Babak's Daily Briefing

Monday, June 22, 2026

Sources: 17 | Total Articles: 85

• 1.The Hidden 6G Bottleneck: RF Hardware Design Is Becoming a Strategic Race

  As 5G-Advanced matures and 6G research moves closer to implementation, the wireless industry faces a deeper challenge than spectrum, standards or AI-native network architecture. Future wireless systems will depend on whether the industry can design, validate and manufacture increasingly complex RF modules fast enough.

• 2.6G in Dalian: What the Latest 3GPP Meetings Reveal About the Future Radio and Network

  The 6G physical layer is starting to converge. The protocol stack is being simplified in meaningful places. But the most consequential architecture decisions are now moving toward the June plenary in Singapore.

• 3.RF Digital Twins: Why 5G-Advanced and 6G Need Predictive Simulation

  RF Digital Twins: Why 5G-Advanced and 6G Need Predictive Simulation As wireless systems become more tightly coupled across…

• 4.Evaluating 6G PHY Evolution: What the Industry Is Really Trying to Solve

  Summary available at source link.

• 5.Amazon’s Globalstar deal gives Amazon Leo a faster path into D2D

  Amazon’s planned acquisition of Globalstar is about far more than satellites. It gives Amazon Leo a faster path into direct-to-device connectivity, combining spectrum, operational assets, and Apple-facing service continuity in a move that could reshape the hybrid terrestrial-NTN landscape.

• 1.ORAgentBench: Can LLM Agents Solve Challenging Operations Research Tasks End to End?

  Large language models are increasingly deployed as autonomous agents for multi-step tasks in executable environments, yet their ability to perform realistic operations research (OR) work remains unclear. Existing OR evaluations often decouple modeling from solving, rely on pre-formalized or text-only instances, and rarely test the full workflow from operational artifacts to validated decisions. In this work, we introduce ORAgentBench, an execution-grounded benchmark for evaluating autonomous agents on challenging end-to-end operations research tasks. It contains 107 human-reviewed tasks across diverse operational scenarios, each packaged in an isolated environment with a natural-language brief, multi-file data, configuration artifacts, and a required submission schema. Agents must write and run solution code, and their submissions are eva...

• 2.Deontic Policies for Runtime Governance of Agentic AI Systems

  Autonomous agentic AI systems driven by Large Language Models (LLMs) introduce a new class of security, privacy, and compliance challenges: an agent that can invoke tools, manipulate data, install software, and coordinate with peer agents across organizational boundaries must be constrained not just by authentication and access control, but by the full structure of enterprise governance. This includes specifying what agents are permitted and prohibited from doing, what they areobliged to do after certain actions (e.g., notify the CISO), under what conditions a standing obligation may be waived, and which rules take precedence when policies conflict. This governance problem exceeds what current policy engines provide. Systems such as XACML, Rego, and Cedar address only the permit/prohibit subset of this governance structure. They do not pr...

• 3.LLM Consumer Behavior Theory: Foundations of a Novel Research Field

  Large language models (LLMs) are increasingly deployed as autonomous agents that make consumption decisions on behalf of users. This shift raises fundamental questions for consumer theory, which has traditionally modeled humans as the primary decision-makers. In this paper, we introduce LLM Consumer Behavior Theory, a new field of study concerned with analyzing consumer behavior in agentic markets. Drawing on classical and behavioral economics alongside recent advances in Natural Language Processing, we formalize how human preferences are reflected and acted upon by LLM-based agents, and how agent-level decisions aggregate into market demand. We unify previously fragmented literature on LLM decision-making, human behavior simulation, and preference elicitation under a common economic lens, highlighting where assumptions, such as rationali...

• 4.Model Validation of Agentic AI Systems: A POMDP-Based Framework for Belief-State, Forecast, and Policy Validation

  Agentic artificial intelligence systems introduce a new class of model risk. Unlike traditional predictive models, autonomous agents continuously acquire information, form beliefs regarding latent states of the environment, generate forecasts, select actions, and adapt their behavior over time. Existing validation methodologies focus primarily on predictive accuracy and therefore provide limited insight into the quality of the underlying decision process. This paper proposes a model validation framework for agentic AI based on Partially Observable Markov Decision Processes (POMDPs). The framework decomposes autonomous decision making into information, beliefs, forecasts, actions, and utility, allowing each component to be validated independently. Large language models (LLMs) are formalized as approximate Bayesian filtering operators, and ...

• 5.CoffeeBench: Benchmarking Long-Horizon LLM Agents in Heterogeneous Multi-Agent Economies

  As LLM agents become capable of increasingly long-horizon tasks, evaluating their performance in economic systems is becoming increasingly important. Unlike existing benchmarks that primarily evaluate a single agent interacting with a passive environment, economic systems are inherently multi-agent, requiring autonomous agents to communicate, negotiate, and transact while pursuing their own objectives over extended periods. We introduce CoffeeBench, a benchmark for evaluating LLM agents in a long-horizon multi-agent economy composed of heterogeneous firms. In CoffeeBench, two farmers, two roasters, and two retailers autonomously operate their businesses over a 90-day simulation, each seeking to maximize cumulative net income through communication and transactions while managing cash, inventory, and pricing. The evaluated model controls on...

