Mon 1 June 2026
Daily Brief — Curated and contextualised by Best Practice AI
Nvidia Challenges Intel, AI Spurs Inflation, and China Tightens Investment
TL;DR The U.S. relies heavily on global scientific collaboration for innovation, as highlighted in a new study. Nvidia is entering the PC market with a new AI chip, challenging Intel's dominance. AI-related capital expenditures are contributing to economic instability and inflation, complicating the Federal Reserve's interest rate plans. Meanwhile, China is tightening controls on outbound investments to safeguard its technological interests amid rising tensions with the U.S.
The stories that matter most
Selected and contextualised by the Best Practice AI team
Exclusive: Economists have been teaching a broken proof for 50 years. AI just found it
Axiom Math, a $1.6B AI unicorn, is building a formally verified library of economic theorems — and already found gaps in the foundations of antitrust law.
Apollo’s Slok Says AI Will Dash Warsh’s Hopes for Quick Rate Cut
The build-out for artificial intelligence will be inflationary in the early going, preventing new Federal Reserve Chair Kevin Warsh from cutting interest rates as quickly as he has suggested should be possible, according to Torsten Slok of Apollo Global Management Inc.
Global Science Sustains U.S. Innovation
arXiv:2605.30435v1 Announce Type: new Abstract: Like physical products, new technologies are developed using globally sourced inputs. Yet while the supply chains behind physical goods are well understood, we know far less about the international supply chain of scientific knowledge that powers U.S. innovation, or how vulnerable it may be to disruption. Here, I uncover this supply chain by tracing multi-generational citation paths connecting NSF-funded research to downstream patents, and stress-test it by simulating barriers to scientific knowledge flows across the U.S. border. The U.S. knowledge supply chain extends globally, and frictions impeding the movement of ideas across the U.S. border reduce its connectivity, extend its length, and lower innovation productivity. These impacts extend to technology areas deemed critical to national priorities by U.S. Congress, including Semiconductors, Quantum Science, and AI.
Healthcare Mechanisms from Policy-as-Code Search under Strategic Provider Response
arXiv:2605.30680v1 Announce Type: new Abstract: Healthcare mechanisms are inseparable from the strategic provider response they induce: existing healthcare AI benchmarks hold this response fixed and so cannot evaluate mechanisms by the equilibrium they produce. We recast hospital mechanism design as program synthesis for language models: typed, inspectable rule programs are executed and scored by Medi-Sim, a multi-agent simulator with five strategic provider channels (coding, selection, delay, effort, triage). An incentive sweep recovers classical health-economics findings as adjacent regimes -- up-coding and low-complexity-patient selection under profit pressure, and Goodhart-style drift where measured performance becomes anti-correlated with true outcomes -- and a single audit lever exposes pressure migration: closing the coding channel more than doubles low-complexity selection. LLM-guided evolutionary code search over the same rule-program space then synthesizes an inspectable mixed-objective program that eliminates up-coding, halves rejection, and retains most of the profit-oriented baseline's funds.
The Tutoring Effectiveness Index: Predicting LLM Math Tutor Quality from Four Conversation Signals
arXiv:2605.30666v1 Announce Type: new Abstract: Aligning large language models (LLMs) as math tutors typically demands costly reinforcement-learning (RL) training and external LLM judges. We ask whether a frozen model's internal reasoning signals can replace both. We propose the Tutoring Effectiveness Index (TEI), a training-free, judge-free four-signal index that combines a Schoenfeld-Verify keyword ratio, a math-step density, an ends-question rate, and a deep-reasoning gate from the Deep-Thinking Ratio (DTR) probe. Selecting from $N$ candidates with TEI (the TEI@$N$ rule) raises the improvement rate on pre-incorrect scenarios from $59.0\%$ to $81.9\%$ at $N{=}8$ on a frozen DeepSeek-R1-8B base, with no training and no external judge. We also measure the alignment tax of pedagogical GRPO. Thinking length drops from $1{,}764$ to $119$ words per turn ($-93\%$), Content-Knowledge and Pedagogical-Knowledge accuracy fall by $-71\%$ and $-80\%$ relative, and the student's $\Delta$ Solve Rate crosses from $+0.180$ to $-0.012$. To anchor the behavioural reading, we reproduce an 82-code educational codebook on $119{,}009$ tutor sentences with a one-shot structural classifier. Together, these results offer a cost-effective recipe for building math-tutoring LLMs without RL training or external judges.
Claude Mythos exposed a hard truth: Your enterprise patching process is way too slow
The Claude Mythos vulnerability highlights critical delays in enterprise security patching.
Economics & Markets
Wall Street bulls bet US stocks rally will defy bubble fears
Investors and strategists shrug off worries that markets could be overheating by betting on huge gains for AI-linked shares
🔥 We checked. Again. Still no bubble.
A customer-led boom with a few fraying edges.
SoftBank's Masayoshi Son Calls AI ‘50X Bigger’ Than Dot-Com Boom, Welcomes Corrections As ‘Best Investmen - Benzinga
SoftBank CEO Masayoshi Son says AI is 50x bigger than the dot-com boom and market corrections are buying opportunities.
AI Spending Crisis: The Shocking Cost of ChatGPT and Claude Inside Modern Companies
The AI Spending Crisis is forcing companies to rethink ChatGPT and Claude adoption as enterprise AI costs soar and budgets spiral out of control.
It's all AI (and the war). - by Patrick O'Hearn
The AI hyperscalers and adjacent companies have done well since the start of the war (in particular AMD, a rival chip designer to Nvidia, that is benefiting from the market belief that hyperscalers are starting to diversify away from Nvidia’s chips), with one notable exception.
This AI Hardware Bottleneck Is Determining the Next Trillion-Dollar Tech Companies | The Motley Fool
The technology sector wasn't quite ready to fully meet this need, but the resulting price increase isn't stifling demand.
Apollo’s Slok Says AI Will Dash Warsh’s Hopes for Quick Rate Cut
The build-out for artificial intelligence will be inflationary in the early going, preventing new Federal Reserve Chair Kevin Warsh from cutting interest rates as quickly as he has suggested should be possible, according to Torsten Slok of Apollo Global Management Inc.
