Babak's Daily Briefing

Wednesday, April 8, 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.TDA-RC: Task-Driven Alignment for Knowledge-Based Reasoning Chains in Large Language Models

  arXiv:2604.04942v1 Announce Type: new Abstract: Enhancing the reasoning capability of large language models (LLMs) remains a core challenge in natural language processing. The Chain-of-Thought (CoT) paradigm dominates practical applications for its single-round efficiency, yet its reasoning chains often exhibit logical gaps. While multi-round paradigms like Graph-of-Thoughts (GoT), Tree-of-Thoughts (ToT), and Atom of Thought (AoT) achieve strong performance and reveal effective reasoning structures, their high cost limits practical use. To address this problem, this paper proposes a topology-based method for optimizing reasoning chains. The framework embeds essential topological patterns of effective reasoning into the lightweight CoT paradigm. Using persistent homology, we map CoT, ToT, and GoT into a unified topological space to quanti...

• 2.The Illusion of Latent Generalization: Bi-directionality and the Reversal Curse

  arXiv:2604.04943v1 Announce Type: new Abstract: The reversal curse describes a failure of autoregressive language models to retrieve a fact in reverse order (e.g., training on ``$A > B$'' but failing on ``$B < A$''). Recent work shows that objectives with bidirectional supervision (e.g., bidirectional attention or masking-based reconstruction for decoder-only models) can mitigate the reversal curse. We extend this evaluation to include a vanilla masked language modeling (MLM) objective and compare it to decoder-only masking-based training across four reversal benchmarks and then provide a minimal mechanistic study of \emph{how} these objectives succeed. We show that reversal accuracy requires training signal that explicitly makes the source entity a prediction target, and we find little evidence that success corresponds to a single direc...

• 3.Inclusion-of-Thoughts: Mitigating Preference Instability via Purifying the Decision Space

  arXiv:2604.04944v1 Announce Type: new Abstract: Multiple-choice questions (MCQs) are widely used to evaluate large language models (LLMs). However, LLMs remain vulnerable to the presence of plausible distractors. This often diverts attention toward irrelevant choices, resulting in unstable oscillation between correct and incorrect answers. In this paper, we propose Inclusion-of-Thoughts (IoT), a progressive self-filtering strategy that is designed to mitigate this cognitive load (i.e., instability of model preferences under the presence of distractors) and enable the model to focus more effectively on plausible answers. Our method operates to reconstruct the MCQ using only plausible option choices, providing a controlled setting for examining comparative judgements and therefore the stability of the model's internal reasoning under pertu...

• 4.Phase-Associative Memory: Sequence Modeling in Complex Hilbert Space

  arXiv:2604.05030v1 Announce Type: new Abstract: We present Phase-Associative Memory (PAM), a recurrent sequence model in which all representations are complex-valued, associations accumulate in a matrix state $S_{t}$ $\in$ $\mathbb{C}^{d \times d}$ via outer products, and retrieval operates through the conjugate inner product $K_t^* \cdot Q_t / \sqrt{d}$. At $\sim$100M parameters on WikiText-103, PAM reaches validation perplexity 30.0, within $\sim$10\% of a matched transformer (27.1) trained under identical conditions, despite $4\times$ arithmetic overhead from complex computation and no custom kernels. We trace the experimental path from vector-state models, where holographic binding fails due to the $O(1/\sqrt{n})$ capacity degradation of superposed associations, to the matrix state that resolves it. The competitiveness of an architec...

• 5.This Treatment Works, Right? Evaluating LLM Sensitivity to Patient Question Framing in Medical QA

  arXiv:2604.05051v1 Announce Type: new Abstract: Patients are increasingly turning to large language models (LLMs) with medical questions that are complex and difficult to articulate clearly. However, LLMs are sensitive to prompt phrasings and can be influenced by the way questions are worded. Ideally, LLMs should respond consistently regardless of phrasing, particularly when grounded in the same underlying evidence. We investigate this through a systematic evaluation in a controlled retrieval-augmented generation (RAG) setting for medical question answering (QA), where expert-selected documents are used rather than retrieved automatically. We examine two dimensions of patient query variation: question framing (positive vs. negative) and language style (technical vs. plain language). We construct a dataset of 6,614 query pairs grounded in...

