Babak's Daily Briefing

Monday, May 25, 2026

Sources: 16 | Total Articles: 64

• 1.RF Digital Twins: Why 5G-Advanced and 6G Need Predictive Simulation

  RF Digital Twins: Why 5G-Advanced and 6G Need Predictive Simulation As wireless systems become more tightly coupled across…

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

  Summary available at source link.

• 3.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.

• 4.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.

• 5.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.

• 1.Evaluating Large Language Models in a Complex Hidden Role Game

  arXiv:2605.22826v1 Announce Type: new Abstract: Quantifying the deceptive potential of Large Language Models (LLMs) is critical for AI safety, yet difficult to achieve in uncontrolled environments. This work investigates the reasoning, persuasion, and deceptive capabilities of LLMs within the social deduction game Secret Hitler. I introduce an open-source framework and novel metrics to measure performance: Role Identification Accuracy, Deception Retention Rate, and Game State Impact Rate. By benchmarking models against rule-based algorithms and human games, I identify a gap between conversational ability and strategic depth. The study also analyzes the impact of reasoning-enhancement techniques on win rates and strategic reasoning. Neither Chain-of-Thought prompting nor internal memory bring improvements in performance, with up to 23.2% ...

• 2.A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development

  arXiv:2605.22828v1 Announce Type: new Abstract: This survey provides a comprehensive catalog of publicly available text and speech resources for two West African languages: Hausa, an Afroasiatic language with approximately 80-100 million speakers, and Fongbe, a Niger-Congo language spoken by approximately 2 million people in Benin. These languages represent contrasting cases on the resource availability spectrum. We address the question: \textit{What is the current state of publicly available NLP resources for Hausa and Fongbe, and what gaps remain?} Through systematic search of academic repositories, data platforms, and web sources, we catalog parallel corpora, monolingual text collections, speech datasets, pre-trained models, and evaluation benchmarks. For each resource, we document size, domain coverage, format, licensing, and accessi...

• 3.Query-Adaptive Semantic Chunking for Retrieval-Augmented Generation: A Dynamic Strategy with Contextual Window Expansion

  arXiv:2605.22834v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems depend critically on document chunking quality for retrieving relevant context. Fixed chunking segments documents into uniform units irrespective of semantics or user intent, producing a precision-recall trade-off unresolvable by tuning chunk size alone. Semantic and agentic methods partially address these limitations but do not integrate user queries at the chunking stage. We present Query-Adaptive Semantic Chunking (QASC), which dynamically constructs chunks by integrating queries into segmentation through three mechanisms: cosine similarity scoring between sentence and query embeddings to identify seed sentences, contextual window expansion around seeds to preserve coherence, and chunk-level score aggregation to ensure holistic relevance. We e...

• 4.Knowledge Distillation for Low-Resource Open-source Text-to-SQL Model

  arXiv:2605.22843v1 Announce Type: new Abstract: Text-to-SQL converts natural language questions into executable SQL queries, enabling non-technical users to access relational databases for analytics and intelligent data services. In real-world scenarios, performance is often constrained by low-resource settings, where high-quality annotated \texttt{} pairs are scarce, particularly for domain-specific databases. Additional challenges include opaque schema definitions, abbreviations, and implicit business logic that are not explicitly encoded in the schema. Existing data synthesis and prompting techniques improve coverage but often fail to produce task-specific, semantically grounded examples aligned with database constraints. To address these challenges, we propose a knowledge-aware Text-to-SQL framework that constructs tas...

• 5.How Far Will They Go? Red-Teaming Online Influence with Large Language Models

  arXiv:2605.22880v1 Announce Type: new Abstract: As large language model (LLM)-based agents increasingly participate in online discourse, red-teaming their capacity to support political influence campaigns is critical for information integrity. In pursuit of this goal, we focus on locally deployed open-source LLMs, as opposed to frontier API-only models, given their superior alignment with the operational constraints of privacy-conscious malicious actors deployed in social media environments. We introduce an empirical red-teaming framework for measuring LLM Overton Windows (OWs), defined as the range of political opinions a model can reliably express on controversial topics, and for quantifying how simple natural-language jailbreaks expand that range. We evaluate more than 30 LLMs spanning 10 model families and five countries of origin. W...