Global Science Sustains U.S. Innovation
arXiv:2605.30435v1 Announce Type: new Abstract: Like physical products, new technologies are developed using globally sourced inputs. Yet while the supply chains behind physical goods are well understood, we know far less about the international supply chain of scientific knowledge that powers U.S. innovation, or how vulnerable it may be to disruption. Here, I uncover this supply chain by tracing multi-generational citation paths connecting NSF-funded research to downstream patents, and stress-test it by simulating barriers to scientific knowledge flows across the U.S. border. The U.S. knowledge supply chain extends globally, and frictions impeding the movement of ideas across the U.S. border reduce its connectivity, extend its length, and lower innovation productivity. These impacts extend to technology areas deemed critical to national priorities by U.S. Congress, including Semiconductors, Quantum Science, and AI.
Context-Conditioned Generative Models Enable Subnational Refinement of Sparse Humanitarian Surveys
arXiv:2605.31489v1 Announce Type: new Abstract: Data scarcity limits inference in many scientific and policy domains. Survey data are essential for decision-making, but sparse samples often fail to capture fine spatial granularities. We evaluate normalizing flows, a generative model that learns complex data distributions and can be conditioned on exogenous contextual features, in controlled data scarcity scenarios. Across eight household survey datasets spanning six low-income or middle-income countries in the humanitarian domain, we show that context-conditioned generative models can refine sub-national survey distributions under severe data scarcity, and that performance increases systematically with the richness of the conditioning information. These findings support a general principle for survey data augmentation: generative models can improve sub-national estimates when the sparse sample retains sufficient support and contextual covariates encode relevant local heterogeneity. By learning full conditional distributions rather than point estimates, the approach provides fine-grained evidence for humanitarian decision-making and resource allocation.
Meredith Whitney on AI's Impact, Rates and Debt Markets
Meredith Whitney Advisory Group CEO Meredith Whitney says the economy is doing "well" amid the expansion of AI and rising inflation on "Bloomberg Open Interest." (Source: Bloomberg)
Towards an Ideometrics-Based General Theory of Human Progress
arXiv:2605.30683v1 Announce Type: new Abstract: This paper proposes ideometrics as the foundation for a generalised and potentially testable theory of human progress and civilisational progress, thus linking ideometrics to studies in economics and history. Building on prior work that conceptualises the human brain as a sensor of ideas, human progress is understood not primarily through outcomes such as wealth, health, or technological advancement, but through the dynamic process of the "idea life cycle" that shapes future states. The paper advances a formal definition of human progress as a measurable improvement in the ability of individuals and societies to generate, evaluate, prioritise, and implement ideas in a way that increasingly aligns prioritised ideas with those that truly lead to preferred future states, given available information and uncertainty, and under scarcity of human capacity, energy, time and resources. It introduces the Ideometric Index of Human Progress (IIHP) that captures the quality of idea generation (G), accuracy of their evaluation (E), efficiency of their prioritisation (P), and effectiveness of their implementation (Ie). It shows that the future progress will be realised if there is good alignment between the perceived future value of ideas and their true, realised future value, assessed as outcome monitoring (O). This formulation shifts the analytical focus from static outcomes to the quality of evaluating ideas, thereby offering a novel lens for understanding progress and regress. The concept can also be extended to long periods of history through the Ideometric Index of Civilisational Progress (IICP), where additional parameters of successful documentation of outcomes (D) and successful intergenerational transmission of gathered knowledge (T) are added. By transforming ideas into measurable units of analysis, ideometrics offers a potentially transformative approach to understanding human progress.
Reuters Reuters | Breaking International News & Views
Nvidia has unveiled a new chip that puts AI capabilities directly into laptops and desktop computers, pitting it against the likes of Advanced Micro Devices, Intel and Apple.
Measuring Social Media Network Effects
arXiv:2507.04545v2 Announce Type: replace Abstract: Network effects -- the utility gains from additional consumers of a good -- are widely regarded as critical to the digital economy. Yet recent theory and evidence suggest that local network effects -- the economic value created by specific social network connections -- drive value in networked online platforms. Using incentive-compatible online choice experiments with 19,923 Facebook, Instagram, LinkedIn, and X users in the United States, we provide the first large-scale empirical measurement of local network effects in the digital economy and measure heterogeneity in connection value across platforms. Platform value ranges from \$78 to \$101 per consumer per month, with 8.1-23.7% explained by local network effects. We find that 1) stronger ties are more valuable on Facebook and Instagram, while weaker ties are more valuable on LinkedIn and X; 2) work connections are most valuable on LinkedIn and least on Facebook, and job-seekers value LinkedIn significantly more and Facebook significantly less; 3) men value connections to women significantly more than to other men, particularly on Instagram, Facebook, and X, while women value connections to men and women equally across platforms; 4) consumers value connections on any platform more if they are also connected on other platforms, suggesting that platforms are complements, not substitutes; 5) white consumers disproportionately value same-race connections on Facebook while, on Instagram, connections to alters eighteen or younger are valued significantly more than any other age group -- two patterns not seen on other platforms. Each platform generates between \$53B and \$215B in annual US consumer surplus. These results suggest that social media generates significant value, that local network effects drive a substantial fraction of it, and that the sources and contours of these effects vary across platforms, consumers, and connections.
To YouTube and beyond: how online gen Z directors stormed Hollywood
Record-breaking box office for Backrooms and Obsession has opened the door for twentysomething YouTube creators as the industry rethinks what audiences want At this time last year, the idea of a wide-release feature film-maker cutting their teeth on YouTube was, if not unheard of, certainly still a niche origin story. Siblings Michael and Danny Philippou had just released Bring Her Back, the follow-up to their surprise horror hit Talk to Me, to pretty-good reviews and OK box office; clearly they would continue to work, but the slightly diminished returns didn’t predict a YouTube explosion. Nor did the outright lousiness of Shelby Oaks, from longtime YouTube film critic Chris Stuckmann, when it premiered in theaters later in 2025. Generous horror-festival buzz died down as more people actually laid eyes on the movie; Stuckmann was an obvious enthusiast, and some saw promise in his first effort, but a clumsy found-footage pastiche without much emotional sense didn’t seem like the next big thing, either. But in 2026, something has shifted. In January, YouTuber Markiplier self-released his adaptation of the video game Iron Lung to theaters, and it outgrossed any number of big-studio titles. Then Curry Barker, whose comedy sketches have been a YouTube fixture, unveiled his feature debut Obsession. The film, made for under a million dollars, has become the box office phenomenon of the summer so far, managing a virtually unheard-of feat when its second and third weekends actually outgrossed its first. Obsession is sharing multiplex space with Backrooms, directed by 20-year-old Kane Parsons, who previously brought the spooky internet meme to life in a series of YouTube shorts. Despite being set in a series of purgatorial, sparsely furnished, fluorescent-lit “liminal spaces”, it was the top movie at the North American box office this weekend, poised to become the biggest-grossing movie from distributor A24 in a matter of days. Backrooms also opened to bigger numbers than any number of starrier or bigger-brand 2026 titles like Wuthering Heights, Scream 7, The Devil Wears Prada 2 or the last Pixar movie. That makes three YouTube-trained film-makers who have presided over some of this year’s biggest and/or most surprising hits. With them have come countless social media posts about how YouTube, not film school, provides the real training tomorrow’s directors need. Continue reading...