• 1.Integrating Artificial Intelligence, Physics, and Internet of Things: A Framework for Cultural Heritage Conservation

  arXiv:2604.03233v1 Announce Type: new Abstract: The conservation of cultural heritage increasingly relies on integrating technological innovation with domain expertise to ensure effective monitoring and predictive maintenance. This paper presents a novel framework to support the preservation of cultural assets, combining Internet of Things (IoT) and Artificial Intelligence (AI) technologies, enhanced with the physical knowledge of phenomena. The framework is structured into four functional layers that permit the analysis of 3D models of cultural assets and elaborate simulations based on the knowledge acquired from data and physics. A central component of the proposed framework consists of Scientific Machine Learning, particularly Physics-Informed Neural Networks (PINNs), which incorporate physical laws into deep learning models. To enhanc...

• 2.Scaling DPPs for RAG: Density Meets Diversity

  arXiv:2604.03240v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding generation in external knowledge, yielding relevance responses that are aligned with factual evidence and evolving corpora. Standard RAG pipelines construct context through relevance ranking, performing point-wise scoring between the user query and each corpora chunk. This formulation, however, ignores interactions among retrieved candidates, leading to redundant contexts that dilute density and fail to surface complementary evidence. We argue that effective retrieval should optimize jointly for both density and diversity, ensuring the grounding evidence that is dense in information yet diverse in coverage. In this study, we propose ScalDPP, a diversity-aware retrieval mechanism for RAG that incorporates ...

• 3.DRAFT: Task Decoupled Latent Reasoning for Agent Safety

  arXiv:2604.03242v1 Announce Type: new Abstract: The advent of tool-using LLM agents shifts safety monitoring from output moderation to auditing long, noisy interaction trajectories, where risk-critical evidence is sparse-making standard binary supervision poorly suited for credit assignment. To address this, we propose DRAFT (Task Decoupled Latent Reasoning for Agent Safety), a latent reasoning framework that decouples safety judgment into two trainable stages: an Extractor that distills the full trajectory into a compact continuous latent draft, and a Reasoner that jointly attends to the draft and the original trajectory to predict safety. DRAFT avoids lossy explicit summarize-then-judge pipelines by performing evidence aggregation in latent space, enabling end-to-end differentiable training.Across benchmarks including ASSEBench and R-Ju...

• 4.General Explicit Network (GEN): A novel deep learning architecture for solving partial differential equations

  arXiv:2604.03321v1 Announce Type: new Abstract: Machine learning, especially physics-informed neural networks (PINNs) and their neural network variants, has been widely used to solve problems involving partial differential equations (PDEs). The successful deployment of such methods beyond academic research remains limited. For example, PINN methods primarily consider discrete point-to-point fitting and fail to account for the potential properties of real solutions. The adoption of continuous activation functions in these approaches leads to local characteristics that align with the equation solutions while resulting in poor extensibility and robustness. A general explicit network (GEN) that implements point-to-function PDE solving is proposed in this paper. The "function" component can be constructed based on our prior knowledge of the or...

• 5.Apparent Age Estimation: Challenges and Outcomes

  arXiv:2604.03335v1 Announce Type: new Abstract: Apparent age estimation is a valuable tool for business personalization, yet current models frequently exhibit demographic biases. We review prior works on the DEX method by applying distribution learning techniques such as Mean-Variance Loss (MVL) and Adaptive Mean-Residue Loss (AMRL), and evaluate them in both accuracy and fairness. Using IMDB-WIKI, APPA-REAL, and FairFace, we demonstrate that while AMRL achieves state-of-the-art accuracy, trade-offs between precision and demographic equity persist. Despite clear age clustering in UMAP embeddings, our saliency maps indicate inconsistent feature focus across demographics, leading to significant performance degradation for Asian and African American populations. We argue that technical improvements alone are insufficient; accurate and fair a...

