Babak's Daily Briefing

Monday, April 13, 2026

Sources: 16 | Total Articles: 64

• 1.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.

• 2.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.

• 3.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.

• 4.ODC’s $45M raise signals a bigger shift in AI-RAN, from network optimization to edge intelligence

  ORAN Development Company said it has closed a $45 million Series A backed by Booz Allen, Cisco Investments, Nokia, NVIDIA, AT&T, MTN and Telecom Italia to scale its U.S.-based Odyssey platform, which it positions as an AI-native RAN architecture combining communications, sensing and edge intelligence. The company said it plans to accelerate commercial deployment through 2026.

• 5.Lockheed Martin’s NetSense points to a bigger shift: 5G as drone-detection infrastructure

  Lockheed Martin’s latest NetSense prototype suggests that commercial 5G infrastructure could play a growing role in drone detection, adding momentum to the broader move toward sensing-enabled wireless networks.

• 1.Drift and selection in LLM text ecosystems

  arXiv:2604.08554v1 Announce Type: new Abstract: The public text record -- the material from which both people and AI systems now learn -- is increasingly shaped by its own outputs. Generated text enters the public record, later agents learn from it, and the cycle repeats. Here we develop an exactly solvable mathematical framework for this recursive process, based on variable-order $n$-gram agents, and separate two forces acting on the public corpus. The first is drift: unfiltered reuse progressively removes rare forms, and in the infinite-corpus limit we characterise the stable distributions exactly. The second is selection: publication, ranking and verification filter what enters the record, and the outcome depends on what is selected. When publication merely reflects the statistical status quo, the corpus converges to a shallow state i...

• 2.SynDocDis: A Metadata-Driven Framework for Generating Synthetic Physician Discussions Using Large Language Models

  arXiv:2604.08555v1 Announce Type: new Abstract: Physician-physician discussions of patient cases represent a rich source of clinical knowledge and reasoning that could feed AI agents to enrich and even participate in subsequent interactions. However, privacy regulations and ethical considerations severely restrict access to such data. While synthetic data generation using Large Language Models offers a promising alternative, existing approaches primarily focus on patient-physician interactions or structured medical records, leaving a significant gap in physician-to-physician communication synthesis. We present SynDocDis, a novel framework that combines structured prompting techniques with privacy-preserving de-identified case metadata to generate clinically accurate physician-to-physician dialogues. Evaluation by five practicing physicia...

• 3.EMA Is Not All You Need: Mapping the Boundary Between Structure and Content in Recurrent Context

  arXiv:2604.08556v1 Announce Type: new Abstract: What exactly do efficient sequence models gain over simple temporal averaging? We use exponential moving average (EMA) traces, the simplest recurrent context (no gating, no content-based retrieval), as a controlled probe to map the boundary between what fixed-coefficient accumulation can and cannot represent. EMA traces encode temporal structure: a Hebbian architecture with multi-timescale traces achieves 96% of a supervised BiGRU on grammatical role assignment with zero labels, surpassing the supervised model on structure-dependent roles. EMA traces destroy token identity: a 130M-parameter language model using only EMA context reaches C4 perplexity 260 (8x GPT-2), and a predictor ablation (replacing the linear predictor with full softmax attention) yields identical loss, localizing the ent...

• 4.Re-Mask and Redirect: Exploiting Denoising Irreversibility in Diffusion Language Models

  arXiv:2604.08557v1 Announce Type: new Abstract: Diffusion-based language models (dLLMs) generate text by iteratively denoising masked token sequences. We show that their safety alignment rests on a single fragile assumption: that the denoising schedule is monotonic and committed tokens are never re-evaluated. Safety-aligned dLLMs commit refusal tokens within the first 8-16 of 64 denoising steps, and the schedule treats these commitments as permanent. A trivial two-step intervention - re-masking these tokens and injecting a 12-token affirmative prefix - achieves 76.1% ASR on HarmBench (n=159, Lg=128) against LLaDA-8B-Instruct and 81.8% ASR (n=159) against Dream-7B-Instruct, without any gradient computation or adversarial search. The simplicity of this exploit is itself the central finding: augmenting with gradient-optimized perturbation v...