• 1.Latent Cache Flow: Model-to-Model Communication Without Text

  arXiv:2605.22863v1 Announce Type: new Abstract: LLM agents today communicate via text, which incurs considerable latency and information loss due to the need to autoregressively decode the sharer model's state and encode at the receiver model. Recent work such as Cache-to-Cache (C2C; Fu et al., 2026) seeks to exchange KV caches by learning adapters that translate sharer KV matrices to the receiver model. However, the adapters are large and expensive to train, and translate individual tokens, which requires the target context to be identical. This is unsuitable for agent communication, where the LLMs have differing context. We introduce Latent Cache Flow (LCF). To address efficiency, we observe that keys and values can be jointly translated and compressed, reducing the adapter to about 4% of C2C's size. To address differing context, we des...

• 2.Reading Calibrated Uncertainty from Language Model Trajectories

  arXiv:2605.22864v1 Announce Type: new Abstract: The maximum softmax probability (MSP) represents a default approach when evaluating uncertainty quantification for language model generation with structured output. Although cheap, it is often miscalibrated. Methods that probe the model's internal activations feed raw hidden states into opaque classifiers, reading activations as static snapshots and leaving implicit the layer-wise trajectory by which a representation is formed. Yet, similar endpoints can arise from very different paths, and how evidence accumulates, reinforces, or reverses across depth might reveal uncertainty that final probabilities obscure. We extract eleven scale-invariant geometric features, tracing the cumulative path of per-layer MLP updates, and feed them to a sparse linear probe. The probe outperforms MSP under sele...

• 3.FusionSense: Tri-Stage Near-Sensor Learning for Runtime-Adaptive Multimodal Edge Intelligence

  arXiv:2605.22868v1 Announce Type: new Abstract: Autonomous systems and smart-industry deployments increasingly split computation across near-sensor, edge, and cloud resources, where tight energy, latency, and reliability budgets demand run-time adaptivity. In practice, deciding what to compute and transmit at each point is pivotal; yet as multimodal sensor suites (cameras, LiDAR/depth, etc.) proliferate at the edge, most prior approaches either (i) fuse modalities on powerful servers or (ii) apply uni-modal near-sensor filters that ignore cross-modal dependencies, leading to redundant transmissions or missed events. We present FusionSense, a fusion-aware intelligent sensing framework for energy-constrained autonomous edge systems. Lightweight near-sensor classifiers are trained via a three-step procedure: (i) a server-side fusion model le...

• 4.FuRA: Full-Rank Parameter-Efficient Fine-Tuning with Spectral Preconditioning

  arXiv:2605.22869v1 Announce Type: new Abstract: Both full fine-tuning (Full FT) and parameter-efficient fine-tuning methods such as LoRA introduce weight updates without accounting for the spectral structure established during pretraining. As a result, noisy gradients from limited fine-tuning data can perturb robust pretrained features. We identify spectral preconditioning as the missing ingredient: reparameterizing each weight matrix through its full-rank singular value decomposition (SVD) and freezing one singular basis constrains updates to the pretrained column space, yielding a preconditioned optimization scheme that outperforms unconstrained Full FT at the same trainable parameter count. Building on this insight, we propose FuRA (Full-Rank Adaptation), an efficient full-rank adaptation framework based on a block tensor-train factori...

• 5.The Readout Shortcut: Positional Number Copying Dominates Arithmetic CoT Readout in Small Language Models

  arXiv:2605.22870v1 Announce Type: new Abstract: Chain-of-thought (CoT) prompting is necessary for arithmetic in small language models, yet shuffling its steps preserves most performance. What does CoT contribute if not logical sequencing? In three 1-3B instruction-tuned LMs on GSM8K, we isolate the answer-readout stage via prefix completion and identify a positional shortcut: the model copies whichever number occupies the trailing position before the answer delimiter, regardless of intermediate reasoning. Gold-answer presence accounts for 54-92 pp of accuracy (89-92% of each model's teacher-forcing ceiling); even on incorrect items, the final answer matches the last CoT number 95-96% of the time. The copy channel takes precedence over retained-context completion: replacing the trailing number with a wrong value collapses accuracy to near-...