• 1.AI Economist Agent: An Agentic Framework for Model-Grounded Economic Analysis with RAG, Knowledge Graphs, and Large Language Models

  We propose a model-grounded RAG-based AI economist with an agentic framework for economic scenario analysis using large language models (LLMs) and knowledge graphs. While LLMs can generate fluent economic narratives, economists are often required to make economic claims grounded by economic theory and real-world data. Based on this motivation, this study proposes an RAG-based AI economist, which utilizes knowledge graphs including economic data and theory and LLM-based agents to plan the analysis, retrieve relevant evidence, select appropriate models, and generate reports. In our framework, we do not produce quantitative claims directly with the language model alone; instead, we generate narratives grounded in explicit model-based computations and linked to the retrieved evidence via AI agents. We refer to our framework as an AI economist...

• 2.DeXposure-Claw: An Agentic System for DeFi Risk Supervision

  Decentralized finance exposes supervisors to fast-moving, networked credit risks. General-purpose LLM agents fit this setting poorly: they over-read weak evidence and recommend high-stakes interventions, while existing evaluations offer no regulator-aligned way to measure the resulting false alarms. We introduce DeXposure-Claw, a forecast-grounded agentic supervision system that routes LLM decisions through structured evidence: (1) DeXposure-FM, a graph time-series foundation model, forecasts future exposure networks; (2) deterministic monitors and stress scenarios then turn those forecasts into typed alerts, attribution signals, and scenario evidence; and (3) data-health and confidence gates constrain escalation before DeXposure-Claw emits auditable supervisory tickets with rationales. We further develop DeXposure-Bench, a six-axis evalu...

• 3.Conformal Prediction Intervals with Tail-Specific Guarantees

  This paper extends classical conformal frameworks for constructing prediction intervals with global marginal coverage $1-α$ to intervals that provide explicitly calibrated guarantees for the upper and lower tails separately. Focusing on split conformal prediction, we first construct lower and upper one-sided conformal intervals that achieve marginal validity, and then derive the induced two-sided interval by intersection. Theoretical results prove both tail-specific and global marginal coverage of the induced two-sided interval. Results are presented first for the exchangeable setting, where coverage has finite-sample guarantees, and then for non-exchangeable data, where guarantees are asymptotic. Simulation studies show that the proposed approach achieves improved directional calibration relative to classical two-sided intervals, especia...

• 4.Continuous-time Optimal Stopping through Deep Reinforcement Learning

  Simulation based solvers for optimal stopping problems must discretize the stopping decision. Under classical dynamic programming, a coarse exercise grid with only a few stopping opportunities can materially undervalue the optimal expected reward, whereas on a very fine grid, approximation errors accumulate through the backward recursion. To remove this limitation, we develop a new reinforcement-learning inspired algorithm that enables us to learn the exercise rule at arbitrarily fine time resolution. Our CARLOS (Continuous-time Adaptive Reinforcement Learning for Optimal Stopping) algorithm utilizes an aggregate deep neural network (ADNN) to learn a joint space-time decision boundary. Starting from a coarse time grid, we progressively increase the frequency of stopping opportunities, while in parallel training the ADNN to refine its timi...

• 5.Martingale Doppelgänger-Eval: An Identification Framework for Auditing Candlestick Understanding in Vision-Language Models

  We introduce Martingale Doppelgänger-Eval, a public shadow-market benchmark for auditing whether vision-language models (VLMs) use candlestick evidence rather than extrapolate past trends. The central difficulty is identification: on real market histories, chart evidence and trend are strongly coupled, so an observational score cannot determine whether a fluent technical-analysis narrative is grounded in local visual evidence. We prove this limitation formally: no evaluation functional computed from observational chart--label data can distinguish a grounded responder from a trend-shortcut responder under strong coupling, whereas matched evidence interventions separate the same responders at an exponential rate and trend--label swaps provide an independent shortcut stress test. The benchmark therefore evaluates frozen VLMs on rendered OHLC...

• 1.New GSMA Report: Digital Reforms Could Unlock FCFA 870 Billion and Connect Over 540,000 More People in the Republic of the Congo by 2030

  Summary available at source link.

• 2.First-ever GLOMOs Africa winners announced alongside GSMA Open Gateway Africa Ignite Hackathon champions

  Summary available at source link.

• 3.Mobile Technologies Contributed $240 Billion to Africa’s Economy in 2025 as the Continent Enters a New Phase of Digital Transformation

  Summary available at source link.

• 4.Europe’s €1 trillion mobile industry at a crossroads ahead of Irish EU presidency

  Summary available at source link.

• 5.810 million women still not using mobile internet in low- and middle-income countries, compared to 595 million men

  Summary available at source link.

• 1.Multi-LCB: Extending LiveCodeBench to Multiple Programming Languages

  LiveCodeBench (LCB) has recently become a widely adopted benchmark for evaluating large language models (LLMs) on code-generation tasks. By curating competitive programming problems, constantly adding fresh problems to the set, and filtering them by release dates, LCB provides contamination-aware evaluation and offers a holistic view of coding capability. However, LCB remains restricted to Python, leaving open the question of whether LLMs can generalize across the diverse programming languages required in real-world software engineering. We introduce Multi-LCB, a benchmark for evaluating LLMs across twelve programming languages, including Python. Multi-LCB transforms Python tasks from the LCB dataset into equivalent tasks in other languages while preserving LCB's contamination controls and evaluation protocol. Because it is fully compatib...

