Babak's Daily Briefing

Monday, May 18, 2026

Sources: 16 | Total Articles: 68

• 1.Evaluating 6G PHY Evolution: What the Industry Is Really Trying to Solve

  Summary available at source link.

• 2.Amazon’s Globalstar deal gives Amazon Leo a faster path into D2D

  Amazon’s planned acquisition of Globalstar is about far more than satellites. It gives Amazon Leo a faster path into direct-to-device connectivity, combining spectrum, operational assets, and Apple-facing service continuity in a move that could reshape the hybrid terrestrial-NTN landscape.

• 3.SoftBank’s Physical AI push gives AI-RAN a sharper purpose

  SoftBank is starting to give AI-RAN a more concrete job description: not just running AI workloads near the network, but serving as the real-time infrastructure layer for robots and other physical systems. The company’s recent materials suggest it wants to move the AI-RAN conversation from telecom architecture to real-world machine action.

• 4.South Korea puts 6G inside its national AI push

  South Korea has unveiled a three-year national roadmap aimed at becoming one of the world’s top three AI powers by 2028, with 6G commercialization positioned as part of that broader push.

• 5.b-com’s Open XG Hub targets one of telecom’s biggest gaps: turning experimentation into deployment

  In an interview with Peter Pietrzyk, Managing Director of 6GWorld, Patrick Savell, Head of Connectivity at b-com, said platforms such as Open XG Hub are designed to help bridge one of the industry’s most persistent challenges: moving promising ideas from research environments into deployable network systems. The bigger point is that, as telecom becomes more software-driven and AI-native, the bottleneck is increasingly less about invention and more about validation, integration, and operational readiness.

• 1.Always Learning, Always Mixing: Efficient and Simple Data Mixing All The Time

  arXiv:2605.15220v1 Announce Type: new Abstract: Data mixing decides how to combine different sources or types of data and is a consequential problem throughout language model training. In pretraining, data composition is a key determinant of model quality; in continual learning and adaptation, it governs what is retained and acquired. Yet existing data mixing methods address only one phase of this lifecycle at a time: some require smaller proxy models tied to a single training phase, others assume a fixed domain set, and continual learning lacks principled guidance altogether. We argue that data mixing is fundamentally an online decision making problem -- one that recurs throughout training and demands a single, unified solution. We introduce OP-Mix (On-Policy Mix), a data mixing algorithm that operates across the entire language model t...

• 2.Fluency and Faithfulness in Human and Machine Literary Translation

  arXiv:2605.15282v1 Announce Type: new Abstract: Literary translation requires balancing target-language fluency with faithfulness to the source. Recent large language models (LLMs) often produce fluent translations, but it remains unclear whether fluency corresponds to semantic preservation in literary text. We examine this relationship using 130,486 translated paragraphs from 106 novels in 16 source languages, including human, Google Translate, and TranslateGemma translations. Fluency is measured as original-likeness with a translationese classifier trained on paragraph part-of-speech n-grams, and faithfulness with the automatic translation evaluation metric COMET-KIWI. We control for paragraph length and find a consistent negative correlation between fluency and faithfulness. The pattern appears for both human and Google Translate, but...

• 3.DiscoExplorer: An Open Interface for the Study of Multilingual Discourse Relations

  arXiv:2605.15304v1 Announce Type: new Abstract: The relations connecting propositions in discourse such as cause (A because B) or concession (A although B) are a subject of intense interest in Computational Linguistics and Pragmatics, but challenging to study and compare across languages. Recent progress in standardizing discourse relation inventories across datasets offers the potential to facilitate such studies, but is hindered by the complexity of relevant data and the lack of easily accessible interfaces to analyze it. In this paper we present DiscoExplorer, a new open source web interface, capable of running on local computers, which we use to make datasets from the DISRPT Shared Task on discourse relation classification publicly available, covering 16 different languages. We present the query language, search and visualization fac...