• 1.Belief Dynamics for Detecting Behavioral Shifts in Safe Collaborative Manipulation

  arXiv:2604.04967v1 Announce Type: new Abstract: Robots operating in shared workspaces must maintain safe coordination with other agents whose behavior may change during task execution. When a collaborating agent switches strategy mid-episode, continuing under outdated assumptions can lead to unsafe actions and increased collision risk. Reliable detection of such behavioral regime changes is therefore critical. We study regime-switch detection under controlled non-stationarity in ManiSkill shared-workspace manipulation tasks. Across ten detection methods and five random seeds, enabling detection reduces post-switch collisions by 52%. However, average performance hides significant reliability differences: under a realistic tolerance of +-3 steps, detection ranges from 86% to 30%, while under +-5 steps all methods achieve 100%. We introduce ...

• 2.From Video to Control: A Survey of Learning Manipulation Interfaces from Temporal Visual Data

  arXiv:2604.04974v1 Announce Type: new Abstract: Video is a scalable observation of physical dynamics: it captures how objects move, how contact unfolds, and how scenes evolve under interaction -- all without requiring robot action labels. Yet translating this temporal structure into reliable robotic control remains an open challenge, because video lacks action supervision and differs from robot experience in embodiment, viewpoint, and physical constraints. This survey reviews methods that exploit non-action-annotated temporal video to learn control interfaces for robotic manipulation. We introduce an \emph{interface-centric taxonomy} organized by where the video-to-control interface is constructed and what control properties it enables, identifying three families: direct video--action policies, which keep the interface implicit; latent-ac...

• 3.COMB: Common Open Modular robotic platform for Bees

  arXiv:2604.04980v1 Announce Type: new Abstract: Experimental access to real honeybee colonies requires robotic systems capable of operating within limited spatial constraints, tolerating hive-specific fouling and environmental conditions, and supporting both sensing and localized actuation without frequent hardware redesign. This paper introduces COMB, a compact, open-source, modular mechatronic platform designed for in-hive experiments within standard observation-hive frames. The platform integrates a XY positioning stage, a Movable Access Window (MAW) for sealed tool access through the hive boundary, interchangeable payload modules, and an embedded control architecture that enables repeatable trajectory execution and signal generation. The platform's capabilities are demonstrated through three representative modules: a biomimetic dance-...

• 4.A Survey on Sensor-based Planning and Control for Unmanned Underwater Vehicles

  arXiv:2604.05003v1 Announce Type: new Abstract: This survey examines recent sensor-based planning and control methods for Unmanned Underwater Vehicles (UUVs). In complex, uncertain underwater environments, UUVs require advanced planning and control strategies for effective navigation. These vehicles face significant challenges including drifting and noisy sensor measurements, absence of Global Navigation Satellite System (GNSS) signals, and low-bandwidth, high-latency underwater acoustic communications. The focus is on reactive local planning layers that adapt to real-time sensor inputs such as SONAR and Inertial Measurement Units (IMU) to improve localization accuracy and autonomy in dynamic ocean conditions, enabling dynamic obstacle avoidance and on-the-fly re-planning. The survey categorizes the existing literature into decoupled and ...

• 5.StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing

  arXiv:2604.05014v1 Announce Type: new Abstract: Building generalist embodied agents requires integrating perception, language understanding, and action, which are core capabilities addressed by Vision-Language-Action (VLA) approaches based on multimodal foundation models, including recent advances in vision-language models and world models. Despite rapid progress, VLA methods remain fragmented across incompatible architectures, codebases, and evaluation protocols, hindering principled comparison and reproducibility. We present StarVLA, an open-source codebase for VLA research. StarVLA addresses these challenges in three aspects. First, it provides a modular backbone--action-head architecture that supports both VLM backbones (e.g., Qwen-VL) and world-model backbones (e.g., Cosmos) alongside representative action-decoding paradigms, all und...