• 5.WAND: Windowed Attention and Knowledge Distillation for Efficient Autoregressive Text-to-Speech Models

  arXiv:2604.08558v1 Announce Type: new Abstract: Recent decoder-only autoregressive text-to-speech (AR-TTS) models produce high-fidelity speech, but their memory and compute costs scale quadratically with sequence length due to full self-attention. In this paper, we propose WAND, Windowed Attention and Knowledge Distillation, a framework that adapts pretrained AR-TTS models to operate with constant computational and memory complexity. WAND separates the attention mechanism into two: persistent global attention over conditioning tokens and local sliding-window attention over generated tokens. To stabilize fine-tuning, we employ a curriculum learning strategy that progressively tightens the attention window. We further utilize knowledge distillation from a full-attention teacher to recover high-fidelity synthesis quality with high data effi...

• 1.GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback

  arXiv:2604.08553v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic understanding ability on textual node features. However, their effectiveness as predictors in the low-resource setting, where labeled nodes are severely limited and scarce, remains constrained since fine-tuning LLMs usually requires sufficient labeled data, especially when the TAG shows complex structural patterns. In essence, this paper targets two key challenges: (i) the difficulty of generating and selecting reliable pseudo labels on TAGs for LLMs, and (ii) the need to mitigate potential label noise when fine-tuning LLMs with pseudo labels. To counter the challenges, we propose a new framework, GNN-as-Judge, which can unleash the power of LLMs for few-shot semi-superv...

• 2.Memory-Guided Trust-Region Bayesian Optimization (MG-TuRBO) for High Dimensions

  arXiv:2604.08569v1 Announce Type: new Abstract: Traffic simulation and digital-twin calibration is a challenging optimization problem with a limited simulation budget. Each trial requires an expensive simulation run, and the relationship between calibration inputs and model error is often nonconvex, and noisy. The problem becomes more difficult as the number of calibration parameters increases. We compare a commonly used automatic calibration method, a genetic algorithm (GA), with Bayesian optimization methods (BOMs): classical Bayesian optimization (BO), Trust-Region BO (TuRBO), Multi-TuRBO, and a proposed Memory-Guided TuRBO (MG-TuRBO) method. We compare performance on 2 real-world traffic simulation calibration problems with 14 and 84 decision variables, representing lower- and higher-dimensional (14D and 84D) settings. For BOMs, we st...

• 3.QuanBench+: A Unified Multi-Framework Benchmark for LLM-Based Quantum Code Generation

  arXiv:2604.08570v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used for code generation, yet quantum code generation is still evaluated mostly within single frameworks, making it difficult to separate quantum reasoning from framework familiarity. We introduce QuanBench+, a unified benchmark spanning Qiskit, PennyLane, and Cirq, with 42 aligned tasks covering quantum algorithms, gate decomposition, and state preparation. We evaluate models with executable functional tests, report Pass@1 and Pass@5, and use KL-divergence-based acceptance for probabilistic outputs. We additionally study Pass@1 after feedback-based repair, where a model may revise code after a runtime error or wrong answer. Across frameworks, the strongest one-shot scores reach 59.5% in Qiskit, 54.8% in Cirq, and 42.9% in PennyLane; with feedbac...

• 4.Robust Reasoning Benchmark

  arXiv:2604.08571v1 Announce Type: new Abstract: While Large Language Models (LLMs) achieve high performance on standard mathematical benchmarks, their underlying reasoning processes remain highly overfit to standard textual formatting. We propose a perturbation pipeline consisting of 14 techniques to evaluate robustness of LLM reasoning. We apply this pipeline to AIME 2024 dataset and evalute 8 state-of-the-art models on the resulting benchmark. While frontier models exhibit resilience, open weights reasoning models suffer catastrophic collapses (up to 55% average accuracy drops across perturbations and up to 100% on some), exposing structural fragility. To further disentangle mechanical parsing failures from downstream reasoning failures, we strictly isolate the models' working memory capacity by forcing models to solve multiple unpertur...