• 4.Automatic Construction of a Legal Citation Graph from 100 Million Ukrainian Court Decisions: Large-Scale Extraction, Topological Analysis, and Ontology-Driven Clustering

  arXiv:2605.15362v1 Announce Type: new Abstract: Half a billion citation edges extracted from 100.7 million Ukrainian court decisions reveal that judicial citation structure encodes legal domain boundaries without supervision and predicts future legislative importance with near-perfect accuracy. We construct the first large-scale citation graph from the complete EDRSR registry (99.5 million full texts, 1.1 TB), extracting 502 million citation links across six types via regex on commodity hardware in approximately 5 hours, with precision of 1.00 on a 200-decision validation sample (95% Wilson CI: [0.982, 1.000]). Three principal findings emerge. (1) The degree distribution follows a power law (alpha = 1.57 +/- 0.008), placing the Ukrainian court network near the EU Court of Justice and below the US Supreme Court, with hub articles cited ...

• 5.Greedy or not, here I come: Language production under vocabulary constraints in humans and resource-rational models

  arXiv:2605.15365v1 Announce Type: new Abstract: Communicating using only a limited vocabulary is a common but challenging cognitive phenomenon, requiring an ideal communicator to plan carefully to optimize for intelligibility while circumventing a constrained lexicon. In this work, we investigate how humans respond to a broad array of questions under variable vocabulary limitations, consisting of only 250 highly frequent words at the most restrictive. We provide theoretically motivated comparisons to greedy and globally optimal sampling algorithms using Sequential Monte Carlo inference with large language models. Humans generally resemble greedy sampling more than globally optimal sampling, though more skilled humans are more likely to backtrack and revise -- a non-greedy behavior. An observed human pattern of leaning on semantically lig...

• 1.AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices

  arXiv:2605.15206v1 Announce Type: new Abstract: Autonomous agents powered by large language models (LLMs) are increasingly used to automate complex, multi-step tasks such as coding or web-based question answering. While remote, cloud-based agents offer scalability and ease of deployment, they raise privacy concerns, depend on network connectivity, and incur recurring API costs. Deploying agents locally on user devices mitigates these issues by preserving data privacy and eliminating usage-based fees. However, agentic workflows are far more resource-intensive than typical LLM interactions. Iterative reasoning, tool use, and failure retries substantially increase token consumption, often expending significant compute without successfully completing tasks. In this work, we investigate the time, token, and energy overhead of locally deployed ...

• 2.TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination

  arXiv:2605.15207v1 Announce Type: new Abstract: Multi-agent LLM systems have shown promise for complex reasoning, yet recent evaluations reveal they often underperform single-model baselines. We identify a structural failure mode in sequential fine-tuning of shared-context teams: updating one agent shifts the team's context distribution, and when subsequent updates are evaluated on cached rollouts, this mismatch compounds. We formalize this as the compounding occupancy shift and prove that stale-occupancy evaluation incurs a penalty that scales quadratically with the number of agents. In contrast, intermediate-occupancy evaluation reduces this to linear scaling. We propose TeamTR, a trust-region framework that resamples trajectories after each component update and enforces per-agent divergence control, yielding rigorous per-update and per...

• 3.Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels

  arXiv:2605.15208v1 Announce Type: new Abstract: Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for cloud and edge deployment, yet the impact of this compression on model quality remains poorly understood. Existing studies typically compare only two conditions (full-precision vs. a single quantized variant), rely on aggregate bias metrics, and evaluate a single model family, making it impossible to distinguish gradual degradation from threshold-dependent safety failures. We conduct a controlled empirical study of three instruction-tuned models (Qwen2.5-7B, Mistral-7B, Phi-3.5-mini) at five precision levels (BF16 through 3-bit) on 12,148 BBQ bias benchmark items across 5 random seeds, totaling 911,100 inference records. Our results reveal that 3-bit quantization c...

• 4.Mask-Morph Graph U-Net: A Generalisable Mesh-Based Surrogate for Crashworthiness Field Prediction under Large Geometric Variation

  arXiv:2605.15231v1 Announce Type: new Abstract: Nonlinear finite element crash simulations are accurate but computationally expensive, limiting their use in iterative design optimisation. Machine-learning surrogate models based on graph neural networks (GNNs) offer a faster alternative. Message-passing GNNs are widely used for mesh simulation, and their shared node and edge update functions are relatively generalisable across varying graph structures. By contrast, non-shareable edge-specific aggregation layers can capture nonlinear relationships more accurately but usually require fixed graph connectivity, which limits generalisability. This paper presents Mask-Morph Graph U-Net (MMGUNet), a practical approach to addressing the limitation of hierarchical Graph U-Net architectures that use edge-specific downsampling and upsampling layers. ...

