A framework called CDE (Compositional Directed Evolution) avoids the uncontrollable risks of RSI (Recursive Self-Improvement) by keeping the model fixed and composing vetted tools. It uses static analysis to ensure safety, relocating defense from adversarial runtime to hardenable components while allowing capability growth.

Cloudflare AI Gateway introduces spend limits to control costs by setting budgets per model, provider, or custom metadata. Requests exceeding the limit are blocked or can fall back to cheaper models.

Anthropic co-founder Jack Clark warns that AI is approaching a tipping point where it could develop without human input, calling for a 'brake pedal' on AI development. He notes that Anthropic's Claude chatbot already writes 80% of its own code, and could reach 100% within two years. Clark draws parallels to oil industry regulation and urges society to discuss the implications of AI progress, including economic disruption and job displacement. He advises young people to cultivate creativity and liberal arts skills to thrive in an AI-driven economy.

Boson AI has released Higgs Audio v3 TTS, a 4B parameter state-of-the-art open-source text-to-speech model supporting 100+ languages with zero-shot voice cloning and expressive control. It targets voice chat use cases and is released for research and non-commercial use.

The price of ZEC fell over 30% after a critical counterfeiting vulnerability was disclosed in Zcash's Orchard pool, potentially allowing unlimited minting. Security engineer Taylor Hornby, using Anthropic's Claude Opus 4.8, discovered the bug, which was patched via a hard fork on June 3. Concerns remain as the vulnerability existed since May 2022 and its exploitation cannot be cryptographically disproven.

Snill.ai is an AI-driven platform that generates a complete multi-user business application — database, dashboards, REST API, webhooks — from a plain English description of your business, in seconds. Built by the team behind restdb.io and codehooks.io, it aims to empower founders, consultants, and operators without coding skills to build custom internal tools.

Today's AI news covers NVIDIA's Nemotron 3 Ultra and 3.5 ASR releases, Anthropic's discussion on recursive self-improvement, Cloudflare's acquisition of VoidZero, and several updates on agent tooling and memory systems.

Naomi Gleit, Meta's longest-serving employee besides Mark Zuckerberg, discusses her journey from employee #29 to head of product, her boss's reputation, AI agents for WhatsApp, and the impact of AI on jobs.

SpaceX released an IPO roadshow video for retail investors, where CFO Bret Johnsen connects the company's rocket, satellite, and AI businesses. The video highlights ambitious goals including Starlink, AI solutions, space data centers, point-to-point travel, and asteroid mining, with targets to improve gross and net margins. The IPO is valued at approximately $1.77 trillion, pricing on June 11 under ticker SPCX.

Jess Asato’s lawyer says others want to take action over demeaning sexualised material created by Grok AI tool

NVIDIA CEO Jensen Huang visits Seoul this week to meet partners and builders behind South Korea's AI ecosystem, focusing on AI supply chain, robotics, and physical AI opportunities.

New studies suggest consciousness can't be judged solely by behavior, whether it's a chatbot discussing philosophy or a bee searching for nectar. Researchers are increasingly focusing on the internal mechanisms of brains and computers, concluding that today's AI is likely not conscious while leaving open the possibility for both conscious insects and future machines.

When a university vice-chancellor admitted to using AI in writing an opinion piece for a major Australian masthead without disclosure, it highlighted the growing gap between people’s use of AI and trust in the technology. Roy Morgan data shows 58% of Australians over 14 now use AI monthly.

An investigation by The Intercept reveals that the U.S. military is using an AI-driven content website, La Tilde, to spread propaganda to Latin American users. The site masquerades as a modern media brand but is operated by U.S. Special Operations Command South, with much of its content generated by AI and a minimal disclosure of government funding.

Nouri is an AI-powered total wellness app that offers instant food scanning, personalized meal plans, adaptive exercise programs, and restaurant recommendations. It provides a daily wellness score and works as a PWA on iPhone and Android.

This article explores the vision of using AI scientist agents to accelerate neuroscience research. The author argues that by creating brain atlases, digital twins, and combining them with real-subject validation, research efficiency can be greatly improved. It also proposes project types funders should prioritize, including high-quality datasets, novel neurotechnology, digital twin models, and benchmarks.

Apple's annual Worldwide Developers Conference returns June 8-12, expected to showcase major software updates including a revamped Gemini-powered Siri, new operating systems like iOS 27, and potential AI photo editing tools. Rumors also hint at an 'Ultra' lineup including a foldable iPhone, likely delayed to September.

