Babak's Daily Briefing

Saturday, March 28, 2026

Sources: 17 | Total Articles: 85

• 1.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.

• 2.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.

• 3.ODC launches RANIQ and pushes AI-RAN beyond automation

  ODC has launched RANIQ, a new edge intelligence platform for AI-native RAN that the company says runs directly on the DU/CU compute node, exposing structured real-time signal intelligence to applications without adding new RF hardware or affecting live RAN operations.

• 4.The SRS acquisition gives Rohde & Schwarz a stronger hand in the 6G software stack

  Rohde & Schwarz has acquired Software Radio Systems, a specialist in software-defined mobile communications, in a move that strengthens its position not only in wireless test and measurement, but increasingly in the software foundations behind AI-native networks, NTN and early 6G. The wider signal is clear: the next strategic layer in wireless may be less about radio hardware alone and more about who controls the software stack underneath.

• 5.Linux Foundation launches OCUDU to turn AI-RAN into an open software foundation

  The Linux Foundation has launched the OCUDU Ecosystem Foundation, a public-private effort to build a production-ready open-source CU/DU base for 5G and early 6G, backed by founding premier members including AMD, AT&T, DeepSig, Ericsson, Nokia, NVIDIA, SoftBank, SRS and Verizon.

• 1.PII Shield: A Browser-Level Overlay for User-Controlled Personal Identifiable Information (PII) Management in AI Interactions

  AI chatbots have quietly become the world's most popular therapists, coaches, and confidants. Users of cloud-based LLM services are increasingly shifting from simple queries like idea generation and poem writing, to deeply personal interactions. As Large Language Models increasingly assume the role of our confessors, we are witnessing a massive, unregulated transfer of sensitive personal identifiable information (PII) to powerful tech companies with opaque privacy practices. While the enterprise sector has made great strides in addressing data leakage concerns through sophisticated guardrails and PII redaction pipelines, these powerful tools have functionally remained inaccessible for the average user due to their technical complexity. This results in a dangerous trade off for individual users. In order to receive the therapeutic or produ...

• 2.GameplayQA: A Benchmarking Framework for Decision-Dense POV-Synced Multi-Video Understanding of 3D Virtual Agents

  Multimodal LLMs are increasingly deployed as perceptual backbones for autonomous agents in 3D environments, from robotics to virtual worlds. These applications require agents to perceive rapid state changes, attribute actions to the correct entities, and reason about concurrent multi-agent behaviors from a first-person perspective, capabilities that existing benchmarks do not adequately evaluate. We introduce GameplayQA, a framework for evaluating agentic-centric perception and reasoning through video understanding. Specifically, we densely annotate multiplayer 3D gameplay videos at 1.22 labels/second, with time-synced, concurrent captions of states, actions, and events structured around a triadic system of Self, Other Agents, and the World, a natural decomposition for multi-agent environments. From these annotations, we refined 2.4K diag...

• 3.Experiential Reflective Learning for Self-Improving LLM Agents

  Recent advances in large language models (LLMs) have enabled the development of autonomous agents capable of complex reasoning and multi-step problem solving. However, these agents struggle to adapt to specialized environments and do not leverage past interactions, approaching each new task from scratch regardless of their accumulated experience. We introduce Experiential Reflective Learning (ERL), a simple self-improvement framework that enables rapid environment adaptation through experiential learning. ERL reflects on task trajectories and outcomes to generate heuristics, capturing actionable lessons that transfer across tasks. At test time, relevant heuristics are retrieved based on the current task and injected into the agent's context to guide execution. On the Gaia2 benchmark, ERL improves success rate by 7.8% over a ReAct baseline...

• 4.Language-Grounded Multi-Agent Planning for Personalized and Fair Participatory Urban Sensing

  Participatory urban sensing leverages human mobility for large-scale urban data collection, yet existing methods typically rely on centralized optimization and assume homogeneous participants, resulting in rigid assignments that overlook personal preferences and heterogeneous urban contexts. We propose MAPUS, an LLM-based multi-agent framework for personalized and fair participatory urban sensing. In our framework, participants are modeled as autonomous agents with individual profiles and schedules, while a coordinator agent performs fairness-aware selection and refines sensing routes through language-based negotiation. Experiments on real-world datasets show that MAPUS achieves competitive sensing coverage while substantially improving participant satisfaction and fairness, promoting more human-centric and sustainable urban sensing syste...