• 1.Remote Teleoperation of Endovascular Intervention Robots: A Systematic Review

  arXiv:2605.22889v1 Announce Type: new Abstract: Remote robotic-assisted endovascular intervention offers a promising approach to reduce clinician radiation exposure and physical strain, while extending specialized vascular care to geographically distant regions. Despite advancements, teleoperated endovascular intervention remains underexplored, especially for time-sensitive interventions like mechanical thrombectomy for acute stroke. The aim of the current review was to determine the evidence regarding teleoperated endovascular robotic systems, covering technical feasibility, communication infrastructure, and clinical outcomes. The review further identified research gaps and future directions. Following PRISMA guidelines, 16 studies were included that met the inclusion criteria out of 2501 initial search results. We found that teleoperate...

• 2.Extending Deep Event Visual Odometry with Sparse Point-Cloud Export

  arXiv:2605.22890v1 Announce Type: new Abstract: Event cameras are well suited for visual odometry under high-speed motion and challenging lighting conditions due to their low latency, high temporal resolution, and high dynamic range. Deep Event Visual Odometry (DEVO) demonstrated that monocular event-only odometry can achieve strong performance by combining sparse patch tracking, learned patch selection, recurrent correspondence refinement, and differentiable bundle adjustment. In this project, we extend DEVO with a sparse point-cloud export pipeline. Rather than modifying the core odometry formulation, our approach exposes the internal 3D structure already estimated by DEVO and converts it into an explicit point-cloud representation for visualization and further processing. In addition, we implement a practical workflow for data export, ...

• 3.Agentic-VLA: Efficient Online Adaptation for Vision-Language-Action Models

  arXiv:2605.22896v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for robotic manipulation by leveraging pre-trained vision-language representations. However, current VLA training methods suffer from two critical limitations: poor generalization to novel environments and low training efficiency requiring extensive demonstrations. We introduce Agentic-VLA, an agentic training framework that enables VLAs to efficiently adapt online through three key innovations: (1) Adaptive Reward Synthesis, which dynamically generates and adjusts reward functions based on the VLA's current capabilities and task complexity, decomposing complex tasks into learnable sub-goals for curriculum learning; (2) Language-Guided Exploration, where a critic model provides structured guidance for systematic explora...

• 4.Robots That Know What to Ask: Recovering Misaligned Rewards through Targeted Explanations

  arXiv:2605.22986v1 Announce Type: new Abstract: Learning reward functions from demonstrations assumes that demonstrations provide adequate supervision over all features -- or task-relevant aspects of behavior. In practice, demonstrations are often imperfect: humans may under-emphasize certain features due to cognitive load or physical difficulty, or the training regime may fail to sufficiently cover all relevant situations. In either case, important features may be underspecified, leading to ambiguity in the learned reward function and misaligned behavior at deployment. We propose a framework that detects such underspecified features and actively solicits targeted corrective demonstrations. Our key insight is that demonstrations implicitly reveal which features are well specified: features that are consistently optimized show little varia...

• 5.Verified Task-Space Motion Planning Under Joint-Space Constraints

  arXiv:2605.22991v1 Announce Type: new Abstract: Reactive task-space planners such as Bug2 operate with fixed Cartesian step sizes and are unaware of the manipulator's joint-angle limits. When the Jacobian is poorly conditioned, even small Cartesian steps can demand joint changes that exceed admissible bounds; clipping the joints to their limits causes tracking drift and can prevent goal reaching entirely. We address this by computing, at each planning step, the largest Cartesian hyperrectangle that is \emph{certifiably reachable} under joint displacement bounds. Using a second-order polynomial approximation of the inverse kinematics and the S-procedure, we formulate a small semidefinite program whose solution yields the certified half-width~$\lambda^\star$. An equivalent bisection procedure exploiting the quadratic structure solves the ce...

• 1.Network Slicing to Help Drive Next Wave of 5G Innovation in India

  Summary available at source link.

• 2.Canadian Telecommunications Association and GSMA Convene Industry Leaders at “Inflection Point” for Canada’s Connectivity Future

  Summary available at source link.

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

  Summary available at source link.

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

  Summary available at source link.

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

  Summary available at source link.

