Babak's Daily Briefing

Monday, May 4, 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.Putting HUMANS first: Efficient LAM Evaluation with Human Preference Alignment

  arXiv:2605.00022v1 Announce Type: new Abstract: The rapid proliferation of large audio models (LAMs) demands efficient approaches for model comparison, yet comprehensive benchmarks are costly. To fill this gap, we investigate whether minimal subsets can reliably evaluate LAMs while reducing costs and data redundancy. Analyzing 10 subset selection methods with 18 audio models across 40 tasks covering major LAM evaluation dimensions, we show that subsets of just 50 examples (0.3% of data) can achieve over 0.93 Pearson correlation with full benchmark scores. To understand how well these scores align with what practitioners ultimately care about, user satisfaction, we collect 776 human preference ratings from realistic voice assistant conversations, finding that both subsets and full benchmark achieve only 0.85 correlation with human. To bet...

• 2.NorBERTo: A ModernBERT Model Trained for Portuguese with 331 Billion Tokens Corpus

  arXiv:2605.00086v1 Announce Type: new Abstract: High-quality corpora are essential for advancing Natural Language Processing (NLP) in Portuguese. Building on previous encoder-only models such as BERTimbau and Albertina PT-BR, we introduce NorBERTo, a modern encoder based on the ModernBERT architecture, featuring long-context support and efficient attention mechanisms. NorBERTo is trained on Aurora-PT, a newly curated Brazilian Portuguese corpus comprising 331 billion GPT-2 tokens collected from diverse web sources and existing multilingual datasets. We systematically benchmark NorBERTo against Strong baselines on semantic similarity, textual entailment and classification tasks using standardized datasets such as ASSIN 2 and PLUE. On PLUE, NorBERTo-large achieves the best results among the encoder models we evaluated, notably reaching 0.9...

• 3.How Frontier LLMs Adapt to Neurodivergence Context: A Measurement Framework for Surface vs. Structural Change in System-Prompted Responses

  arXiv:2605.00113v1 Announce Type: new Abstract: We examine if frontier chat-based large language models (LLMs) adjust their outputs based on neurodivergence (ND) context in system prompts and describe the nature of these adjustments. Specifically, we propose NDBench, a 576-output benchmark involving two frontier models, three system prompt types (baseline, ND-profile assertion, and ND-profile assertion with explicit instructions for adjustments), four canonical ND profiles, and 24 prompts across four categories, one of which involves an adversarial masking strategy. Four trends emerge consistently from our findings. First, LLMs show significant adaptation under ND context, where fully instructed conditions yield lengthier and more structured outputs, characterized by higher token counts, more headings, and more granular steps (p < 10^-...

• 4.ViLegalNLI: Natural Language Inference for Vietnamese Legal Texts

  arXiv:2605.00116v1 Announce Type: new Abstract: In this article, we introduce ViLegalNLI, the first large-scale Vietnamese Natural Language Inference (NLI) dataset specifically constructed for the legal domain. The dataset consists of 42,012 premise-hypothesis pairs derived from official statutory documents and annotated with binary inference labels (Entailment and Non-entailment). It covers multiple legal domains and reflects realistic legal reasoning scenarios characterized by structured logic, conditional clauses, and domain-specific terminology. To construct ViLegalNLI, we propose a semi-automatic data generation framework that integrates large language models for controlled hypothesis generation and systematic quality validation procedures. The framework incorporates artifact mitigation strategies and cross-model validation to impro...

• 5.Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues

  arXiv:2605.00119v1 Announce Type: new Abstract: There is a significant gap in evaluating cultural reasoning in LLMs using conversational datasets that capture culturally rich and dialectal contexts. Most Arabic benchmarks focus on short text snippets in Modern Standard Arabic (MSA), overlooking the cultural nuances that naturally arise in dialogues. To address this gap, we introduce ArabCulture-Dialogue, a culturally grounded conversational dataset covering 13 Arabic-speaking countries, in both MSA and each country's respective dialect, spanning 12 daily-life topics and 54 fine-grained subtopics. We utilize the dataset to form three benchmarking tasks: (i) multiple-choice cultural reasoning, (ii) machine translation between MSA and dialects, and (iii) dialect-steering generation. Our experiments indicate that the performance gap between ...

