leaves…

Exploring the Trade-Off Between Structured Pruning and Fault Tolerance in Deep Neural Networks for Space Applications

arXiv:2610.03117v1 Announce Type: new Abstract: Deep Neural Networks (DNNs) inherently exhibit a degree of robustness to bit-level faults due to their distributed representation of information. As a model increases in…

Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows

arXiv:2608.11544v2 Announce Type: replace-cross Abstract: In many high-stakes domains, extreme events carry substantial consequences, yet learning the heavy-tailed distributions that govern them from finite samples…

An Irreducible Quantum Advantage in Aligning World Models with Reality

arXiv:2608.19779v2 Announce Type: replace-cross Abstract: World models provide digital simulacra of the true world, allowing agents to be trained and tested before costly real-world deployment. At each time step, they…

Beyond Trained Models: Compiling GNNs for a Sound Explainer Benchmark

arXiv:2610.03526v1 Announce Type: new Abstract: Explainers for Graph Neural Networks (GNNs) are commonly evaluated by their plausibility, i.e., how well their explanations recover a predefined ground truth, such as a…

Sample complexity of variance-reduced policy gradient: weaker assumptions and lower bounds

arXiv:2610.03165v1 Announce Type: new Abstract: Several variance-reduced versions of REINFORCE based on importance sampling achieve an improved $O(\epsilon^{-3})$ sample complexity to find an $\epsilon$-stationary…

Timescale Separation Enables Deep Reinforcement Learning Control of Rotating Detonation Engine Mode Transitions

arXiv:2604.14398v2 Announce Type: replace-cross Abstract: Rotating detonation engines (RDEs) are a promising propulsion concept that may offer higher thermodynamic efficiency and specific impulse than conventional…

Provable Benefits of Regularization: Fast Rates for Adversarial Imitation Learning

arXiv:2609.35698v3 Announce Type: replace Abstract: We study adversarial imitation learning (AIL), in which an agent learns to imitate expert demonstrations by optimizing a policy against an adversarial reward that…

High-Dimensional Asymptotics and Dataset Selection for Private Transfer Learning

arXiv:2610.02578v1 Announce Type: cross Abstract: To commit to buying external data or participate in collaborative learning, one must decide whether the additional data will improve prediction enough to justify the…

Heads, Tails, and AI Fails: LLMs, Randomness, and Human Judgments

arXiv:2406.00092v2 Announce Type: replace-cross Abstract: Randomness is central to human cognition and to many applications in which large language models are deployed, yet probabilistic token generation does not imply…

Cephalonauts One: A deep fMRI dataset for decoding naturalistic speech in the human brain

arXiv:2610.03558v1 Announce Type: new Abstract: Cephalonauts One is a whole-brain 3 Tesla (3T) functional magnetic resonance imaging (fMRI) dataset recorded while subjects listened to audio podcasts. Three healthy…

A hybrid CNN-adjoint optimization framework for reconstruction of viscoelastic tissue properties in magnetic resonance elastography

arXiv:2610.02634v1 Announce Type: cross Abstract: Magnetic resonance elastography (MRE) is a noninvasive imaging modality for quantifying the viscoelastic properties of soft tissues from shear wave propagation.…