• 5.MuteBench: Modality Unavailability Tolerance Evaluation for Incomplete Multimodal Fusion

  arXiv:2605.15235v1 Announce Type: new Abstract: Multimodal physiological data powers clinical AI systems from intensive care units to wearable devices, but sensors routinely fail in practice. Two failure modes are common: modality missing, where an entire channel is absent, and within-modality missing, where a contiguous time segment is lost. No existing benchmark evaluates multiple fusion architectures under both failure modes at controlled severity levels across diverse clinical datasets. We present MuteBench, a benchmark covering 9 datasets from 7 clinical domains, 6 fusion architectures, and 2 missing-data modes over 125,000 samples. Through this benchmark, we find that architecture family is the strongest predictor of robustness, outweighing parameter count. Channel-independent models tolerate modality missing well but can be sensiti...

• 1.PhysBrain 1.0 Technical Report

  arXiv:2605.15298v1 Announce Type: new Abstract: Vision-language-action models have advanced rapidly, but robot trajectories alone provide limited coverage for learning broad physical understanding. PhysBrain 1.0 studies a complementary route: converting large-scale human egocentric video into structured physical commonsense supervision before robot adaptation. Our data engine extracts scene elements, spatial dynamics, action execution, and depth-aware relations, then turns them into question-answer supervision for training PhysBrain VLMs. The resulting physical priors are further transferred to VLA policies through a capability-preserving and language-sensitive adaptation design. Across multimodal QA benchmarks and embodied control benchmarks, including ERQA, PhysBench, SimplerEnv-WidowX, LIBERO, and RoboCasa, PhysBrain 1.0 achieves SOTA ...

• 2.HoloMotion-1 Technical Report

  arXiv:2605.15336v1 Announce Type: new Abstract: In this report, we present HoloMotion-1, a humanoid motion foundation model for zero-shot whole-body motion tracking. A key innovation of HoloMotion-1 is to scale control-policy training with a large-scale hybrid motion corpus, where video-reconstructed motions from in-the-wild videos provide the dominant source of motion diversity, while curated motion-capture and in-house motion data provide higher-fidelity supervision and deployment-oriented coverage. This data regime enables HoloMotion-1 to move beyond conventional MoCap-only training and exposes the policy to substantially broader behaviors, capture conditions, and motion styles. Learning from such heterogeneous data introduces new challenges, including reconstruction noise, source-domain mismatch, uneven motion quality, and the need fo...

• 3.Diffusion Policy for Coordinated Control of a Nonholonomic Mobile Base and Dual Arms in Door Opening and Passing

  arXiv:2605.15352v1 Announce Type: new Abstract: Opening heavy, self closing doors, especially those that require pulling remains a long standing challenge in robotics. Humans naturally employ both arms in a dexterous manner, rotating the handle, widening the gap, holding the door, switching arms when needed, and moving through while maintaining clearance. To replicate such behaviors, a robot must perform a long sequence of motions spanning multiple stages and interactions with different parts of the door. Traditional approaches rely on state machines that transition between manually defined stages (e.g., pulling after the knob is rotated, passing after the gap is sufficiently wide). While intuitive, these methods lack robustness, as hand crafted trajectories fail to generalize to the diversity of real world conditions without extensive en...

• 4.Where to Perch in a Tree: Vision-Guidance for Tree-Grasping Drones

  arXiv:2605.15430v1 Announce Type: new Abstract: This study demonstrates a method to locate an ideal perch location on a tree for vision-guided autonomous tree-perching drones. Various image processing algorithms, including those used for machine learning, image segmentation and binary image morphology, are implemented to assess the shape and structure of a tree. Rather than identifying the closest available branch, this study builds on vision methods by evaluating the potential of each branch, determining its suitability for perching based on factors such as branch width, slope (angle to the horizontal) and curvature. For a given tree-perching drone and a dataset of more than 10,000 urban tree images taken from February to October in a subtropical and temperate monsoon climate, the proposed method successfully produces a result for 76% of...