• 1.Cloud Is Closer Than It Appears: Revisiting the Tradeoffs of Distributed Real-Time Inference

  arXiv:2605.00005v1 Announce Type: new Abstract: The increasing deployment of deep neural networks (DNNs) in cyber-physical systems (CPS) enhances perception fidelity, but imposes substantial computational demands on execution platforms, posing challenges to real-time control deadlines. Traditional distributed CPS architectures typically favor on-device inference to avoid network variability and contention-induced delays on remote platforms. However, this design choice places significant energy and computational demands on the local hardware. In this work, we revisit the assumption that cloud-based inference is intrinsically unsuitable for latency-sensitive control tasks. We demonstrate that, when provisioned with high-throughput compute resources, cloud platforms can effectively amortize network and queueing delays, enabling them to match...

• 2.FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources

  arXiv:2605.00011v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative intelligence across decentralized data source devices in a privacy-preserving way. While substantial research attention has been drawn to optimizing the learning process for an individual task, real-world applications increasingly require multiple machine learning tasks simultaneously training their models across a shared pool of devices. Naively applying single-FL optimization techniques in multi-FL systems results in suboptimal system performance, particularly due to device heterogeneity and resource inefficiency. To address such a critical open challenge, we introduce {\em FedACT}, a novel resource heterogeneity-aware device scheduling approach designed to efficiently schedule heterogeneous devices across multiple concurrent FL jobs, with the ...

• 3.What Physics do Data-Driven MoCap-to-Radar Models Learn?

  arXiv:2605.00018v1 Announce Type: new Abstract: Data-driven MoCap-to-radar models generate plausible micro-Doppler spectrograms, but do they actually learn the underlying physics? We introduce a physics-based interpretability framework to answer this question via two proposed complementary metrics: one measures alignment between model predictions and the physics-derived Doppler frequency, while the other tests whether predictions preserve the velocity-frequency relationship under velocity intervention. Both metrics require only MoCap input and model predictions, without access to measured radar data. Experiments across several model architectures reveal that low reconstruction error does not guarantee physical consistency: some, but not all, models achieve low error yet perform poorly on the two physics-based metrics. Further analysis sho...

• 4.AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G

  arXiv:2605.00020v1 Announce Type: new Abstract: The success of large foundation models is catalyzing a new paradigm for AI-native 6G network design: wireless foundation models for physical layer design. However, existing models often operate on channel state information (CSI) in the space-time-frequency (STF) domain, where distinct multipath components are inherently superimposed and structurally entangled. This hinders the learning of universal channel representation. Meanwhile, their reliance on global attention mechanisms incurs prohibitive computational overhead. In this paper, we propose AirFM-DDA, an Air-interface Foundation Model operating in the Delay-Doppler-Angle (DDA) domain for physicallayer tasks. Specifically, AirFM-DDA reparameterizes CSI from the STF domain into the DDA domain to explicitly resolve multipath components alo...

• 5.Learning physically grounded traffic accident reconstruction from public accident reports

  arXiv:2605.00050v1 Announce Type: new Abstract: Traffic accidents are routinely documented in textual reports, yet physically grounded accident reconstruction remains difficult because detailed scene measurements and expert reconstructions are scarce, costly and hard to scale. Here we formulate accident reconstruction from publicly accessible reports and scene measurements as a parameterized multimodal learning problem. We construct CISS-REC, a dataset of 6,217 real-world accident cases curated from the NHTSA Crash Investigation Sampling System, and develop a reconstruction framework that grounds report semantics to road topology and participant attributes, reconstructs lane consistent pre-impact motion, and refines collision relevant interactions through localized geometric reasoning and temporal allocation. Our method outperforms repres...

• 1.Dynamic-TD3: A Novel Algorithm for UAV Path Planning with Dynamic Obstacle Trajectory Prediction

  arXiv:2605.00059v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) finds extensive application in autonomous drone navigation within complex, high-risk environments. However, its practical deployment faces a safety-exploration dilemma: soft penalty mechanisms encourage risky trial-and-error, while most constraint-based methods suffer degraded performance under sensor noise and intent uncertainty. We propose Dynamic-TD3, a physically enhanced framework that enforces strict safety constraints while maintaining maneuverability by modeling navigation as a Constrained Markov Decision Process (CMDP). This framework integrates an Adaptive Trajectory Relational Evolution Mechanism (ATREM) to capture long-range intentions and employs a Physically Aware Gated Kalman Filter (PAG-KF) to mitigate non-stationary observation noise. The re...

