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Highlights
**Top Highlights:**
1. **OpenAI's Leadership by Gartner**: OpenAI has been recognized as a leader in enterprise AI coding agents, following a comprehensive analysis of its innovations and enterprise applications.
2. **Critical Security Vulnerability in License Portal System**: A critical SQL Injection vulnerability (CVE-2023-3522) exists in the License Portal System before version 1.48, which could lead to unauthorized access and malicious actions without validation or integrity checking.
3. **Improper Neutralization of Special Elements in LLMs Vulnerability**: Video Large Language Models like Video-LLMs struggle with signed image-plane motion direction detection, impacting their ability to accurately interpret video content.
4. **NeuroTechX's Open BCI Resources**: A curated collection of resources for neurotechnology and brain-computer interfaces (BCIs), maintained by the EEG-ExPy team within NeuroTechX, is free and accessible to anyone interested in learning about neurotech and BCIs.
**Action Plan (Next 24–72 Hours):**
1. **Stay Updated on Vulnerability Updates**: Monitor for any new CVEs related to SQL Injection and other critical security vulnerabilities.
2. **Enhance BCI Safety Protocols**: Review the guidelines provided by NeuroTechX and OpenViBE Consortium to ensure that all BCIs are implemented safely, particularly in educational and healthcare contexts.
3. **Implement Enhanced Code Review with Codex**: Continue using GPT-5.5 within Ramp for improved code review efficiency and accuracy.
**Learning / Skill Upgrades:**
1. **Deep Dive into NLP Models**: Study the limitations of current BPE and Unigram algorithms, focusing on how they affect the overall vocabulary in tokenization processes.
2. **Understanding Directional Motion Blindness**: Explore studies that highlight challenges in detecting image-plane motion direction in video content, with practical tips for improving detection accuracy.
**Watchlist:**
1. **Monitor Critical Vulnerabilities Announcements**: Keep an eye on security advisories for SQL Injection vulnerabilities affecting systems like License Portal and Veribase.
2. **Stay Informed on Open Source Quantum Computing Tools**: Track developments in open source quantum computer training to stay updated with practical skills for developers and researchers.
This briefing is designed to provide a clear, factual overview of the most relevant items without revealing proprietary or sensitive information from sources.
1. **OpenAI's Leadership by Gartner**: OpenAI has been recognized as a leader in enterprise AI coding agents, following a comprehensive analysis of its innovations and enterprise applications.
2. **Critical Security Vulnerability in License Portal System**: A critical SQL Injection vulnerability (CVE-2023-3522) exists in the License Portal System before version 1.48, which could lead to unauthorized access and malicious actions without validation or integrity checking.
3. **Improper Neutralization of Special Elements in LLMs Vulnerability**: Video Large Language Models like Video-LLMs struggle with signed image-plane motion direction detection, impacting their ability to accurately interpret video content.
4. **NeuroTechX's Open BCI Resources**: A curated collection of resources for neurotechnology and brain-computer interfaces (BCIs), maintained by the EEG-ExPy team within NeuroTechX, is free and accessible to anyone interested in learning about neurotech and BCIs.
**Action Plan (Next 24–72 Hours):**
1. **Stay Updated on Vulnerability Updates**: Monitor for any new CVEs related to SQL Injection and other critical security vulnerabilities.
2. **Enhance BCI Safety Protocols**: Review the guidelines provided by NeuroTechX and OpenViBE Consortium to ensure that all BCIs are implemented safely, particularly in educational and healthcare contexts.
3. **Implement Enhanced Code Review with Codex**: Continue using GPT-5.5 within Ramp for improved code review efficiency and accuracy.
**Learning / Skill Upgrades:**
1. **Deep Dive into NLP Models**: Study the limitations of current BPE and Unigram algorithms, focusing on how they affect the overall vocabulary in tokenization processes.
2. **Understanding Directional Motion Blindness**: Explore studies that highlight challenges in detecting image-plane motion direction in video content, with practical tips for improving detection accuracy.
**Watchlist:**
1. **Monitor Critical Vulnerabilities Announcements**: Keep an eye on security advisories for SQL Injection vulnerabilities affecting systems like License Portal and Veribase.
2. **Stay Informed on Open Source Quantum Computing Tools**: Track developments in open source quantum computer training to stay updated with practical skills for developers and researchers.
This briefing is designed to provide a clear, factual overview of the most relevant items without revealing proprietary or sensitive information from sources.
Digest
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Tech + AI + Science News
Betting on biotech: Prediction markets set sights on clinical trials fallback
One day in early May, web-based prediction market platform Kalshi listed two contracts tied to Compass Pathways’ psilocybin drug, COMP360. | Several biotech contracts are rotating through Kalshi and Polymarket, while a prediction market specifically focused on clinical trials has recently ...