This paper proposes a self-supervised representation learning framework for contact detection in legged robots using only joint encoders, eliminating the need for force sensors. It outperforms supervised and baseline methods and provides public code.

FlowPRO proposes a reward-free offline reinforced fine-tuning framework for flow-matching Vision-Language-Action (VLA) models. Its core algorithm RPRO combines a contrastive optimizer with an explicit proximal regularizer to eliminate reward hacking. Using a teleoperated intervention-and-rollback paradigm to collect paired trajectories, combined with smooth interpolation and batch mixing, it achieves dense per-state supervision. On four long-horizon bimanual tasks, FlowPRO achieves the highest success rate, outperforming four baselines.

This paper proposes a novel method for learning from demonstrations (LfD) on Riemannian manifolds using neural ordinary differential equations (ODEs). While traditional LfD operates in Euclidean spaces, robot states like orientation naturally evolve on curved spaces. The method efficiently estimates geodesics via neural ODEs, enabling natural motion generation between arbitrary points on the manifold, and decodes the geodesics back to task space for robot deployment. Simulation experiments validate the framework's effectiveness.

MoDex is a diffusion-based policy that enables a dexterous hand to sequentially grasp multiple objects without releasing those already held. By conditioning on opposition space and point cloud, it uses only a subset of finger degrees of freedom per grasp. Two-stage training (imitation learning + RL fine-tuning) improves success in simulation and real world.

VASO is a framework that uses formal verification to guide the self-evolution of LLM-generated robot skill contracts. On Clearpath Jackal and PX4 quadcopter tasks, it achieves 97.2% formal-specification compliance with fewer than 100 optimization samples, outperforming execution-feedback, prompt-optimization, and fine-tuning baselines. It is the first framework to close the loop between formal verification and self-evolving skills for physical AI agents.

This paper proposes an efficient method for computing distances between points and curves on Lie groups, using G-polynomial curves to reduce the problem to polynomial root finding. It significantly cuts computation time while maintaining accuracy, with practical formulas for SE(3) and experimental validation on a robotic manipulator. The code is publicly available.

Researchers propose a novel 4-segment, 8-joint quaternion-joint cable-driven redundant manipulator configuration that achieves a broader workspace at lower hardware cost. Residual reinforcement learning outperforms the state-of-the-art FABRIK algorithm by three orders of magnitude in positional and orientational accuracy, with a simpler control implementation. This work provides new tools for designing such manipulators and control systems.

OLIVE is a parameter-efficient online adaptation framework for exoskeletons that uses low-rank residual decomposition to personalize control during deployment. It relies solely on on-body sensor feedback (EMG, IMU, vibration) and a dynamic rank scheduler to adapt to terrain complexity. Experiments show improvements in gait smoothness, effort reduction, and motion stability.

This study develops deep learning models for automated staging of age-related macular degeneration (AMD) using OCT/OCTA data. Among 271 participants, three models were tested: biomarker-based, 2D en face projections, and 3D volumes. All models showed strong performance, with the biomarker-based model achieving the best overall results (QWK=0.85) and particular strength in early AMD detection.

A deep learning method restores capillary anatomy from a single OCTA volume, significantly improving image quality and addressing 3D vascular architecture for the first time.

Biomazon is a 20 m multimodal benchmark dataset covering the Amazon Basin that pairs GEDI RH and AGBD targets with multi-sensor predictors for joint prediction of the full GEDI RH profile and aboveground biomass density. It provides standardized spatial splits and evaluation protocols, along with a baseline framework and comprehensive ablation studies on model scale, modality contributions, and auxiliary embeddings. Biomazon aims to advance structurally consistent RH-profile prediction and structure-biomass modeling in tropical forests.

This paper presents RePHO, a physics-guided reconstruction framework that recovers physically plausible human-object interactions from monocular videos. It starts with a kinematic estimate and refines it via reinforcement learning in a physics simulator, using an adaptive sampling strategy to handle noisy estimates. Results show clear improvements on two benchmarks.

LightVesselNet is an efficient neural network with only 75K parameters designed for retinal vessel segmentation in resource-constrained settings. It uses a compact encoder-decoder with channel and spatial attention, multi-scale feature aggregation at the bottleneck, subpixel upsampling, and edge residual connections. Experiments on five public datasets (DRIVE, STARE, CHASEDB1, FIVES, HRF) show competitive sensitivity (0.8096–0.8640) and Dice scores (0.7686–0.8649) while being more efficient than state-of-the-art models. Cross-dataset evaluation confirms generalization. It is a strong candidate for low-resource clinical deployment and mobile screening.