• 2.PowerAgentBench-Dyn: A Benchmark for Agentic AI in Power System Dynamic Studies

  Large Language Model (LLM)-based agents are increasingly being used to automate multi-step engineering work flows by interacting with software tools, interpreting intermediate results, and autonomously planning subsequent actions. Power system dynamic studies represent a particularly promising yet largely unexplored application domain for these agents. Unlike static computational tasks, dynamic studies often require more time on model parameter calibration, engineering judgment, and decision making under constrained action spaces. This paper introduces PowerAgentBench-Dyn, a benchmark designed to evaluate Agentic AI systems on power system dynamic-analysis tasks. The benchmark targets problems that cannot be reduced to a single optimization or coding task, but instead require a type of reasoning, tool usage, and iterative experimentation ...

• 3.Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference

  Large language models (LLMs) have achieved strong performance across a wide range of language-based tasks by leveraging both extensive parametric knowledge and in-context learning ability, enabling them to incorporate external information provided in the input prompt. However, the integration of external knowledge can introduce conflicts, not only between the model's internal parametric knowledge and the external information, but also among multiple pieces of external contexts. Existing approaches typically assume that either the model or the provided context is reliable, overlooking the possibility that both sources may contain errors, and avoid conflicts by privileging one source over the other, rather than actively resolving inconsistencies. To address these limitations, we propose a novel framework MACR for LLM knowledge conflict reso...

• 4.QMFOL: Benchmarking Large Language Model Reasoning via Quantifiable Monadic First-Order Logic Test Case Generation

  Large Language Models (LLMs) have made significant progress in reasoning, particularly in deductive reasoning, which is crucial for high-stakes decision-making. As models improve, evaluation benchmarks should evolve to keep pace. However, existing benchmarks lack fine-grained control over logical complexity and struggle to balance semantic diversity with logical consistency. To address these issues, we propose QMFOL, an automated framework for generating monadic first-order logic reasoning tasks with quantifiable and controllable complexity. It constructs formal logical structures using conjunction and disjunction patterns, enabling precise control over reasoning depth, width, label types, and distractors. These structures are then translated into natural language via LLMs, with logical consistency ensured through round-trip verification ...

• 5.MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization

  Real-world clinical decision support requires reasoning over heterogeneous and longitudinal patient information rather than answering isolated medical questions. However, current medical large language models and retrieval-augmented generation systems often rely on single-step prompting or retrieval, which can be fragile when clinical evidence is distributed across long electronic health records, medical images, sensor streams, guidelines, and referral constraints. This paper proposes MedRLM, a Recursive Multimodal Health Intelligence framework for long-context clinical reasoning, sensor-guided screening, and community-to-tertiary referral support. Instead of compressing all patient information into one prompt, MedRLM treats the patient case as an external clinical environment that can be recursively inspected, decomposed, retrieved, veri...

• 1.The Chandra-Gaia Catalog of Counterparts: Resolving ambiguous Gaia matches to X-ray sources in the Chandra Source Catalog using Machine Learning

  We present a framework to cross-match sources from the Chandra Source Catalog (CSC v2.1) with optical sources from Gaia Data Release 3. Unlike purely spatial approaches, we use source properties such as magnitudes, colors, and distances to identify true counterparts, detect chance coincidences, and resolve ambiguities when multiple plausible candidates exist. We define a training set of high-confidence matches using NWAY, a Bayesian cross-matching framework that accounts for positional errors and source densities. We train a gradient-boosted classifier (LightGBM) on a variety of features from both catalogs. Of the ~$254$k unique X-ray sources, we find counterparts for ~$113$k sources, of which plausible multiple counterparts are found for ~$7$k. We find no counterparts for ~$20$k sources for which separation-based cross-matching does find...

• 2.Rethinking Reward Supervision: Rubric-Conditioned Self-Distillation

  Post-training of reasoning language models is commonly driven by supervised distillation and reinforcement learning with verifiable rewards. Distillation often relies on chain-of-thought annotations that are expensive to obtain and may themselves be noisy, incomplete, or partially incorrect; even when the final solution is correct, an imperfect rationale can interfere with learning. Reinforcement learning with verified rewards, on the other hand, typically compresses evaluative feedback into a scalar signal, obscuring which aspects of a response should be improved. We propose \textbf{Rubric-Conditioned Self-Distillation}, a framework that incorporates rubrics as structured, fine-grained feedback for on-policy self-distillation. Our method conditions the teacher model on criterion-level rubrics and uses it to provide token-level guidance o...

• 3.Reference-Driven Multi-Speaker Audio Scene Generation from In-the-Wild Priors

  Existing multi-speaker dialogue systems bind speakers to utterances through structured supervision: per-turn tags, multi-stream transcriptions, or learnable speaker embeddings. These systems operate within speech-only pipelines that produce clean vocal sequences without the ambient texture of real conversations. We take a different approach. Our method, ScenA, conditions a text-to-audio flow-matching foundation model, pretrained on large-scale in-the-wild data, directly on multiple reference voices and a free-form natural language prompt that describes an entire multi-speaker audio scene. Leveraging such a foundational model allows us to inherit its capacity for natural, non-studio audio: background noise, room acoustics, overlapping dialogue, and spontaneous paralinguistic events, while adding multi-speaker control without any per-turn s...