Mistral AI Launches Vibe, Expands into Industrial AI And Announces Data Center Push to Challenge OpenAI
Mistral AI is expanding with Vibe, industrial AI capabilities, and a data center strategy aimed at enterprise and sovereign AI needs.
AWS reportedly to tuck Elon Musk's Grok into Bedrock, despite zero enterprise demand
The energy drink of frontier models.
OECD Says 60% of Chinese Gains in Market Share Driven by Subsidies
Over the past two decades, Chinese businesses have received three to eight times more support than their competitors, according to the Paris-based research body.
Everyone Has Their Targets Set on the MacBook Neo
Industry analysts and competitors are closely watching the development of the MacBook Neo.
Netflix wiz creates app to slash AI bills, then open sources it
Project Headroom could save you big money, too.
AI still has not solved software pricing, and Snowflake knows it - Startup Fortune
Snowflake's latest quarter shows why AI is forcing enterprise software companies to rethink seat-based pricing. Its consumption model looks better suited to var
Price Pass-Through of Austria's Single-Use Plastics Producer Charges: Evidence from Retail Offer Spells
arXiv:2510.15617v4 Announce Type: replace Abstract: Single use plastics (SUPs) impose substantial environmental costs. Following Directive (EU) 2019/904, Austria introduced producer charges and mandatory participation in collection and recycling systems. This paper exploits a monthly panel of retail offer spells drawn from a price comparison platform to estimate the extent to which compliance costs pass through to posted online prices in Austria. The treated sample comprises keyword matched SUP products including balloons, to go cups, wet wipes, plastic bags, food containers, tobacco filter items, beverage bottles, and plastic wraps observed alongside a control group of non SUP listings over 2020-2024. A two way fixed effects (TWFE) specification places the average post treatment price increase at approximately 4.1 percent. A sequential TWFE model separating the administrative reporting phase from March 2023 and the payment due phase from March 2024 reveals that the larger adjustment occurred during the earlier reporting stage, with a reporting only effect of approximately 8.1 percent and an incremental payment phase effect of 5.6 percent. For balloons, a category subject to pronounced regulatory fee exposure, event study estimates exceed 50 percent immediately following the initial payment date and remain elevated throughout most of the post treatment window. These findings indicate that Austrian online retailers adjusted prices in advance of fee payment deadlines, consistent with anticipatory pass through of expected compliance costs rather than a discrete response to realized payments. As the data contain price observations but not quantity data, the analysis speaks to price incidence and not to consumption or environmental outcomes.
Pricing - Claude API Docs
Learn about Anthropic's pricing structure for models and features
SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning
arXiv:2605.30832v1 Announce Type: new Abstract: Recent advances in Large Reasoning Models have significantly improved chain-of-thought (CoT) capabilities via reinforcement learning (RL). However, generated reasoning chains frequently suffer from structural redundancy (i.e., \emph{overthinking}), incurring high computational overhead without improving answer correctness. Existing mitigation strategies typically rely on token-uniform length penalties, which provide coarse, segment-agnostic pressure toward shorter outputs and can inadvertently suppress useful reasoning alongside redundancy. To address this, we demonstrate that inefficiency concentrates in high-probability segments with low marginal utility. We derive a theoretical characterization of segment suboptimality under the correctness-length trade-off objective and propose \textsc{SLAT} (Segment-Level Adaptive Trimming), an RL framework that selectively suppresses redundant segments based on this criterion. Empirical results on standard benchmarks indicate that \textsc{SLAT} establishes a superior accuracy-efficiency Pareto frontier, reducing reasoning length by $50\%$ relative to uncompressed baselines while maintaining competitive accuracy. Overall, our results suggest that theoretically grounded, segment-aware trimming is a promising direction for efficient CoT reasoning in large language models.
Anahata ASI Studio Revolutionizes IDE with Dynamic AI-Driven Developer Tools and Open-Source Integration
Anahata ASI Studio introduces advanced in-IDE capabilities with a robust open-source platform supporting real-time AI integration and dynamic provider flexibility.
Healthcare Mechanisms from Policy-as-Code Search under Strategic Provider Response
arXiv:2605.30680v1 Announce Type: new Abstract: Healthcare mechanisms are inseparable from the strategic provider response they induce: existing healthcare AI benchmarks hold this response fixed and so cannot evaluate mechanisms by the equilibrium they produce. We recast hospital mechanism design as program synthesis for language models: typed, inspectable rule programs are executed and scored by Medi-Sim, a multi-agent simulator with five strategic provider channels (coding, selection, delay, effort, triage). An incentive sweep recovers classical health-economics findings as adjacent regimes -- up-coding and low-complexity-patient selection under profit pressure, and Goodhart-style drift where measured performance becomes anti-correlated with true outcomes -- and a single audit lever exposes pressure migration: closing the coding channel more than doubles low-complexity selection. LLM-guided evolutionary code search over the same rule-program space then synthesizes an inspectable mixed-objective program that eliminates up-coding, halves rejection, and retains most of the profit-oriented baseline's funds.
Exclusive: Economists have been teaching a broken proof for 50 years. AI just found it
Axiom Math, a $1.6B AI unicorn, is building a formally verified library of economic theorems — and already found gaps in the foundations of antitrust law.
African Startup Spiro Raises $215 Million, Nearing Unicorn Value
African electric-mobility startup Spiro raised $215 million backed by European and African investors as it nears $1 billion in value, or so-called unicorn status, founder Gagan Gupta said.
AI crushes startup valuations for pre-ChatGPT companies
More than 220 former unicorns have lost their billion-dollar status as AI redirects venture capital, with SaaS firms hit hardest and 2021-era startups worth 68% less.
CVS Health Ventures Leads $40M Investment in H1 AI - DistilINFO Publications
CVS Health Ventures led a $40 million investment in H1 on May 28, 2026, following a successful collaboration that produced an AI model improving provider directory accuracy, with H1's platform serving 85% of the top 20 pharma companies and nine out of ten leading health plans through its Doctor ...