• 5.Ranked Activation Shift for Post-Hoc Out-of-Distribution Detection

  arXiv:2604.08572v1 Announce Type: new Abstract: State-of-the-art post-hoc out-of-distribution detection methods rely on intermediate layer activation editing. However, they exhibit inconsistent performance across datasets and models. We show that this instability is driven by differences in the activation distributions, and identify a failure mode of scaling-based methods that arises when penultimate layer activations are not rectified. Motivated by this analysis, we propose \ours, a hyperparameter-free post-hoc method that replaces sorted activation magnitudes with a fixed in-distribution reference profile. Our simple plug-and-play method shows strong and consistent performance across datasets and architectures without assumptions on the penultimate layer activation function, and without requiring any hyperparameter tuning, while preserv...

• 1.LEGO: Latent-space Exploration for Geometry-aware Optimization of Humanoid Kinematic Design

  arXiv:2604.08636v1 Announce Type: new Abstract: Designing robot morphologies and kinematics has traditionally relied on human intuition, with little systematic foundation. Motion-design co-optimization offers a promising path toward automation, but two major challenges remain: (i) the vast, unstructured design space and (ii) the difficulty of constructing task-specific loss functions. We propose a new paradigm that minimizes human involvement by (i) learning the design search space from existing mechanical designs, rather than hand-crafting it, and (ii) defining the loss directly from human motion data via motion retargeting and Procrustes analysis. Using screw-theory-based joint axis representation and isometric manifold learning, we construct a compact, geometry-preserving latent space of humanoid upper body designs in which optimizatio...

• 2.Generative Simulation for Policy Learning in Physical Human-Robot Interaction

  arXiv:2604.08664v1 Announce Type: new Abstract: Developing autonomous physical human-robot interaction (pHRI) systems is limited by the scarcity of large-scale training data to learn robust robot behaviors for real-world applications. In this paper, we introduce a zero-shot "text2sim2real" generative simulation framework that automatically synthesizes diverse pHRI scenarios from high-level natural-language prompts. Leveraging Large Language Models (LLMs) and Vision-Language Models (VLMs), our pipeline procedurally generates soft-body human models, scene layouts, and robot motion trajectories for assistive tasks. We utilize this framework to autonomously collect large-scale synthetic demonstration datasets and then train vision-based imitation learning policies operating on segmented point clouds. We evaluate our approach through a user st...

• 3.Task-Aware Bimanual Affordance Prediction via VLM-Guided Semantic-Geometric Reasoning

  arXiv:2604.08726v1 Announce Type: new Abstract: Bimanual manipulation requires reasoning about where to interact with an object and which arm should perform each action, a joint affordance localization and arm allocation problem that geometry-only planners cannot resolve without semantic understanding of task intent. Existing approaches either treat affordance prediction as coarse part segmentation or rely on geometric heuristics for arm assignment, failing to jointly reason about task-relevant contact regions and arm allocation. We reframe bimanual manipulation as a joint affordance localization and arm allocation problem and propose a hierarchical framework for task-aware bimanual affordance prediction that leverages a Vision-Language Model (VLM) to generalize across object categories and task descriptions without requiring category-spe...

• 4.Toward Hardware-Agnostic Quadrupedal World Models via Morphology Conditioning

  arXiv:2604.08780v1 Announce Type: new Abstract: World models promise a paradigm shift in robotics, where an agent learns the underlying physics of its environment once to enable efficient planning and behavior learning. However, current world models are often hardware-locked specialists: a model trained on a Boston Dynamics Spot robot fails catastrophically on a Unitree Go1 due to the mismatch in kinematic and dynamic properties, as the model overfits to specific embodiment constraints rather than capturing the universal locomotion dynamics. Consequently, a slight change in actuator dynamics or limb length necessitates training a new model from scratch. In this work, we take a step towards a framework for training a generalizable Quadrupedal World Model (QWM) that disentangles environmental dynamics from robot morphology. We address the l...

