digitado

Modern machine learning systems, such as generative models and recommendation systems, often evolve through a cycle of deployment, user interaction, and periodic model updates. This differs from standard supervised learning frameworks, which focus on loss or regret minimization over a fixed sequence of prediction tasks. Motivated by this setting, we revisit the classical model of learning from equivalence queries, introduced by Angluin (1988). In this model, a learner repeatedly proposes hypotheses and, when a deployed hypothesis is inadequate, […]

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User interests typically encompass both long-term preferences and short-term intentions, reflecting the dynamic nature of user behaviors across different timeframes. The uneven temporal distribution of user interactions highlights the evolving patterns of interests, making it challenging to accurately capture shifts in interests using comprehensive historical behaviors. To address this, we propose SLSRec, a novel Session-based model with the fusion of Long- and Short-term Recommendations that effectively captures the temporal dynamics of user interests by segmenting historical behaviors over […]

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Deep Neural Networks (DNNs) are increasingly utilized in high-stakes domains like medical diagnostics and autonomous driving where model reliability is critical. However, the research landscape for ensuring this reliability is terminologically fractured across communities that pursue the same goal of ensuring models rely on causally relevant features rather than confounding signals. While frameworks such as distributionally robust optimization (DRO), invariant risk minimization (IRM), shortcut learning, simplicity bias, and the Clever Hans effect all address model failure due to […]

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Hey all, I’m building an RL agent to play an Asteroids-style arcade game that I made. I can get decent models working now, and I’ve definitely improved compared to the first RL version I ever built. The agent survives way longer than it did in the beginning, and by watching it play after training I can actually make some decisions about what seems to be helping or hurting. So it’s not totally random guessing anymore, but I still […]

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If you’ve had a penetration test performed for your business, you likely know that not all pentesting vendors are created equal. It can be difficult to find the best vendor for your specific business type, delivery speed needs, and one whose strengths fit your environment. This is why I’ve compiled this list of the top penetration testing vendors and what each of their strengths are, who they’re the best fit for, and how fast they can deliver your […]

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OpenClaw is an open-source AI agent with 320k+ stars on GitHub. If you haven’t seen it: it connects to your actual chat apps (Telegram, WhatsApp, Discord, Signal) and does real things on your behalf. Email, calendar, shell commands, browser automation, cron jobs, memory that persists between conversations. It runs around the clock. It’s not a chatbot you ask questions. It’s closer to a coworker who never logs off and never forgets what you told it last Tuesday. But […]

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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. ## Why Your “Profitable” Backtest Fails the Moment You Go Live By @grigorychikishev [ 6 Min read ] Latency, queue position, market impact, and adverse selection all distort the theoretical edge a model appears to have. Read More. Enabling BDX On-Ramp and Off-Ramp to Onboard […]

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Autonomous agents are increasingly deployed in both offensive and defensive cyber operations, creating high-speed, closed-loop interactions in critical infrastructure environments. Advanced Persistent Threat (APT) actors exploit "Living off the Land" techniques and targeted telemetry perturbations to induce ambiguity in monitoring systems, causing automated defenses to overreact or misclassify benign behavior as malicious activity. Existing monolithic and multi-agent defense pipelines largely operate on correlation-based signals, lack structural constraints on response actions, and are vulnerable to reasoning drift under ambiguous […]

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