• 5.Residual Reinforcement Learning for Robot Teleoperation under Stochastic Delays

  arXiv:2605.15480v1 Announce Type: new Abstract: Stochastic communication delays in teleoperation introduce signal discontinuities that undermine control stability and degrade control performance. Consequently, the conventional reinforcement learning (RL) methods struggle with the delayed observations due to the delay-induced observations, leading to high-frequency chattering. To address this, we propose a hybrid control framework, delay-resilient RL, integrating a state estimator utilizing Long Short-Term Memory (LSTM) with a residual RL policy, which is resilient to stochastic delays. The LSTM reconstructs smooth, continuous state estimates from delayed observations, enabling the RL agent to learn a residual torque compensation policy that balances tracking accuracy with velocity smoothness. Experimental validation on Franka Panda robots...

• 1.iOS 26.5 brings E2EE  for RCS: A new milestone for secure cross‑platform messaging

  Summary available at source link.

• 2.GSMA Calls for Urgent Action to Protect Connectivity Resilience Across Africa

  Summary available at source link.

• 3.€475 billion required for Europe to complete its 5G journey and regain digital leadership, new GSMA study finds

  Summary available at source link.

• 4.GSMA Announces Senior Leadership Changes Across Events and Industry Services Portfolio

  Summary available at source link.

• 5.Zain launches landmark ‘Regulatory Academy’ with GSMA Advance to empower its Policy and Regulatory professionals

  Summary available at source link.

• 1.GeoGS-CE: Learning Delay--Beam Channel Priors with 3D Gaussians for High-Mobility Scenarios

  Wideband channel estimation (CE) in high-mobility scenarios remains challenging because channel responses vary rapidly, while practical systems can allocate only sparse pilots to accommodate dense users. Fortunately, many high-mobility environments, such as high-speed railways, exhibit scheduled trajectories, predictable velocities, and a limited number of dominant propagation paths. These properties induce a delay--beam power spectrum that is more stable than the instantaneous complex channel frequency response (CFR), less sensitive to the random phase coherence, and rich in geometric information. To exploit such environmental properties, we propose GeoGS-CE, a two-stage channel estimation framework for sparse-pilot high-mobility scenarios. In the offline stage, GeoGS-CE jointly models: 1) a scene-level 3D Gaussian representation that ca...

• 2.Linked Multi-Model Data on Russian Domestic and Foreign Policy Speeches

  This paper introduces a dataset of interlinked multimodal political communications from the Russian government, addressing persistent deficiencies in the availability of social text- and image-based data for authoritarian politics contexts. The dataset comprises two large corpora of official speeches delivered by senior actors within the Kremlin and the Russian Ministry of Foreign Affairs over multiple decades. For each speech, we provide Russian- and English-language texts, associated images and captions where available, and harmonized metadata including (e.g.) dates, speakers, (geo)locations, and official government content tags. Unique identifiers link images to speeches and align Russian and English versions of the same communication texts. We further augment these linked datasets with validated topical annotations for both speech tex...

• 3.Learning Context-conditioned Gaussian Overbounds for Convolution-Based Uncertainty Propagation

  Uncertainty quantification is essential in safety-critical settings--from autonomous driving to aviation, finance, and health--where decisions must rely on conservative bounds rather than point estimates. Predictor-level intervals (e.g., from quantile regression, conformal prediction, variance networks, or Bayesian models) generally do not compose: adding two per-variable intervals need not yield a valid interval for their sum or preserve coverage. In aviation, Gaussian overbounding replaces complex error distributions with a conservative Gaussian whose tails dominate the truth, so conservatism propagates through linear operations. Yet classical overbounds are global, often overly conservative, and hard to adapt to feature-conditioned errors. We propose a unified learning framework that trains neural networks to produce context-aware Gaus...

• 4.AGOP-IxG: A Gradient Covariance Filter for Local Feature Attribution on Tabular Data, with a Controlled Benchmark

  Automated machine learning pipelines increasingly produce models whose predictions must be explained to end users, auditors, and downstream decision systems. The most widely used feature attribution methods (SHAP, Integrated Gradients, LIME) are typically chosen by convention rather than measured fidelity, because rigorous evaluation is impeded by the absence of ground-truth attribution on real data. We propose AGOP-IxG, a fast per-sample attribution method for tabular classifiers that pre-multiplies the per-sample gradient by a top-$K$ rank-truncated Average Gradient Outer Product matrix, and evaluate it against four widely-used baselines on a controlled tabular benchmark designed for AutoML practitioners. In Part 1, we construct three synthetic multi-class tabular tasks (linear, sparse nonlinear, interaction-based) where ground-truth at...