• 5.Self-Evolving Multi-Agent Framework for Efficient Decision Making in Real-Time Strategy Scenarios

  Large language models (LLMs) have demonstrated exceptional potential in complex reasoning,pioneering a new paradigm for autonomous agent decision making in dynamic settings. However, in Real-Time Strategy (RTS) scenarios, LLMs suffer from a critical speed-quality trade-off. Specifically expansive state spaces and time limits render inference delays prohibitive, while stochastic planning errors undermine logical consistency. To address these challenges, we present SEMA (Self-Evolving Multi-Agent), a novel framework designed for high-performance, low-latency decision-making in RTS environments. This collaborative multi-agent framework facilitates self-evolution by adaptively calibrating model bias through in-episode assessment and cross-episode analysis. We further incorporate dynamic observation pruning based on structural entropy to model...

• 1.Adapting Altman's bankruptcy prediction model to the compositional data methodology

  Using standard financial ratios as variables in statistical analyses has been related to several serious problems, such as extreme outliers, asymmetry, non-normality, and non-linearity. The compositional-data methodology has been successfully applied to solve these problems and has always yielded substantially different results when compared to standard financial ratios. An under-researched area is the use of financial log-ratios computed with the compositional-data methodology to predict bankruptcy or the related terms of business default, insolvency or failure. Another under-researched area is the use of machine learning methods in combination with compositional log-ratios. The present article adapts the classical Altman bankruptcy prediction model and some of its extensions to the compositional methodology with pairwise log-ratios and ...

• 2.LineMVGNN: Anti-Money Laundering with Line-Graph-Assisted Multi-View Graph Neural Networks

  Anti-money laundering (AML) systems are important for protecting the global economy. However, conventional rule-based methods rely on domain knowledge, leading to suboptimal accuracy and a lack of scalability. Graph neural networks (GNNs) for digraphs (directed graphs) can be applied to transaction graphs and capture suspicious transactions or accounts. However, most spectral GNNs do not naturally support multi-dimensional edge features, lack interpretability due to edge modifications, and have limited scalability owing to their spectral nature. Conversely, most spatial methods may not capture the money flow well. Therefore, in this work, we propose LineMVGNN (Line-Graph-Assisted Multi-View Graph Neural Network), a novel spatial method that considers payment and receipt transactions. Specifically, the LineMVGNN model extends a lightweight...

• 3.High-Resolution Tensor-Network Fourier Methods for Exponentially Compressed Non-Gaussian Aggregate Distributions

  Characteristic functions of weighted sums of independent random variables exhibit low-rank structure in the quantized tensor train (QTT) representation, also known as matrix product states (MPS), enabling up to exponential compression of their fully non-Gaussian probability distributions. Under variable independence, the global characteristic function factorizes into local terms. Its low-rank QTT structure arises from intrinsic spectral smoothness in continuous models, or from spectral energy concentration as the number of components $D$ grows in discrete models. We demonstrate this on weighted sums of Bernoulli and lognormal random variables. In the former, despite an adversarial, incompressible small-$D$ regime, the characteristic function undergoes a sharp bond-dimension collapse for $D \gtrsim 300$ components, enabling polylogarithmic...

• 4.Conditionally Identifiable Latent Representation for Multivariate Time Series with Structural Dynamics

  We propose the Identifiable Variational Dynamic Factor Model (iVDFM), which learns latent factors from multivariate time series with identifiability guarantees. By applying iVAE-style conditioning to the innovation process driving the dynamics rather than to the latent states, we show that factors are identifiable up to permutation and component-wise affine (or monotone invertible) transformations. Linear diagonal dynamics preserve this identifiability and admit scalable computation via companion-matrix and Krylov methods. We demonstrate improved factor recovery on synthetic data, stable intervention accuracy on synthetic SCMs, and competitive probabilistic forecasting on real-world benchmarks.

• 5.FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading

  We present FinRL-X, a modular and deployment-consistent trading architecture that unifies data processing, strategy construction, backtesting, and broker execution under a weight-centric interface. While existing open-source platforms are often backtesting- or model-centric, they rarely provide system-level consistency between research evaluation and live deployment. FinRL-X addresses this gap through a composable strategy pipeline that integrates stock selection, portfolio allocation, timing, and portfolio-level risk overlays within a unified protocol. The framework supports both rule-based and AI-driven components, including reinforcement learning allocators and LLM-based sentiment signals, without altering downstream execution semantics. FinRL-X provides an extensible foundation for reproducible, end-to-end quantitative trading researc...

