Babak's Daily Briefing

Monday, April 6, 2026

Sources: 16 | Total Articles: 68

• 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.Using LLM-as-a-Judge/Jury to Advance Scalable, Clinically-Validated Safety Evaluations of Model Responses to Users Demonstrating Psychosis

  arXiv:2604.02359v1 Announce Type: new Abstract: General-purpose Large Language Models (LLMs) are becoming widely adopted by people for mental health support. Yet emerging evidence suggests there are significant risks associated with high-frequency use, particularly for individuals suffering from psychosis, as LLMs may reinforce delusions and hallucinations. Existing evaluations of LLMs in mental health contexts are limited by a lack of clinical validation and scalability of assessment. To address these issues, this research focuses on psychosis as a critical condition for LLM safety evaluation by (1) developing and validating seven clinician-informed safety criteria, (2) constructing a human-consensus dataset, and (3) testing automated assessment using an LLM as an evaluator (LLM-as-a-Judge) or taking the majority vote of several LLM jud...

• 2.CIPHER: Conformer-based Inference of Phonemes from High-density EEG

  arXiv:2604.02362v1 Announce Type: new Abstract: Decoding speech information from scalp EEG remains difficult due to low SNR and spatial blurring. We present CIPHER (Conformer-based Inference of Phonemes from High-density EEG Representations), a dual-pathway model using (i) ERP features and (ii) broadband DDA coefficients. On OpenNeuro ds006104 (24 participants, two studies with concurrent TMS), binary articulatory tasks reach near-ceiling performance but are highly confound-vulnerable (acoustic onset separability and TMS-target blocking). On the primary 11-class CVC phoneme task under full Study 2 LOSO (16 held-out subjects), performance is substantially lower (real-word WER: ERP 0.671 +/- 0.080, DDA 0.688 +/- 0.096, indicating limited fine-grained discriminability. We therefore position this work as a benchmark and feature-comparison st...

• 3.SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancy

  arXiv:2604.02423v1 Announce Type: new Abstract: Large language models exhibit sycophancy: the tendency to shift outputs toward user-expressed stances, regardless of correctness or consistency. While prior work has studied this issue and its impacts, rigorous computational linguistic metrics are needed to identify when models are being sycophantic. Here, we introduce SWAY, an unsupervised computational linguistic measure of sycophancy. We develop a counterfactual prompting mechanism to identify how much a model's agreement shifts under positive versus negative linguistic pressure, isolating framing effects from content. Applying this metric to benchmark 6 models, we find that sycophancy increases with epistemic commitment. Leveraging our metric, we introduce a counterfactual mitigation strategy teaching models to consider what the answer ...

• 4.Skeleton-based Coherence Modeling in Narratives

  arXiv:2604.02451v1 Announce Type: new Abstract: Modeling coherence in text has been a task that has excited NLP researchers since a long time. It has applications in detecting incoherent structures and helping the author fix them. There has been recent work in using neural networks to extract a skeleton from one sentence, and then use that skeleton to generate the next sentence for coherent narrative story generation. In this project, we aim to study if the consistency of skeletons across subsequent sentences is a good metric to characterize the coherence of a given body of text. We propose a new Sentence/Skeleton Similarity Network (SSN) for modeling coherence across pairs of sentences, and show that this network performs much better than baseline similarity techniques like cosine similarity and Euclidean distance. Although skeletons ap...

• 5.Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets

  arXiv:2604.02460v1 Announce Type: new Abstract: Recent work reports strong performance from multi-agent LLM systems (MAS), but these gains are often confounded by increased test-time computation. When computation is normalized, single-agent systems (SAS) can match or outperform MAS, yet the theoretical basis and evaluation methodology behind this comparison remain unclear. We present an information-theoretic argument, grounded in the Data Processing Inequality, suggesting that under a fixed reasoning-token budget and with perfect context utilization, single-agent systems are more information-efficient. This perspective further predicts that multi-agent systems become competitive when a single agent's effective context utilization is degraded, or when more compute is expended. We test these predictions in a controlled empirical study acro...

