technocracy

Epileptic seizure forecasting is a clinically important yet challenging problem in epilepsy research. Existing approaches predominantly rely on neural signals such as electroencephalography (EEG), which require specialized equipment and limit long-term deployment in real-world settings. In contrast, video data provide a non-invasive and accessible alternative, yet existing video-based studies mainly focus on post-onset seizure detection, leaving seizure forecasting largely unexplored. In this work, we formulate a novel task of video-based epileptic seizure forecasting, where short pre-ictal video segments […]

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The technical design decisions that turned a personal hobby project into 2026’s most-discussed AI agent An open-source AI agent hit 100,000 GitHub stars in under two months. That’s not hype, that’s a signal. OpenClaw (née Clawdbot, briefly Moltbot) isn’t just another chatbot wrapper. It’s a locally-deployed autonomous agent that lives in your messaging apps and executes tasks on your machine. And beneath the viral TikTok demos and space lobster memes lies an architecture worth studying. Let’s dissect it. The Problem OpenClaw […]

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arXiv:2604.05013v1 Announce Type: new Abstract: Current LLM coding agents are predominantly trained on composite benchmarks (e.g., bug fixing), which often leads to task-specific overfitting and limited generalization. To address this, we propose a novel scaling paradigm that shifts the focus from task-level optimization to atomic skill mastery. We first formalize five fundamental atomic skills, code localization, code editing, unit-test generation, issue reproduction, and code review, that serve as the basis vectors for complex software engineering tasks. Compared with […]

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We derive explicit non-asymptotic PAC-Bayes generalization bounds for Gibbs posteriors, that is, data-dependent distributions over model parameters obtained by exponentially tilting a prior with the empirical risk. Unlike classical worst-case complexity bounds based on uniform laws of large numbers, which require explicit control of the model space in terms of metric entropy (integrals), our analysis yields posterior-averaged risk bounds that can be applied to overparameterized models and adapt to the data structure and the intrinsic model complexity. The […]

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With the widespread adoption of deep learning in visual tasks, Class-Incremental Learning (CIL) has become an important paradigm for handling dynamically evolving data distributions. However, CIL faces the core challenge of catastrophic forgetting, often manifested as a prediction bias toward new classes. Existing methods mainly attribute this bias to intra-task class imbalance and focus on corrections at the classifier head. In this paper, we highlight an overlooked factor — temporal imbalance — as a key cause of this […]

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Reinforcement learning with verifiable rewards has emerged as a promising paradigm for enhancing the reasoning capabilities of large language models particularly in mathematics. Current approaches in this domain present a clear trade-off: PPO-style methods (e.g., GRPO/DAPO) offer training stability but exhibit slow learning trajectories due to their trust-region constraints on policy updates, while REINFORCE-style approaches (e.g., CISPO) demonstrate improved learning efficiency but suffer from performance instability as they clip importance sampling weights while still permitting non-zero gradients outside […]

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The current development of artificial intelligence creativity tools is rapidly progressing, with Google Mixboard being another entrant. The Mixboard platform functions as an AI-driven idea board that enables creators to brainstorm ideas and create visual elements while organizing their thoughts within a single workspace. The process of developing new ideas becomes difficult for creators, marketers, and designers because they need to switch between different tools that offer inspiration, design, and execution capabilities. Google Mixboard promises to bring all […]

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An AI is world-class at writing test-pleasing code. It knows exactly how to write the code that will result in the coveted green checkmark, but not necessarily solve for the edge cases. Code coverage measures what was executed. Mutation testing measures what your tests would detect if the code was wrong. And in the world of AI, that is the only thing that matters. You could think of it like this: You want to make sure all the […]

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