• 5.LRCP: Low-Rank Compressibility Guided Visual Token Pruning for Efficient LVLMs

  Large vision-language models (LVLMs) achieve strong multimodal understanding, but their inference cost grows rapidly with the number of visual tokens, especially for high-resolution images and long videos. Existing attention-based methods estimate token importance from attention scores, which may introduce positional bias, while representation-based methods reduce visual redundancy based on feature relations or reconstruction errors, overlooking the global structure of the visual token set. In this paper, we revisit visual token compression from the perspective of low-rank compressibility. Across models and datasets, we observe that visual token representations exhibit a pronounced low-rank structure, with a dominant subspace that remains stable even after a large fraction of tokens is randomly removed. Motivated by this finding, we propo...

• 1.How Melbourne’s AI and Data Center Flywheel Is Accelerating Research Innovation

  This sponsored article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia . Melbourne’s reputation as a global events city, from the Australian Open tennis and Formula 1 Australian Grand Prix to hosting NFL regular season games, now intersects with a different form of scale: large-scale compute, data-intensive research, and advanced engineering. Long recognized for delivering complex international events, the city is applying the same organisational capability to the infrastructure that underpins modern AI research, positioning Melbourne at the convergence of global convening and high-performance digital systems. Consistently ranked among the world’s most livable cities, Melbourne was named Time Out’s Best City in the World in 2026 , the first Australian city to hold the title. More materially fo...

• 2.Agentic AI for Robot Teams

  This presentation highlights recent efforts at the Johns Hopkins Applied Physics Laboratory to advance agentic AI for collaborative robotic teams. It begins by framing the core challenges of enabling autonomy, coordination, and adaptability across heterogeneous systems, then introduces a scalable architecture designed to support agentic behaviors in multi-robot environments. The talk concludes with key challenges encountered and practical lessons learned from ongoing research and development. Key learnings Provides an introduction to LLM-based AI Agents Describes an approach to applying LLM-based AI Agents to robotic teams Provides demonstrations of the approach running in hardware with a heterogeneous team of robots Presents lessons learned and future work in this area Download this free whitepaper now!

• 3.Voice AI Systems Are Vulnerable to Hidden Audio Attacks

  AI-powered voice and audio tools are becoming increasingly embedded in daily life, from digital assistants to smart speakers and customer service bots. Advances in large audio-language models (LALMs), which can both analyze and generate audio , now make it possible to control devices using voice commands, transcribe meetings automatically, or identify a song playing in the background. These models are also increasingly equipped with the ability to communicate with external services and operate other applications and tools. But these tools can be “hijacked” through imperceptible sounds embedded in audio, forcing them to execute unauthorized commands without a user’s knowledge. New research due to be presented at the IEEE Symposium on Security and Privacy in San Francisco next week shows that a modified audio clip undetectable by human ears...

• 4.AI Rings on Fingers Can Interpret Sign Language

  Electronic rings wirelessly connected to an AI system are capable of translating multiple sign languages into text, a new study finds. “I believe this is an important step toward making sign language translation systems more practical, lightweight, and usable in real-world environments,” says Ki Jun Yu , an associate professor of electrical and electronic engineering at Yonsei University in Korea. More than 300 differen t sign languages are used worldwide, and many research projects are developing translation devices for communicating with people who do not know a sign language. However, these projects have faced many setbacks. For example, some projects used cameras and computer vision algorithms to recognize hand gestures. However, these were typically limited to controlled settings with fixed cameras, and were sensitive to lighting var...

• 5.Graphene “Tattoos” for Plants Could Form Neural Networks

  A hydrated leaf is a healthy leaf. That’s true for the leaves of crop plants in a farmer’s field and for the leaves of trees in an area vulnerable to forest fires. But the traditional techniques to monitor leaf hydration require cutting them from their plants, which is time-consuming and cannot give live measurements. That’s why many researchers are building sensors that measure a plant’s health in real time. Now, researchers in Texas have developed a graphene “tattoo” that can be stuck directly onto a leaf to provide real-time moisture readings. The researchers also believe it could one day be the building block for a new kind of plant monitoring, by turning the patches into a neural network that computes on the plants themselves. “Not only are we just sensing the moisture level, but we can have that sensor act as this artificial synapse...