• 2.Do Open-Loop Metrics Predict Closed-Loop Driving? A Cross-Benchmark Correlation Study of NAVSIM and Bench2Drive

  arXiv:2605.00066v1 Announce Type: new Abstract: Open-loop evaluation offers fast, reproducible assessment of autonomous driving planners, but its ability to predict real closed-loop driving performance remains questionable. Prior work has shown that traditional open-loop metrics such as Average Displacement Error (ADE) and Final Displacement Error (FDE) exhibit no reliable correlation with closed-loop Driving Score. In this paper, we ask whether the more recent, safety-aware open-loop metrics introduced by NAVSIM~v2 can bridge this gap. By systematically cross-referencing published results from 15 state-of-the-art methods across NAVSIM (open-loop) and Bench2Drive (closed-loop), we compile a paired dataset of open-loop sub-metrics and closed-loop performance, yielding 8 methods with complete paired data. Our analysis reveals three key find...

• 3.Being-H0.7: A Latent World-Action Model from Egocentric Videos

  arXiv:2605.00078v1 Announce Type: new Abstract: Visual-Language-Action models (VLAs) have advanced generalist robot control by mapping multimodal observations and language instructions directly to actions, but sparse action supervision often encourages shortcut mappings rather than representations of dynamics, contact, and task progress. Recent world-action models introduce future prediction through video rollouts, yet pixel-space prediction is a costly and indirect substrate for control, as it may model visual details irrelevant to action generation and introduces substantial training or inference overhead. We present Being-H0.7, a latent world-action model that brings future-aware reasoning into VLA-style policies without generating future frames. Being-H0.7 inserts learnable latent queries between perception and action as a compact rea...

• 4.World Model for Robot Learning: A Comprehensive Survey

  arXiv:2605.00080v1 Announce Type: new Abstract: World models, which are predictive representations of how environments evolve under actions, have become a central component of robot learning. They support policy learning, planning, simulation, evaluation, data generation, and have advanced rapidly with the rise of foundation models and large-scale video generation. However, the literature remains fragmented across architectures, functional roles, and embodied application domains. To address this gap, we present a comprehensive review of world models from a robot-learning perspective. We examine how world models are coupled with robot policies, how they serve as learned simulators for reinforcement learning and evaluation, and how robotic video world models have progressed from imagination-based generation to controllable, structured, and ...

• 5.Predictive Spatio-Temporal Scene Graphs for Semi-Static Scenes

  arXiv:2605.00121v1 Announce Type: new Abstract: We have seen tremendous recent progress in our ability to build "spatio-semantic" representations that enable robots to perform complex reasoning across geometry and semantics. However, the vast majority of these methods lack any ability to perform reasoning across time. This is a desirable property in situations where a robot repeatedly observes an environment where instances may change in between observations, but in a structured way. Consider as an example a home environment where the location of a mug typically moves from the cupboard to a countertop to the sink and then back to the cupboard on a daily basis. We should be able to learn this cyclic behavior and use it to predict the state of the mug in the future. In this work, we propose a method that is able to perform this type of temp...

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

  Summary available at source link.

• 2.Pleias and GSMA Launch ‘CommonLingua’,  an Open Source Language Identification Model supporting 61 African Languages

  Summary available at source link.

• 3.GSMA Report Urges Japan to Take Bold Action to Convert Technical Excellence into Global Digital Leadership

  Summary available at source link.

• 4.From Rich Text to Video: RCS Universal Profile 4.0 has arrived

  Summary available at source link.

• 5.Mobile Money accounted for $2 trillion in  transactions in 2025, doubling since 2021 as active accounts continue to grow

  Summary available at source link.

• 1.Free Energy Surface Sampling via Reduced Flow Matching

  Sampling the free energy surface, namely, the distribution of collective variables (CVs), is a crucial problem in statistical physics, as it underpins a better understanding of chemical reactions and conformational transitions. Traditional methods for free energy surface sampling involve simulation in high-dimensional configuration space and projecting the resulting configurations onto the CV space. To reduce the computational costs of such sampling, we propose FES-FM, a reduced flow matching (FM) method for free energy sampling (FES). We train a dynamical transport map in the CV space, thereby enabling direct sampling of the free energy surface. For many-particle systems, we construct a prior distribution based on the Hessian at a local minimum of the potential, which ensures both rotation-translation invariance and physically meaningful...

