Intelligence Digest

OpenAI Blog · 2026-04-08

Discover OpenAI’s Child Safety Blueprint—a roadmap for building AI responsibly with safeguards, age-appropriate design, and collaboration to protect and empower young people online.

OpenAI Blog · 2026-04-06

A pilot program to support independent safety and alignment research and develop the next generation of talent

OpenAI Blog · 2026-04-06

Explore our ambitious, people-first industrial policy ideas for the AI era—focused on expanding opportunity, sharing prosperity, and building resilient institutions as advanced intelligence evolves.

Hacker News · 2026-04-08

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Hacker News · 2026-04-08

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Hacker News · 2026-04-08

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Hacker News · 2026-04-08

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GitHub Blog · 2026-04-08

Get inspired by five of the most memorable, magical, and quirky Universe sessions to date. The post GitHub Universe is back: We want you to take the stage appeared first on The GitHub Blog .

GitHub Blog · 2026-04-06

Discover how Rubber Duck provides a different perspective to GitHub Copilot CLI. The post GitHub Copilot CLI combines model families for a second opinion appeared first on The GitHub Blog .

ScienceDaily — Artificial Intelligence · 2026-04-07

A team of engineers has created a breakthrough memory device that keeps working at temperatures hotter than molten lava, shattering one of electronics’ biggest limits. Built from an unusual stack of ultra-durable materials, the tiny component can store data and perform calculations even at 700°C (1300°F), far beyond what today’s chips can handle. The discovery was partly accidental, but it revealed a powerful new mechanism that prevents heat-induced failure at the atomic level.

ScienceDaily — Artificial Intelligence · 2026-04-06

AI is consuming staggering amounts of energy—already over 10% of U.S. electricity—and the demand is only accelerating. Now, researchers have unveiled a radically more efficient approach that could slash AI energy use by up to 100× while actually improving accuracy. By combining neural networks with human-like symbolic reasoning, their system helps robots think more logically instead of relying on brute-force trial and error.

ScienceDaily — Stem Cells · 2026-04-08

Scientists at Cornell University may be closing in on the long-sought “holy grail” of male contraception: a safe, reversible, nonhormonal method that completely halts sperm production. In a breakthrough mouse study, researchers used a compound called JQ1 to temporarily shut down meiosis—the critical process that produces sperm—without causing lasting harm. After treatment stopped, sperm production bounced back, fertility returned, and the animals produced healthy offspring.

arXiv · 2026-04-07

The rapid growth of scientific literature has made it increasingly difficult for researchers to efficiently discover, evaluate, and synthesize relevant work. Recent advances in multi-agent large language models (LLMs) have demonstrated strong potential for understanding user intent and are being trained to utilize various tools. In this paper, we introduce Paper Circle, a multi-agent research discovery and analysis system designed to reduce the effort required to find, assess, organize, and understand academic literature. The system comprises two complementary pipelines: (1) a Discovery Pipeline that integrates offline and online retrieval from multiple sources, multi-criteria scoring, diversity-aware ranking, and structured outputs; and (2) an Analysis Pipeline that transforms individual papers into structured knowledge graphs with typed nodes such as concepts, methods, experiments, and

arXiv · 2026-04-07

The static ``train then deploy" paradigm fundamentally limits Large Language Models (LLMs) from dynamically adapting their weights in response to continuous streams of new information inherent in real-world tasks. Test-Time Training (TTT) offers a compelling alternative by updating a subset of model parameters (fast weights) at inference time, yet its potential in the current LLM ecosystem is hindered by critical barriers including architectural incompatibility, computational inefficiency and misaligned fast weight objectives for language modeling. In this work, we introduce In-Place Test-Time Training (In-Place TTT), a framework that seamlessly endows LLMs with Test-Time Training ability. In-Place TTT treats the final projection matrix of the ubiquitous MLP blocks as its adaptable fast weights, enabling a ``drop-in" enhancement for LLMs without costly retraining from scratch. Furthermor