• 1.How Job Design for Disability Improves Work for Everyone

  Gary Waters / Ikon Images Disability-related innovations are all around us. Curb cuts in sidewalks, originally designed for wheelchair users, benefit caregivers with strollers, travelers with suitcases, and delivery workers with hand trucks. Automatic doors intended for individuals with mobility impairments are convenient for all. Blurred backgrounds in video calls, standing desks and ergonomic keyboards, […]

• 2.Resolve the Conflict Between Efficiency and Resilience

  Ellice Weaver/Ikon Images Operational efficiency is critical for both financial success and customer satisfaction. Efficient systems, characterized by minimal buffers and idle time, tight schedules, and maximum asset utilization, allow organizations to do more with less, thereby boosting revenue and appealing to time-sensitive customers. However, such systems often lack resilience, increasing an organization’s vulnerability to […]

• 3.Beyond Verification — What Responsible AI Really Demands of Human Experts

  For the fifth year in a row, MIT Sloan Management Review and Boston Consulting Group (BCG) have assembled an international panel of AI experts that includes academics and practitioners to help us understand how responsible artificial intelligence is being implemented across organizations worldwide. In our first post this year, we explored how organizations should think […]

• 4.How Leaders Can Move Past Personal Obstacles

  Brian Stauffer/theispot.com Imagine you’re Gabrielle, a senior leader at a fast-growing tech company. Two of your top performers are also your biggest headaches, and they’re making everyone miserable — most of all, you. One is technically brilliant but undermines colleagues’ ideas with sly sarcasm and strategic inaction. The other is a creative powerhouse but belittles […]

• 5.Why Businesses Should Experiment With Quantum Computing Now

  Matt Chinworth/theispot.com Executives tracking the latest news about quantum computing might conclude that with technical milestones still to be reached, the prudent approach is to watch and wait before investing. But that overlooks what other, bolder companies recognize: Quantum computing is an enabling technology, and user organizations have a critical role to play in shaping […]

• 1.AlphaPortfolio: Goal-Oriented Investment Management Through Deep Reinforcement Learning -- by Lin William Cong, Ke Tang, Jingyuan Wang

  We adapt attention-based neural networks and reinforcement learning to direct portfolio construction, allowing broader portfolio-management objectives (including non-time-additively separable ones) and in a data-driven way, searching over a much richer policy/strategy space than low-dimensional parametric rules or human-specified strategies. As arguably the first non-text-based, “large” GenAI model in Finance, AlphaPortfolio accommodates long- and short-range path dependence in firm and market states (e.g., using Transformer encoder), cross-asset information, flexible (path-dependent) objectives (incl. Sharpe ratio, which is non-additively separable across periods) for end-to-end (rather than step-by-step) optimizations. In U.S. equities, AlphaPortfolio yields superior out-of-sample performance (e.g., Sharpe ratio above two and risk-adjus...

• 2.Making Sense of Labor Market Indicators Amid Data Imperfections -- by Scott A. Brave, Erin E. Crust, Stefano Eusepi, Bart Hobijn, Ayşegül Şahin

  Interpreting real-time labor market conditions is challenging because commonly used indicators are noisy, revised over time, and often send conflicting signals. In practice, policymakers and market participants describe labor market developments using a shared narrative language centered on labor demand, labor supply, and matching frictions. In this paper, we show that empirical measures of these narrative concepts can be recovered from latent factors that summarize the joint movements of a broad set of high-frequency U.S. labor-market indicators. We use ninety-four labor-market indicators, over the period from 1960 to 2026, and construct measures for labor demand, long-run labor supply, short-run labor supply, and matching efficiency by selecting the factors that satisfy a limited set of restrictions on how underlying forces map into obs...