• 1.Convolutional Surrogate for 3D Discrete Fracture-Matrix Tensor Upscaling

  arXiv:2604.02335v1 Announce Type: new Abstract: Modeling groundwater flow in three-dimensional fractured crystalline media requires accounting for strong spatial heterogeneity induced by fractures. Fine-scale discrete fracture-matrix (DFM) simulations can capture this complexity but are computationally expensive, especially when repeated evaluations are needed. To address this, we aim to employ a multilevel Monte Carlo (MLMC) framework in which numerical homogenization is used to upscale sub-resolution fracture effects when transitioning between accuracy levels. To reduce the cost of conventional 3D numerical homogenization, we develop a surrogate model that predicts the equivalent hydraulic conductivity tensor Keq from a voxelized 3D domain representing tensor-valued random fields of matrix and fracture conductivities. Fracture size, ori...

• 2.Generating Counterfactual Patient Timelines from Real-World Data

  arXiv:2604.02337v1 Announce Type: new Abstract: Counterfactual simulation - exploring hypothetical consequences under alternative clinical scenarios - holds promise for transformative applications such as personalized medicine and in silico trials. However, it remains challenging due to methodological limitations. Here, we show that an autoregressive generative model trained on real-world data from over 300,000 patients and 400 million patient timeline entries can generate clinically plausible counterfactual trajectories. As a validation task, we applied the model to patients hospitalized with COVID-19 in 2023, modifying age, serum C-reactive protein (CRP), and serum creatinine to simulate 7-day outcomes. Increased in-hospital mortality was observed in counterfactual simulations with older age, elevated CRP, and elevated serum creatinine....

• 3.LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning

  arXiv:2604.02338v1 Announce Type: new Abstract: MoE-PEFT methods combine Mixture of Experts with parameter-efficient fine-tuning for multi-task adaptation, but require separate adapters per expert causing trainable parameters to scale linearly with expert count and limiting applicability to adapter-based architectures. We propose LiME (Lightweight Mixture of Experts), which achieves expert specialization through lightweight modulation rather than adapter replication. Instead of separate adapters, LiME uses a single shared PEFT module and modulates its output with lightweight expert vectors, reducing expert parameters while generalizing to any PEFT method. Notably, LiME introduces zero-parameter routing by leveraging existing frozen and adapted representations eliminating learned router parameters typically required per layer. Theoreticall...

• 4.SIEVE: Sample-Efficient Parametric Learning from Natural Language

  arXiv:2604.02339v1 Announce Type: new Abstract: Natural language context-such as instructions, knowledge, or feedback-contains rich signal for adapting language models. While in-context learning provides adaptation via the prompt, parametric learning persists into model weights and can improve performance further, though is data hungry and heavily relies on either high-quality traces or automated verifiers. We propose SIEVE, a method for sample-efficient parametric learning from natural language context that requires as few as three query examples. SIEVE uses a novel synthetic data generation pipeline, SIEVE-GEN, that leverages the insight that context is decomposable. Decomposing context allows us to generate higher quality rollouts by pairing synthetic queries with only the applicable context rather than the entirety, then using context...

• 5.Not All Denoising Steps Are Equal: Model Scheduling for Faster Masked Diffusion Language Models

  arXiv:2604.02340v1 Announce Type: new Abstract: Recent advances in masked diffusion language models (MDLMs) narrow the quality gap to autoregressive LMs, but their sampling remains expensive because generation requires many full-sequence denoising passes with a large Transformer and, unlike autoregressive decoding, cannot benefit from KV caching. In this work, we exploit the flexibility of the diffusion framework and study model scheduling, where a smaller MDLM replaces the full model at a subset of denoising steps. On OpenWebText, we show that early and late denoising steps are substantially more robust to such replacement than middle steps, enabling up to a 17% reduction in FLOPs with only modest degradation in generative perplexity. We support these findings with a step-importance analysis based on loss and KL divergence between small ...

• 1.A Dynamic Toolkit for Transmission Characteristics of Precision Reducers with Explicit Contact Geometry

  arXiv:2604.02387v1 Announce Type: new Abstract: Precision reducers are critical components in robotic systems, directly affecting the motion accuracy and dynamic performance of humanoid robots, quadruped robots, collaborative robots, industrial robots, and SCARA robots. This paper presents a dynamic toolkit for analyzing the transmission characteristics of precision reducers with explicit contact geometry. A unified framework is proposed to address the challenges in modeling accurate contact behaviors, evaluating gear stiffness, and predicting system vibrations. By integrating advanced contact theories and numerical solving methods, the proposed toolkit offers higher precision and computational efficiency compared to traditional dynamics software. The toolkit is designed with a modular, scriptable architecture that supports rapid reconfig...