• 5.One Interface, Many Robots: Unified Real-Time Low-Level Motion Planning for Collaborative Arms

  arXiv:2604.08787v1 Announce Type: new Abstract: This paper proposes a common interface for real-time low-level motion planning of collaborative robotic arms, aimed at enabling broader applicability and improved portability across heterogeneous hardware platforms. In previous work, we introduced WinGs Operating Studio (WOS), a middleware solution that abstracts diverse robotic components into uniform software resources and provides a broad suite of language-agnostic APIs. This paper specifically focuses on its minimal yet flexible interface for real-time end-effector trajectory control. By employing an n-degree polynomial interpolator in conjunction with a quadratic programming solver, the proposed method generates smooth, continuously differentiable trajectories with precise position, velocity, and acceleration profiles. We validate our a...

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

  Summary available at source link.

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

  Summary available at source link.

• 3.Strengthening the Global Fight Against Fraud and Scams – Takeaways from the Global Fraud Summit in Vienna

  Summary available at source link.

• 4.GSMA MWC26 Barcelona closes 20th anniversary edition

  Summary available at source link.

• 5.From Ambition to Execution: How Open Gateway Is Scaling the Global API Economy

  Summary available at source link.

• 1.Hidden in Plain Sight: Visual-to-Symbolic Analytical Solution Inference from Field Visualizations

  Recovering analytical solutions of physical fields from visual observations is a fundamental yet underexplored capability for AI-assisted scientific reasoning. We study visual-to-symbolic analytical solution inference (ViSA) for two-dimensional linear steady-state fields: given field visualizations (and first-order derivatives) plus minimal auxiliary metadata, the model must output a single executable SymPy expression with fully instantiated numeric constants. We introduce ViSA-R2 and align it with a self-verifying, solution-centric chain-of-thought pipeline that follows a physicist-like pathway: structural pattern recognition solution-family (ansatz) hypothesis parameter derivation consistency verification. We also release ViSA-Bench, a VLM-ready synthetic benchmark covering 30 linear steady-state scenarios with verifiable analytical/sym...

• 2.EfficientSign: An Attention-Enhanced Lightweight Architecture for Indian Sign Language Recognition

  How do you build a sign language recognizer that works on a phone? That question drove this work. We built EfficientSign, a lightweight model which takes EfficientNet-B0 and focuses on two attention modules (Squeeze-and-Excitation for channel focus, and a spatial attention layer that focuses on the hand gestures). We tested it against five other approaches on 12,637 images of Indian Sign Language alphabets, all 26 classes, using 5-fold cross-validation. EfficientSign achieves the accuracy of 99.94% (+/-0.05%), which matches the performance of ResNet18's 99.97% accuracy, but with 62% fewer parameters (4.2M vs 11.2M). We also experimented with feeding deep features (1,280-dimensional vectors pulled from EfficientNet-B0's pooling layer) into classical classifiers. SVM achieved the accuracy of 99.63%, Logistic Regression achieved the accuracy...

• 3.CrashSight: A Phase-Aware, Infrastructure-Centric Video Benchmark for Traffic Crash Scene Understanding and Reasoning

  Cooperative autonomous driving requires traffic scene understanding from both vehicle and infrastructure perspectives. While vision-language models (VLMs) show strong general reasoning capabilities, their performance in safety-critical traffic scenarios remains insufficiently evaluated due to the ego-vehicle focus of existing benchmarks. To bridge this gap, we present \textbf{CrashSight}, a large-scale vision-language benchmark for roadway crash understanding using real-world roadside camera data. The dataset comprises 250 crash videos, annotated with 13K multiple-choice question-answer pairs organized under a two-tier taxonomy. Tier 1 evaluates the visual grounding of scene context and involved parties, while Tier 2 probes higher-level reasoning, including crash mechanics, causal attribution, temporal progression, and post-crash outcomes...