• 4.Data Intelligence Agents: Interpreting, Modeling, and Querying Enterprise Data via Autonomous Coding Agents

  Production data integration is bottlenecked by repeated, lossy handoffs between data owners, engineers, and analysts who must collaboratively discover, structure, and query enterprise data. We present Data Intelligence Agents (DIA), a system of three agents (Data Interpreter, Schema Creator, and Query Generator) that compresses this workflow by treating autonomous coding agents (ACAs) as a first-class abstraction: rather than emitting text, the agents generate, execute, validate, and repair concrete artifacts, draw on a shared memory for experience reuse, and surface each for review by domain experts. DIA is deployed in production for enterprise customers. We study the Query Generator in depth and evaluate it in fully autonomous mode across seven SQL benchmarks spanning four task categories and four dialects. It matches or surpasses the b...

• 5.Explaining Attention with Program Synthesis

  A longstanding goal of research on interpretable deep learning is to replace opaque neural computations with human-meaningful symbolic descriptions. In this paper, we propose an approach for approximating the behavior of components of deep networks with executable programs. We focus on attention heads in transformer language models. For a given head, we first compute its associated attention matrices on a collection of randomly selected training examples. Next, we prompt a pre-trained language model with a summary of these matrices, and instruct it to generate a set of Python programs that can reproduce the associated attention patterns given only text from the input sentence. Finally, we re-rank programs according to how well our final set of programs predict behavior on held-out inputs. We demonstrate that a set of fewer than 1,000 such...

• 1.IEEE Rolls Out Large Language Models Virtual Training Course

  Large language models have moved out of the research lab and into engineers’ daily workflow. LLMs serve as reasoning engines that can orchestrate complex tasks including identifying vulnerabilities in source code and transforming fragmented project discussions into rigorous technical specifications. While the general public uses AI tools to write email and plan vacations, technical professionals use LLMs as core architectural elements that are fundamentally changing how digital infrastructures are built and maintained. As the AI models move into mainstream engineering practice, the demand for technical expertise is rising. The LLM technology market is expected to grow by about 33 percent every year through 2030 , according to MarketsandMarkets . The rapid expansion suggests that proficiency in implementing and securing the models is trans...

• 2.Sound Waves Give Neuromorphic Chips a Brain-Simulating Edge

  By mimicking how the brain operates, neuromorphic computing can use dramatically less energy than conventional electronic AI chips. However, even the most sophisticated neuromorphic devices today are still quite simple, using only a small fraction of the number of connections found in human neurons. Now, a new study suggests that by using sound waves, neuromorphic devices can better mimic biological neurons and operate faster and with greater energy efficiency than their electronic counterparts. “This could make future neuromorphic hardware more compact, more parallel, and more efficient for tasks that require combining many features, such as pattern recognition, sensory processing, and data analysis,” says Xiaodong Yan , an assistant professor of materials science and engineering and electrical and computer engineering at the University ...

• 3.How Musicians Can Get Paid for Training AI

  Musicians are accustomed to getting paid each time their creative work is used. Across vinyl/CD sales, streams, radio, cover versions, and those numerous niches like karaoke, there are agreements in place about what “use” means. Underlying this is a simple economic principle: The more something is used, the more money it makes. Generative AI has complicated the definition of use . On the one hand, you could argue that the use of a piece of musical training data happens just once, at the point of training. On the other hand, creators would be right to complain that the creative essence of their work lives on in the structure of the model, used every time the model produces an output. Now, companies like Sureel and SoundVerse are working to re-create the essential economic principle that motivates creativity in an era of AI. Such initiative...

• 4.General Motors Is Cutting Its Development Cycles in Half

  For decades, automakers enjoyed a luxury that had nothing to do with the softest leather or the smoothest engines. Their luxury was time, with some popular cars and trucks enduring for a decade or longer before they received a full redesign. The clock is ticking faster now, thanks to China. BYD and other automakers there are speeding electric vehicles (EVs) and other models from drawing board to showrooms in two years or less. General Motors is among the Western automakers striving to match that blistering pace, by harnessing AI and simulation to dramatically shorten development times. GM’s effort is being spearheaded by Sterling Anderson , the technologist and robotics guru who led development teams for Tesla’s Autopilot and the Model X before cofounding Aurora Innovation , the autonomous trucking company. GM lured Anderson last June as ...

• 5.Visual Language Models Train Robots to Read Human Emotions

  This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore. As robots advance in terms of dexterity and other physical capabilities , it becomes more likely that humans may find themselves working alongside them. If that happens, how will robots’ emotional capabilities need to advance for them to successfully work with people? In a recent study, researchers trained collaborative robots to read human emotions by not only accounting for facial expressions, but also contextual factors in the interactions as well. Through experiments with 40 volunteers, the researchers then evaluated how a robot’s ability to read human emotions and adjust its behavior in turn impacted a human’s perception of the robot and its capabilities as the two collaborated on tasks. The results —which show that the emotional capabili...