• 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.SlotVTG: Object-Centric Adapter for Generalizable Video Temporal Grounding

  Multimodal Large Language Models (MLLMs) have shown strong performance on Video Temporal Grounding (VTG). However, their coarse recognition capabilities are insufficient for fine-grained temporal understanding, making task-specific fine-tuning indispensable. This fine-tuning causes models to memorize dataset-specific shortcuts rather than faithfully grounding in the actual visual content, leading to poor Out-of-Domain (OOD) generalization. Object-centric learning offers a promising remedy by decomposing scenes into entity-level representations, but existing approaches require re-running the entire multi-stage training pipeline from scratch. We propose SlotVTG, a framework that steers MLLMs toward object-centric, input-grounded visual reasoning at minimal cost. SlotVTG introduces a lightweight slot adapter that decomposes visual tokens int...

• 2.Is Mathematical Problem-Solving Expertise in Large Language Models Associated with Assessment Performance?

  Large Language Models (LLMs) are increasingly used in math education not only as problem solvers but also as assessors of learners' reasoning. However, it remains unclear whether stronger math problem-solving ability is associated with stronger step-level assessment performance. This study examines that relationship using the GSM8K and MATH subsets of PROCESSBENCH, a human-annotated benchmark for identifying the earliest erroneous step in mathematical reasoning. We evaluate two LLM-based math tutor agent settings, instantiated with GPT-4 and GPT-5, in two independent tasks on the same math problems: solving the original problem and assessing a benchmark-provided solution by predicting the earliest erroneous step. Results show a consistent within-model pattern: assessment accuracy is substantially higher on math problem items the same mode...

• 3.Demographic Fairness in Multimodal LLMs: A Benchmark of Gender and Ethnicity Bias in Face Verification

  Multimodal Large Language Models (MLLMs) have recently been explored as face verification systems that determine whether two face images are of the same person. Unlike dedicated face recognition systems, MLLMs approach this task through visual prompting and rely on general visual and reasoning abilities. However, the demographic fairness of these models remains largely unexplored. In this paper, we present a benchmarking study that evaluates nine open-source MLLMs from six model families, ranging from 2B to 8B parameters, on the IJB-C and RFW face verification protocols across four ethnicity groups and two gender groups. We measure verification accuracy with the Equal Error Rate and True Match Rate at multiple operating points per demographic group, and we quantify demographic disparity with four FMR-based fairness metrics. Our results sh...

• 4.Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes

  On-policy distillation (OPD) is appealing for large language model (LLM) post-training because it evaluates teacher feedback on student-generated rollouts rather than fixed teacher traces. In long-horizon settings, however, the common sampled-token variant is fragile: it reduces distribution matching to a one-token signal and becomes increasingly unreliable as rollouts drift away from prefixes the teacher commonly visits. We revisit OPD from the estimator and implementation sides. Theoretically, token-level OPD is biased relative to sequence-level reverse-KL, but it has a much tighter worst-case variance bound; our toy study shows the same tradeoff empirically, with stronger future-reward coupling producing higher gradient variance and less stable learning. Empirically, we identify three failure modes of sampled-token OPD: an imbalanced o...

• 5.EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agents

  As the Web transitions from static retrieval to generative interaction, the escalating environmental footprint of Large Language Models (LLMs) presents a critical sustainability challenge. Current paradigms indiscriminately apply computation-intensive strategies like Chain-of-Thought (CoT) to billions of daily queries, causing LLM overthinking, a redundancy that amplifies carbon emissions and operational barriers. This inefficiency directly undermines UN Sustainable Development Goals 13 (Climate Action) and 10 (Reduced Inequalities) by hindering equitable AI access in resource-constrained regions. To address this, we introduce EcoThink, an energy-aware adaptive inference framework designed to reconcile high-performance AI intelligence with environmental responsibility. EcoThink employs a lightweight, distillation-based router to dynamical...