The great high-tech shift: Israel’s startups grow, but not necessarily in Israel | Ctech
New report reveals expanding overseas operations, rising relocation, and a shrinking share of local employment.
Labor, Society & Culture
Should I State or Should I Show? Aligning AI with Human Preferences
arXiv:2603.29317v2 Announce Type: replace Abstract: As AI agents become more autonomous, properly aligning their objectives with human preferences becomes increasingly important. We study how effectively an AI agent learns a human principal's preference in choice under risk via stated versus revealed preferences. We conduct an online experiment in which subjects state their preferences through written instructions ("prompts") and reveal them through choices in a series of binary lottery questions ("data"). We find that on average, an AI agent given revealed-preference data predicts subjects' choices more accurately than an AI agent given stated-preference prompts. Further analysis suggests that the gap is driven by subjects' difficulty in translating their own preferences into written instructions. When given a choice between which information source to give to an AI agent, a large portion of subjects fail to select the more informative one. Moreover, when predictions from the two sources conflict, we find that the AI agent aligns more frequently with the prompt, despite its lower accuracy. Overall, these results highlight the revealed preference approach as a powerful mechanism for communicating human preferences to AI agents, but its success depends on careful implementation.
AI Loss of Control Incident Management: Response & Resilience
arXiv:2605.30406v1 Announce Type: new Abstract: Recent research demonstrating AI systems exhibiting deception and shutdown resistance suggests that AI loss of control (LOC) is an urgent policy concern , yet current literature focuses almost exclusively on alignment and prevention. To address this gap, this paper introduces a foundational framework and taxonomy for managing catastrophic AI LOC incidents. The taxonomy's first level distinguishes between scenarios where regaining control is 'extremely costly' versus 'impossible'. While impossible scenarios demand immediate resilience investments to fundamentally restrict an AI's attack surface , extremely costly scenarios require active incident management via Containment and Threat Neutralization. The framework further categorizes these manageable events into accidental LOC (requiring automated circuit-breaker responses) and adversarial LOC (requiring graduated escalatory measures). By mapping three severity classes to specific scenario matrices, this paper provides a concrete, proportional guide for managing unprecedented AI risks.
Charities decry UK plan to use AI to assess age of young asylum seekers
Coalition of more than 100 organisations says move could lead to more children ending up in adult detention facilities A coalition of more than a hundred refugee children’s organisations has said controversial plans to use AI to assess the age of young asylum seekers could lead to more children wrongly ending up in adult prisons or detention centres. The warning follows a Home Office announcement on Friday of a contract to roll out AI facial age estimation technology on young asylum seekers whose age is disputed. Continue reading...
Meta's AI training tool for employees may spark new EU privacy concerns | Tech News - Business Standard
The initiative highlights how technology companies are increasingly seeking large datasets to train AI systems capable of performing complex workplace tasks. Reuters reported that MCI forms part of Meta Chief Executive Officer Mark Zuckerberg’s broader push towards AI agents that can automate ...
AI Behavioral Science
arXiv:2509.13323v2 Announce Type: replace-cross Abstract: We outline a foundation for a new field of ``AI Behavioral Science,'' covering three perspectives. First, as AI becomes ubiquitous and is increasingly proprietary and opaque, it becomes vital to develop techniques for assessing AI behavior. We outline how tools developed to assess people's behaviors by social scientists can be used to assess and infer AI's behaviors biases, tendencies, and heuristics. Second, we also discuss how AI can change the ways in which we learn about human behavior. Beyond its computational power, AI offers new techniques for simulating, inferring, and predicting human behaviors that we outline and discuss. Third, as humans and AI are interacting in increasingly complex and intertwined systems, we need to understand the implications for the resulting economic and political outcomes. We outline issues that are increasingly pressing concerning the future of human-AI interactions and potential changes and disruptions that can ensue.
Technology & Infrastructure
Vertice acquires Vendr to create the world’s largest procurement intelligence dataset and lead autonomous AI negotiation
LONDON and NEW YORK, June 1, 2026 /PRNewswire/ — Vertice, the AI procurement platform built for the modern enterprise, today announced its acquisition of Vendr, the US software pricing leader. The deal creates the world’s largest procurement intelligence dataset, as Vertice will integrate Vendr’s software insights with its own. The combined data represents more than $75+ […]
Agentic AI in the Enterprise: Key Trends and Use Cases for 2026 - AngelHack DevLabs
AI has moved past the chatbot phase. In 2026, it is planning, deciding, and acting on your behalf.
AI Agents and Crude Oil Trading: How Geopolitical Shocks, Inventory Draws, and Machine Learning Are Reshaping WTI Strategy | FXEmpire
Crude oil’s 2026 rally has been shaped by geopolitical shocks, tight U.S. inventories, and major disruptions across key supply routes like the Strait of Hormuz. While prices could ease as tensions fade and shipping normalizes, AI Agents are now adding a new trading edge by helping analyze ...
Operation Jailbreak: lessons from Ukraine on making weapons talk to each other
Defence companies join with Army personnel in hackathon to apply AI to ‘interoperability’ puzzle
Learning Agent-Compatible Context Management for Long-Horizon Tasks
arXiv:2605.30785v1 Announce Type: new Abstract: LLM agents increasingly face long-horizon tasks such as web search and deep research in real-world applications, where accumulated context can cause long-context degradation and reasoning failures. Prior work mitigates this through context management with agent-side context control or fixed strategies such as summarization, which require training the agent itself for adaptation - making it impractical for closed-source agents and ignoring that different agents may require different strategies. We introduce Adaptive Context Management (AdaCoM), which trains an external LLM to manage the context of a frozen agent through flexible modification actions and end-to-end reinforcement learning. Across diverse agents on web search and deep research benchmarks, AdaCoM substantially improves performance by preserving task constraints and progress while pruning stale content. The learned strategies reveal a Fidelity-Reliability Trade-off: agents with higher vanilla ReAct performance benefit from higher-fidelity context preservation, whereas lower-performing agents require more aggressive compression to stay within a reliable reasoning regime. Transfer experiments show that AdaCoM generalizes most effectively across agents with similar capability (measured by vanilla ReAct performance), suggesting a practical path toward reusable context managers for agent systems.