• 1.Resolving Muddled Objectives in Corporate Venture Capital

  Carolyn Geason-Beissel/MIT SMR | Getty Images The Research The authors compared the approaches of prominent corporate venture capital (CVC) units, including those owned by Intel, Cisco, General Electric, Siemens, NTT Docomo, Hitachi, Panasonic, and Sompo. They examined 59 of the most active CVCs tracked by research firm CB Insights from 2017 through 2024 and mapped […]

• 2.Leaders at All Levels: How DBS Bank Makes Everyone an Innovator

  DBS Bank believes that innovation is critical to its survival, and to reinforce that objective, it made innovation a KPI representing 20% of every team and individual’s performance review. In this episode of Leaders at All Levels, hosts Katherine W. Isaacs and Michele Zanini speak with Bidyut Dumra, group head of innovation and future of […]

• 3.AI Upskilling at Scale: Bank of America’s Bernard Hampton

  Today’s episode of the Me, Myself, and AI podcast, the final one of Season 13, explores how Bank of America is preparing a massive global workforce for an AI future through upskilling and reskilling. Bernard Hampton, head of the financial institution’s Academy, explains how the learning and development organization focuses on workforce agility and a […]

• 4.How to Grow Without Betting Big

  Matt Harrison Clough/Ikon Images Some of the most spectacular stories of corporate growth revolve around big bets — long-term investments, bold pivots, and major acquisitions. Think of ASML, which pursued next-generation semiconductor manufacturing technologies for more than 30 years; Adobe, which abandoned perpetual licenses in favor of cloud subscriptions; or Disney, which acquired Pixar, Marvel, […]

• 5.Agentic AI: What Leaders Wish They Knew Sooner

  As AI agents go beyond the hypothetical and enter actual workflows, many leaders see a gap between the promise and the reality. Are the agents ready? Moreover, are the humans? At the 2026 MIT Sloan CIO Symposium, we sought expert perspective and advice. We asked technology and business leaders, “What have you learned this year […]

• 1.US Monetary Spillovers, Foreign Exchange, and Gold Reserves at Times of Geopolitical Fragmentation -- by Joshua Aizenman, Jamel Saadaoui, Gazi Salah Uddin, Naoki Yago

  This paper studies the role of foreign exchange and gold reserves in mitigating the US monetary policy spillovers to exchange rates at times of geopolitical fragmentation and de-dollarization. US dollar reserves mitigate depreciation driven by US monetary tightening, while non-dollar reserves do not. Gold reserves are also associated with smaller exchange-rate responses, though less strongly than dollar reserves, suggesting novel complementarity between dollar and gold reserves. Moreover, the estimated effects of dollar and gold reserves are concentrated in countries without swap and repo lines. These findings are consistent with recent large-scale purchases and sales of gold reserves by emerging economies amid sanctions-related restrictions and geopolitical concerns about access to dollar liquidity. Our results suggest that not only the ...

• 2.Beta for Alpha: Neural Engagement in Financial Trading -- by Liang Chen, Tse-Chun Lin, Fei Wu, Xingjian Zheng, Eric Zou

  The emergence of wearable electroencephalography (EEG) technologies presents new opportunities to identify and quantify neural correlates of decision-making in real-time, financially consequential settings. We report a field study in which we use wearable EEG devices to record brainwave activity from professional day traders during real-world, high-stakes trading. Using these signals, we construct an established psychophysiological Engagement Index (EI), which is defined as beta wave power divided by the sum of alpha and theta wave power, and document clear spike-and-decay patterns around trade execution. Linking EI dynamics to trading performance, we find that more successful trades are preceded by larger EI spikes in the 30 seconds before execution, while baseline or background EI levels have no predictive power and, if anything, are ne...

• 3.Elderly Health and Longevity in the US: Evidence and Implications -- by Liran Einav, Amy Finkelstein

  Rising elderly life expectancy is a well-known source of fiscal pressure on Social Security and Medicare – but how have declining mortality and morbidity affected the two programs’ relative finances? Using nearly three decades of Medicare Current Beneficiary Survey data (1992-2019), we estimate that these demographic changes raised expected lifetime Social Security spending by over twice as much as expected lifetime Medicare spending: 14% compared to 6%. The slower growth of elderly lifetime health care spending than annuity spending reflects two features of how longevity has increased: the additional 2.4 years of remaining life expectancy were entirely healthy – free of physical or cognitive limitations – while the expected amount of time spent with severe health limitations fell by about 30%, reducing expected lifetime nursing-home and ...

• 4.AI Diffusion Gaps: Unequal Integration of AI Across K-12 Schools -- by Christopher Campos, John D. Singleton

  Although use of generative AI tools has quickly become widespread in education settings, emerging evidence suggests that effects on learning will depend on how that use is supported and guided. This paper reports findings from an original national survey of K-12 school principals designed to measure institutional integration of AI in schools through policies, teacher training, guidance for student use, leadership engagement, and the availability of AI-enabled tools. We find that AI use has spread rapidly across schools, largely as a productivity aid. Students mainly use AI for homework help and writing, while educators primarily use it for lesson planning and administrative tasks. The development of teacher training, guidance, and school policies has lagged adoption. We next document two diffusion gaps across schools: First, lower AI inte...