• 2.Backup-Based Safety Filters: A Comparative Review of Backup CBF, Model Predictive Shielding, and gatekeeper

  arXiv:2604.02401v1 Announce Type: new Abstract: This paper revisits three backup-based safety filters -- Backup Control Barrier Functions (Backup CBF), Model Predictive Shielding (MPS), and gatekeeper -- through a unified comparative framework. Using a common safety-filter abstraction and shared notation, we make explicit both their common backup-policy structure and their key algorithmic differences. We compare the three methods through their filter-inactive sets, i.e., the states where the nominal policy is left unchanged. In particular, we show that MPS is a special case of gatekeeper, and we further relate gatekeeper to the interior of the Backup CBF inactive set within the implicit safe set. This unified view also highlights a key source of conservatism in backup-based safety filters: safety is often evaluated through the feasibility...

• 3.F2F-AP: Flow-to-Future Asynchronous Policy for Real-time Dynamic Manipulation

  arXiv:2604.02408v1 Announce Type: new Abstract: Asynchronous inference has emerged as a prevalent paradigm in robotic manipulation, achieving significant progress in ensuring trajectory smoothness and efficiency. However, a systemic challenge remains unresolved, as inherent latency causes generated actions to inevitably lag behind the real-time environment. This issue is particularly exacerbated in dynamic scenarios, where such temporal misalignment severely compromises the policy's ability to interpret and react to rapidly evolving surroundings. In this paper, we propose a novel framework that leverages predicted object flow to synthesize future observations, incorporating a flow-based contrastive learning objective to align the visual feature representations of predicted observations with ground-truth future states. Empowered by this an...

• 4.Adaptive Learned State Estimation based on KalmanNet

  arXiv:2604.02441v1 Announce Type: new Abstract: Hybrid state estimators that combine model-based Kalman filtering with learned components have shown promise on simulated data, yet their performance on real-world automotive data remains insufficient. In this work we present Adaptive Multi-modal KalmanNet (AM-KNet), an advancement of KalmanNet tailored to the multi-sensor autonomous driving setting. AM-KNet introduces sensor-specific measurement modules that enable the network to learn the distinct noise characteristics of radar, lidar, and camera independently. A hypernetwork with context modulation conditions the filter on target type, motion state, and relative pose, allowing adaptation to diverse traffic scenarios. We further incorporate a covariance estimation branch based on the Josephs form and supervise it through negative log-likel...

• 5.Tune to Learn: How Controller Gains Shape Robot Policy Learning

  arXiv:2604.02523v1 Announce Type: new Abstract: Position controllers have become the dominant interface for executing learned manipulation policies. Yet a critical design decision remains understudied: how should we choose controller gains for policy learning? The conventional wisdom is to select gains based on desired task compliance or stiffness. However, this logic breaks down when controllers are paired with state-conditioned policies: effective stiffness emerges from the interplay between learned reactions and control dynamics, not from gains alone. We argue that gain selection should instead be guided by learnability: how amenable different gain settings are to the learning algorithm in use. In this work, we systematically investigate how position controller gains affect three core components of modern robot learning pipelines: beha...

• 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.Drift-Resilient Temporal Priors for Visual Tracking

  Temporal information is crucial for visual tracking, but existing multi-frame trackers are vulnerable to model drift caused by naively aggregating noisy historical predictions. In this paper, we introduce DTPTrack, a lightweight and generalizable module designed to be seamlessly integrated into existing trackers to suppress drift. Our framework consists of two core components: (1) a Temporal Reliability Calibrator (TRC) mechanism that learns to assign a per-frame reliability score to historical states, filtering out noise while anchoring on the ground-truth template; and (2) a Temporal Guidance Synthesizer (TGS) module that synthesizes this calibrated history into a compact set of dynamic temporal priors to provide predictive guidance. To demonstrate its versatility, we integrate DTPTrack into three diverse tracking architectures--OSTrack...