• 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.Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework

  The rapid growth of scientific literature has made it increasingly difficult for researchers to efficiently discover, evaluate, and synthesize relevant work. Recent advances in multi-agent large language models (LLMs) have demonstrated strong potential for understanding user intent and are being trained to utilize various tools. In this paper, we introduce Paper Circle, a multi-agent research discovery and analysis system designed to reduce the effort required to find, assess, organize, and understand academic literature. The system comprises two complementary pipelines: (1) a Discovery Pipeline that integrates offline and online retrieval from multiple sources, multi-criteria scoring, diversity-aware ranking, and structured outputs; and (2) an Analysis Pipeline that transforms individual papers into structured knowledge graphs with typed...

• 2.In-Place Test-Time Training

  The static ``train then deploy" paradigm fundamentally limits Large Language Models (LLMs) from dynamically adapting their weights in response to continuous streams of new information inherent in real-world tasks. Test-Time Training (TTT) offers a compelling alternative by updating a subset of model parameters (fast weights) at inference time, yet its potential in the current LLM ecosystem is hindered by critical barriers including architectural incompatibility, computational inefficiency and misaligned fast weight objectives for language modeling. In this work, we introduce In-Place Test-Time Training (In-Place TTT), a framework that seamlessly endows LLMs with Test-Time Training ability. In-Place TTT treats the final projection matrix of the ubiquitous MLP blocks as its adaptable fast weights, enabling a ``drop-in" enhancement for LLMs ...

• 3.Action Images: End-to-End Policy Learning via Multiview Video Generation

  World action models (WAMs) have emerged as a promising direction for robot policy learning, as they can leverage powerful video backbones to model the future states. However, existing approaches often rely on separate action modules, or use action representations that are not pixel-grounded, making it difficult to fully exploit the pretrained knowledge of video models and limiting transfer across viewpoints and environments. In this work, we present Action Images, a unified world action model that formulates policy learning as multiview video generation. Instead of encoding control as low-dimensional tokens, we translate 7-DoF robot actions into interpretable action images: multi-view action videos that are grounded in 2D pixels and explicitly track robot-arm motion. This pixel-grounded action representation allows the video backbone itse...

• 4.The Character Error Vector: Decomposable errors for page-level OCR evaluation

  The Character Error Rate (CER) is a key metric for evaluating the quality of Optical Character Recognition (OCR). However, this metric assumes that text has been perfectly parsed, which is often not the case. Under page-parsing errors, CER becomes undefined, limiting its use as a metric and making evaluating page-level OCR challenging, particularly when using data that do not share a labelling schema. We introduce the Character Error Vector (CEV), a bag-of-characters evaluator for OCR. The CEV can be decomposed into parsing and OCR, and interaction error components. This decomposability allows practitioners to focus on the part of the Document Understanding pipeline that will have the greatest impact on overall text extraction quality. The CEV can be implemented using a variety of methods, of which we demonstrate SpACER (Spatially Aware C...

• 5.Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement

  Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (NTP) focuses on one-step-ahead supervision, Multi-Token Prediction (MTP) has shown promise in learning more structured representations. In this work, we provide a theoretical perspective analyzing the gradient inductive bias of MTP, supported by empirical evidence, showing that MTP promotes the convergence toward internal belief states by inducing representational contractivity via gradient coupling. However, we reveal that standard MTP often suffers from structural hallucinations, where discrete token supervision encourages illegal shortcuts in latent space that violate environmental constraints. To address this, we propose a novel method Latent Semantic Enhancement MTP (LSE-MTP), which anchors pred...

• 1.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 ...

• 2.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...