• 3.Global Policy Spillovers: How Environmental Policies Propagate through Product Attributes -- by Koichiro Ito, James M. Sallee, Jonathan (Andrew) Smith

  How should policymakers evaluate policy impacts when firms design products for global markets? Standard economic analyses typically focus on domestic outcomes, implicitly assuming that policies affect only the jurisdiction in which they are enacted. Yet multinational firms often harmonize product design across markets, creating the potential for policies implemented in one country to generate global spillovers through changes in product attributes. We call this phenomenon "attribute propagation" and develop a framework to measure and assess its quantitative importance. Applying this framework to an environmental policy affecting automobiles, we find that a fuel-economy subsidy in Japan led to significant improvements in the fuel economy of vehicles sold in the United States. We then develop a model of multinational automobile markets feat...

• 4.The Effects of GLP-1 Use on Mental Health, Self-rated Health, Employment and Marriage -- by Robert Kaestner, Cuiping Schiman

  In this article, we exploit the recent, rapid diffusion of the use of GLP-1 drugs among individuals with diabetes to measure the effect of the use of these drugs on mental health, self-rated health, employment, and marriage. The documented large weight loss from GLP-1 use may plausibly affect these outcomes and evidence of these broader impacts of GLP-1 use is necessary to evaluate their full value. Estimates are obtained using a longitudinal (within-person) regression approach. Results indicate that GLP-1 use is not meaningfully associated with mental health, self-rated health, employment, and marriage. Overall, our analysis adds new evidence about how GLP-1 use is affecting the lives of individuals with diabetes.

• 5.How do Workers Learn? Theory and Evidence on the Roots of Lifecycle Human Capital Accumulation -- by Xiao Ma, Alejandro Nakab, Daniela Vidart

  How do the sources of worker learning change over the lifecycle, and how does this affect human capital and wages? Using data from Germany and the US, we document that internal learning (from coworkers) decreases with experience, while external learning (on-the-job training) follows an inverted U-shape. We develop a search model featuring multiple learning sources whose returns evolve as workers age and accumulate human capital. Quantitative results indicate that the interaction between sources is key to lifecycle wage dynamics and the effects of remote work, which disrupts internal learning and early-career wage growth, though external learning partially offsets these losses.

• 1.Honey, Who Shrunk the U.S. Income Surplus?

  Foreign holdings of U.S. financial assets are immense, with official estimates putting their current market value at $69 trillion. U.S. holdings of foreign assets are also impressive but much smaller, at $41 trillion. The shortfall in U.S. foreign assets relative to foreign liabilities has been mounting for decades. Yet U.S. investment income receipts—in profits, dividends, and interest—comfortably exceeded income payments until recently. We show that the fading of the net investment income surplus stems from the upward shift in interest rates in the aftermath of the pandemic along with the continued net sales of U.S. assets to foreign investors.

• 2.Do Job Postings Show Early Labor‑Market Effects of AI?

  As generative AI tools become more widely used, a key issue is the technology’s impact on labor demand. Where might we find evidence of that impact? In this post, we examine whether early evidence of AI’s effect on the labor market appears in firms’ job postings. We combine an occupational measure of AI exposure with detailed U.S. job-posting data from Lightcast, which aggregates listings from company career pages, national and local job boards, and job-listing aggregators. Using this data, we test whether postings for AI-exposed occupations declined disproportionately since the release of ChatGPT in late 2022. We find that, while overall hiring has slowed since then, the evidence from job postings provides little indication of a distinct AI-driven decline in labor demand.

• 3.Federal Student Loan Defaults Return After Pandemic Pause

  During 2026:Q1, household debt balances increased slightly, by $18 billion, to reach $18.8 trillion, according to the latest Quarterly Report on Household Debt and Credit from the New York Fed’s Center for Microeconomic Data. Amid upticks in mortgage, HELOC, and auto balances and a seasonal decline in credit card balances, student loan balances remained unchanged. However, the share of student loan balances past due increased, nearing pre-pandemic levels at just over 10 percent. In this post, we focus on which borrowers entered default on their federal student loans over the past two quarters. We find that the average borrower entering default is nearly 40 years old, was not past due on their student loans prior to...

• 4.Will Mounting Supply Chain Strains Hamstring the AI Investment Boom?