• 2.Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training

  Graph neural networks (GNNs) are widely used for learning on graph datasets derived from various real-world scenarios. Learning from extremely large graphs requires distributed training, and mini-batching with sampling is a popular approach for parallelizing GNN training. Existing distributed mini-batch approaches have significant performance bottlenecks due to expensive sampling methods and limited scaling when using data parallelism. In this work, we present ScaleGNN, a 4D parallel framework for scalable mini-batch GNN training that combines communication-free distributed sampling, 3D parallel matrix multiplication (PMM), and data parallelism. ScaleGNN introduces a uniform vertex sampling algorithm, enabling each process (GPU device) to construct its local mini-batch, i.e., subgraph partitions without any inter-process communication. 3D...

• 3.EventHub: Data Factory for Generalizable Event-Based Stereo Networks without Active Sensors

  We propose EventHub, a novel framework for training deep-event stereo networks without ground truth annotations from costly active sensors, relying instead on standard color images. From these images, we derive either proxy annotations and proxy events through state-of-the-art novel view synthesis techniques, or simply proxy annotations when images are already paired with event data. Using the training set generated by our data factory, we repurpose state-of-the-art stereo models from RGB literature to process event data, obtaining new event stereo models with unprecedented generalization capabilities. Experiments on widely used event stereo datasets support the effectiveness of EventHub and show how the same data distillation mechanism can improve the accuracy of RGB stereo foundation models in challenging conditions such as nighttime sc...

• 4.Steerable Visual Representations

  Pretrained Vision Transformers (ViTs) such as DINOv2 and MAE provide generic image features that can be applied to a variety of downstream tasks such as retrieval, classification, and segmentation. However, such representations tend to focus on the most salient visual cues in the image, with no way to direct them toward less prominent concepts of interest. In contrast, Multimodal LLMs can be guided with textual prompts, but the resulting representations tend to be language-centric and lose their effectiveness for generic visual tasks. To address this, we introduce Steerable Visual Representations, a new class of visual representations, whose global and local features can be steered with natural language. While most vision-language models (e.g., CLIP) fuse text with visual features after encoding (late fusion), we inject text directly into...

• 5.Novel Memory Forgetting Techniques for Autonomous AI Agents: Balancing Relevance and Efficiency

  Long-horizon conversational agents require persistent memory for coherent reasoning, yet uncontrolled accumulation causes temporal decay and false memory propagation. Benchmarks such as LOCOMO and LOCCO report performance degradation from 0.455 to 0.05 across stages, while MultiWOZ shows 78.2% accuracy with 6.8% false memory rate under persistent retention. This work introduces an adaptive budgeted forgetting framework that regulates memory through relevanceguided scoring and bounded optimization. The approach integrates recency, frequency, and semantic alignment to maintain stability under constrained context. Comparative analysis demonstrates improved long-horizon F1 beyond 0.583 baseline levels, higher retention consistency, and reduced false memory behavior without increasing context usage. These findings confirm that structured forge...

• 1.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...

• 2.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 ...

• 3.How NYU’s Quantum Institute Bridges Science and Application

  This sponsored article is brought to you by NYU Tandon School of Engineering . Within a 6 mile radius of New York University’s (NYU) campus, there are more than 500 tech industry giants, banks, and hospitals. This isn’t just a fact about real estate, it’s the foundation for advancing quantum discovery and application. While the world races to harness quantum technology, NYU is betting that the ultimate advantage lies not solely in a lab, but in the dense, demanding, and hyper-connected urban ecosystem that surrounds it. With the launch of its NYU Quantum Institute (NYUQI), NYU is positioning itself as the central node in this network; a “full stack” powerhouse built on the conviction that it has found the right place, and the right time, to turn quantum science into tangible reality. Proximity advantage is essential because quantum scienc...

• 4.Training Driving AI at 50,000× Real Time

  This is a sponsored article brought to you by General Motors. Visit their new Engineering Blog for more insights. Autonomous driving is one of the most demanding problems in physical AI. An automated system must interpret a chaotic, ever-changing world in real time—navigating uncertainty, predicting human behavior, and operating safely across an immense range of environments and edge cases. At General Motors, we approach this problem from a simple premise: while most moments on the road are predictable, the rare, ambiguous, and unexpected events — the long tail — are what ultimately defines whether an autonomous system is safe, reliable, and ready for deployment at scale. (Note: While here we discuss research and emerging technologies to solve the long tail required for full general autonomy, we also discuss our current approach or solvin...