• 3.AI Is Insatiable

  While browsing our website a few weeks ago, I stumbled upon “ How and When the Memory Chip Shortage Will End ” by Senior Editor Samuel K. Moore. His analysis focuses on the current DRAM shortage caused by AI hyperscalers’ ravenous appetite for memory, a major constraint on the speed at which large language models run. Moore provides a clear explanation of the shortage, particularly for high bandwidth memory (HBM). As we and the rest of the tech media have documented, AI is a resource hog. AI electricity consumption could account for up to 12 percent of all U.S. power by 2028. Generative AI queries consumed 15 terawatt-hours in 2025 and are projected to consume 347 TWh by 2030. Water consumption for cooling AI data centers is predicted to double or even quadruple by 2028 compared to 2023. But Moore’s reporting shines a light on an obscure ...

• 4.The AI Data Centers That Fit on a Truck

  A traditional data center protects the expensive hardware inside it with a “shell” constructed from steel and concrete. Constructing a data center’s shell is inexpensive compared to the cost of the hardware and infrastructure inside it, but it’s not trivial. It takes time for engineers to consider potential sites, apply for permits, and coordinate with construction contractors. That’s a problem for those looking to quickly deploy AI hardware, which has led companies like Duos Edge AI and LG CNS to respond with a more modular approach. They use pre-fabricated, self-contained boxes that can be deployed in months instead of years. The boxes can operate alone or in tandem with others, providing the option to add more if required. “I just came back from Nvidia’s GTC, and a lot of [companies] are sitting on their deployment because their data c...

• 5.Why Are Large Language Models so Terrible at Video Games?

  Large language models (LLMs) have improved so quickly that the benchmarks themselves have evolved, adding more complex problems in an effort to challenge the latest models. Yet LLMs haven’t improved across all domains, and one task remains far outside their grasp: They have no idea how to play video games. While a few have managed to beat a few games (for example, Gemini 2.5 Pro beat Pokemon Blue in May of 2025), these exceptions prove the rule. The eventually victorious AI completed games far more slowly than a typical human player, made bizarre and often repetitive mistakes, and required custom software to guide their interactions with the game. Julian Togelius , the director of New York University’s Game Innovation Lab and co-founder of AI game testing company Modl.ai, explored the implications of LLMs’ limitations in video games in a ...

• 1.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, […]

• 2.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. […]

• 3.How to Reap Compound Benefits From Generative AI

  Carolyn Geason-Beissel/MIT SMR | Minneapolis Institute of Art In domain after domain, AI has compressed work that used to be expensive — generating drafts, code, prototypes, and analyses. The marginal cost of a first attempt has dropped sharply. What remains expensive is what happens after the output arrives: evaluating what gets generated. That involves separating […]

• 4.Job Pivots in the Age of AI: Lessons From Mike Mulligan and His Steam Shovel

  Matt Harrison Clough As organizations like Amazon, PwC, and Microsoft have announced AI-fueled layoffs, it’s no surprise that half of Americans have expressed concern about AI’s larger potential impact on their jobs. Of course, companies can attribute layoffs to AI efficiencies while trimming workforces for various reasons. Yet there is no question that artificial intelligence […]

• 5.The Best Customers to Study When Scaling Into a New Market

  Carolyn Geason-Beissel/MIT SMR | Getty Images For tech companies worldwide, expanding into a new market is both a rite of passage and a moment of truth. It represents the transition from early promise to meaningful scale — an opportunity to increase revenue, signal growth potential to investors, and unlock powerful sources of differentiation, such as […]

• 1.Can Personal Access to Medical Expertise Overcome Vaccine Hesitancy? -- by D. Mark Anderson, Ron Diris, Raymond Montizaan, Daniel I. Rees

  Using data on applicants to Dutch medical schools and their older relatives (i.e., parents, aunts, and uncles ages 60+), we estimate the effect of personal access to medical expertise on vaccine hesitancy. Leveraging variation in lottery outcomes that determine admission to medical schools, we find that having a physician in the family increases the likelihood of complying with government recommendations that anyone over the age of 59 receive a second booster dose of a COVID-19 vaccine. Our estimated effects are strongest for having a female physician in the family, suggesting important gender-based differences in how medical expertise is communicated.