• 1.CHRONOS: Temporally-Aware Multi-Agent Coordination for Evolving Data Marketplaces

  Temporal knowledge-graph data marketplaces face three coupled failures in static designs: stale hybrid index shortcuts reduce recall as edges evolve, stationary Shapley pricing misattributes value after distribution shifts, and uncoordinated agents over-consume a shared differential-privacy budget. We present CHRONOS, a three-layer architecture providing a unified treatment of these challenges with explicit public and private separation. Layer one applies neural-ODE temporal decay to shortcut edges, providing a per-query expected recall-loss bound of Big-O of Pq lambda delta t, with a monotone-envelope guarantee reducing bound looseness to 1.8 to 3.2 times observed loss. Layer two conditions Shapley valuation on detected changepoints and provides finite-sample error guarantees under noise. Layer three uses EXP3-IX to achieve Big-O of the ...

• 2.Preisach Attention: A Hysteretic Model of Sequential Memory

  We introduce the Preisach Attention Layer (PAL), a novel sequence modelling architecture grounded in the classical Preisach hysteresis operator from mathematical physics. PAL replaces the softmax attention mechanism with a binary relay operator parameterised by learned activation and deactivation thresholds, maintaining a stack of local extrema as its internal state. A single-layer PAL-Transformer with O(1) depth is Turing-complete under arbitrary precision arithmetic, achievable through simulation of a two-stack pushdown automaton -- in contrast to the O(log n) depth required by standard hard-attention transformers. Second, we prove that the function classes computable by PAL and by the transformer are incomparable: PAL computes historical range statistics in O(1) layers that require O(log n) layers for transformers, while transformers s...

• 3.Score-Based One-step MeanFlow Policy Optimization

  Diffusion and flow matching have emerged as expressive policy classes in reinforcement learning, but their reliance on multi-step denoising imposes substantial computational overhead at inference time, which is particularly problematic in online RL. MeanFlow offers a promising alternative by learning an average velocity field that maps noise to data in a single network evaluation. However, MeanFlow typically requires samples from the target distribution to construct its target velocity field, which are unavailable in online RL. We propose Score-Based One-step MeanFlow Policy Optimization (SOM), an actor-critic algorithm that resolves this by constructing the target velocity field directly from the Q-function via score estimation and a probability flow ODE, thereby concentrating probability mass on high-value modes. In the fully online RL ...

• 4.Turning Adaptation into Assets: Cross-Domain Bridging for Online Vision-Language Navigation

  Navigating under non-stationary environment shifts poses a critical challenge for a Vision-and-Language Navigation (VLN) agent deployed in the wild. Yet, existing Test-Time Adaptation (TTA) methods for VLN largely treat online adaptation as transient, isolated updates, leading to catastrophic forgetting and negative transfer. To overcome these issues, we propose Inter-Domain BridgE with Historical Assets (IDEA), a novel TTA framework that transforms adaptation into the accumulation and composition of assets. Specifically, IDEA introduces soft prompts optimized via a Fisher-guided weighting scheme to capture the transferable knowledge. These optimized prompts are then augmented with domain coordinates to form a dynamic asset library. Leveraging this library, IDEA constructs a cross-domain bridge by projecting the target domain onto the con...

• 5.FastKernels: Benchmarking GPU Kernel Generation in Production

  LLM-based agents for GPU kernel generation are advancing rapidly, yet their progress is fundamentally constrained by the benchmarks they optimize against. Existing benchmarks are poorly aligned with production inference frameworks: they evaluate kernels on a single GPU with synthetic inputs, ignore the surrounding compilation stack, and reward replicating known optimizations rather than discovering new ones. The resulting reward signals are misleading: agents learn to generate kernels that score well in sandboxes but introduce interface incompatibilities, compilation-stack conflicts, and silent correctness degradation when integrated into real systems. We introduce FastKernels, a kernel benchmark built around a minimal set of 46 representative architectures spanning 8 categories, whose kernels collectively subsume those of 96.2% (409/425)...

• 1.AI with Model-Based Design: Virtual Sensor Modeling

  This webinar presents a workflow offering end-to-end solutions for designing, training, validating and verifying, compressing, and deploying AI-based virtual sensor models to embedded processors within a single environment. Highlights Integrate AI models into Simulink for system-level simulation, verification, and simulation-based testing Apply formal verification techniques to assert neural network behavior Compress the AI model for memory footprint reduction and execution speedup Generate library-free C code from AI models and performing PIL tests Profile code performance and evaluate design and model selection tradeoffs Design and train AI-based virtual sensors using MATLAB Register now for this free webinar!