• 5.The Informed Insider: A Leading Measure of Quality -- by Wei Cai, Dennis Campbell, Yaxuan Chen, Yufei Chen, Andrea Prat

  Product and service quality is fundamental to firm value creation, yet it is well recognized as difficult to observe ex ante. Existing quality proxies are limited in coverage, lack cross-firm comparability, and primarily rely on lagging indicators that capture product or service failures only after they materialize. Exploiting employees’ informational advantage as informed insiders and firsthand observers of firms’ internal operations, we develop and validate a novel, forward-looking measure of firm-year-level product and service quality using over 4.3 million employee reviews on Glassdoor. Leveraging machine learning models trained on a subset of firms with third-party customer satisfaction data, we construct quality indices for S&P 1500 firms spanning 2008 to 2023. The resulting quality measures exhibit meaningful variation across firms...

• 1.The New York Fed DSGE Model Forecast—June 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 March 2026. To summarize, inflation forecasts are higher in 2026 than predicted in March. Projections for the short-run real natural rate of interest (r*) increased slightly relative to March.

• 2.The Unintended Effects of Interest Rate Caps: Credit Reallocation to Safer Borrowers

  Several states have recently capped consumer loan rates with the stated purpose of protecting borrowers. In a recent Staff Report, we study how these interventions have played out in three states. In our first post about that study, we showed that rate caps lead riskier borrowers to face rationing in the credit market. One question that naturally arises is what lenders do with the credit they used to provide to high-risk borrowers before the caps were imposed. Lenders that lend exclusively to high-risk borrowers (at rates above the cap) may decide to stop lending to high-risk borrowers in that state. Others, however, may ...

• 3.The Unintended Effects of Interest Rate Caps: Credit Rationing for Risky Borrowers

  In imperial China, 3 percent was the maximum legal monthly loan rate; charging more was punishable by 40 to 100 blows with the “light cane.” (Rockoff 2003) Centuries later, many U.S. states are imposing the same cap (without corporal penalties) on alternative credit providers, such as payday, installment, and auto-title lenders, with the goal of lowering credit costs and delinquency for the high-risk borrowers that rely on these funding sources. A concern, however, is that lenders will simply refuse to lend to these borrowers at lower interest rates. Our recent Staff Report studies how interest rate caps have played out in several states that recently adopted them. Using hou...

• 4.Struggling Regional Small Businesses Deeply Pessimistic About 2026 Prospects

  We recently updated the suite of indicators describing the performance of small businesses in the Second District (defined, for the purpose of this study, as New York, New Jersey, and Connecticut) and nationally with data from the 2025 edition of the Small Business Credit Survey (SBCS). In this post, we find that regional small businesses reported severe declines in employment and revenue growth in 2025 and became more pessimistic about growth in 2026. In contrast, small firms in the rest of the nation enjoyed stable revenues and employment in 2025 and, while they also had lower expectations of futur...

• 5.Remote Work Leaves Younger Workers Sidelined

  Youth unemployment has risen dramatically since the pandemic—as has the prevalence of remote work. Our analysis suggests that these trends are related, with remote work making it more difficult for managers to train and mentor new employees. Accordingly, companies may be reluctant to hire less-experienced workers in distributed work arrangements. We estimate that remote work can explain 64 percent of the recent increase in unemployment among young college graduates. Further, the timing of this surge suggests that remote work—not generative AI—explains the bulk of the rise in youth unemployment.

• 1.What the Iran War Taught the World About Food Security

  Perhaps the most unsettling lesson from the closure of the Strait of Hormuz is the gap between what we knew and how we prepared. Countries should use whatever easing of immediate pressures occurs to strengthen resilience before the next shock arrives.

• 2.The Orange Bandit

  According to the Roman Emperor Tiberius, a ruler can either “shear the sheep” for many years, by encouraging productive activity from which to generate tax revenues, or he can “skin them alive” just once. By brazenly using the US government to pillage the country, President Donald Trump has apparently chosen the knife.

• 3.How (Not) to Conserve Tropical Forests

  Although the Tropical Forest Forever Facility that emerged from the last United Nations Climate Conference is unlikely to succeed, critically important global conservation efforts are not doomed. Through sustainability-linked sovereign bonds and loans, tropical countries can overcome the TFFF’s fatal flaws.

• 4.Was Brexit Inevitable?

  If counterfactual history is motivated by a refusal to accept what many have deemed inevitable, it is newly relevant now that the West is marking the tenth anniversary of the Brexit referendum. Even if Britain's fateful choice was caused by deeper structural and historical forces, that does not justify fatalism.

• 5.Questioning the Just War Doctrine

  With war becoming more frequent and more lethal to civilians, Pope Leo XIV and the College of Cardinals are wading into the debate over its legitimacy. The best outcome of the “consistory” Leo is convening in in late June would be to authorize an overhaul of the just war doctrine for the modern era—and ask the necessary questions.