• 2.Why Do Americans No Longer Work So Much More Than Non-Americans? -- by Serdar Birinci, Loukas Karabarbounis, Kurt See

  In the 1990s, Americans used to work much more than non-Americans. Nowadays, about half of the gap in hours worked has reversed. To evaluate the convergence of working hours, we develop a tractable model of labor supply enriched with multiple sources of heterogeneity across individuals, an extensive margin of participation, multi-member households, and an elaborate system of taxes and benefits upon non-employment. Using detailed measurements from micro-level and aggregate datasets, we identify model parameters and sources of heterogeneity across individuals for various countries. We run a horse race between competing explanations and find that U.S. hours per person declined after 2000 owing mainly to the rise of government health benefits provided to the non-employed. Non-U.S. countries have generous benefits for the non-employed, but th...

• 3.AI Patents in the United States and China: Measurement, Organization, and Knowledge Flows -- by Hanming Fang, Xian Gu, Hanyin Yan, Wu Zhu

  We develop a high-precision classifier to measure artificial intelligence (AI) patents by fine-tuning PatentSBERTa on manually labeled data from the USPTO’s AI Patent Dataset. Our classifier substantially improves the existing USPTO approach, achieving 97.0% precision, 91.3% recall, and a 94.0% F1 score, and it generalizes well to Chinese patents based on citation and lexical validation. Applying it to granted U.S. patents (1976–2023) and Chinese patents (2010–2023), we document rapid growth in AI patenting in both countries and broad convergence in AI patenting intensity and subfield composition, even as China surpasses the United States in recent annual patent counts. The organization of AI innovation nevertheless differs sharply: U.S. AI patenting is concentrated among large private incumbents and established hubs, whereas Chinese AI p...

• 4.Tariffs, Global Value Chains, and the Incidence of Protection: Evidence from US Automobiles -- by Luke Heeney, Christopher R. Knittel, Jasdeep Mandia

  In many modern industries, firms compete in differentiated-product markets while relying on complex global value chains for intermediate inputs. In such settings, trade policies such as tariffs on vehicles and parts operate not only through consumer substitution and firm pricing, but also through firms’ cost structures and sourcing decisions. We develop a structural model of the U.S. automobile market that integrates random-coefficients demand, multiproduct firm pricing, and a flexible supply-side framework in which shocks to the cost of imported parts transmit imperfectly into manufacturers’ marginal costs. The model is disciplined by novel model-level data on imported-parts exposure and exploits exchange-rate variation to identify cost pass-through. Our counterfactual analysis quantifies the effects of alternative tariff policies on pri...

• 5.Learning How To Borrow in a Fintech World: Consumer Behavior When Search Costs Are (Near) Zero -- by Alex Günsberg, Camelia M. Kuhnen

  Online loan marketplaces are changing consumer lending. Here we investigate consumer behavior in these markets with near-zero search costs. Using administrative data on 730,000 applications, 750,000 offers, and 200,000 individuals, together with credit registry records, we document four facts. First, substantial within-applicant dispersion in offered terms makes search highly valuable. Second, marketplace nudges mitigate choice complexity. Third, applicants search significantly, applying repeatedly, asking for different terms, and rejecting offers, in ways consistent with their creditworthiness. Fourth, dynamic adverse selection constrains search, as lenders penalize repeat applicants. Our findings highlight trade-offs between informational gains from search, and reputational and cognitive costs.

• 1.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. 

• 2.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.

• 3.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.