• 5.What Happens When You Host an AI Café

  “Can I get an interview?” “Can I get a job when I graduate?” Those questions came from students during a candid discussion about artificial intelligence, capturing the anxiety many young people feel today. As companies adopt AI-driven interview screeners, restructure their workforces, and redirect billions of dollars toward AI infrastructure , students are increasingly unsure of what the future of work will look like. We had gathered people together at a coffee shop in Auburn, Alabama, for what we called an AI Café. The event was designed to confront concerns about AI directly, demystifying the technology while pushing back against the growing narrative of technological doom. AI is reshaping society at breathtaking speed. Yet the trajectory of this transformation is being charted primarily by for-profit tech companies, whose priorities re...

• 1.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 […]

• 2.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 […]

• 3.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 […]

• 4.Level Up Your Crisis Management Skills

  Michael Austin/theispot.com The Research The authors conducted in-depth interviews with senior leaders with direct experience guiding large, complex systems through unexpected shocks. Their sample included a former prime minister, CEOs, board chairs and directors of multinational corporations, a central bank governor, a national chief of defense, and a national fire marshal. Participants represented a diversity […]

• 5.When Not to Use AI

  Carolyn Geason-Beissel/MIT SMR | Getty Images AI promises to make managers more productive and give them access to more information more quickly. It can draft plans, summarize reports, and even coach you on how to deliver feedback. Yet the same technology that accelerates decision-making can also erode your judgment, if you let it. Rely on […]

• 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.America’s Currency Is the Global South’s Problem

  While the US dollar will not retain its preeminent status forever, countries in the Global South cannot afford to wait out its dominance. With a strategic approach, they can reduce their dependence on the dollar, thereby mitigating the associated risks and fostering a more balanced and resilient global financial landscape.

• 2.Bombing for Freedom

  The idea that bombing civilians will create the conditions for “regime change” is more than a century old. It has never worked, and it won’t work in Iran, because when people are expending all their energy trying to make it to the next day, they don’t have any left over for rebellion.

• 3.The Mispricing of War

  The US-Israeli campaign against Iran is reminding everyone that war is one of the most globally subsidized of human activities. Those who initiate it rarely bear the full costs, which tend to be displaced across borders, markets, and time, making decisions to initiate hostilities more likely.

• 4.Tragedy Is Reborn as Hope

  As the world confronts the twin crises of democratic backsliding and climate change, a new sense of possibility has emerged from tragedy. US President Donald Trump has shown that the person holding political power does, in fact, matter, while the senseless war in the Middle East has renewed hope for completing the energy transition.

• 5.Iran on the Edge of Breakdown

  The sustained US-Israeli attacks on Iranian infrastructure have left the country’s legal system without functional enforcement mechanisms. Regardless of whether the Islamic Republic collapses or endures, the country is likely to face a prolonged period of instability.

• 1.The building is the network (part 1) – dumb Wi-Fi is dead (Analyst Angle)

  Multi-dwelling unit (MDU) service providers have spent years building excellent Wi-Fi networks and collecting predictable fees. The ceiling on what that network can generate is gone. For years, the managed Wi-Fi industry sold itself on a straightforward value proposition: take…

• 2.Global telecom capex set to fall in 2026: Dell’Oro

  Dell’Oro forecasts a 2% decline in global telecom capex in 2026, followed by modest growth at a CAGR of around 1% through 2030 In sum – what to know: Capex set to decline – Global telecom capex is expected to…

• 3.ABI on AI infra | AI grid may be the next telecoms arms race (Analyst Angle)

  Telcos are exploring NVIDIA’s AI grid, but edge GPU deployment lacks a strong latency or cost case today; only physical AI use cases justify it, with gradual rollout expected toward future 6G networks. The diffusion of AI in telco networks…

• 4.Analysts weigh in on potential Amazon-Globalstar deal

  Amazon is reportedly weighing a $9 million acquisition of Globalstar. Here’s why Amazon is reportedly in talks with Globalstar — the company behind Apple’s emergency direct-to-device (D2D) service — for an acquisition, which, until recently, rumors were, that SpaceX was…

• 5.Nokia review (part 2) | Has Nokia left a Trojan horse in the private 5G market?