• 1.Taara beams fiber-class data through the air to keep AI buildouts moving

  Taara Beam swaps mechanical mirrors for a fingernail-sized photonic chip In sum – what we know: Fiber gets all the credit, and for good reason. It’s the backbone of global connectivity, it’s high-capacity, and it’s proven. But it’s also slow…

• 2.Complexity, convergence, AI and the demand for trust are reshaping telecom testing

  In sum, what to know: –Telecom testing is shifting from point-in-time verification to continuous validation. As networks become increasingly cloud-native, multi-technology and automated, operators are moving toward continuous, end-to-end testing that combines active and passive elements and real-world performance monitoring.…

• 3.Report: Scaling Optical Networks For The Hyperscale And AI Era

  AI is not just driving demand for more network capacity. It is fundamentally changing what networks need to do. AI workloads introduce a traffic profile unlike anything cloud computing has required before, with massive east-west data flows, tightly synchronized compute…

• 4.SK Telecom wants to give every employee their own AI agent

  SK Telecom’s AX Innovation 2.0 program treats AI agents as “digital employees” In sum – what we know: SK Telecom wants you to think of AI agents less as software and more as colleagues. With the launch of AX Innovation 2.0,…

• 5.Mavenir, Red Hat target AI monetization opportunity for telcos

  According to Mavenir, a key element of the offering is a token-based charging model that allows telcos to bill AI usage through mechanisms similar to those already used for traditional telecom services In sum – what to know: AI monetization…

• 1.Physics-informed machine learning

  Abstract not available.

• 2.Machine Learning: Algorithms, Real-World Applications and Research Directions

  In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intellig...

• 3.Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

  We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits. The dataset is freely available at this https URL

• 4.A Survey on Bias and Fairness in Machine Learning

  With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various...

• 5.Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead

  Black box machine learning models are currently being used for high-stakes decision making throughout society, causing problems in healthcare, criminal justice and other domains. Some people hope that creating methods for explaining these black box models will alleviate some of the problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several...

• 1.ConsisFormer: Compute-Efficient Transformer for Wireless Foundation Models Based on Channel Consistency

  Wireless foundation models (WFMs) have recently emerged as a promising paradigm for AI-native 6G networks, enabling universal channel representations adaptable to diverse communication and sensing tasks. Existing WFMs are predominantly built upon the Transformer architecture, which delivers superior performance but incurs computational complexity proportional to the square of the input sequence length, posing a significant barrier to their deployment under stringent inference latency constraints. To address this issue, in this paper, we propose ConsisFormer, a compute-efficient Transformer design based on short-term consistency of wireless channels, as a WFM backbone. By utilizing the observation that adjacent time or frequency instances share similar clusters of scatterers and thus exhibit similar channel characteristics, we develop an a...

• 2.TelcoAgent: A Scalable 5G Multi-KPM Forecasting With 3GPP-Grounded Explainability

  Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks. However, existing machine learning (ML) approaches face significant limitations in scalability and explainability, restricting their effectiveness in real-world deployments. We propose TelcoAgent, a foundation model-based framework that enables accurate, scalable, and explainable forecasting of multiple KPMs across diverse network cells without the need for site-specific training. Specifically, the framework comprises three key components: (i) an automated three-agent pipeline that constructs a 3rd Generation Partnership Project (3GPP) knowledge graph directly from specification documents, (ii) a scalable, time-series foundation model (TSFM)-based prediction pipeline to deliver accurate, zero-shot forecas...

• 3.Ray Antenna Array Enhanced Low-Altitude ISAC: Performance Analysis and Beamforming Design

  The low-altitude economy (LAE) heavily relies on aerial vehicles, yet these platforms remain vulnerable to environmental and security risks, necessitating robust airspace monitoring. Integrated sensing and communication (ISAC) as one of the key technologies of 6G provides potential solutions for safe LAE. However, conventional antenna arrays face limitations in cost, scalability, and coverage, especially directly above the base station, due to hardware complexity and degraded angular resolution. By exploiting the recently proposed ray antenna array (RAA), this paper considers a RAA-enhanced low-altitude ISAC system. RAA architecture employs multiple ray-arranged arrays directly connected without phase shifters, significantly reducing hardware costs while supporting flexible beamforming via dynamic ray selection. Moreover, RAA can provide ...

• 4.Atomic Handover for 6G Nomadic Non-Public Networks Using Edge-Based Spectrum Brokering

  Nomadic Non-Public Networks (NNPN) are expected to play an important role in future 6G systems by enabling mobile and rapidly deployable network infrastructures for scenarios such as emergency response or temporary events. In such environments, maintaining seamless connectivity is challenging, as both network attachment and spectrum access may need to be adapted simultaneously when moving across heterogeneous infrastructures. In this paper, we investigate handover mechanisms for NNPN and propose a zero-touch approach that jointly considers mobility management and dynamic spectrum coordination. The proposed architecture introduces an edge-based Spectrum Broker in combination with a Cognitive Spectrum Manager to support an atomic handover procedure, where network selection and spectrum allocation are performed in a single step. The concept ...

• 5.Channel Charting With Physical Channel Fingerprints For Massive MIMO-OFDM Channel Acquisition

  The advancement of 6G mobile communication and positioning technologies has amplified the significance of location-aware tools, such as location-indexed channel fingerprints (CFs) and channel charting, which are becoming key enablers for massive MIMO-OFDM systems. In this paper, we propose a novel channel charting with physical CFs (PCFs) and demonstrate its effectiveness in channel state information (CSI) acquisition. First, we define the PCF based on a cluster-based geometric stochastic channel model (GBSM), enabling a comprehensive representation of physical channel characteristics using a compact set of parameters. We then develop a methodology for PCF acquisition in massive MIMO-OFDM systems. By exploiting the relationship between PCFs and the space-frequency-time (SFT) domain channel, the proposed method extracts PCFs from multi-loc...