  The conflict in the Middle East has precipitated a global supply shock—the third in six years following the pandemic in 2020 and Russia’s invasion of Ukraine in 2022. The current shock raises the specter of spillovers to the U.S. through both prices and physical shortages of goods. A critical conduit for spillovers through these channels is via Asian supply chains, especially from middle- to lower-middle income countries in southeast Asia, which are key suppliers for goods needed for the AI infrastructure build-out in the U.S. These countries are also heavily reliant on Middle East energy imports. This post examines key factors related to these Asian supply chain vulnerabilities.

• 5.Stress and Strain from NBFIs to Banks

  Do the recent stresses in the NBFI space—notably the bankruptcies of Tricolor and First Brands, and the decision of Blue Owl Capital Corp II (OBDC II) to end its redemption program and return capital through a wind-down of the fund—create distress for banks? The general sentiment is that the recent stresses are unlikely to amount to systemic concerns, although it does not mean there might not be “some stress and strain” for banks and that policymakers are “watching carefully” for exposure across banks. In a series of previous posts, we showed that shoc...

• 1.Africa Is the Key to Sustained Global Growth

  The rest of the world continues to shunt Africa to the periphery, regarding it mainly as a source of raw materials. But as more advanced economies face structural problems, the continent’s unmatched demographic momentum and enormous industrialization potential imply that it will play a central role in global economic expansion.

• 2.The Root of Today’s Global Imbalances

  Today’s global imbalances are driven primarily by domestic saving-investment dynamics and the interaction between geopolitical rivalry, technological competition, and capital flows. Only by taking coordinated action to manage the associated risks can the world prevent today’s tensions from erupting in another global economic crisis.

• 3.When Volatility Is Good Politics

  The Iran war has revealed how deeply material interests are embedded in strategic calculations, concentrating rewards among powerful insiders while leaving society to absorb the costs. As long as chaos remains politically and economically rewarding, such conflicts will remain difficult to contain.

• 4.The World Economy After the Trump-Xi Summit

  If Donald Trump and Xi Jinping's Beijing summit produces a sustained Sino-American trade truce and a path to reopening the Strait of Hormuz, that will give the world economy something it has lacked for the past year and half: a reduction in tail risks. In a year when so much has gone wrong, that is a welcome prospect.

• 5.California Capitalism Meets the Moment

  The United States needs AI governance that protects workers without stifling innovation; tax strategies that actually address inequality; and innovation policies that do not kill the goose that lays the golden egg. If there is one place where these demands will be met, it is the Golden State.

• 1.Dan Schulman was right – networks won’t differentiate; experience will (Reader Forum)

  Why CSPs are losing the customer relationship betting on half the experience and where they can grab the other half. Just three weeks ago, Verizon CEO Dan Schulman said the quiet part out loud at a Semafor fireside chat: network…

• 2.Defense ISAC panel sees real capabilities and a 5–10 year runway

  A panel at the Defense Communications Forum put timelines on drone detection, waveform debates, and the limits of sensing from space At the Defense Communications Forum, a panel moderated by the GSMA’s James Joiner dissected where integrated sensing and communications…

• 3.Foxconn, Bull, and Amini team up for AI data centers

  Foxconn’s modular facilities are designed to deploy in under 12 months In sum – what we know: Three companies that don’t usually work together have announced an AI infrastructure project for Africa and the Global South. Amini, a Nairobi-based sovereign…

• 4.Major US telcos back D2D expansion with ‘dead-zone’ joint venture

  New joint venture between AT&T, T-Mobile, and Verizon in the US reflects industry momentum around satellite-enabled mobile connectivity and broader ecosystem development, reckon analysts. In sum – what to know: Shared D2D push – AT&T, Verizon, and T-Mobile plan a…

• 5.Vodafone talks-up federated European telco edge-cloud for sovereign IoT and AI

  Vodafone and other major European operators are advancing a federated telco edge-cloud designed to support sovereign AI and IoT workloads, positioning it as a home-grown alternative to hyperscaler platforms. The initiative promises “seamless” cross-border services, but remains in early-stage validation.…

• 1.Physics-informed machine learning

  Abstract not available.

• 2.Machine Learning: Algorithms, Real-World Applications and Research Directions

  In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intellig...

• 3.Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

  We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits. The dataset is freely available at this https URL

• 4.A Survey on Bias and Fairness in Machine Learning

  With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various...

• 5.Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

  Abstract not available.

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