In the age of graph data – such as social networks, business relationship graphs, or knowledge maps – sharing these datasets for research or application purposes is increasingly common. But what if the structure of a graph itself contains sensitive information? Even without revealing the node contents, simply disclosing the existence of edges can lead to privacy breaches. Traditional approaches to Differential Privacy (DP) focus on protecting data during model training. In this paper, the authors go a step further — they aim to protect privacy at the moment of graph data publishing. They propose an elegant method based on Gaussian Differential Privacy (GDP) that enables learning the structure of a graph while maintaining strong privacy guarantees. ...

Can AI improve how call centers operate? The paper “Optimising Call Centre Operations using Reinforcement Learning: Value Iteration versus Proximal Policy Optimisation” by Kwong Ho Li and Wathsala Karunarathne shows that it can — and with strong results. The authors compare two reinforcement learning (RL) approaches to optimize call routing: the classical Value Iteration (VI) and the modern Proximal Policy Optimisation (PPO). What is Reinforcement Learning? Reinforcement Learning is an AI method where an agent takes actions in an environment and receives rewards based on how good those actions are. The goal is to maximize the cumulative reward — essentially, to learn the best decisions. ...

In many real-world tasks—like forecasting the paths of cars at a busy intersection, coordinating fleets of delivery robots, or simulating pedestrian movement—models must reason about not just where things are, but how they face or rotate relative to each other. That’s the SE(2) geometry: 2D position + heading. Traditional Transformer models that account for rotation and translation invariance (SE(2)-invariant) need to compute relative poses between every pair of objects. If you have $n$ objects, this leads to memory cost growing like $O(n^2)$—which becomes prohibitively expensive when $n$ is large. ...

Skin cancer is one of the most common cancers globally – and early detection significantly improves the chances of successful treatment. Unfortunately, many people lack access to dermatologists or advanced diagnostic tools. This research addresses the problem by bringing AI-based diagnostics to low-cost wearable devices. What did the authors do? Used MobileNetV2: A compact neural network architecture optimized for mobile environments. With transfer learning, the model was fine-tuned to classify skin lesions as cancerous or non-cancerous. ...

In recent years, Large Vision‑Language Models (LVLMs) have shown impressive abilities to understand and generate text about images—but they often struggle with long, multi‑step reasoning. The paper “SOPHIA: Semi‑Off‑Policy Reinforcement Learning for Slow‑Thinking in LVLMs” presents a new approach that significantly improves their capacity for slow‑thinking reasoning. What Is Slow‑Thinking? Slow‑thinking is a deliberate, step‑by‑step reasoning process where the model: Breaks down complex problems into smaller steps, Verifies intermediate conclusions, Provides transparency into each decision. This contrasts with fast, intuitive “snap” judgments and helps avoid hallucinations—invented details not supported by the image. ...

Modern satellite mega-constellations—groups of hundreds or thousands of small satellites working together—are transforming how we connect the world. Yet, managing these networks presents unique challenges: constantly moving nodes, limited onboard computing power, and a need to minimize communication delays. The ConstellAI project, supported by the European Space Agency, explores how artificial intelligence (AI) can optimize two critical tasks: Data Routing: Choosing the best path through the network to send data quickly and reliably. Resource Allocation: Distributing limited resources (bandwidth, power, time slots) among satellites and ground stations. Data Routing with Reinforcement Learning Traditional routing algorithms, like finding the shortest path on a map, don’t account for traffic jams (long queues) at network nodes. ConstellAI uses a technique called reinforcement learning (RL). In RL, a software agent learns from experience: it tries different routes, observes delays, and gradually discovers which paths minimize overall transit time. ...

When making decisions—from financial investments to routing autonomous vehicles—we care not only about average outcomes but also about risk. A widely used risk metric is the Conditional Value at Risk, or CVaR, defined for confidence level $\alpha\in(0,1)$ by: $$ CVaR_\alpha(X) =\inf_{\xi}{\xi + \tfrac{1}{1-\alpha},E[(X-\xi)_+]}. $$ In their recent paper, Godbout and Durand (2025) examine how to reliably compute this metric in Markov Decision Processes (MDPs). They reveal that the most common method—the dual decomposition—suffers from inherent limitations. ...

The paper “PinFM: Foundation Model for User Activity Sequences at a Billion-Scale Visual Discovery Platform” introduces a $>$20B-parameter transformer pretrained on Pinterest user interaction sequences. Its goal is to build a universal sequence model applicable to various recommendation tasks, including content ranking, related Pins, and personalized feeds. Background and Motivation Traditional recommendation systems rely on specialized models for each task. The explosion of data volume and signal diversity calls for a generalized pretraining–finetuning paradigm. PinFM was developed to: ...

Optimal Transport (OT) is the mathematical problem of moving “mass” from one distribution to another in the most efficient way. Think of reshaping a pile of sand into a new shape with minimal effort. GradNetOT is a novel machine‑learning method that learns exactly these efficient maps using neural networks equipped with a built‑in “bias” toward physically correct solutions. What Is Optimal Transport? Classic formulation: Given two probability distributions (e.g., piles of sand and holes to fill), find a mapping that moves mass at minimal total cost. Monge’s theorem: For certain costs (like squared distance), the optimal map is the gradient of a convex function satisfying a Monge–Ampère equation. The GradNetOT Approach GradNetOT leverages a special neural network architecture called a Monotone Gradient Network (mGradNet) to represent convex functions implicitly. By enforcing convexity and monotonicity, the network’s output gradient automatically yields a valid OT map. ...

The paper “Understanding the Evolution of the Neural Tangent Kernel at the Edge of Stability” by Kaiqi Jiang, Jeremy Cohen, and Yuanzhi Li explores how the Neural Tangent Kernel (NTK) evolves during deep network training, especially under the Edge of Stability (EoS) regime. What is the NTK? The Neural Tangent Kernel (NTK) is a matrix that captures how tiny weight changes affect network outputs on each training example. It lets us analyze neural networks with tools from kernel methods, offering theoretical insights into learning dynamics. What is the Edge of Stability? When training with a large learning rate $\eta$, the largest eigenvalue of the NTK (or the loss Hessian) exceeds the stability threshold $2/\eta$ and then oscillates around it. This phenomenon, called Edge of Stability, combines elements of instability with phases of rapid learning. Key Findings Alignment Shift Higher $\eta$ leads to stronger final Kernel Target Alignment (KTA) between the NTK and the label vector $y$. ...