• 1.Less Gaussians, Texture More: 4K Feed-Forward Textured Splatting

  Existing feed-forward 3D Gaussian Splatting methods predict pixel-aligned primitives, leading to a quadratic growth in primitive count as resolution increases. This fundamentally limits their scalability, making high-resolution synthesis such as 4K intractable. We introduce LGTM (Less Gaussians, Texture More), a feed-forward framework that overcomes this resolution scaling barrier. By predicting compact Gaussian primitives coupled with per-primitive textures, LGTM decouples geometric complexity from rendering resolution. This approach enables high-fidelity 4K novel view synthesis without per-scene optimization, a capability previously out of reach for feed-forward methods, all while using significantly fewer Gaussian primitives. Project page: https://yxlao.github.io/lgtm/

• 2.MuRF: Unlocking the Multi-Scale Potential of Vision Foundation Models

  Vision Foundation Models (VFMs) have become the cornerstone of modern computer vision, offering robust representations across a wide array of tasks. While recent advances allow these models to handle varying input sizes during training, inference typically remains restricted to a single, fixed scale. This prevalent single-scale paradigm overlooks a fundamental property of visual perception: varying resolutions offer complementary inductive biases, where low-resolution views excel at global semantic recognition and high-resolution views are essential for fine-grained refinement. In this work, we propose Multi-Resolution Fusion (MuRF), a simple yet universally effective strategy to harness this synergy at inference time. Instead of relying on a single view, MuRF constructs a unified representation by processing an image at multiple resoluti...

• 3.Measuring What Matters -- or What's Convenient?: Robustness of LLM-Based Scoring Systems to Construct-Irrelevant Factors

  Automated systems have been widely adopted across the educational testing industry for open-response assessment and essay scoring. These systems commonly achieve performance levels comparable to or superior than trained human raters, but have frequently been demonstrated to be vulnerable to the influence of construct-irrelevant factors (i.e., features of responses that are unrelated to the construct assessed) and adversarial conditions. Given the rising usage of large language models in automated scoring systems, there is a renewed focus on ``hallucinations'' and the robustness of these LLM-based automated scoring approaches to construct-irrelevant factors. This study investigates the effects of construct-irrelevant factors on a dual-architecture LLM-based scoring system designed to score short essay-like open-response items in a situatio...

• 4.Visual or Textual: Effects of Explanation Format and Personal Characteristics on the Perception of Explanations in an Educational Recommender System

  Explanations are central to improving transparency, trust, and user satisfaction in recommender systems (RS), yet it remains unclear how different explanation formats (visual vs. textual) are suited to users with different personal characteristics (PCs). To this end, we report a within-subject user study (n=54) comparing visual and textual explanations and examine how explanation format and PCs jointly influence perceived control, transparency, trust, and satisfaction in an educational recommender system (ERS). Using robust mixed-effects models, we analyze the moderating effects of a wide range of PCs, including Big Five traits, need for cognition, decision making style, visualization familiarity, and technical expertise. Our results show that a well-designed visual, simple, interactive, selective, easy to understand visualization that cl...

• 5.UNIC: Neural Garment Deformation Field for Real-time Clothed Character Animation

  Simulating physically realistic garment deformations is an essential task for virtual immersive experience, which is often achieved by physics simulation methods. However, these methods are typically time-consuming, computationally demanding, and require costly hardware, which is not suitable for real-time applications. Recent learning-based methods tried to resolve this problem by training graph neural networks to learn the garment deformation on vertices, which, however, fail to capture the intricate deformation of complex garment meshes with complex topologies. In this paper, we introduce a novel neural deformation field-based method, named UNIC, to animate the garments of an avatar in real time, given the motion sequences. Our key idea is to learn the instance-specific neural deformation field to animate the garment meshes. Such an in...

• 1.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...

• 2.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...

• 3.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...

• 4.These AI Workstations Look Like PCs but Pack a Stronger Punch

  The rise of generative AI has spurred demand for AI workstations that can run or train models on local hardware. Yet modern PCs have proven inadequate for this task . A typical laptop has only enough memory to load a large language model (LLM) with 8 billion to 13 billion parameters—much smaller, and much less intelligent, than frontier models that are presumed to have over a trillion parameters. Even the most capable workstation PCs struggle to serve LLMs with more than 70 billion parameters. Tenstorrent’s QuietBox 2 is an attempt to fill that gap. Though it looks like a PC workstation, the QuietBox 2 contains four of the company’s custom Blackhole AI accelerators, 128 gigabytes of GDDR6 memory—specialized memory used in GPUs—and 256 GB of DDR5 system memory (for a total of 384 GB). This configuration provides enough memory to load OpenA...