White House Considers AI Vetting, Sparks Tech Industry Panic new fallback
The White House is scrambling to find its footing on AI policy, as the development of new, more powerful models forces the Trump administration to rethink its strategy on AI safety. From the outset of President Trump’s second term, the White House has promoted a pro-innovation, light-touch ...
GitHub recognized as a Leader in the Gartner® Magic Quadrant™ for Enterprise AI Coding Agents for the third year in a row article
We are committed to empowering every developer by building an open, secure, and AI-powered platform that defines the future of software development. The post GitHub recognized as a Leader in the Gartner® Magic Quadrant™ for Enterprise AI Coding Agents for the third year in a row appeared first on The GitHub Blog .
Beyond the engine: 10 open source projects shaping how games actually get made article
Check out these 10 open source tools that help game developers create art, animation, levels, audio, dialogue, debug UIs, and engine-ready assets. The post Beyond the engine: 10 open source projects shaping how games actually get made appeared first on The GitHub Blog .
Investigating unauthorized access to GitHub-owned repositories article
If any impact is discovered, customers will be notified via established incident response and notification channels. The post Investigating unauthorized access to GitHub-owned repositories appeared first on The GitHub Blog .
Think you’re bad at languages? Experts say these 5 myths are to blame fallback
Many people avoid learning a new language because they remember stressful grammar lessons or fear making mistakes. But language experts say communication, culture, and connection matter far more than perfection. Modern apps, entertainment, travel, and online communities have made learning easier, more social, and surprisingly fun.
Research (arXiv)
Tokenisation via Convex Relaxations fallbackTokenisation is an integral part of the current NLP pipeline. Current tokenisation algorithms such as BPE and Unigram are greedy algorithms -- they make locally optimal decisions without considering the resulting vocabulary as a whole. We instead formulate tokeniser construction as a linear program and solve it using convex optimisation tools, yielding a new algorithm we call ConvexTok. We find ConvexTok consistently improves intrinsic tokenisation metrics and the bits-per-byte (BpB) achieved by language models; it also improves downstream task performance, but less consistently. Furthermore, ConvexTok allows the user to certify how far their tokeniser is from optimal, with respect to a certain objective, via a lower bound, and we empirically find it to be within 1\% of optimal at common vocabulary sizes.
Which Way Did It Move? Diagnosing and Overcoming Directional Motion Blindness in Video-LLMs fallbackVideo Large Language Models (Video-LLMs) have made rapid progress on temporal video understanding, yet many fail at a basic perceptual primitive: signed image-plane motion direction. On simple videos of a single object moving left, right, up, or down, most Video-LLMs perform near chance, with above-chance cases largely attributable to prediction biases rather than genuine direction understanding. We call this failure directional motion blindness. We localize the failure by tracing motion direction information through the Video-LLM pipeline. Motion direction remains linearly accessible from the vision encoder, projector, and LLM hidden states, but the readout fails to bind this signal to the correct verbal answer option, revealing a direction binding gap. Although synthetic motion direction instruction tuning reduces this gap on the source domain, motion direction concept vector analysis
Bottom-up open EFT for non-Abelian gauge theory with dynamical color environment fallbackWe develop a bottom-up open effective field theory (EFT) for non-Abelian gauge theories within the Schwinger--Keldysh formalism. Instead of integrating out the environment completely and starting from a nonlocal influence functional, we retain the slow environmental response variables explicitly and construct a local system-environment EFT. The environmental sector is described by a dynamical color-frame variable, Stückelberg-like field, and an associated color-current sector, which gives the nontrivial interactions and dissipation between the system and the environment. The resulting construction provides a gauge-covariant Markov embedding of nonlocal and non-Markovian color response. After integrating out the retained environmental variables with retarded boundary conditions, the reduced system theory acquires nonlocal dissipative kernels and stochastic sources. We show that the hard t
Integrable Elasticity via Neural Demand Potentials fallbackWe propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. The model learns log-demand as a smooth, context-conditioned function of log-prices, allowing elasticities to be derived exactly from the learned demand surface. On the Dominick's beer dataset, ICDN improves out-of-sample generalization over a directed log-log benchmark and yields more stable, economically plausible elasticity estimates, especially for weakly identified cross-price effects.