MAVEN: Improving Generalization in Agentic Tool Calling
arXiv:2605.30738v1 Announce Type: new Abstract: Generalization across agentic tool-calling environments remains a central challenge for reliable agentic reasoning systems. Although large language models achieve strong results on individual benchmarks, their ability to compose reasoning strategies, preserve intermediate states, and coordinate tools across domains remains underexplored. We present MAVEN (Modular Agentic Verification and Execution Network), a lightweight symbolic reasoning scaffold for structured decomposition, adaptive tool orchestration, and intermediate verification. We evaluate MAVEN across established tool-calling benchmarks, including BFCL v3, TauBench, Tau2Bench, AceBench, and introduce MAVEN-Bench, a stress-test benchmark for multi-step mathematical and physical reasoning with explicit verification and adversarial task composition. MAVEN-Bench exposes a substantial gap between partial reasoning quality and end-to-end task success; in direct MAVEN-Bench runs, MAVEN improves its GPT-OSS-120b base model from 48% to 71% accuracy without additional training. It also remains competitive with frontier proprietary baselines while using an open-weight backbone with an estimated cost ratio of roughly 1/10, suggesting that lightweight verification-centered scaffolds can strengthen compositional reasoning and motivate more process-aware evaluation of agents in the wild.
Planner-Centric Reinforcement Learning for Deep Research with Structure-Aware Reward
arXiv:2605.30824v1 Announce Type: new Abstract: Deep research tasks require LLMs to plan what to investigate, retrieve evidence, and synthesize long-form answers across multiple branches of inquiry. Existing training paradigms either rely on short-form verifiable QA as a proxy or optimize monolithic long trajectories, which makes planning and execution difficult to disentangle and yields weak credit assignment for the planning process. We propose DecomposeR, a planner-centric deep research framework that represents research plans as typed directed acyclic graphs (DAGs), allowing planning to be made explicit, structured, and rewardable. We train a Qwen3-8B model in two stages: planner reinforcement learning (RL) first learns graph structure and query decomposition to improve research planning, and answerer reinforcement learning (RL) then learns branch-level execution and final synthesis conditioned on the learned plan. By assigning rewards to explicit planner tokens and structured components rather than to a flat trajectory, DecomposeR enables finer-grained optimization of planning while reducing the ambiguity of end-to-end training. Experiments show that DecomposeR-8B improves over strong comparable open baselines by 5.1-8.0 points on popular long-form benchmarks due to improved planning and answering capabilities.
NVIDIA unveils Agent Toolkit for enterprise AI agents | NVDA Stock News
Nemotron 3 Ultra gives long-running agents up to 5x faster inference and 30% lower cost, as Microsoft and CrowdStrike adopt NVIDIA OpenShell runtime.
Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving
arXiv:2605.30576v1 Announce Type: new Abstract: Exploration in reinforcement learning for autonomous driving is inherently unsafe: agents must experience novel behaviors to learn, yet exploration can lead to collisions or off-road driving. We propose an uncertainty-aware framework that leverages expert advice to guide exploration while avoiding long-term dependence. Advice is triggered when epistemic or aleatoric uncertainty exceeds adaptive thresholds derived from rolling buffers, ensuring advice evolves with the agent's confidence. A commitment-cooldown strategy with a stochastic early-stop heuristic regulates the duration and frequency of guidance, exposing the agent to coherent maneuvers without exhausting the advice budget. Expert and agent experiences are combined in a shared replay buffer within an off-policy implicit quantile network (IQN) backbone, enabling efficient reuse of expert trajectories. Experiments in CARLA show that our method outperforms the IQN baseline, improving success by 5-7% and reducing failures, demonstrating that risk-sensitive uncertainty coupled with regulated expert integration enables safer and more efficient exploration for sensor-based RL policy learning in unsignalized intersection navigation.
Build Skill-Augmented AI Agents with SkillNet for Search, Evaluation, Graph Analysis, and Task Planning - MarkTechPost
A Coding Implementation to Build Skill-Augmented AI Agents with SkillNet for Search, Evaluation, Graph Analysis, and Task Planning
Intel targets new AI data centre chip by year end
Leader of data centre unit says company aims to release ‘inference’ GPU as shares rally more than 200% this year
High Bandwidth Memory: The Backbone of AI and Its Potential Return to Gaming GPUs
High Bandwidth Memory (HBM) advances to HBM4E, increasing efficiency and capacity for AI workloads, with potential reentry into gaming GPUs if bandwidth demands warrant the cost.
Ohio hits pause on datacenter tax breaks draining its coffers
Buckeye State found it had inadvertently joined the billion dollar losers' club
Jensen Huang’s 5-Layer AI “Cake”: Energy, Chips, Data Centers, Models, Apps | Windows Forum
Jensen Huang’s “five-layer cake” frames artificial intelligence in 2026 as an industrial stack built from energy, chips, cloud infrastructure, models, and applications, with Nvidia’s CEO arguing that each layer must expand together for AI to become economically useful.
Cryptominer DMG signs LOI to secure 50MW AI customer in Canada
Cryptominer DMG Blockchain is set to secure an AI customer at a facility in Canada. The company this week announced it has signed a letter of intent to offer 50MW critical IT load of artificial intelligence data center colocation services to a single tenant at its Christina Lake facility in British Columbia. – Google Maps […]
Data centers could help determine who wins the next war, and a shortage of compute would be 'catastrophic,' retired general says | Fortune
"Nearly every function in the military depends on the ability to store, move, process, secure and exploit vast quantities of data at speed and scale."
Agent-led devs need serverless OpenSearch, Amazon claims
System relies on a proprietary storage layer as AWS moves to separate storage and compute to fit mega AI demands
Erin Brockovich takes aim at data center secrecy
Environmental advocate Erin Brockovich is challenging the lack of transparency surrounding data center operations.
NVIDIA AI Cloud Ecosystem Expands Worldwide to Meet Global AI Compute Demand | NVIDIA Blog
Fast-growing ecosystem helps enterprises, startups, nations, AI labs and developers scale agentic AI applications.
NVIDIA DGX Station for Windows Puts a Trillion-Parameter AI Supercomputer on Every Enterprise Desk
NVIDIA is extending enterprise AI infrastructure to Windows desktops with a desk-side system designed for trillion-parameter AI workloads.
How Modular Data Centers Could Solve AI's Infrastructure Problem
As AI infrastructure expands, modular data centers may offer a path to lower resource consumption and greater community acceptance.