• 4.Small-scale photonic Kolmogorov-Arnold networks using standard telecom nonlinear modules

  Photonic neural networks promise ultrafast inference, yet most architectures rely on linear optical meshes with electronic nonlinearities, reintroducing optical-electrical-optical bottlenecks. Here we introduce small-scale photonic Kolmogorov-Arnold networks (SSP-KANs) implemented entirely with standard telecommunications components. Each network edge employs a trainable nonlinear module composed of a Mach-Zehnder interferometer, semiconductor optical amplifier, and variable optical attenuators, providing a four-parameter transfer function derived from gain saturation and interferometric mixing. Despite this constrained expressivity, SSP-KANs comprising only a few optical modules achieve strong nonlinear inference performance across classification, regression, and image recognition tasks, approaching software baselines with significantly ...

• 5.HyperMem: Hypergraph Memory for Long-Term Conversations

  Long-term memory is essential for conversational agents to maintain coherence, track persistent tasks, and provide personalized interactions across extended dialogues. However, existing approaches as Retrieval-Augmented Generation (RAG) and graph-based memory mostly rely on pairwise relations, which can hardly capture high-order associations, i.e., joint dependencies among multiple elements, causing fragmented retrieval. To this end, we propose HyperMem, a hypergraph-based hierarchical memory architecture that explicitly models such associations using hyperedges. Particularly, HyperMem structures memory into three levels: topics, episodes, and facts, and groups related episodes and their facts via hyperedges, unifying scattered content into coherent units. Leveraging this structure, we design a hybrid lexical-semantic index and a coarse-t...

• 1.12 Graphs That Explain the State of AI in 2026

  The capabilities of leading AI models continue to accelerate and the largest AI companies, including OpenAI and Anthropic , are hurtling toward IPOs later this year. Yet resentment towards AI continues to simmer and in some cases has boiled over, especially in the United States, where local governments are beginning to embrace restrictions or outright bans on new data center development. It’s a lot to keep track of, but the 2026 edition of the AI Index from Stanford University’s Human-Centered Artificial Intelligence center pulls it off. The report, which comes in at over 400 pages, includes dozens of data points and graphs that approach the topic from multiple angles, from benchmark scores to investment and public perception. As in prior years (see our coverage from 2021 , 2022 , 2023 , 2024 , and 2025 ), we’ve read the report and identi...

• 2.GoZTASP: A Zero-Trust Platform for Governing Autonomous Systems at Mission Scale

  ZTASP is a mission-scale assurance and governance platform designed for autonomous systems operating in real-world environments. It integrates heterogeneous systems—including drones, robots, sensors, and human operators—into a unified zero-trust architecture. Through Secure Runtime Assurance (SRTA) and Secure Spatio-Temporal Reasoning (SSTR), ZTASP continuously verifies system integrity, enforces safety constraints, and enables resilient operation even under degraded conditions. ZTASP has progressed beyond conceptual design, with operational validation at Technology Readiness Level (TRL) 7 in mission critical environments. Core components, including Saluki secure flight controllers, have reached TRL8 and are deployed in customer systems. While initially developed for high-consequence mission environments, the same assurance challenges are...

• 3.AI Models Map the Colorado River’s Hard Choices

  The Colorado River begins as snow. Every spring, the mountain snowpack of the Rockies melts into streams that feed into reservoirs that supply 40 million people across seven U.S. states. The system has worked, more or less, for a century. That century is over. By some measures, 2026 is shaping up to be the worst year the river has seen since records began. Flows are down 20 percent from 2000 levels . Lake Powell, the reservoir straddling Utah and Arizona, may drop below the threshold for generating hydropower before the year is out . The negotiations between the seven states over how to share what’s left have collapsed twice , and the U.S. federal government is threatening to impose its own plan. While the states argue and the river shrinks, a growing set of machine learning tools is being deployed across the basin. Federal water managers...