• 5.The Coming Drone-War Inflection in Ukraine

  WHEN KYIV-BORN ENGINEER Yaroslav Azhnyuk thinks about the future, his mind conjures up dystopian images. He talks about “swarms of autonomous drones carrying other autonomous drones to protect them against autonomous drones, which are trying to intercept them, controlled by AI agents overseen by a human general somewhere.” He also imagines flotillas of autonomous submarines, each carrying hundreds of drones, suddenly emerging off the coast of California or Great Britain and discharging their cargoes en masse to the sky. “How do you protect from that?” he asks as we speak in late December 2025; me at my quiet home office in London, he in Kyiv, which is bracing for another wave of missile attacks . Azhnyuk is not an alarmist. He cofounded and was formerly CEO of Petcube , a California-based company that uses smart cameras and an app to let ...

• 1.How Morningstar’s CEO Drives Relentless Execution

  Aleksandar Savic Many investors rely on Morningstar for independent financial analysis and insights, but few people are familiar with the company behind the ratings. From Morningstar’s origins rating mutual funds, the company has expanded its product line, customer base, and global footprint and realized a tenfold increase in revenues and profits between 2005 and 2025. […]

• 2.An AI Reckoning for HR: Transform or Fade Away

  Carolyn Geason-Beissel/MIT SMR | Getty Images For decades, human resource leaders have talked about the need to shift their focus from having responsibility for compliance to acting as architects of talent strategy. And for decades, the pattern of HR being stuck in age-old roles has persisted. But there is new pressure to redefine the role. […]

• 3.Shifting AI From Fear to Optimism: U.S. Department of Labor’s Taylor Stockton

  In this episode of the Me, Myself, and AI podcast, host Sam Ransbotham speaks with Taylor Stockton, chief innovation officer at the U.S. Department of Labor, about how artificial intelligence is reshaping the workforce. Taylor emphasizes that AI is having an economywide impact, transforming tasks within nearly every job rather than affecting only certain industries […]

• 4.Why Leaders Lose the Room in High-Stakes Meetings

  Carolyn Geason-Beissel/MIT SMR | Getty Images Most advice about leadership communication focuses on presentation skills: Be concise, be clear, tell better stories. But the most consequential leadership communication happens in meetings where tough issues are being discussed and real decisions are being made. Even some of the most skilled leaders find themselves in moments where […]

• 5.How Goldman Sachs Stays Agile: HR Leader Jacqueline Arthur

  Aleksandar Savic After World War II, Goldman Sachs ranked 10th among the top 30 U.S. investment banks. Twenty-seven of those once-mighty Wall Street rivals, including Salomon, Lehman, and First Boston, have been relegated to the annals of business history. Goldman, in contrast, is a global powerhouse, employing more than 46,000 people, operating in more than […]

• 1.Medicaid Coverage for Obesity Medications: Utilization and Net-of-Rebate Spending -- by Coady Wing, Wei-Lun Lo, Maddie Potter, Tarik Yuce, Alberto Ortega, John Cawley, Thuy D. Nguyen, Kosali I. Simon

  We document state variation in Medicaid coverage for obesity-indicated GLP-1 medications over time, and use a stacked difference-in-differences design to estimate the effects of coverage on utilization and net-of-rebate spending. Nine quarters out, coverage increases prescriptions for obesity-indicated GLP-1 medications by 0.82 per 100 enrollee-months (SE = 0.10). Coverage had no effect on GLP-1 prescribing for diabetes or cardiovascular indications, suggesting that off-label prescribing of diabetes formulations for obesity is not very common in the Medicaid program. The expansions do not appear to affect consumer spending at major online GLP-1 compounding firms, which suggests that the utilization response in our main analysis reflects new utilization rather than crowd-out. We find that coverage increases net-of-rebate Medicaid spending ...

• 2.Reserve Demand Estimation with Minimal Theory -- by Ricardo Lagos, Gastón Navarro

  We propose a new reserve-demand estimation strategy---a middle ground between atheoretical reduced-form econometric approaches and fully structural quantitative-theoretic approaches. The strategy consists of an econometric specification that satisfies core restrictions implied by theory and controls for changes in administered-rate spreads that induce rotations and shifts in reserve demand. The resulting approach is as user-friendly as existing reduced-form econometric methods but improves upon them by incorporating a minimal set of theoretical restrictions that any reserve demand must satisfy. We apply this approach to U.S. data and obtain reserve-demand estimates that are broadly consistent with the structural estimates.