Arcem buys land in Joroinen, Finland, for 500MW data center campus
Arcem has acquired land in eastern Finland for a planned data center development. The company this week announced it has signed an agreement with the municipality of Joroinen to acquire a site for a data center project. – Arcem The first development phase will make 60MW available as early as 2027, scaling to 100MW before […]
The future of AI healthcare lies in a solid infrastructure backbone - IT-Online
In most walks of life, AI’s presence can already be felt. In healthcare, the benefits are, quite frankly, mindboggling; AI-powered platforms are unlocking new levels of efficiency and precision across medical practices. By Steven Santini, vice-president for secure power: SSA at Schneider ...
TUX: Measuring Human--AI Tacit Understanding
arXiv:2605.30930v1 Announce Type: cross Abstract: As large language models (LLMs) increasingly act as collaborative partners, human--AI alignment is often evaluated through explicit task success, accuracy, or reward optimization. Yet many collaborative settings depend on tacit understanding: whether an agent can align with a human's evaluative stance or representational priors without clear objectives, communication, or feedback. To study this capacity, we develop a spectrum-placement task inspired by the social party game Wavelength, in which humans and agents independently place concepts along subjective spectra. We operationalize the Tacit Understanding Index (TUX) as a pairwise measure of similarity between human and agent judgments, and evaluate it with 241 human participants and 200 profile-conditioned LLM agents across four models. We find that nearest human--agent pairs in trait space achieve significantly higher TUX, suggesting that tacit alignment is structured by person-level characteristics rather than random similarity. Regression analyses show that TUX becomes more explainable as predictor sets become richer, with individual traits, decision-making styles, and confidence improving over aggregate trait-distance baselines. These findings suggest that tacit understanding between humans and LLMs is measurable, while revealing the limits of profile-based conditioning for capturing deeper representational alignment.
Structure-Induced Information for Rerooting Levin Tree Search
arXiv:2605.30664v1 Announce Type: new Abstract: Subgoal-based policy tree search, which uses a policy to guide search, is effective for complex single-agent deterministic problems but often relies on explicit subgoal generation that can incur substantial overhead and hinders scalability. In this paper, we overcome these limitations by using a learned ``rerooter'' through the recently-introduced $\sqrt{\text{LTS}}$ algorithm. A rerooter implicitly decomposes the problem into soft subtasks. While previous work focused on the formal guarantees for given or handcrafted rerooters, in this work we propose three rerooter designs: (i) a clustering-based rerooter that exploits global state-space structure, (ii) a heuristic-based rerooter that leverages learned cost-to-go estimates, and (iii) a hybrid that combines both signals. Our framework avoids having to explicitly reconstruct and reason over generated subgoals, thereby enabling scalable allocation of search effort with significantly lower computational overhead. Empirically, our rerooting-based methods scale to complex environments where subgoal-based policy tree search fails, and achieve state-of-the-art online training efficiency on the domains tested.
If LLMs Have Human-Like Attributes, Then So Does Age of Empires II
arXiv:2605.31514v1 Announce Type: cross Abstract: Much research has been carried out on large language models (LLMs) and LLM-powered agentic workflows. However, many works within the field state emergence of, ascribe to, or assume, generalised anthropomorphic attributes to them (e.g., morality or understanding of natural language). Our goal is not to argue in favour or against the existence of these attributes, but to point out that these conclusions could be incorrect. For this we build and train a simple neural network on the videogame Age of Empires II, and note that any entity in a sufficiently-powerful substrate, such as LEGO or the Greater Boston Area, could also present such attributes. Hence, the purported anthropomorphic attributes of LLMs are empirically non-unique: although some properties (e.g., responses to prompts) could remain constant, others, such as the interpretation of their perceived behaviour, might change with the substrate. Thus, any empirically-grounded discussion requires explicit measurement criteria; otherwise the interpretation is left to the representation. We then show that assuming that these attributes exist or not in a system, independent of the substrate and in a generalised way, leads to either circular or uninformative conclusions, regardless of the experimenter's viewpoint on the subject. Finally we propose a 'null' assumption, where one assumes LLM non-uniqueness instead of assuming anthropomorphic attributes to set up an experiment, along with examples of it. We also discuss potential objections to our work, briefly survey the field, and prove that \textit{Age of Empires II} is functionally- and Turing-complete.
Physically Viable World Models: A Case for Query-Conditioned Embodied AI
arXiv:2605.30542v1 Announce Type: new Abstract: World models for embodied AI must be physically viable: constructed to answer intervention queries by representing the physical structure governing action outcomes, rather than merely predicting future observations. Existing observation-predictive world models can produce visually plausible but physically wrong rollouts. This failure is structural; distinct physical systems can look identical yet diverge under intervention. We expose this problem with controlled benchmarks that fix the visible scene while varying latent physics. We show that such models may recommend infeasible actions, mispredict interaction outcomes, or certify unsafe behavior. We argue that embodied AI requires world models that identify the simplest physical abstraction sufficient to answer an intervention query. Such a model comprises modular components, including environment representation, latent state and parameter estimation, action specification, interventional dynamics, and query-level response. An autonomous orchestrator should identify the relevant abstraction and compose compatible learned and structured components per query. When closed-form physics is unavailable, uncertain, or costly, the transition model may be analytic, simulated, learned, or hybrid, but it must preserve the structure that determines interventional outcomes. This decomposition makes the model interpretable, its components verifiable, and its outputs auditable against the query. It also provides a design principle for new world models and a feasibility test for existing ones: the right abstraction is not the most detailed model of the world, but the simplest model that preserves the distinctions relevant to the query. We demonstrate this approach on queries that existing systems fail to answer correctly, and outline how an orchestrator can dynamically assemble and adapt physically viable models for planning, control, and verification.
Proxy-Pointer RAG: Eliminating Wasteful Entity & Relations Extraction in Knowledge Graphs
A new RAG technique designed to optimize entity and relation extraction in knowledge graphs.
PhyDrawGen: Physically Grounded Diagram Generation from Natural Language
arXiv:2605.30512v1 Announce Type: new Abstract: Generating physics diagrams from text requires strict adherence to physical laws. While current generative models produce visually plausible outputs, they systematically hallucinate force vectors, ignore conservation laws, and violate geometric constraints. We present PhyDrawGen, a neuro-symbolic pipeline that decouples semantic scene understanding from physical constraint satisfaction. First, a large language model extracts a typed scene graph from the problem text. A deterministic solver then converts this graph into a Planar Straight-Line Graph (PSLG), encoding force balance, optical paths, and field topologies as exact geometric primitives. Finally, a fine-tuned Qwen-VL model implements a visually grounded propose-verify loop to iteratively correct any constraint violations. Evaluated on a benchmark of 1,449 problems spanning mechanics, optics, and electromagnetism, PhyDrawGen significantly outperforms GPT-5-image, Gemini 2.5 Flash, and Gemini 3 Pro, demonstrating robust physical accuracy even on unusual-object problems.