• 4.Decentralized Training Can Help Solve AI’s Energy Woes

  Artificial intelligence harbors an enormous energy appetite. Such constant cravings are evident in the hefty carbon footprint of the data centers behind the AI boom and the steady increase over time of carbon emissions from training frontier AI models . No wonder big tech companies are warming up to nuclear energy , envisioning a future fueled by reliable, carbon-free sources. But while nuclear-powered data centers might still be years away, some in the research and industry spheres are taking action right now to curb AI’s growing energy demands. They’re tackling training as one of the most energy-intensive phases in a model’s life cycle, focusing their efforts on decentralization. Decentralization allocates model training across a network of independent nodes rather than relying on one platform or provider. It allows compute to go where ...

• 5.Why AI Systems Fail Quietly

  In late-stage testing of a distributed AI platform, engineers sometimes encounter a perplexing situation: every monitoring dashboard reads “healthy,” yet users report that the system’s decisions are slowly becoming wrong. Engineers are trained to recognize failure in familiar ways: a service crashes, a sensor stops responding, a constraint violation triggers a shutdown. Something breaks, and the system tells you. But a growing class of software failures looks very different. The system keeps running, logs appear normal, and monitoring dashboards stay green. Yet the system’s behavior quietly drifts away from what it was designed to do. This pattern is becoming more common as autonomy spreads across software systems. Quiet failure is emerging as one of the defining engineering challenges of autonomous systems because correctness now depends...

• 1.Managing Up: A Skill Set That Matters Now

  Carolyn Geason-Beissel/MIT SMR | Getty Images Are you skilled at managing up? If your talents are lacking when it comes to managing and dealing with the people above you in the organizational hierarchy, you can find yourself mired in some unpleasant and career-harming situations. Maybe you’re frustrated by a micromanaging supervisor or feeling marginalized by […]

• 2.The Trap That Skilled Negotiators Miss

  Brian Stauffer/theispot.com Say you walk into a car dealership determined to stay within budget. The salesperson shows you a car you like and quotes a price of $41,435. You know there’s room to negotiate, but when it’s time to counter, that first number quietly takes over. Your counteroffer, the concessions, and the final deal all […]

• 3.Rethink Responsibility in the Age of AI

  Mark Airs/Ikon Images Early one morning in 2018, a self-driving Uber vehicle fatally struck a pedestrian in Tempe, Arizona. The world had questions: Who was responsible? Was it the safety driver behind the wheel? The engineers who designed the algorithms? Uber’s leadership? Or the regulators who had allowed autonomous-vehicle testing? The inability to name a […]

• 4.Gain Consumer Insight With Generative AI

  Stuart Kinlough/Ikon Images Marketing leaders often face a dilemma: Deriving the insights they need in order to make confident decisions can cost tens of thousands of dollars and involve several months of data gathering and analysis, by which time market conditions may have shifted. Can generative AI fundamentally reshape this calculus? Drawing on recent research, […]

• 5.Disintegrating the Org Chart: ServiceNow’s Jacqui Canney

  In this episode of the Me, Myself, and AI podcast, Sam Ransbotham is joined by Jacqui Canney, chief people and AI enablement officer at ServiceNow. Jacqui outlines how the software company has embedded AI agents into processes like employee onboarding to automate tasks, personalize experiences, and free up people’s time to focus on higher-value work. […]

• 1.The Empathy Channel in Fertility -- by Sebastian Galiani, Raul A. Sosa

  Being around babies makes people want babies. We formalize this observation as the empathy channel: exposure to infants in the social environment activates neurobiological mechanisms that increase the desire for parenthood. As children become scarcer, this affective stimulus weakens, further eroding the motivation to have children. We embed the mechanism in a two-group overlapping-generations quantity-quality model. The empathy channel generates a positive externality, since each birth raises others’ desire for children, making the decentralized equilibrium inefficient. We characterize the optimal per-child subsidy and show that the first-order Pigouvian rate substantially overshoots the general-equilibrium optimum. The optimal targeting rule follows a Ramsey-like logic, directing the subsidy at the group with the most externality per fis...