Cambrian-P: Pose-Grounded Video Understanding fallbackCamera pose matters. The position and orientation of each viewpoint define a shared spatial coordinate frame that relates observations across video frames. Yet this signal is largely absent from multimodal LLMs (MLLMs) for video understanding, which process frames as isolated 2D snapshots, instead of the persistent scene humans perceive. We revisit pose as a lightweight supervisory signal and introduce Cambrian-P, a video MLLM augmented with per-frame learnable camera tokens and a pose regression head. With a carefully designed sampling scheme, the model achieves substantial gains of 4.5-6.5% on spatial reasoning benchmarks such as VSI-Bench, generalizes across eight additional spatial and general video QA benchmarks, and, as a byproduct, achieves state of the art streaming pose estimation on ScanNet. Surprisingly, training on pseudo-annotated poses from in-the-wild video further improve
MotiMotion: Motion-Controlled Video Generation with Visual Reasoning fallbackCurrent motion-controlled image-to-video generation models rigidly follow user-provided trajectories that are often sparse, imprecise, and causally incomplete. Such reliance often yields unnatural or implausible outcomes, especially by missing secondary causal consequences. To address this, we introduce MotiMotion, a novel framework that reformulates motion control as a reasoning-then-generation problem. To encourage causally grounded and commonsense-consistent interactions, we leverage a training-free vision-language reasoner to refine image-space coordinates of primary trajectories and to hallucinate plausible secondary motions. To further improve motion naturalness, we propose a confidence-aware control scheme that modulates guidance strength, enabling the model to closely follow high-confidence plans while correcting artifacts under low-confidence inputs with its internal generative
Vector Policy Optimization: Training for Diversity Improves Test-Time Search fallbackLanguage models must now generalize out of the box to novel environments and work inside inference-scaling search procedures, such as AlphaEvolve, that select rollouts with a variety of task-specific reward functions. Unfortunately, the standard paradigm of LLM post-training optimizes a pre-specified scalar reward, often leading current LLMs to produce low-entropy response distributions and thus to struggle at displaying the diversity that inference-time search will require. We propose Vector Policy Optimization (VPO), an RL algorithm that explicitly trains policies to anticipate diverse downstream reward functions and to produce diverse solutions. VPO exploits that rewards are often vector-valued in practice, like per-test-case correctness in code generation or, say, multiple different user personas or reward models. VPO is essentially a drop-in replacement for the GRPO advantage estima
AwareVLN: Reasoning with Self-awareness for Vision-Language Navigation fallbackVision-and-Language Navigation (VLN) requires an agent to ground language instructions to its own movement within a visual environment. While state-of-the-art methods leverage the reasoning capabilities of Vision-Language Models (VLMs) for end-to-end action prediction, they often lack an explicit and explainable understanding of the relationships between the agent, the instruction, and the scene. Conversely, explicitly building a scene map for heuristic planning is intuitively appealing but relies on additional 3D sensors and hinders large-scale vision-language pre-training. To bridge this gap, we propose AwareVLN, a novel framework that equips the navigation model with a self-aware reasoning mechanism, enabling it to understand the agent's state and task progress in a fully end-to-end and data-driven manner. Our approach features two key innovations: (1) a structural reasoning module th
Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration fallbackExploration is a prerequisite for learning useful behaviors in sparse-reward, long-horizon tasks, particularly within 3D environments. Curiosity-driven reinforcement learning addresses this via intrinsic rewards derived from the mismatch between the agent's predictive model of the world and reality. However, translating this intrinsic motivation to complex, photorealistic environments remains difficult, as agents can become trapped in local loops and receive fresh rewards for revisiting forgotten states. In this work, we demonstrate that this failure stems from a lack of spatial persistence and episodic context. We show that effective curiosity requires a model of the world that is persistent and continuously updated, paired with an agent that maintains an episodic trajectory history to navigate toward novel regions. We achieve this using an online 3D reconstruction as a persistent model
GesVLA: Gesture-Aware Vision-Language-Action Model Embedded Representations fallbackVision-Language-Action (VLA) models have shown strong potential for general-purpose robot manipulation by unifying perception and action. However, existing VLA systems primarily rely on textual instructions and struggle to resolve spatial ambiguity in complex scenes with multiple similar objects. To address this limitation, we introduce gesture as a parallel instruction modality and propose a Gesture-aware Vision-Language-Action model (GesVLA). Our approach encodes gesture features directly into the latent space, enabling them to participate in both high-level reasoning and low-level action generation, and adopts a dual-VLM architecture to achieve tight coupling between gesture representations and action policies. At the data level, we construct a scalable gesture data generation pipeline by rendering hand models onto real-world scene images. This reduces the sim-to-real visual gap while
Sensor2Sensor: Cross-Embodiment Sensor Conversion for Autonomous Driving fallbackRobust training and validation of Autonomous Driving Systems (ADS) require massive, diverse datasets. Proprietary data collected by Autonomous Vehicle (AV) fleets, while high-fidelity, are limited in scale, diversity of sensor configurations, as well as geographic and long-tail-behavioral coverage. In contrast, in-the-wild data from sources like dashcams offers immense scale and diversity, capturing critical long-tail scenarios and novel environments. However, this unstructured, in-the-wild video data is incompatible with ADS expecting structured, multi-modal sensor inputs for validation and training. To bridge this data gap, we propose Sensor2Sensor, a novel generative modeling paradigm that translates in-the-wild monocular dashcam videos into a high-fidelity, multi-modal sensor suite (AV logs) comprising multi-view camera images and LiDAR point clouds. A core challenge is the lack of p
How many systems can be dephased before the quantum switch becomes causally definite? fallbackQuantum processes with indefinite causal order -- so-called causally nonseparable processes -- can exhibit various advantages over quantum circuits with a fixed or a well-defined causal structure. A natural question is how much nonclassicality is required for a process to display causal nonseparability. Here we address this by investigating how many systems can be dephased (or decohered) before this property vanishes. First, for bipartite processes with open past and future we show that if all systems are dephased, or if only the future system is kept undephased, then the process becomes causally separable. However, if any single system other than the future system remains undephased, then there exist processes that retain causal nonseparability. Next, we demonstrate a similar behaviour in the multipartite case, when restricted to the physically motivated class of quantum circuits with q
Security (NVD + CISA KEV)
CVE-2023-3377· CVSS 9.8 CRITICAL new fallbackImproper Neutralization of Special Elements used in an SQL Command ('SQL Injection') vulnerability in Veribilim Software Computer Veribase allows SQL Injection. This issue affects Veribase: through 20231123. NOTE: The vendor was contacted early about this disclosure but did not respond in any way.