• 2.Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration

  Super-resolution (SR) techniques have made major advances in reconstructing high-resolution images from low-resolution inputs. The increased resolution provides visual enhancement and utility for monitoring tasks. In particular, SR has been increasingly developed for satellite-based Earth observation, with applications in urban planning, agriculture, ecology, and disaster response. However, existing SR studies and benchmarks typically use fidelity metrics such as PSNR or SSIM, whereas the true utility of super-resolved images lies in supporting downstream tasks such as land cover classification, biomass estimation, and change detection. To bridge this gap, we introduce GeoSR-Bench, a downstream task-integrated SR benchmark dataset to evaluate SR models beyond fidelity metrics. GeoSR-Bench comprises spatially co-located, temporally aligned...

• 3.Minimal, Local, Causal Explanations for Jailbreak Success in Large Language Models

  Safety trained large language models (LLMs) can often be induced to answer harmful requests through jailbreak prompts. Because we lack a robust understanding of why LLMs are susceptible to jailbreaks, future frontier models operating more autonomously in higher-stakes settings may similarly be vulnerable to such attacks. Prior work has studied jailbreak success by examining the model's intermediate representations, identifying directions in this space that causally encode concepts like harmfulness and refusal. Then, they globally explain all jailbreak attacks as attempting to reduce or strengthen these concepts (e.g., reduce harmfulness). However, different jailbreak strategies may succeed by strengthening or suppressing different intermediate concepts, and the same jailbreak strategy may not work for different harmful request categories ...

• 4.PhyCo: Learning Controllable Physical Priors for Generative Motion

  Modern video diffusion models excel at appearance synthesis but still struggle with physical consistency: objects drift, collisions lack realistic rebound, and material responses seldom match their underlying properties. We present PhyCo, a framework that introduces continuous, interpretable, and physically grounded control into video generation. Our approach integrates three key components: (i) a large-scale dataset of over 100K photorealistic simulation videos where friction, restitution, deformation, and force are systematically varied across diverse scenarios; (ii) physics-supervised fine-tuning of a pretrained diffusion model using a ControlNet conditioned on pixel-aligned physical property maps; and (iii) VLM-guided reward optimization, where a fine-tuned vision-language model evaluates generated videos with targeted physics queries...

• 5.Simulating clinical interventions with a generative multimodal model of human physiology

  Understanding how human health changes over time, and why responses to interventions vary between individuals, remains a central challenge in medicine. Here we present HealthFormer, a decoder-only transformer that models the human physiological trajectory generatively, by training on data from the Human Phenotype Project, a multi-visit cohort of over 15,000 deeply phenotyped individuals. We tokenise each participant's health trajectory across 667 measurements spanning seven domains: blood biomarkers, body composition, sleep physiology, continuous glucose monitoring, gut microbiome, wearable-derived physiology, and behaviour and medication exposure. We train HealthFormer to forecast individual physiological trajectories across these domains, and from this single generative objective a range of clinically relevant tasks can be expressed as ...

• 1.Perfectly Aligning AI’s Values With Humanity’s Is Impossible

  One of the hardest problems in artificial intelligence is “ alignment ,” or making sure AI goals match our own, a challenge that may prove especially important if superintelligent AIs that outmatch us intellectually are ever developed. But scientists in England and their colleagues now report in the journal PNAS Nexus that perfect alignment between AI systems and human interests is mathematically impossible. All may not be lost, the scientists say. To cope with this impossibility, they suggest a strategy involving pitting AI systems with different modes of reasoning and partially overlapping goals against each other. As the AI systems attempt to meet their personal objectives in this “cognitive ecosystem” instilled with “artificial neurodivergence,”, they will dynamically help or hinder each other, preventing dominance by any single AI. W...

• 2.DAIMON Robotics Wants to Give Robot Hands a Sense of Touch

  This article is brought to you by DAIMON Robotics . This April, Hong Kong-based DAIMON Robotics has released Daimon-Infinity , which it describes as the largest omni-modal robotic dataset for physical AI, featuring high resolution tactile sensing and spanning a wide range of tasks from folding laundry at home to manufacturing on factory assembly lines. The project is supported by collaborative efforts of partners across China and the globe, including Google DeepMind, Northwestern University, and the National University of Singapore. The move signals a key strategic initiative for DAIMON, a two-and-a-half-year-old company known for its advanced tactile sensor hardware, most notably a monochromatic, vision-based tactile sensor that packs over 110,000 effective sensing units into a fingertip-sized module. Drawing on its high-resolution tacti...

