technocracy

Every day, billions of users scroll through personalized feeds on TikTok, Instagram, and YouTube, each experiencing a uniquely tailored stream of content. Behind this seemingly effortless personalization lies one of the most challenging problems in modern machine learning: how do you build recommendation systems that adapt to user interests in real-time, learning from every interaction within the same scrolling session? The engineering required to make this work, processing billions of events per second, updating models every few hours, […]

Ver mais

arXiv:2601.05265v1 Announce Type: new Abstract: Chunking quality determines RAG system performance. Current methods partition documents individually, but complex queries need information scattered across multiple sources: the knowledge fragmentation problem. We introduce Cross-Document Topic-Aligned (CDTA) chunking, which reconstructs knowledge at the corpus level. It first identifies topics across documents, maps segments to each topic, and synthesizes them into unified chunks. On HotpotQA multi-hop reasoning, our method reached 0.93 faithfulness versus 0.83 for contextual retrieval and 0.78 for semantic chunking, […]

Ver mais

How are you, hacker? 🪐 What’s happening in tech today, January 16, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Prohibition went into effect in the United States in 1920, Space Shuttle Columbia disintegrated upon re-entry in 2003, First Youtube video was posted in 2006, and we present you with these top quality stories. From The Secret Math Behind Every Creative Breakthrough to How to Build a DAO from Scratch with […]

Ver mais

Growth hacks work, until they don’t. Most startups begin with quick wins: viral reels, aggressive ads, referral incentives, clever copy. For a while, momentum builds. Metrics go up. Dashboards look healthy. It feels like progress. Then something changes. Your customer acquisition costs start rising. Engagement flattens. What once felt like growth starts to feel like a constant fight. At this point, you usually assume the answer is another tactic. A new channel. A new funnel. A new trick. […]

Ver mais

Mendelian Randomization (MR) is a prominent observational epidemiological research method designed to address unobserved confounding when estimating causal effects. However, core assumptions — particularly the independence between instruments and unobserved confounders — are often violated due to population stratification or assortative mating. Leveraging the increasing availability of multi-environment data, we propose a representation learning framework that exploits cross-environment invariance to recover latent exogenous components of genetic instruments. We provide theoretical guarantees for identifying these latent instruments under various […]

Ver mais

arXiv:2603.16874v1 Announce Type: new Abstract: As conversational AI systems become more realistic and widely deployed, users are increasingly uncertain about whether they are interacting with a human or an AI system. When AI identity is unclear, users may unwittingly share sensitive information, place unwarranted trust in AI-generated advice, or fall victim to AI-enabled fraud. More broadly, a persistent lack of transparency can erode trust in mediated communication. While regulations like the EU AI Act and California’s BOT Act […]

Ver mais

arXiv:2603.12305v1 Announce Type: new Abstract: The ability to understand and reason about cause and effect — encompassing interventions, counterfactuals, and underlying mechanisms — is a cornerstone of robust artificial intelligence. While deep learning excels at pattern recognition, it fundamentally lacks a model of causality, making systems brittle under distribution shifts and unable to answer “what-if” questions. This paper introduces the emph{Hierarchical Causal Primitive Dynamic Composition Network (HCP-DCNet)}, a unified framework that bridges continuous physical dynamics with discrete symbolic […]

Ver mais

In this tutorial, we build an end-to-end implementation around Qwen 3.6-35B-A3B and explore how a modern multimodal MoE model can be used in practical workflows. We begin by setting up the environment, loading the model adaptively based on available GPU memory, and creating a reusable chat framework that supports both standard responses and explicit thinking traces. From there, we work through important capabilities such as thinking-budget control, streamed generation with separated reasoning and answers, vision input handling, tool […]

Ver mais

arXiv:2604.02344v1 Announce Type: new Abstract: WebGPU’s security-focused design imposes per-operation validation that compounds across the many small dispatches in neural network inference, yet the true cost of this overhead is poorly characterized. We present a systematic characterization of WebGPU dispatch overhead for LLM inference at batch size 1, spanning four GPU vendors (NVIDIA, AMD, Apple, Intel), two native implementations (Dawn, wgpu-native) and three browsers (Chrome, Safari, Firefox), and two model sizes (Qwen2.5-0.5B and 1.5B). Our primary contribution is […]

Ver mais