arXiv · 2026-04-07

World action models (WAMs) have emerged as a promising direction for robot policy learning, as they can leverage powerful video backbones to model the future states. However, existing approaches often rely on separate action modules, or use action representations that are not pixel-grounded, making it difficult to fully exploit the pretrained knowledge of video models and limiting transfer across viewpoints and environments. In this work, we present Action Images, a unified world action model that formulates policy learning as multiview video generation. Instead of encoding control as low-dimensional tokens, we translate 7-DoF robot actions into interpretable action images: multi-view action videos that are grounded in 2D pixels and explicitly track robot-arm motion. This pixel-grounded action representation allows the video backbone itself to act as a zero-shot policy, without a separat

arXiv · 2026-04-07

Churn flow-the chaotic, oscillatory regime in vertical two-phase flow-has lacked a quantitative mathematical definition for over $40$ years. We introduce the first topology-based characterization using Euler Characteristic Surfaces (ECS). We formulate unsupervised regime discovery as Multiple Kernel Learning (MKL), blending two complementary ECS-derived kernels-temporal alignment ($L^1$ distance on the $χ(s,t)$ surface) and amplitude statistics (scale-wise mean, standard deviation, max, min)-with gas velocity. Applied to $37$ unlabeled air-water trials from Montana Tech, the self-calibrating framework learns weights $β_{ECS}=0.14$, $β_{amp}=0.50$, $β_{ugs}=0.36$, placing $64\%$ of total weight on topology-derived features ($β_{ECS} + β_{amp}$). The ECS-inferred slug/churn transition lies $+3.81$ m/s above Wu et al.'s (2017) prediction in $2$-in. tubing, quantifying reports that existing

arXiv · 2026-04-07

Large vision-language models can produce object hallucinations in image descriptions, highlighting the need for effective detection and mitigation strategies. Prior work commonly relies on the model's attention weights on visual tokens as a detection signal. We reveal that coarse-grained attention-based analysis is unreliable due to hidden confounders, specifically token position and object repetition in a description. This leads to Simpson's paradox: the attention trends reverse or disappear when statistics are aggregated. Based on this observation, we introduce HaloProbe, a Bayesian framework that factorizes external description statistics and internal decoding signals to estimate token-level hallucination probabilities. HaloProbe uses balanced training to isolate internal evidence and combines it with learned prior over external features to recover the true posterior. While interventi

arXiv · 2026-04-07

Most digital videos are stored in 8-bit low dynamic range (LDR) formats, where much of the original high dynamic range (HDR) scene radiance is lost due to saturation and quantization. This loss of highlight and shadow detail precludes mapping accurate luminance to HDR displays and limits meaningful re-exposure in post-production workflows. Although techniques have been proposed to convert LDR images to HDR through dynamic range expansion, they struggle to restore realistic detail in the over- and underexposed regions. To address this, we present DiffHDR, a framework that formulates LDR-to-HDR conversion as a generative radiance inpainting task within the latent space of a video diffusion model. By operating in Log-Gamma color space, DiffHDR leverages spatio-temporal generative priors from a pretrained video diffusion model to synthesize plausible HDR radiance in over- and underexposed re

arXiv · 2026-04-07

The Character Error Rate (CER) is a key metric for evaluating the quality of Optical Character Recognition (OCR). However, this metric assumes that text has been perfectly parsed, which is often not the case. Under page-parsing errors, CER becomes undefined, limiting its use as a metric and making evaluating page-level OCR challenging, particularly when using data that do not share a labelling schema. We introduce the Character Error Vector (CEV), a bag-of-characters evaluator for OCR. The CEV can be decomposed into parsing and OCR, and interaction error components. This decomposability allows practitioners to focus on the part of the Document Understanding pipeline that will have the greatest impact on overall text extraction quality. The CEV can be implemented using a variety of methods, of which we demonstrate SpACER (Spatially Aware Character Error Rate) and a Character distribution

arXiv · 2026-04-07

In RL, given a prompt, we sample a group of completions from a model and score them. Two questions follow: which completions should gain probability mass, and how should the parameters move to realize that change? Standard policy-gradient methods answer both at once, so the update can overshoot or undershoot depending on the learning rate, clipping, and other optimizer choices. We introduce \emph{Target Policy Optimization} (TPO), which separates the two questions. Given scored completions, TPO constructs a target distribution $q_i \propto p_i^{\,\mathrm{old}} \exp(u_i)$ and fits the policy to it by cross-entropy. The loss gradient on sampled-completion logits is $p^θ- q$, which vanishes once the policy matches the target. On tabular bandits, transformer sequence tasks, and billion-parameter LLM RLVR, TPO matches PG, PPO, GRPO, and DG on easy tasks and substantially outperforms them unde