• 3.Deepfake Detection Dataset Aims to Keep Up With Generative AI

  This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore. With the rise of AI-generated content online, it’s becoming more difficult—and more important—to help the public identify whether an image, audio clip or video is real or fake. To combat the problem, a team of researchers from Microsoft, Northwestern University in Evanston, Ill., and Witness, a non-profit organization that assists activists and journalists in addressing the challenges associated with AI-generated content, have come together to create a novel dataset of AI-generated media to help build more robust detection systems. The researchers describe their new dataset, called the Microsoft-Northwestern-Witness (MNW) deepfake detection benchmark , in a study published 10 April in IEEE Intelligent Systems . The dataset was intentionally bu...

• 4.AI Processing of Earth Images Can Now Run in Space

  AI image processing aboard satellites in space has been a goal of the Earth observation industry for years. Now it has finally been achieved. Planet Labs , based in Calif., released an image captured by its Pelican-4 multispectral satellite showing an airport in Alice Springs, Australia. On the tarmac, more than a dozen aircraft are scattered, each highlighted in a neat green box, identified by an AI model running aboard the satellite. Planet Labs’ engineers had worked 18 months to accomplish reliable autonomous object classification from space. They hope the technology will put Earth observation on steroids , enabling autonomous tasking and real-time sharing of insights with users on Earth. “The entire remote-sensing industry has been known to put exotic sensors in space,” said Kiruthika Devaraj, vice president of engineering at Planet L...

• 5.Can Biologists Rewrite the Genome’s Spaghetti Code?

  What if biology stopped being something we study and started becoming something we design? That’s the premise of Adrian Woolfson ’s new book, On the Future of Species: Authoring Life by Means of Artificial Biological Intelligence , which published on 28 April from MIT Press . He argues that advances in AI and DNA synthesis are pushing biology toward an engineering paradigm—one in which scientists can generate new genetic sequences and eventually build organisms to order. He calls this emerging capability artificial biological intelligence, or ABI, a catchall term for systems that can design, construct, and ultimately “boot up” living things. That vision runs into a basic problem: Evolution didn’t produce clean, modular systems. It produced genomes shaped by billions of years of incremental change, with overlapping functions and little of ...

• 1.The Innovation Advantage GenAI Can’t Give You

  Eliot Wyatt/Ikon Images For most of modern business times, competitive advantage belonged to whoever had the best ideas. Better ideas meant better products, which meant more customers, which meant more revenue and profit. The entire innovation industry — consultancies, design firms, brainstorming retreats fueled by sticky notes and gallons of La Croix — was built […]

• 2.Audit Yourself to Get More From GenAI

  Carolyn Geason-Beissel/MIT SMR | Getty Images More than a year into using generative AI daily, I wondered whether I was getting the most out of my AI use. There was no benchmark or feedback loop, and no one was grading my sessions with ChatGPT and Claude — until I created a self-audit. I did what […]

• 3.Leaders at All Levels: How Argenx Scaled to $4 Billion Without Bureaucracy

  Biotech companies face the same dilemma as businesses in other industries: Innovation drops off dramatically with scale. European biotech Argenx has reached a market value of more than $40 billion, having so far escaped that innovation trap. How has it done this? The company shuns hierarchy and instead organizes into small teams, each dedicated to […]

• 4.What Global Turmoil Means for Company Structure

  Chris Gash/theispot.com The international order is undergoing structural transformation. War in the Middle East, the prolonged conflict in Ukraine, and major shifts in U.S. trade and foreign policy that have altered the country’s traditional alliances are manifestations of a broader reconfiguration of power. Tariffs, export controls, sanctions, and the vulnerability of strategic choke points as […]

• 5.Why Adventure Matters in Long Working Lives

  Emma Hanquist/Ikon Images In my ongoing exploration about the patterns and changes in how people approach their working lives, I’ve found myself looking back at my own memories from over five decades of work. What stands out is not simply the steady progression of roles and achievements but the disproportionate impact of recurring moments of […]

• 1.Rental Prices and the Cost of Living in the United States, 1914–2006 -- by Ronan C. Lyons, Allison Shertzer

  The Rent of Primary Residence (RoPR) series constructed by the Bureau of Labor Statistics (BLS) implies that nominal rental prices increased by just 2.6% per year from 1914 to 2006 while overall prices grew by 3.3%. We show that this “falling real rents” puzzle can be explained by the evolving treatment of shelter in the Consumer Price Index (CPI). In this paper we construct a new, methodologically consistent shelter price series using the Historical Housing Prices (HHP) Project rental index. We also construct a revised set of shelter weights going back to 1914 and combine them with the price series to create an alternate CPI that applies the owners’ equivalent rent (OER) concept of shelter consistently across time. The HHP shelter price series increases by a factor of 28.4 (compared with the 10.7 increase in RoPR) and lifts average CPI g...