• 2.Radar Can Tell the Difference Between Insect Species

  Bees and other pollinating insects play vital roles in food webs and crop pollination, yet monitoring them has proven difficult. That’s why researchers have developed a radar system that could lead to a cost-effective, non-invasive way to track pollinators. Traditionally, identifying pollinators has proven tricky and time-consuming, and typically requires capturing and killing insects to get a close look at them. To find a better way to monitor pollinators , scientists are developing vision systems that use machine learning to automatically classify insects. However, a major limitation these machine learning systems face is acquiring usable images, because of issues such as variable lighting, poor weather, and cluttered backgrounds—not to mention that many insects can just fly away when approached. That’s why researchers based in Europe i...

• 3.Māori Text-to-Speech Model Spurns Big Tech’s Values

  New Zealand is a country famed for its dramatic landscapes, but its linguistic landscape is arguably just as interesting. Of its three official languages, only te reo Māori (the Māori language) could be described as indigenous. Though spoken fluently by just 4.3 percent of the population, national statistics show that about 30 percent of New Zealanders can speak more than a few words or phrases of the language. But ask ChatGPT to write te reo Māori and it will oblige, fluently answering your questions in the standardized form of the language taught in schools and broadcast on national television. Claude and Perplexity can do the same. This impressive language performance is built on text and audio produced by Māori communities and academics, which was scraped and ingested without their permission, processed outside New Zealand, and return...

• 4.Open-Source Software Is Starting to Help Robots Think

  When a group of academics started making open-source robotics hardware , a generation of roboticists got years of their lives back. Now, the bigger challenge is getting robots to think—and that’s starting to be open sourced too. The shift is still early, but companies including Hugging Face, Nvidia, and Alibaba have all made significant bets on open-source robotics in the last two years, releasing tools and models aimed at the higher-level work of getting robots to reason, decide, and act. The open source movement that accelerated other AI applications is now being applied to the problem of making robots smarter. If these attempts to bring AI to robotics with open-source platforms succeed, the barrier to building a capable robot could fall as fast as the barrier to building an AI application did. The world ROS built Open-source robotics s...

• 5.The Future of Physical AI Isn’t Smarter Robots, It’s Smarter Interfaces

  This sponsored article is brought to you by Wetour Robotics . A field technician on a wind turbine, harness clipped, both hands on a wrench, needs to send a command to the diagnostic device hanging at her belt. A logistics worker on a loading dock, gloves on, eyes on the pallet, needs to redirect a connected lift. A person using an assistive mobility device on a crowded street wants to nudge it forward without taking out a phone or speaking aloud. None of these moments call for a smarter robot. They call for a smarter way to be heard by the machines that already exist. The industry has been building from one side The past three years of Physical AI have been a story of remarkable progress on the robot side of the loop. Companies like Boston Dynamics, Figure, and Unitree have advanced actuators, locomotion, and dexterity to a level that wo...

• 1.Data Transformation Is the CEO’s Business

  Aeriform/Ikon Image The Research The MIT Sloan Center for Information Systems Research conducted case study research at Caterpillar, a member of the MIT CISR research consortium since 2007. From July 2023 to December 2024, the authors conducted 56 interviews with 42 stakeholders. Interview participants also reviewed the case narrative as it was developed by the […]

• 2.What It Takes to Scale Value-Based Industrial Solutions

  Dante Terzigni/theispot.com B2B sales is fiercely competitive. Companies selling big-ticket products and services to other businesses must design solutions that meet their customers’ specific needs with a provable value proposition. Increasingly, that means engaging in value-based sales, where the benefits to the customer are defined, quantified, and managed by the vendor. That’s a challenging practice […]

• 3.Companies Don’t Have to Slash Jobs Because of AI

  Harry Haysom/Ikon Images | Carolyn Geason-Beissel “If AI is going to destroy all the jobs, why don’t we just stop?” That was the rhetorical question my college-age son asked after we talked about the possibility of drastic changes to career paths and society thanks to AI (technically, generative AI). It was in line with what […]

