March 2026

Banks have been operating worldwide for centuries, so it’s fair to wonder how money could work without them. That’s cryptocurrencies, by the way: money. A new type, a digital type, a decentralized type; specifically designed to be out of reach for banks, governments, and any other big central party.  Crypto networks, unlike banks, are systems that run day and night, across borders, with rules enforced by code rather than by a central authority. Let’s break this down. How […]

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Introduction to the 2026 AI Arms Race Welcome to the AI arms race of 2026 — and what a race it has become! Three years ago, ChatGPT was a novelty. Today, four AI titans are locked in a war for your attention, your workflow, your trust, and ultimately your subscription dollars. Google Gemini, Anthropic Claude, OpenAI ChatGPT, and xAI Grok have each staked out their territory, sharpened their claws, and are sprinting toward a finish line that keeps […]

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For most of the internet era, commerce followed a predictable path. Consumers searched for a product, visited a website, compared options, and eventually checked out. The brand’s website was the center of gravity for discovery and purchase. That model is quietly evolving. Today, consumers increasingly discover and buy products directly inside content, communities, and AI-powered experiences—a shift I call moment commerce. Instead of pulling customers to a store or homepage, brands must meet them where attention already exists. […]

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:::info Authors: Elizabeth A. Gibson Huichu Li Victoria Fruh Malaika Gabra Gowtham Asokan Anne Marie Z. Jukic Donna D. Baird Christine L. Curry Tyler Fischer-Colbrie Jukka-Pekka Onnela Michelle A. Williams Russ Hauser Brent A. Coull Shruthi Mahalingaiah ::: Abstract COVID-19 vaccination may be associated with change in menstrual cycle length following vaccination. We estimated covariate-adjusted differences in mean cycle length (MCL), measured in days, between pre-vaccination cycles, vaccination cycles, and post-vaccination cycles within vaccinated participants who met eligibility […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## SERP Benchmarks: Success Rates and Latency at Scale By @brightdata [ 8 Min read ] ​​We benchmark SERP APIs for success rate, ​​speed, and stability under load. Learn which setup delivers consistent results for AI agents ​​and deep research. Read More. Create a Website […]

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Many real-world machine learning tasks are anti-causal: they require inferring latent causes from observed effects. In practice, we often face multiple related tasks where part of the forward causal mechanism is invariant across tasks, while other components are task-specific. We propose Multi-Task Anti-Causal learning (MTAC), a framework for estimating causes from outcomes and confounders by explicitly exploiting such cross-task invariances. MTAC first performs causal discovery to learn a shared causal graph and then instantiates a structured multi-task structural […]

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arXiv:2603.10092v1 Announce Type: new Abstract: OpenClaw-style agent stacks turn language into privileged execution: LLM intents flow through tool interception, policy gates, and a local executor. In parallel, skill marketplaces such as skills.sh make capability acquisition as easy as installing skills and CLIs, creating a growing capability supply chain. Together, these trends shift the dominant safety failure mode from “wrong answers” to execution-induced loss, where untrusted prompts, compromised skills, or narrative manipulation can trigger real trades and irreversible side […]

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arXiv:2603.10091v1 Announce Type: new Abstract: The widespread adoption of thinking mode in large language models (LLMs) has significantly enhanced complex task processing capabilities while introducing new security risks. When subjected to jailbreak attacks, the step-by-step reasoning process may cause models to generate more detailed harmful content. We observe that thinking mode exhibits unique vulnerabilities when processing interleaved multiple tasks. Based on this observation, we propose multi-stream perturbation attack, which generates superimposed interference by interweaving multiple task streams within […]

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arXiv:2603.10090v1 Announce Type: new Abstract: Neural network weights are typically viewed as the end product of training, while most deep learning research focuses on data, features, and architectures. However, recent advances show that the set of all possible weight values (weight space) itself contains rich structure: pretrained models form organized distributions, exhibit symmetries, and can be embedded, compared, or even generated. Understanding such structures has tremendous impact on how neural networks are analyzed and compared, and on how […]

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arXiv:2603.10088v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) are emerging as a promising alternative to autoregressive models (ARMs) due to their ability to capture bidirectional context and the potential for parallel generation. Despite the advantages, dLLM inference remains computationally expensive as the full input context is processed at every iteration. In this work, we analyze the generation dynamics of dLLMs and find that intermediate representations, including key, value, and hidden states, change only subtly across successive […]

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