• 2.Profit Regulation and Strategic Transfer Pricing by Vertically Integrated Firms: Evidence from Health Care -- by Pragya Kakani, Eric Yde, Genevieve P. Kanter, Richard G. Frank, Amelia M. Bond

  We provide evidence of strategic transfer pricing by vertically integrated health care firms in response to insurer profit regulations. Insurers increased prices at vertically integrated pharmacies by 9.5% following the introduction of caps on insurer profits in Medicare Part D. We detect larger price increases by insurers that were at greatest risk of exceeding the allowable profit level. More than one-fifth of these higher prices were borne by the federal government. Our analysis illustrates that vertically integrated firms can evade profit regulation by “tunneling” profits to unregulated subsidiaries, undermining regulatory intent and increasing health care spending.

• 3.Predicted Incrementality by Experimentation (PIE) for Ad Measurement -- by Brett R. Gordon, Robert Moakler, Florian Zettelmeyer

  Randomized controlled trials (RCTs) provide the most credible estimates of advertising incrementality but are difficult to scale. We propose Predicted Incrementality by Experimentation (PIE), which reframes ad measurement as a campaign-level prediction problem. PIE uses a sample of RCTs to learn a mapping from campaign features to causal effects, then applies it to campaigns not run as RCTs. Because the RCTs identify the causal effects, PIE can incorporate post-determined features—campaign-level aggregates such as test-group outcomes, exposure rates, and last-click conversions, computed after campaign completion. These metrics reflect the consumer behaviors that generate treatment effects, so they carry predictive information about incrementality even though they would be invalid controls in a causal model. Using 2,226 Meta ad experiments...

• 4.Bad News and Policy Views: Expectations, Disappointment, and Opposition to Affirmative Action -- by Louis-Pierre Lepage, Heather Sarsons, Michael Thaler

  There is widespread opposition to affirmative action policies. We study whether personal disappointments shape preferences for such policies. Specifically, we test whether individuals' college admissions outcomes, relative to their expectations, influence their attitudes toward affirmative action policies. Using a retrospective survey among recent White and Asian college applicants, we find that disappointed individuals—those who were admitted to fewer schools than anticipated—are relatively more likely to believe that affirmative action played an important role in their admissions outcomes, have the lowest support for affirmative action policies, and are more willing to donate to an anti-affirmative action organization. They also hold more negative views about the academic qualifications of under-represented minorities. To isolate the ca...

• 5.Forecasting the Economic Effects of AI -- by Ezra Karger, Otto Kuusela, Jason Abaluck, Kevin A. Bryan, Basil Halperin, Todd R. Jones, Connacher Murphy, Philip Trammell, Matt Reynolds, Dan Mayland, Ria Viswanathan, Ananaya Mittal, Rebecca Ceppas de Castro, Josh Rosenberg, Philip Tetlock

  We elicit forecasts of how AI will affect the U.S. economy, comparing the beliefs of five groups: academic economists, employees at AI companies, policy researchers focused on AI, highly accurate forecasters, and the general public. The median respondent in each group expects substantial advances in AI capabilities by 2030, small declines in labor force participation consistent with demographic shifts, and an annual GDP growth rate of 2.5%, which exceeds both the typical medium-run (2.0%) and long-run (1.7%) baseline forecasts from government agencies and private-sector forecasters. Conditional on a “rapid” AI progress scenario, in which AI systems surpass human performance on many cognitive and physical tasks, experts forecast substantial, though not historically unprecedented, economic shifts: annualized GDP growth rising to around 4% a...

