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…

Coupled reaction and diffusion governing interface evolution in solid-state batteries

arXiv:2506.10944v2 Announce Type: replace-cross Abstract: Understanding and controlling the atomistic-level reactions governing the formation of the solid-electrolyte interphase (SEI) is crucial for the viability of…

Does Every User Need a Private LoRA? Decoupling Personalization from Per-User Adaptation

arXiv:2610.02353v1 Announce Type: new Abstract: Personalized large language models often require a complete adaptation state for each user. However, this paradigm scales poorly as the user population grows. We revisit…

Diffusion Flow Matching: Dimension-Improved KL Bounds and Wasserstein Guarantees

arXiv:2606.16610v2 Announce Type: replace-cross Abstract: Diffusion Flow Matching (DFM) has recently emerged as a versatile framework for generative modeling, yet its theoretical convergence properties remain only…

Latent-MOPD: Latent Multi-Teacher On-Policy Distillation

arXiv:2610.02381v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on the responses it generates. Existing LLM multi-teacher OPD transfers what specialists predict through their output…

Predictively Oriented Gaussian Process Posteriors

arXiv:2610.03201v1 Announce Type: cross Abstract: Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships. However, they require practitioners to make a number…

Performance Evaluation of Ising and QUBO Variable Encodings in Boltzmann Machine Learning

arXiv:2510.13210v2 Announce Type: replace Abstract: We compare Ising ({-1, +1}) and QUBO ({0, 1}) encodings for Boltzmann machine learning under controlled protocols that fix the sampler, optimizer, and learning-rate…

SlimKV: Joint Token-Feature KV Cache Compression with Reconstruction-Free Beacon Attention

arXiv:2610.02953v1 Announce Type: new Abstract: Long-context LLM serving is increasingly bottlenecked by KV-cache memory, especially in resource-constrained scenarios. Among existing KV-cache compression strategies,…

Learning Style, Forgetting Semantics: A Case Study of SFT and RFT on Classification Tasks

arXiv:2610.02437v1 Announce Type: cross Abstract: Why does supervised fine-tuning (SFT) lead to more forgetting than reinforcement fine-tuning (RFT), even when all teacher demonstrations are semantically correct? We…

Capability Scaling-Down Laws for LLM Compression

arXiv:2610.02462v1 Announce Type: new Abstract: LLM compression reduces inference costs and memory requirements, but selecting a method and configuration remains largely empirical because comparable resource reductions…

Parameter-Free Interval-Dynamic Regret under Heavy-Tailed Noise

arXiv:2610.02258v1 Announce Type: new Abstract: We study online convex optimization with one unbiased stochastic subgradient per round and an unknown finite conditional $p$th noise moment, $1

Adaptive Second-Order Solvers for Fast Stochastic Diffusion Sampling

arXiv:2610.03034v1 Announce Type: new Abstract: Diffusion models rely on numerical solvers requiring time-discretization, which has a large influence on the tradeoff between sampling cost and quality. However, the…

Learning Transferable Policies from Action-free Time Series Through Dynamical Embeddings

arXiv:2610.03065v1 Announce Type: new Abstract: Learning control from action-free recordings is challenging because intervention effects are unobserved and policies may exploit errors in reconstructed dynamics. We…

Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models

arXiv:2610.03647v1 Announce Type: cross Abstract: Many machine learning methods aim to approximate the lower-dimensional manifold on which the data lives. A desirable feature of such methods is that they should capture…