  Final word on this fascinating Nokia narrative, this time from the independent analyst community – about how the Finnish firm is losing a little to Ericsson in its 5G heartlands, but also switching it up with clever short- and long-term…

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• 1.RL-Loop: Reinforcement Learning-Driven Real-Time 5G Slice Control for Connected and Autonomous Mobility Services

  Smart and connected mobility systems rely on 5G edge infrastructure to support real-time communication, control, and service differentiation. Achieving this requires adaptive resource management mechanisms that can react to rapidly changing traffic conditions. In this paper, we propose RL-Loop, a closed-loop reinforcement learning framework for real-time CPU resource control in 5G network slicing environments supporting connected mobility services. RL-Loop employs a Proximal Policy Optimization (PPO) agent that continuously observes slice-level key performance indicators and adjusts edge CPU allocations at one-second granularity on a real testbed. The framework leverages real-time observability and feedback to enable adaptive, software-defined edge intelligence. Experimental results suggest that RL-Loop can reduce average CPU allocation b...

• 2.CIVIC: Cooperative Immersion Via Intelligent Credit-sharing in DRL-Powered Metaverse

  The Metaverse faces complex resource allocation challenges due to diverse Virtual Environments (VEs), Digital Twins (DTs), dynamic user demands, and strict immersion needs. This paper introduces CIVIC (Cooperative Immersion Via Intelligent Credit-sharing), a novel framework optimizing resource sharing among multiple Metaverse Service Providers (MSPs) to enhance user immersion. Unlike existing methods, CIVIC integrates VE rendering, DT synchronization, credit sharing, and immersion-aware provisioning within a cooperative multi-MSP model. The resource allocation problem is formulated as two NP-hard challenges: a non-cooperative setting where MSPs operate independently and a cooperative setting utilizing a General Credit Pool (GCP) for dynamic resource sharing. Using Deep Reinforcement Learning (DRL) for tuning resources and managing coopera...

• 3.Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery

  Next-generation (NextG) cellular networks are designed to support emerging applications with diverse data rate and latency requirements, such as immersive multimedia services and large-scale Internet of Things deployments. A key enabling mechanism is radio access network (RAN) slicing, which dynamically partitions radio resources into virtual resource blocks to efficiently serve heterogeneous traffic classes, including enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). In this paper, we study the impact of adversarial attacks on AI-driven RAN slicing decisions, where a budget-constrained adversary selectively jams slice transmissions to bias deep reinforcement learning (DRL)-based resource allocation, and quantify the resulting service level agreement (SLA) ...

• 4.Online Network Slice Deployment across Multiple Domains under Trust Constraints

  Network slicing across multiple administrative domains raises two coupled challenges: enforcing slice-specific trust constraints while enabling fast online admission and placement decisions. This paper considers a multi-domain infrastructure where each slice request specifies a VNF chain, resource demands, and a set of (un)trusted operators, and formulates the problem as a Node-Link (NL) integer program to obtain an optimal benchmark, before proposing a Path-Link (PL) formulation that pre-generates trust and order-compliant candidate paths to enable real-time operation. To mitigate congestion, resource prices are made dynamic using a Kleinrock congestion function, which inflates marginal costs as utilization approaches capacity, steering traffic away from hotspots. Extensive simulations across different congestion levels and slice types s...

• 5.6GAgentGym: Tool Use, Data Synthesis, and Agentic Learning for Network Management

  Autonomous 6G network management requires agents that can execute tools, observe the resulting state changes, and adapt their decisions accordingly. Existing benchmarks based on static questions or scripted episode replay, however, do not support such closed-loop interaction, limiting agents to passive evaluation without the ability to learn from environmental feedback. This paper presents 6GAgentGym to provide closed-loop capability. The framework provides an interactive environment with 42 typed tools whose effect classification distinguishes read-only observation from state-mutating configuration, backed by a learned Experiment Model calibrated on NS-3 simulation data. 6G-Forge bootstraps closed-loop training trajectories from NS-3 seeds via iterative Self-Instruct generation with execution verification against the Experiment Model. Su...