arXiv · 2026-04-07

We study, to our knowledge, the first tractable multistage ex-ante distributionally robust regret optimization (DRRO) formulation for stochastic control. We consider finite-horizon LQR under common stage-law ambiguity: disturbances are independent across time but share an unknown stage law whose mean and covariance lie in a Gelbrich ball around nominal parameters. Unlike the single-stage quadratic case, the nominal certainty-equivalent (CE) controller is generally not regret-optimal, because reuse of the stage law makes past disturbances informative for future decisions. Despite the general NP-hardness of DRRO, we show that over linear disturbance-feedback policies the resulting multistage DRRO-LQR problem admits an exact semidefinite programming reformulation. The optimal controller is the nominal certainty-equivalent LQR law plus a strictly causal empirical-mean correction. We also cha

arXiv · 2026-04-07

MLLMs have been successfully applied to multimodal embedding tasks, yet their generative reasoning capabilities remain underutilized. Directly incorporating chain-of-thought reasoning into embedding learning introduces two fundamental challenges. First, structural misalignment between instance-level reasoning and pairwise contrastive supervision may lead to shortcut behavior, where the model merely learns the superficial format of reasoning. Second, reasoning is not universally beneficial for embedding tasks. Enforcing reasoning for all inputs may introduce unnecessary computation and latency, and can even obscure salient semantic signals for simple cases. To address these issues, we propose MMEmb-R1, an adaptive reasoning-based multimodal embedding framework. We formulate reasoning as a latent variable and introduce pair-aware reasoning selection that employs counterfactual intervention

NVD · 2022-11-10

xterm before 375 allows code execution via font ops, e.g., because an OSC 50 response may have Ctrl-g and therefore lead to command execution within the vi line-editing mode of Zsh. NOTE: font ops are not allowed in the xterm default configurations of some Linux distributions.

NVD · 2024-11-29

Uncontrolled Search Path Element vulnerability in Mitsubishi Electric GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric ICONICS Suite versions 10.97.3 and prior, Mitsubishi Electric Hyper Historian versions 10.97.3 and prior, Mitsubishi Electric GENESIS32 all versions, Mitsubishi Electric MC Works64 all versions, Mitsubishi Electric Iconics Digital Solutions GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric Iconics Digital Solutions ICONICS Suite versions 10.97.3 and prior, Mitsubishi Electric Iconics Digital Solutions Hyper Historian versions 10.97.3 and prior, and Mitsubishi Electric Iconics Digital Solutions GENESIS32 all versions allows a local authenticated attacker to execute a malicious code by storing a specially crafted DLL in a specific folder. This could lead to disclose, tamper with, destroy, or delete information in the affected products, or to cause a denial

NVD · 2024-11-29

Uncontrolled Search Path Element vulnerability in Mitsubishi Electric GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric ICONICS Suite versions 10.97.3 and prior, Mitsubishi Electric Hyper Historian versions 10.97.3 and prior, Mitsubishi Electric GENESIS32 all versions, Mitsubishi Electric MC Works64 all versions, Mitsubishi Electric Iconics Digital Solutions GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric Iconics Digital Solutions ICONICS Suite versions 10.97.3 and prior, Mitsubishi Electric Iconics Digital Solutions Hyper Historian versions 10.97.3 and prior, and Mitsubishi Electric Iconics Digital Solutions GENESIS32 all versions allows a local authenticated attacker to execute a malicious code by storing a specially crafted DLL in a specific folder. This could lead to disclose, tamper with, destroy, or delete information in the affected products, or cause a denial of

NVD · 2024-01-26

A segment fault (SEGV) flaw was found in libtiff that could be triggered by passing a crafted tiff file to the TIFFReadRGBATileExt() API. This flaw allows a remote attacker to cause a heap-buffer overflow, leading to a denial of service.