Agent Judge: Solving Long Context Evaluations
Agent Judge addresses long-context evaluation challenges for production AI agents by using more capable evaluation agents rather than one-shot LLM judges.
How Iran’s military harnesses western AI
Western AI models are turbocharging Tehran’s cyber operations, helping it develop malware and launch attacks
A Three-Minute Protocol to Reduce AI Manipulation Risk
izusek/Getty Images Of the potential weaknesses of any security system, the human layer has always posed a key risk. The arrival of AI tools has made human cognition even more of a vulnerability. Companies face three overlapping security threats from AI’s effects on human cognition. First, weaponized persuasion lets attackers manipulate employees’ judgment through personalized, […]
Claude Mythos exposed a hard truth: Your enterprise patching process is way too slow
The Claude Mythos vulnerability highlights critical delays in enterprise security patching.
AI-Powered Cyber Attacks Surge: Breakout Times Plummet to 29 Minutes, AI Automation Escalates Threats
CrowdStrike's 2026 Global Threat Report reveals a dramatic reduction in attacker breakout times to an average of 29 minutes, with some as swift as 27 seconds.
Adoption, Deployment & Impact
How Early Adopters Used Generative AI Worldwide: Variation by Country Income and Language
arXiv:2605.30685v1 Announce Type: new Abstract: AI is being used by people globally, but not everyone is using it in the same ways. Using a large-scale dataset of anonymized, de-identified, and privacy-scrubbed interactions with a widely available and free AI chatbot, we empirically characterize differences in early adopters' usage across countries. Schooling is the most common domain of use in most countries, particularly low-income countries, with a strong inverse association evident between schooling and country-level GDP. Leisure-related use, by contrast, is positively associated with country-level income. Language, we find, also shapes use: English-language interactions are overrepresented in places where the predominant languages were not well-served by existing models during the period of the study. Improving performance across languages may be a key factor, our work suggests, in whether this technology expands digital divides or enables leapfrogging.
Billionaires already couldn't talk to their grandchildren. Now they're on opposite sides of the AI divide | Fortune
Citi finds AI use jumped to 22% from 13% in a year, even as principals warn data privacy is “non-negotiable” and fear back-door exposure via SaaS tools.
Trust Is the New KPI: Why AI Governance Has Become a Growth Strategy
AI is moving from experimentation to enterprise reality, and the real constraint is no longer technology but trust.
145 AI laws passed in 2025 and privacy teams aren't catching a break - Help Net Security
AI adoption brings new privacy risks as shadow AI, compliance requirements, and consumer requests put pressure on organizations.
Council Post: Why Central AI Governance Committees Are Failing Healthcare—And Their Fix
If health systems, payers and pharma companies want to move from dozens of AI pilots to hundreds of production systems, the manual committee model has to change.
Joby Demonstrated its Air Taxi in Manhattan, but You Can’t Fly in It Yet
Aviation start-ups and the Trump administration want to replace helicopters with electric aircraft, but the new vehicles still have to pass arduous tests before the public can use them.
AI Is Forcing Big Law to Rethink Business as Usual
In 2021, Volkswagen AG approached the global law firm Freshfields with a problem. The German carmaker’s technology unit was preparing to release new software features and wanted to make sure that they would be compliant in the more than 100 countries where Volkswagens are sold. Ordinarily, Freshfields said, it would bring in lawyers from each jurisdiction to vet the updates, budgeting thousands of euros per country — a process that would need to be repeated if any components changed in the futur
This model is not a real person: how AI is changing online shopping – video
From digital twins to models ‘sculpted’ by programmers, generative AI has been popping up all over the fashion industry. When an Australian e-commerce retailer started using AI-generated models to sell products, lifestyle editor Alyx Gorman had to see if the garments were more than mere pixels. The Iconic, which sells the dress worn in this video, said in a statement: ‘Where AI-generated imagery is used to advertise products for sale on our platform, our expectation is that it is clearly labelled and that the product itself is represented as accurately as possible for customers.’ Meanwhile, Atoir, the designer, said: ‘The Australian fashion industry is highly competitive, particularly for independent brands. We believe that when used responsibly, tools like this can help smaller businesses to operate with greater agility while still maintaining the creative standards and product integrity that matter to both the brand and the customer’ Calling all fashion models … now AI is coming for you Continue reading...
AI for Interoperability in Health Care: Philips’s Carla Goulart Peron
In this episode of the Me, Myself, and AI podcast, Philips’s chief medical officer Carla Goulart Peron shares how artificial intelligence is reshaping health care — not by replacing clinicians but by expanding access, improving diagnostics, and freeing doctors to focus more time on patients. Drawing on her experience practicing medicine in Brazil’s strained public […]
The FDA Real‑Time Clinical Trial Model: Why Sponsors Must Rethink Their Operating Systems Now
The FDA real-time clinical trial model isn't just about faster studies but making clinical development a continuous, data-driven system.
AI Is Already Rewiring the Aftermarket and Services
This article outlines how AI can improve aftermarket sales, service, parts identification, and customer support workflows.
AI Revolutionizes Camp Lejeune Justice Act Claims, Slashing Costs and Boosting Efficiency
A new AI framework, Sublime AI Law Mastery, aims to revolutionize the processing of Camp Lejeune Justice Act claims, promising to cut costs and improve efficiency.
The Tutoring Effectiveness Index: Predicting LLM Math Tutor Quality from Four Conversation Signals
arXiv:2605.30666v1 Announce Type: new Abstract: Aligning large language models (LLMs) as math tutors typically demands costly reinforcement-learning (RL) training and external LLM judges. We ask whether a frozen model's internal reasoning signals can replace both. We propose the Tutoring Effectiveness Index (TEI), a training-free, judge-free four-signal index that combines a Schoenfeld-Verify keyword ratio, a math-step density, an ends-question rate, and a deep-reasoning gate from the Deep-Thinking Ratio (DTR) probe. Selecting from $N$ candidates with TEI (the TEI@$N$ rule) raises the improvement rate on pre-incorrect scenarios from $59.0\%$ to $81.9\%$ at $N{=}8$ on a frozen DeepSeek-R1-8B base, with no training and no external judge. We also measure the alignment tax of pedagogical GRPO. Thinking length drops from $1{,}764$ to $119$ words per turn ($-93\%$), Content-Knowledge and Pedagogical-Knowledge accuracy fall by $-71\%$ and $-80\%$ relative, and the student's $\Delta$ Solve Rate crosses from $+0.180$ to $-0.012$. To anchor the behavioural reading, we reproduce an 82-code educational codebook on $119{,}009$ tutor sentences with a one-shot structural classifier. Together, these results offer a cost-effective recipe for building math-tutoring LLMs without RL training or external judges.