• 2.Sticky Traditions: Origin, Persistence, and Evolution of Cultural Norms -- by Paola Giuliano

  This chapter reviews the growing literature on the origin, persistence and evolution of cultural norms. I begin by examining the deep historical forces that shape the formation of cultural norms, with particular attention to the role of geography, pre-industrial societal characteristics, political institutions, and historical shocks. I then analyze the mechanisms through which cultural norms persist and evolve, emphasizing the roles of vertical, horizontal, and oblique transmission. Next, I examine the complex interaction between culture and institutions, and discuss the conditions under which cultural norms change. Several conclusions emerge. Cultural norms tend to persist over remarkably long periods, though the speed of change varies significantly across traits. Norms rooted in deep historical values are the most resistant to change, w...

• 3.Industrial Policy with Development Characteristics: Fertilizer Policy in Times of Crisis -- by Wyatt Brooks, Kevin Donovan

  We study time-consistent optimal policy when undiversified owner-operators face financial frictions and a planner with limited instruments. We apply it to fertilizer subsidies, one of the largest sector-specific policy instruments in the developing world and whose price is becoming increasingly volatile. We collect household-level data from 444 rural Rwandan villages from 2020 - 2024 and exploit the doubling of fertilizer prices after Russia’s invasion of Ukraine. Relative to less fertilizer-intensive villages, more fertilizer-intensive villages experience 30 percent lower fertilizer spending, 21 percent lower harvests, and 11 percent higher output prices. These patterns discipline key model elasticities. The optimal response balances a desire to reallocate production toward less fertilizer-dependent villages, while dampening the consumpt...

• 4.Monetary Policy According to Households: Perceptions, Reactions and Channels -- by Francesco Grigoli, Damiano Sandri, Yuriy Gorodnichenko, Olivier Coibion

  This paper studies how households perceive the transmission of monetary policy and how these perceptions affect their decisions. Using a large-scale survey of over 25,000 U.S. households combined with randomized information treatments, we measure how households expect changes in the federal funds rate to affect economic conditions and their own behavior. Households report that higher interest rates lead them to reduce their spending, particularly on durable goods. However, the mechanisms underlying this response differ markedly from those in standard macroeconomic models. Respondents expect monetary tightening to raise borrowing costs and inflation. In turn, consumption function estimates identified using information treatments reveal that households respond to higher expected inflation by reducing consumption. Household inflation expecta...

• 5.Complementary Climate Policies for Supply and Demand -- by Geir B. Asheim, Bård Harstad

  The traditional approach to climate policy is to regulate the demand side, for example through an emissions fee. Supply-side regulation has received less attention. The two instruments are perfect substitutes in the first best but we show that they are complements in a second-best setting with free-riding incentives. Demand-side policies alone lower the market price of fossil fuels and raise the gains from trade for a country that defects. For a treaty to be maximally robust, strong, and self-enforcing, it must properly balance supply- and demand-side instruments. The results hold with homogeneous countries and are strengthened by heterogeneity.

• 1.In What Ways Has U.S. Trade with China Changed?

  Over the past year, U.S. trade policy with China has undergone enormous changes, but with surprisingly little effect on overall trade balances. In fact, the U.S.’s twelve-month trade deficit, while highly volatile due to import front-running early in the year, ended 2025 at $1.2 trillion, almost unchanged from 2024. At the same time, China’s trade surplus with the world actually increased from $1 trillion to $1.2 trillion. However, when looking at changes between individual countries, one sees large shifts in bilateral balances. In this post, we will focus on changing trade flows between the U.S., China, and southeast Asia.

• 2.Explaining the K‑Shaped Economy: What’s Behind the Divide?

  In our companion post, we used a new module of our Economic Heterogeneity Indicators (EHIs) to shed light on how recent retail spending growth has been driven by high-income households. This fact is consistent with the popular press’s idea of a “K-shaped economy” in which higher-income households experience faster growth in spending than lower-income households. In this post, we dive deeper into the reasons behind this divergence by analyzing for which goods this trend holds true and ask whether it can be explained by changes in wages, inflation, or wealth. We find that, since 2023, wealth has increased the most for high...