• 3.Identifying Uncertainty, Learning about Productivity, and Human Capital Acquisition: A Reassessment of Labor Market Sorting and Firm Monopsony Power -- by Cristina Gualdani, Elena Pastorino, Áureo de Paula, Sergio Salgado

  We examine the empirical content of a large class of dynamic matching models of the labor market with ex-ante heterogeneous firms and workers, symmetric uncertainty and learning about workers’ productivity, and firms’ monopsony power. We allow workers’ human capital, acquired before and after entry into the labor market, to be general across firms to varying degrees. Such a framework nests and extends known models of worker turnover across firms, occupational choice, wage growth, wage differentials across occupations, firms, and industries, and wage dispersion across workers and over the life cycle. We establish intuitive conditions under which the model primitives are semiparametrically identified solely from data on workers’ wages and jobs, despite the dynamics of these models giving rise to complex patterns of selection based on endoge...

• 4.Financial Conditions Targeting in a Multi-Asset Open Economy -- by Ricardo J. Caballero, Alp Simsek

  We analyze monetary policy responses to noisy financial conditions in an open economy where exchange rates and domestic asset prices affect aggregate demand. Noise traders operate in both markets, and specialized arbitrageurs have limited risk-bearing capacity. Monetary policy creates cross-market spillovers: by adjusting the interest rate to stabilize one market, the central bank influences volatility in the other. We show that targeting a financial conditions index (FCI)—a weighted average of exchange rates and domestic asset prices—delivers substantial macroeconomic benefits. FCI targeting commits the central bank to respond to unexpected movements in financial conditions beyond what discretionary monetary policy implies. These stronger responses improve diversification across markets: each market becomes more exposed to external shock...

• 5.Standardized Test Scores and Academic Performance at a Public University System -- by Theodore J. Joyce, Mina Afrouzi Khosroshahi, Sarah Truelsch, Kerstin Gentsch, Kyle Du

  Recent studies of Ivy-Plus institutions suggest that standardized test scores (SAT/ACT) are far better predictors of college success than high school grade point average (HS-GPA), prompting a return to the requirement that test scores be submitted for admission at elite colleges. We ask whether re-establishing the SAT requirement for admission at a large urban public university system would improve the predictability of academic outcomes. Using administrative data for the 2010-2019 first-year cohorts, we update earlier work of students from public universities as to the relative predictive power of HSGPA and SAT scores on first-year outcomes and graduation rates. Contrary to findings at elite private institutions, we find that HSGPA is the dominant predictor of academic success in this public system. A one-standard-deviation increase in H...

• 1.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,...

• 2.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.

• 3.The New York Fed DSGE Model Forecast—March 2026

  This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model. We describe very briefly our forecast and its change since December 2025. To summarize, growth in 2026 is expected to be more robust, and inflation more persistent, than predicted in December. Stronger investment is the main driver for higher growth, while cost-push shocks, possibly capturing the effects of tariffs, are the key factors behind higher inflation. Projections for the short-run real natural rate of interest (r*) are the same as in December.

• 4.Firms’ Inflation Expectations Return to 2024 Levels

  Businesses experienced substantial cost pressures in 2025 as the cost of insurance and utilities rose sharply, while an increase in tariffs contributed to rising goods and materials costs. This post examines how firms in the New York-Northern New Jersey region adjusted their prices in response to these cost pressures and describes their expectations for future price increases and inflation. Survey results show an acceleration in firms’ price increases in 2025, with an especially sharp increase in the manufacturing sector. While both cost and price increases intensified last year, our surveys re...

• 5.Are Rising Employee Health Insurance Costs Dampening Wage Growth?

  Employer-sponsored health insurance represents a substantial component of total compensation paid by firms to many workers in the United States. Such costs have climbed by close to 20 percent over the past five years. Indeed, the average annual premium for employer-sponsored family health insurance coverage was about $27,000 in 2025—roughly equivalent to the wage of a full-time worker paid $15 per hour. Our February regional business surveys asked firms whether their wage setting decisions were influenced by the rising cost of employee health insurance. As we showed in our

  Project Syndicate

RCR Wireless

Semantic Scholar – Machine Learning

Telecom & 6G AI

arXiv Quantitative Finance

arXiv – 6G & Networking

arXiv – Network Architecture (6G/Slicing)