Projects + Resources (Discovery)
GitHub - NeuroTechX/awesome-bci: Curated Collection of BCI resources · GitHub new fallback
Maintained by the EEG-ExPy team within NeuroTechX. CNS2024 Poster · OpenViBE: A software platform dedicated to designing, testing, and using Brain-Computer Interfaces, maintained by the OpenViBE Consortium. BCI2000: Software suite with GUI based on C++ for data acquisition, stimulus presentation, and brain monitoring applications. Brainstorm: Collaborative, open-source application dedicated to the analysis of brain recordings: MEG, EEG, fNIRS, ECoG, depth electrodes and multiunit electrophysiology.
Open Source Tools for Neuroscience new fallback
<strong>We provide anyone with a computer, the tools necessary to sample the electrical activity of their body</strong>. Our versatile and affordable bio-sensing microcontrollers can be used to sample electrical brain activity (EEG), muscle activity (EMG), heart ...
5 best resources for Neurotech and BCI new fallback
This article highlights five key resources, free and open to anyone on the internet, to start learning about neurotech and Brain Computer Interfaces. The BCI Guys have developed an introductory video series called <strong>the Foundations of Neurotechnology</strong>.
Unlocking Quantum Computing: Free Open Source Training and Resources for Everyone | SpinQ new fallbackLinux Foundation’s Fundamentals of Quantum Computing course. These courses blend theoretical concepts with hands-on programming, mostly using open-source tools, helping learners gain practical skills to run quantum algorithms on simulators and real devices.
Introduction to quantum computing | OpenLearn - Open University new fallback
This OpenLearn course is an adapted extract from the Open University course <strong>SM380 Quantum physics: fundamentals and applications</strong>. This content forms part of the Dangoor Education collection, the educational arm of The Exilarch's Foundation.
Reverse Engineering 101 new fallback
Use open source VM detonation services like hybrid-analysis.com or malwr.com to get the behavior quickly · Use the VM detonation service to capture any network connections or packet data. If you can't do this then we will need to dynamically debug the malware.
FOR610: Reverse-Engineering Malware: Malware Analysis Tools and Techniques | SANS Institute new fallback
This malware analysis course is ideal for those seeking to enhance threat intelligence, incident response, and enterprise defenses. ... Lenny Zeltser is a leader in developing resilient security programs. His invaluable tools, like REMnux, a widely used Linux distribution for malware analysis, have become industry standards in combating malicious software. Read more about Lenny Zeltser ... Anuj Soni, Principal Reverse Engineer at United Healthcare, has over 15 years of experience enhancing organizational security postures.
Introduction to Malware Analysis and Reverse Engineering new fallback
Malware Analysis on a Budget - Discussion of malware analysis tools and research projects out in the open-source community · MalwareDNA - Talk about an instruction-analysis technique I devised in 2013 · Scott Nusbaum taught this class in Spring 2019, here is his great curriculum · Adventures in Security (http://securitykitten.github.io/) - Nick Hoffman: A colleage, former coworker, and friend · RPISEC’s Malware Analysis Course - Rensselaer Polytechnic Institute
Sources
news ok
News (RSS/Atom)
News sources (6/6 ok)
OpenAI News ok
Hacker News ok
GitHub Blog ok
ScienceDaily — Artificial Intelligence ok
ScienceDaily — Education & Learning ok
ScienceDaily — Stem Cells ok
arxiv ok
Research (arXiv)
nvd ok
Security (NVD)
kev ok
Security (CISA KEV)
brave ok
Discovery (Brave)
Archive
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