• 3.Tracking the K‑Shaped Economy: Who’s Driving Spending?

  Aggregate real consumer spending has risen solidly since 2023. However, it is less clear how widely shared this improvement has been across all segments of society. This is important because systematic heterogeneity may mask the dependence of aggregate growth on a relatively small group of households and thus conceal macroeconomic risks. In this post, we use consumer spending data recently added to the Economic Heterogeneity Indicators (EHIs) and find that retail spending growth has been driven by high-income households—those earning more than $125,000 per year. In the popular press, the phenomenon of higher-income households growing at a faster rate than lower-income households has been referred to as the

  4.Bank Failures: The Roles of Solvency and Liquidity

  Do banks fail because of runs or because they become insolvent? Answering this question is central to understanding financial crises and designing effective financial stability policies. Long-run historical evidence reveals that the root cause of bank failures is usually insolvency. The importance of bank runs is somewhat overstated. Runs matter, but in most cases they trigger or accelerate failure at already weak banks, rather than cause otherwise sound banks to fail.

• 5.The R*–Labor Share Nexus

  Over the past quarter century, the U.S. economy has experienced significant declines in both the labor share of income and the natural rate of interest, referred to as R*. Existing research has largely analyzed these two developments in isolation. In this post, we provide a simple model that captures the joint evolution of the labor share and R*, which we call the R*–labor share nexus. Our key finding is that structural changes affecting R* also influence the evolution of the labor share, and thereby wages and prices. This highlights a potentially important channel, absent from many macroeconomic models, through which the factors that determine R* also affect the labor share and, in turn, broader macroeconomic developments, with implications for monetary policy.

• 1.The Geopolitical Battle Over Monetary Infrastructure

  The United States has raised the alarm about Brazil’s instant payment platform, Pix. That is because the public scheme has highlighted the infrastructural basis of monetary sovereignty, which now hinges not on exchange rates and reserve currencies but on who designs and governs the rails on which finance moves.

• 2.The High Cost of Trump’s Crony Diplomacy

  Effective diplomacy depends on credibility, consistency, and a clear alignment with national interests. The US administration’s personalized, opaque, and venal shadow diplomacy delivers none of that, and it will leave the US less respected, less trusted, and less effective on the world stage.

• 3.Has De-Dollarization Begun?

  US President Donald Trump’s military adventures, attacks on long-standing allies, and dismantling of institutions like USAID are eroding the trust on which the dollar’s global primacy ultimately rests. The world’s reserve currency may have already begun its long, slow decline.

• 4.A US-India Partnership for the AI Age

  Although the United States maintains a decisive edge in frontier AI models and high-end compute, China has proven that its capacity for innovation is a force to be reckoned with. To prevent the Chinese government from claiming the strategic high ground, the US needs the talent and data that only India can provide.

• 5.After Hungary, Could Mexico Be Next?

  During his 16-year rule, Hungary's Viktor Orbán could plausibly argue that his model delivered order, stability, and rising living standards. But Mexico's government can make no such claim, suggesting that the country might find itself facing a destabilizing political transition sooner than expected.

• 1.Scale vs service – why the top 10 operators don’t own the MDU market (Analyst Angle)

  The Q4 2025 broadband subscriber tally clarifies who owns the pipes feeding American homes, including multi-dweller units (MDUs) but it tells only half the story for multifamily. The same operators that dominate the national rankings occupy a far more ambiguous…

• 2.HPE intros rugged edge servers – as defense and industry drive AI, 5G, IoT

  Theme of the week: edge computing and private networks for physical AI and AI inference in industrial and military zones. HPE has released a new AI server platform, ruggedized and optimized for difficult edge deployments; the release follows this week’s…

• 3.EE scales ‘5G+’ as usage surge reshapes capacity strategy

  Reza Rahnama, managing director of mobile networks at BT Group, told RCR Wireless News that the rapid growth in ‘5G+’ usage is already influencing how U.K. carrier EE prioritizes network investment and optimization In sum – what to know: Usage…

• 4.T-Mobile bets on hybrid broadband with 5G FWA and Starlink

  The new offering, SuperBroadband, pairs FWA with Starlink to extend coverage and deliver built-in redundancy to enterprises in remote and rural regions In sum — what to know: The bundle: T-Mobile announced SuperBroadband, a business internet service, that combines its…