NVD · 2024-07-04

Uncontrolled Search Path Element vulnerability in Mitsubishi Electric GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric ICONICS Suite versions 10.97.3 and prior, Mitsubishi Electric Hyper Historian versions 10.97.3 and prior, Mitsubishi Electric MC Works64 all versions, Mitsubishi Electric GENESIS32 versions 9.7 and prior, Mitsubishi Electric Iconics Digital Solutions GENESIS64 versions 10.97.3 and prior, Mitsubishi Electric Iconics Digital Solutions ICONICS Suite versions 10.97.3 and prior, Mitsubishi Electric Iconics Digital Solutions Hyper Historian versions 10.97.3 and prior, and Mitsubishi Electric Iconics Digital Solutions GENESIS32 versions 9.7 and prior allows a local attacker to execute a malicious code by storing a specially crafted DLL in a specific folder when GENESIS64, ICONICS Suite, Hyper Historian, GENESIS32, and MC Works64 are installed with the Pager agent in th

NVD · 2024-07-04

Use of Externally-Controlled Input to Select Classes or Code ('Unsafe Reflection') vulnerability in the licensing feature of Mitsubishi Electric GENESIS64 versions 10.97.2 and prior, Mitsubishi Electric ICONICS Suite versions 10.97.2 and prior, Mitsubishi Electric Hyper Historian versions 10.97.2 and prior, Mitsubishi Electric AnalytiX versions 10.97.2 and prior, Mitsubishi Electric MobileHMI versions 10.97.2 and prior, Mitsubishi Electric MC Works64 all versions, Mitsubishi Electric GENESIS32 versions 9.7 and prior, Mitsubishi Electric BizViz versions 9.7 and prior, Mitsubishi Electric Iconics Digital Solutions GENESIS64 versions 10.97.2 and prior, Mitsubishi Electric Iconics Digital Solutions ICONICS Suite versions 10.97.2 and prior, Mitsubishi Electric Iconics Digital Solutions Hyper Historian versions 10.97.2 and prior, Mitsubishi Electric Iconics Digital Solutions AnalytiX versions

Brave Search

ros2 run linorobot2_gazebo create_worlds_from_maps · This reads every YAML file in linorobot2_navigation/maps/, extrudes the occupancy grid into a 3D wall mesh, and writes a Gazebo world for each map. Useful for keeping simulation worlds in sync after a mapping session. Full documentation covering installation, base controller, odometry, sensors, transforms, mapping, and navigation is in the docs/ directory. ... Then open http://127.0.0.1:8000 in your browser.

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This is repository for the course ROS2 Autonomous Driving and SLAM using NAV2 with TurtleBot3 availble at Udemy . <strong>Complete source code is open sourced</strong>.

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OPEN SOURCE SPEECH RECOGNITION TOOLKIT · Jan 10, 2025 · PocketSphinx 5.0.4 is now released. This is a patch release which primarily adds support for Python 3.13. Download source from GitHub or PyPI Yes, these are not exactly the same file. Install binaries for Python: pip3 install pocketsphinx ·

Brave Search

This is changing, today there are a lot of open source speech tools and libraries that you can use right now. They even boomed much more than before, thanks to the trend of AI and generative models. ... It is the software engine responsible for transforming voice to text or vice versa, and It is not meant to be used by end users. Developers will first have to adopt these libraries and use them to create computer programs that can enable speech recognition for users.

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Topics include sparse-matrix/iterative ... analysis, …Show more · <strong>This course offers an advanced introduction to numerical analysis, with a focus on accuracy and efficiency of numerical algorithms</strong>....

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For more practical examples and use cases, explore the official Qwen3-VL cookbooks. DeepSeek-OCR is DeepSeek’s latest open-source VLM that redefines optical character recognition through a concept called Contexts Optical Compression. The core idea works like this: Encode images into compact, high-density vision tokens. Decode those tokens back into text using a language model. Why is this important? LLMs face compute limitations when processing long text sequences.

Brave Search

Discover how to run large language models (LLMs) locally with Ollama. This guide walks you through setup, model selection, and steps to leverage AI on your own machine.

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Abstract. In this article we bring some remarks about use of Open Source Software in · teaching of Numerical Analysis based on our experience with Matlab, Octave, and Python-

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Free self-paced online educator course for getting started with Computer Science Fundamentals!

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LGSVL - Simulation software to accelerate safe autonomous vehicle development. Unity Robotics Hub - This is a central repository for tools, tutorials, resources, and documentation for robotic simulation in Unity. Foxglove Studio - Integrated visualization and diagnosis tool for robotics. ROS2 For Unity - An asset package which enables high-performance communication between Unity3D simulations and ROS2 ecosystem.