• 4.A Need for Nuance: The Economist’s Andrew Palmer

  On today’s episode of the Me, Myself, and AI podcast, Andrew Palmer, senior editor at The Economist, describes how organizations can experiment with generative AI while balancing speed, quality, and risk. At his own organization, Andrew and others test artificial intelligence with human oversight to develop editing and publishing efficiencies. As the host of The […]

• 5.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, […]

• 1.How Should Central Banks Respond to Commodity Price Shocks? Optimal Monetary and Exchange Rate Frameworks for Commodity-Exposed Economies -- by Thomas Drechsel, Michael McLeay, Silvana Tenreyro, Enrico D. Turri

  We show that the optimal monetary policy and exchange rate framework depend critically on the economy’s commodity exposure. We develop a flexible but tractable model economy with commodity exports and imports, in which international financial conditions may vary with the commodity cycle, and we compute the welfare-optimal policy in the presence of price and wage rigidities. Stabilizing domestic prices is welfare-optimal for commodity exporters, in line with standard open-economy policy prescriptions. But for economies that use commodities as inputs in production, optimal policy largely ‘looks through’ the direct and indirect effects of commodity shocks on domestic prices; this contrasts with some earlier findings and policy practice (which only ‘looks through’ the direct effect). Exchange-rate pegs increase welfare for commodity importers...

• 2.State Dependence of Monetary Policy During Global Supply Chain Disruptions -- by Xiwen Bai, Jesús Fernández-Villaverde, Yiliang Li, Francesco Zanetti

  We study how global supply chain disruptions affect monetary policy transmission. Post-pandemic evidence indicates surging transportation costs, goods-market imbalances, and rising prices. We develop a model in which logistical bottlenecks (upstream slack coexisting with downstream shortages) steepen the aggregate supply curve. This convexity amplifies price responses to monetary policy while dampening output effects. Threshold VAR and Local Projection estimates are consistent with this mechanism: during disruptions, contractionary policy reduces prices more at smaller output cost, easing the stabilization trade-off.

• 3.California Billionaires: Wealth, Taxes, and Wealth Tax Revenue Estimates -- by Jasper Boll, Emmanuel Saez, Gabriel Zucman

  This paper documents the wealth of California’s billionaires and the taxes they pay. California billionaires’ wealth exceeds $2 trillion today, the equivalent of 50% of California’s GDP. It has grown 144% from 2023 to 2025, fueled by the AI boom. Over the longer run, the real wealth of California’s billionaire class—the 0.0002% richest households—has been multiplied by 30 from 1982 to 2025, while average real family income in California has about doubled. California billionaires pay about 0.2% of their wealth in California income tax ($3.2 billion/year), representing 2.4% of total California income tax revenue on average over 2023-2025. Using Securities and Exchange Commission data from Alphabet, Meta, Oracle, and Nvidia since 2004, we estimate the trajectory of wealth, income, and taxes paid by the top 4 California billionaires—Page, Bri...

• 4.Explaining Movements in Government Debt -- by Tatiana Kirsanova, Eric M. Leeper, Campbell B. Leith, Ding Liu

  Standard New Keynesian models with time-consistent policy predict minimal debt responses to conventional shocks, as a debt stabilization bias dominates tax-smoothing motives. We show that two mechanisms can generate debt movements of the magnitude observed in the data: increases in policymaker myopia and declines in real interest rates, such as during flight-to-safety episodes. Other potential drivers—changes in markups, debt maturity, government transfers, or large recessions—cannot account for such fluctuations.

• 5.Management of Health Care Facilities and Patient Attendance during Major Disruptions: Evidence from Kenya -- by Kathryn Andrews, Fabiano Dal-Ri, Roberta Gatti, Renata Lemos, Mario Macis, Lydia Nakhone

  This paper measures and analyzes management practices in the Kenyan health care sector, drawing on a nationally representative survey and linked administrative data. The paper adapts the World Management Survey to measure management quality in primary health care facilities and hospitals, surveying 429 primary health care facilities and 73 hospitals. Primary health care facilities are the primary point of contact for most patients, providing treatment for common infectious diseases and chronic conditions, as well as services related to maternal and child health. Management quality is low on average, and the distribution is highly compressed. The analysis uses administrative data to test the association between the management quality and performance of primary health care facilities, measured by outpatient attendance, during a period of di...