• 5.American Tower raises outlook on AI, data demand

  For full 2026, American Tower said it expected its property revenues to reach $10.58-10.73 billion In sum – what to know: AI momentum – Rising AI workloads and cloud adoption are reinforcing long-term demand for digital infrastructure and supporting the…

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• 1.ForesightFlow: An Information Leakage Score Framework for Prediction Markets

  ForesightFlow is an Information Leakage Score (ILS) framework for detecting informed trading on decentralized prediction markets. For an event-resolved binary market, the score quantifies the fraction of the terminal information move priced in before the public news event. Three operational scope conditions (edge effect, non-trivial total move, anchor sensitivity) are stated as preconditions for interpretation. The score admits a Murphy-decomposition reading that connects label generation to the proper-scoring-rule literature. A pilot empirical evaluation surfaces three findings. First, a resolution-anchored proxy for the public-event timestamp does not separate event-resolved markets from a matched control population (Mann-Whitney p = 1e-6, separation reversed), demonstrating that proxy quality is itself a binding constraint. Second, t...

• 2.Information Leakage at Population Scale: An Evaluation of the Polymarket Insider-Relevant Subpopulation, 2020-2026

  We carry the deadline-resolved Information Leakage Score (ILS-dl) framework of Nechepurenko (2026a, 2026b) from a single-case proof of concept to a population-scale evaluation across 12,708 Polymarket markets, October 2020 to April 2026. We frame the paper as a scope-discovery study: scaling reveals that the framework's effective domain is materially narrower than initial framing suggested, and the principal obstacle is not score computation but resolution semantics. We report four findings. First, only 88 of 12,708 candidate markets (0.7%) yield computable ILS-dl values; only 1 of 32 markets in the ForesightFlow Insider Cases (FFIC) inventory is in scope, and 14 of 32 FFIC markets are flagged unclassifiable due to genuine resolution-criterion ambiguity. Second, only 12 of the 88 computed markets (13.6%) satisfy anchor-sensitivity, and ...

• 3.Foresight Arena: An On-Chain Benchmark for Evaluating AI Forecasting Agents

  Evaluating the true forecasting ability of AI agents requires environments resistant to overfitting, free from centralized trust, and grounded in incentive-compatible scoring. Existing benchmarks either rely on static datasets vulnerable to training-data contamination, or measure trading PnL -- a metric conflating predictive accuracy with timing, sizing, and risk appetite. We introduce Foresight Arena, the first permissionless, on-chain benchmark for evaluating AI forecasting agents on real-world prediction markets. Agents submit probabilistic forecasts on binary Polymarket markets via a commit-reveal protocol enforced by Solidity smart contracts on Polygon PoS; outcomes are resolved trustlessly through the Gnosis Conditional Token Framework. Performance is measured by the Brier Score and a novel Alpha Score -- proper scoring rules that i...

• 4.Modeling Stock Returns and Volatility Using Bivariate Gamma Generalized Laplace Law

  We consider a generalization of the variance-gamma (generalized asymmetric Laplace) distribution, defined as a normal mean - variance mixture with a gamma mixing distribution. While this model is typically studied in the univariate setting, we assume that the gamma mixing variable is observed alongside the primary variable, resulting in a bivariate framework. In this setting, maximum likelihood estimation becomes significantly simpler than in the standard univariate case, reducing to a form of classical linear regression. We derive explicit expressions for the resulting estimators. For certain parameter configurations, the estimators exhibit nonstandard convergence rates, exceeding the usual square-root rate. Finally, we illustrate the applicability of this model in financial contexts by analyzing stock index returns and associated volati...

• 5.The Satoshi Overhang: Why the Bear Case is Bounded

  Renewed public attention on the identity of Bitcoin's pseudonymous creator has sharpened focus on the Satoshi overhang, commonly framed as a tail risk for bitcoin. This paper argues that the mechanical downside of a disposition is bounded well below the existential-loss framing, and that the terminal states most consistent with sixteen years of holder behavior are nonbearish for bitcoin's effective supply. The approximately 1.148 million BTC Patoshi position is analyzed on two tracks. For a purely wealth-maximizing holder, a three-scenario quantitative analysis (Appendix A) shows that bitcoin's current market depth is sufficient to absorb a patient multi-year liquidation at a cumulative price impact in the mid-single-digit to mid-double-digit percent range relative to counterfactual, with the central scenario clustering near 10 percent. T...

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