• 4.Sports Betting Is Everywhere, Especially on Credit Reports

  Since 2018, more than thirty states have legalized mobile sports betting, leading to more than a half trillion dollars in wagers. In our recent Staff Report, we examine how legalized sports betting affects household financial health by comparing betting activity and consumer credit outcomes between states that legalized to those that have not. We find that legalization increases spending at online sportsbooks roughly tenfold, but betting does not stop at state boundaries. Nearby areas where betting is not legal still experience roughly 15 percent the increase of counties where it is legal. At the same time, consumer financial health suffers. Our analysis finds rising delinquencies in participating states,...

• 5.China’s Electric Trade

  China has spent considerable government resources to develop advanced electric technology industries, such as those that produce electric vehicles, lithium batteries, and solar panels. These efforts have spilled over to international trade as improvements in price and quality have increased the global demand for these goods. One consequence is that passenger cars and batteries have been disproportionately large contributors to the rise in the country’s trade surplus in recent years. This has not been the case, though, for solar panels, as falling prices due to a supply glut pulled down export revenues despite higher volumes.

• 1.Can Hungary Reverse Course?

  As Hungarians head to the polls, the rest of the world will be watching closely to see if Prime Minister Viktor Orbán's carefully crafted system of free but unfair elections is still functioning as intended. With his challenger commanding a double-digit lead in opinion polls, three scenarios remain possible.

• 2.The Real Question About the AI Future

  Whereas US power in the 20th century rested heavily on manufacturing scale, military reach, and dollar strength, in the 21st century it may rest increasingly on ownership of indispensable AI infrastructure. The challenge for the world will be how to pay for access to it.

• 3.The Coming Inflation-Deflation Whipsaw

  Other than the pandemic-era supply-chain disruptions, business leaders and investors have not had occasion to think much about either inflation or deflation. But that needs to change, because we are now facing both an immediate threat of higher prices and a longer-term prospect of rapidly falling labor, goods, and services costs.

• 4.The Road to De-Escalation With Iran

  Around the world, cash-strapped governments and people already struggling with poverty and precarity are asking with increasing urgency: Is there an off-ramp for the latest conflict in the Middle East? There is, but it requires taking economic incentives seriously.

• 5.Israel’s Buffer-Zone Fallacy

  In an era when ballistic missiles, drones, and other projectiles can travel long distances to hit targets with growing accuracy, the idea of a protective buffer zone is not only faulty; it is complete nonsense. Yet this is precisely the rationale that Israel has offered for its occupation of southern Lebanon.

• 1.Will AI Replace the Org Chart? Suman Kanuganti on the Rise of Personal AI

  In this episode, Suman Kanuganti, CEO of Personal AI, shares how he is redefining artificial intelligence by shifting the focus from generalized models to deeply personalized, human-centered intelligence. From founding Aira—an AI platform supporting the blind and low-vision community—to building…

• 2.Calix brings agentic AI to small ISPs for easy service differentiation

  “Speed is one of the attributes, not the main attribute,” says Kerry Haughan, SVP of Commercial Strategy Recently, Calix launched Calix One, an AI-native platform designed to automate broadband operations for rural and regional providers. With embedded agentic AI workflows…

• 3.China shifts 5G focus to monetization and enterprise growth

  As China’s 5G market nears saturation, operators are shifting focus from subscriber growth to revenue monetization. Enterprise services, industrial applications, IoT, and premium experiences are emerging as the key drivers of higher-value, longer-term growth, supported by government policy and network…

• 4.Industry 4.0’s hourglass figure – AI and IoT put squeeze on OT hardware spend

  Bain & Company warns that traditional industrial control systems are losing their central role as AI and smart devices redefine the economic landscape. By 2030, software, AI workflows, and intelligent field devices will capture the bulk of industry profit. In…

• 5.Restoring trust in voice: The next generation of authenticated and branded communications (Reader Forum)

  The global voice ecosystem is at an inflection point. For more than a decade, the rise of robocalls, spoofing, and impersonation scams has steadily eroded consumer confidence in answering the phone. What was once the most direct and trusted communications…

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