When we train deep neural networks, they often get stuck — not in a bad result, but in a “flat region” of the loss landscape. The authors of this paper introduce ghost nodes: extra, fake output nodes that aren’t real classes, but help the model explore better paths during training. Imagine you’re rolling a ball into a valley. Sometimes the valley floor is flat and the ball slows down. Ghost nodes are like adding new dimensions to the terrain — giving the ball more freedom to move and find a better path. ...

Large‑scale epidemic modeling is a key tool for public health—but it often requires sensitive data (e.g., hospital admissions, financial records, mobility). A recent paper, “A Framework for Multi‑source Privacy Preserving Epidemic Analysis” (June 27, 2025), introduces a hybrid neural‑mechanistic model that respects Differential Privacy (DP). This means we can use private data without compromising individuals’ privacy. 🌍 Why It Matters 🚑 Accurate predictions help allocate resources (like vaccines, ICU beds). 🕵️‍♂️ But using private data poses a privacy risk. 🔐 Differential Privacy (DP) adds controlled randomness—protecting individuals at a formal, mathematical level. 🧠 Inside the Framework: Neural + Mechanistic The model is a hybrid system combining: ...

Introduction Unmanned Aerial Vehicles (UAVs) play pivotal roles today in photography, deliveries, rescue missions, border surveillance, and military operations. However, the growing availability of signal disruption tools (GPS spoofing, gyroscope jamming, magnetometer manipulation) poses significant threats to autonomous systems. Even a slight navigational drift can turn a mission into a disaster. Why Physical-Attack Robustness Matters Traditional safe RL methods or adversarial trainings rely on known attack scenarios. In practice, it’s impossible to anticipate every possible manipulation—an adversary could employ novel jamming or optical disruption techniques. Iterative adversarial training is computationally expensive and often poorly generalizes to unseen scenarios. ...

🧠 Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge Agentic Search is one of the most promising applications of modern AI. Imagine a virtual assistant that doesn’t just look up information for you but can autonomously search the web, navigate pages, find facts, and return well-structured answers with citations. That’s the idea behind tools like OpenAI’s Deep Research. However, how do we evaluate if such an AI is doing a good job? ...

Can a machine discover the laws of physics by itself—like Newton, but without the apple and without writing the equation by hand? In June 2025, a new method called H-FEX (Hamiltonian Finite Expression) was published. It doesn’t just predict system behavior—it writes down the math behind it. And crucially, in a form humans can understand. It’s a form of symbolic learning, increasingly popular over black-box neural networks that work, but don’t tell us why. ...

Imagine having to choose the best ad variant, but each time you only learn how many users clicked on the one you showed. This is the essence of bandit learning: it balances exploration (trying out new options) with exploitation (using the current best) to discover the winner as quickly as possible. In a world where every experiment has a cost—from ad budgets to a patient’s time in experimental therapy—bandit algorithms can significantly accelerate optimal decision-making. Yet, despite their practical power, these solutions are surprisingly hard to analyze theoretically! ...