• 1.AI’s Macroeconomic Challenges and Promises

  In the third quarter of 2025, America's largest tech firms for the first time spent more on capital investment than they earned from operations. The implication is that AI, a technology with the potential to make the economy more productive, is, for now, absorbing resources faster than it is generating returns. This post discusses how the tension between AI's long-run promise and its short-run costs affects the outlooks for inflation, real activity, and financial stability.

• 2.The Global Credit Cycle in Corporate Bond Returns

  The global corporate nonfinancial bond market is both a large investment asset class and a vital source of funding for nonfinancial firms. With $19 trillion outstanding at the end of 2024, a broad portfolio of corporate bonds would be expected to be well diversified. Yet, in 37 percent of months between 1998 and 2024, more than 80 percent of bonds in the ICE Global Bond Indices—a portfolio with over 10,000 constituents spanning diverse industries, credit ratings, and regions—moved in the same direction, suggesting a large degree of synchronization. In this post, we introduce the global credit factor, which proxies for the global price of risk in international corporate bond markets. The global credit factor creates a global credit cycle in bond risk premia and generates predictable comovement in bond prices.

• 3.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.

• 4.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.

• 5.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...

• 1.Whose Vote Will Count in America’s Midterms?

  Democrats and Republicans alike are more focused on giving themselves an electoral advantage through redistricting than on appealing to voters, because both parties lack a credible vision for middle- and working-class Americans. Without an attractive program, all that is left are tactics.

• 2.How to Build a Sovereign AI Stack

  Paradoxically, no economy can build the architecture of AI sovereignty alone. Coalitions of countries must work together to regulate the sector, design and operate the rails, open the model layer, and universalize the agent interface, thereby embedding contestability and interoperability into every layer.

• 3.Why Gender Inequality Still Haunts the Economy

  Why do gender wage gaps and other disparities persist despite converging education levels and mounting evidence that the public opposes such forms of inequality? The problem lies less in outdated beliefs and biases than in entrenched structural constraints to women's economic advancement.

• 4.The Affordability Crisis Is About More than Prices

  Too many governments around the world are responding to affordability concerns by treating the symptoms rather than the underlying causes. Negotiating marginal price reductions with retailers might create the appearance of responsiveness, but the results will inevitably disappoint.

• 5.NATO Must Die

  Some claim that the threats Europe is facing, especially after Russia’s invasion of Ukraine, can create the momentum toward political union that the euro crisis and then the pandemic failed to generate. Right or wrong, one thing is clear: a functional defense union requires political union, and NATO's existence is inimical to it.

• 1.Bharti Airtel launches 5G slicing service for postpaid users

  Nicole McCormick, chief analyst for consumer 5G and broadband monetization at Omdia, said the launch by Airtel reflects a broader shift among operators toward protecting premium subscriber experiences while seeking new revenue opportunities from 5G services In sum – what…

• 2.German sovereignty play builds – DT/SAP win federal gig, Thales expands Google model

  Germany’s sovereign cloud push is advancing with Deutsche Telekom and SAP winning a BMDS AI platform tender built on the “Deutschland-Stack”, while Thales expands its Google Cloud-powered sovereign model in Germany alongside its French PREMI3NS infrastructure. STL Partners has a…

• 3.Defense Communications Market Pulse Report

  Defense communications has entered a new phase of urgency. Networks are no longer just the pipes that move information across the battlespace; they are becoming the sensing, compute, coordination and decision infrastructure that modern missions depend on. At the same…

• 4.“Poor” and “cynical” – about GSMA and CTIA on US midband spectrum (Analyst Angle)

  The GSMA and CTIA have misrepresented early 6GHz Wi-Fi adoption data to justify reallocating spectrum for mobile use, arguing the technology remains in a normal growth phase with significant long-term strategic and economic value. GSMA last month published a report…

• 5.The end of the broadband duopoly (Analyst Angle)

  The U.S. broadband duopoly of cable and telcos is fading as fixed wireless, Starlink, WISPs, and fiber overbuilders expand rapidly. Increased competition from carriers and alternative providers is giving consumers more choices, wider availability, easier setup, and lower prices. The…

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