FTSE 100’s likely new entrant puts a British spin on the AI boom
Hardware reseller’s trick will be to convince investors artificial intelligence can augment its services rather than replace them
The Missing Variable In Every AI Business Case: Your Customer
Leaders are running incomplete math on AI automation. Efficiency gains are real—but the erosion of customer trust may cost far more than the savings.
Rerankers Aren’t Magic Either: When the Cross-Encoder Layer Is Worth the Cost
An analysis of the performance and cost trade-offs of using cross-encoder layers in reranking systems.
How Verizon Connect Scaled Agentic AI to 100,000 Users
Verizon Connect scaled agentic AI to turn massive vehicle-fleet data streams into actionable insights. The project demonstrated impressive scale and cost optimization across models.
The solution might be cancelling my AI subscription
A personal reflection on the diminishing returns of AI subscriptions.
Geopolitics, Policy & Governance
Traceable by Design: An LLM Pipeline and Dashboard for EU Regulatory Consultation Analysis
arXiv:2605.30995v1 Announce Type: new Abstract: Public consultations generate large volumes of data in the form of stakeholder submissions that are practically unfeasible to analyse manually. We present an end-to-end LLM-based pipeline and interactive dashboard for structured topic extraction from regulatory consultation submissions, demonstrated on the European Commission's Digital Fairness Act (DFA) public call for evidence as a case study. The system processes raw PDF attachments and web-form responses, extracts topic annotations, and grounds every extraction in a verbatim quote from the source text. Applied to 4,322 DFA submissions, the pipeline produced 15,368 topic annotations supported by 20,951 verbatim evidence quotes. Three principles govern the proposed design: verbatim grounding, full traceability, and transparency by design. The dashboard exposes the full extraction dataset through five analytical views, from dataset-level topic overviews to individual paragraph drill-downs, with every result traceable to its source. Beyond the predefined DFA topic categories, the pipeline generated certain stakeholder concerns, such as Age Verification, Payment Processor Censorship, and Digital Ownership, that a fixed-taxonomy approach would have missed. The pipeline is domain-generic; adapting it to a new consultation requires only a prompt update and a new dataset. A live demo is available at https://dfa-dashboard.thalesbertaglia.com/. The code and processed data are publicly available at https://github.com/thalesbertaglia/dfa-dashboard.
Tech billionaires are spending unprecedented sums in California races. Experts say it’s the tip of the iceberg
From Google co-founder Brin spending $66m to fight a billionaire tax to Google and Meta funding a joint Super Pac, Silicon Valley is engaged in an existential fight for its political power at home Tech billionaires have shelled out hundreds of millions of dollars ahead of the 2 June primary election in California, in an unrivaled attempt to influence who gets to run the state that Silicon Valley calls home. The industry has used a cover-all-bases approach, funding candidates and ballot measures big and small, contributing to what looks to be the most expensive primary season in California history. The goal, experts say, is to gain both political and regulatory leverage that will perpetuate dominance in business. Google co-founder Sergey Brin has spent $66m since January, more than any other donor, to fight a billionaire tax that’s up for a vote on the November ballot. Democratic gubernatorial candidate Matt Mahan has received more donations than any other candidate, including from top executives at Google, Amazon, Snap, LinkedIn, Reddit and Palantir. Crypto mogul Chris Larsen has funded three Super Pacs with $26m to sway campaigns across California, including giving $1m to back a primary candidate for state insurance commissioner. Google and Meta have collectively funded a Super Pac with $10m to back assembly and senate candidates in local district races across the state. Silicon Valley money is flowing toward city primaries as well as state-level ones, with tech-backed Pacs sponsoring voter guides suggesting how to vote on local tax measures. Continue reading...
China Aims A.I. at Predicting Who Could Pose a Political Risk
New research examines how a Chinese company struggled to develop its predictive surveillance technology while U.S. restrictions were in place.
Meta legal action forces Facebook whistleblower to sit in silence at Hay festival
Sarah Wynn-Williams did not speak during event after lawyers warned of possible sanctions from tech firm Facebook whistleblower Sarah Wynn-Williams was forced to sit in silence on stage at an event at Hay festival, after lawyers advised her not to speak because of ongoing legal action brought by Meta. Wynn-Williams, whose bestselling memoir, Careless People, details her years working at Facebook, was due to appear in conversation with the investigative journalist Carole Cadwalladr and academic Tim Wu. Continue reading...
Emerging data centers: New TN law to protect ratepayers goes into effect in July
A new Tennessee law to protect utility customers from the growing energy demands of data centers will take effect in July.
AI Regulation 2026 Opens Three Fronts: CNN Sues Perplexity as OpenAI Aligns With EU Rules
AI regulation 2026 split into three simultaneous fronts: CNN filed a copyright lawsuit against Perplexity AI for scraping 17,000 news items, the DOJ blocked Colorado’s AI law in a historic first-ever federal challenge, and OpenAI published its EU AI Act compliance framework. Each front demands a
AI chatbots: California Invasion of Privacy Act's next frontier
AI companies face class actions under California's anti-wiretapping law for allegedly embedding tracking technology in ChatGPT that transmitted user queries to third parties.
California passes bill declaring death-by-algorithm to 3D-printed ghost guns
Last-hour amendments aim to allay privacy concerns, but broad scepticism about feasibility remains
Hidden digital voter manipulation ahead of 2026 elections | ITWeb
Local voters will enter the 2026 elections with fewer safeguards against online manipulation and discriminatory political adverts than their global counterparts, says CODE.
Ctrl+AI+Reg - 1 June 2026 (new format) - Ctrl+AI+Reg
Your shortcut to AI regulation, law and policy updates around the world.
Russia’s Supreme Court to Launch First Comprehensive Review of AI Legal Cases | Bull Source
Paragraph 1: Russia's Supreme Court Embarks on a Landmark AI Legal Review
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