• 1.What Millions of Homeowner’s Insurance Contracts Reveal About Risk Sharing

  Housing is the largest component of assets held by households in the United States, totaling $48 trillion in 2025. When natural disasters strike, the resulting damage to homes can be large relative to households’ liquid savings. Homeowner’s insurance is the primary financial tool households use to protect themselves against property risk. Despite the economic importance of homeowner’s insurance, we know surprisingly little about how insurance contracts are actually designed with respect to property risk. In this post, which is based on our new paper, “Economics of Property Insurance,” we examine how homeowner’s insurance contracts are structured in practice. Using a new granular dataset covering millions of homeowner’s insurance policies, we document ...

• 2.A Closer Look at Emerging Market Resilience During Recent Shocks

  A succession of shocks to the global economy in recent years has focused attention on the improved economic and financial resilience of emerging market economies. For some of these economies, this assessment is well-founded and highlights the fruits of deep, structural economic reforms since the 1990s. However, for a much larger universe of countries, the ability to weather shocks is still mixed and many remain vulnerable. In this post, we explore the divide between the two sets of countries and focus on the effects of recent economic shocks, including the ongoing conflict in the Middle East.

• 3.The Fed Has Two Tools to Influence Money Market Conditions 

  The Federal Reserve’s 2022-23 tightening cycle involved the use of two monetary policy tools: changes in administrative rates and changes in the size of its balance sheet. This post highlights the results of a recent Staff Report that explores how these tools affect money market conditions. Using confidential trade-level data, we find that both tools have significant effects on the pricing of funds sourced through repo. These results suggest that the Fed can manage how financing conditions are affected even as it influences economic conditions. For example, the Fed can lower its administrative rates to loosen economic conditions, while shrinking its balance sheet to maintain financing conditions in the money markets. 

• 4.Treasury Market Liquidity Since April 2025

  In this post, we examine the evolution of U.S. Treasury market liquidity over the past year, which has witnessed myriad economic and political developments. Liquidity worsened markedly one year ago as volatility increased following the announcement of higher-than-expected tariffs. Liquidity quickly improved when the tariff increases were partially rolled back and then remained fairly stable thereafter (through the end of our sample in February 2026), including after the recent Supreme Court decision striking down the emergency tariffs and the subsequent announcement of new tariffs.

• 5.Behind the ATM: Exploring the Structure of Bank Holding Companies

  Many modern banking organizations are highly complex. A “bank” is often a larger structure made up of distinct entities, each subject to different regulatory, supervisory, and reporting requirements. For researchers and policymakers, understanding how these institutions are structured and how they have evolved over time is essential. In this post, we illustrate what a modern financial holding company looks like in practice, document how banks’ organizational structures have changed over time, and explain why these details matter for conducting accurate analyses of the financial system.

• 1.After Orbán, Hungary Faces an Even Harder Battle

  Center-right Tisza’s victory in Hungary’s election shows that even a highly entrenched new-right regime can be defeated at the polls. But the myths, resentments, and paranoia that fueled popular support for Viktor Orbán’s brand of illiberalism have not suddenly vanished with his defeat.

• 2.Why Orbán Lost

  The downfall of Hungary's autocratic prime minister, whose model of "illiberal democracy"  became a lodestar to many on the right—was a consequence of that model's own logic. Whatever happens next, one thing is certain: democracy's friends and foes alike will be watching closely.

• 3.Hedging Security in the Gulf Is Risky

  The Gulf states have long hedged their diplomatic and security bets, attempting to strike a balance between those that might protect them from threats (especially the US) and those doing the threatening (such as Iran). But this approach has left them highly vulnerable and must be replaced by a unified approach to allies and foes.

• 4.The Iran War Has Ended a Year of Economic Promise

  Before US President Donald Trump launched his war of choice against Iran, financial markets were booming in many countries, and private-sector confidence was recovering. But the outlook has suddenly become bleaker, and many governments have only limited policy buffers available to cushion the inflationary shock.

• 5.Africa Is Losing the Iran War

  The fallout from the latest war in the Middle East has made visible a problem that many preferred to ignore: the international financial architecture is not fit for a world of cascading shocks, tightening fiscal constraints, and rising human need. Nowhere is this more obvious than in Africa.

• 1.How GNSS satellites power positioning and timing

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