• 1.Trends, Volatility, Correlations, and Critical Phenomena in Financial Markets

  We forecast future volatilities and correlations of financial markets based on the current trends in these markets. This complements previous work that models future expected returns by a cubic polynomial of the current trend strength. Empirically, we observe that volatilities and correlations tend to increase day after day in times of strong up- or down-trends. This effect is particularly pronounced in down-trends. It can be accurately quantified by quadratic polynomials of today's trend strengths, which refine common mean-reversion models of volatilities and correlations. Our results improve the prediction of market risk by accounting for market trends. They also support a recent proposal to model financial markets by a lattice gas near its critical point.

• 2.AI Economist Agent: An Agentic Framework for Model-Grounded Economic Analysis with RAG, Knowledge Graphs, and Large Language Models

  We propose a model-grounded RAG-based AI economist with an agentic framework for economic scenario analysis using large language models (LLMs) and knowledge graphs. While LLMs can generate fluent economic narratives, economists are often required to make economic claims grounded by economic theory and real-world data. Based on this motivation, this study proposes an RAG-based AI economist, which utilizes knowledge graphs including economic data and theory and LLM-based agents to plan the analysis, retrieve relevant evidence, select appropriate models, and generate reports. In our framework, we do not produce quantitative claims directly with the language model alone; instead, we generate narratives grounded in explicit model-based computations and linked to the retrieved evidence via AI agents. We refer to our framework as an AI economist...

• 3.Which Portfolios? The Construction Dependence of Factor Model Performance

  Factor-model performance depends not only on the model but also on how test assets are constructed. We form characteristic-unsorted random portfolios from a broad CRSP universe and vary stock selection, initial weighting, holding, and rebalancing. Rankings shift materially: buy-and-hold favors FF5 and FF6, whereas daily constant-weighting favors FF3, the most stable model across designs. Although q5 attains the highest maximum Sharpe ratio in factor-spanning tests, it leaves comparatively large and construction-sensitive pricing errors on random portfolios. These results reflect construction-specific weighting of each model's pricing-error vector. Test-asset construction, including dynamic weight management, is therefore a design choice in model evaluation.

• 4.Fitting Accumulated Stock Returns with Tempered Skew t-Distribution

  We analyze distributions of historic S&P500 multi-day returns, for the number of days of accumulation from 20 to 120. With the increase of the number of days of accumulation, we observe clear tempering of power-law tails toward a seemingly finite value. To explain this phenomenon, we employ a model that produces a "capped Inverse Gamma" stationary (steady-state) distribution for stochastic volatility which, in turn, produces a "tempered Student-t" distribution for returns. We then employ Jones-Faddy-like symmetry breaking mechanism that produces a "tempered Skew-t" distribution. This distribution provides rather good fits to the distributions of accumulated multi-day S&P500 returns, which exhibit symmetry breaking between gains and losses -- as reflected by positive mean and negative skew. Tempered Skew-t fits are also consistent ...

• 5.Crashing Together, Rallying Apart: Dynamic Conditional Tail Dependence in Cryptocurrency Markets

  Cryptocurrency markets are prone to violent, synchronised drawdowns, challenging the claim that a basket of crypto-assets offers genuine internal diversification. Because standard covariance-based metrics fail to capture asymptotic tail dependence, they systematically understate systemic risk and overstate diversification benefits precisely when markets crash. This study maps the conditional dependence structure of the cryptocurrency market directly in the joint tails, isolating direct extremal linkages from those mediated by the rest of the system. We analyse the daily returns of the thirteen largest cryptocurrencies over a sequence of 89 overlapping windows spanning late 2021 to 2025. We apply dynamic Hüsler-Reiss graphical models of extremes, estimated separately for joint crashes and rallies, and benchmark them against a Gaussian grap...

• 1.ConsisFormer: Compute-Efficient Transformer for Wireless Foundation Models Based on Channel Consistency

  Wireless foundation models (WFMs) have recently emerged as a promising paradigm for AI-native 6G networks, enabling universal channel representations adaptable to diverse communication and sensing tasks. Existing WFMs are predominantly built upon the Transformer architecture, which delivers superior performance but incurs computational complexity proportional to the square of the input sequence length, posing a significant barrier to their deployment under stringent inference latency constraints. To address this issue, in this paper, we propose ConsisFormer, a compute-efficient Transformer design based on short-term consistency of wireless channels, as a WFM backbone. By utilizing the observation that adjacent time or frequency instances share similar clusters of scatterers and thus exhibit similar channel characteristics, we develop an a...

• 2.Ray Antenna Array Enhanced Low-Altitude ISAC: Performance Analysis and Beamforming Design

  The low-altitude economy (LAE) heavily relies on aerial vehicles, yet these platforms remain vulnerable to environmental and security risks, necessitating robust airspace monitoring. Integrated sensing and communication (ISAC) as one of the key technologies of 6G provides potential solutions for safe LAE. However, conventional antenna arrays face limitations in cost, scalability, and coverage, especially directly above the base station, due to hardware complexity and degraded angular resolution. By exploiting the recently proposed ray antenna array (RAA), this paper considers a RAA-enhanced low-altitude ISAC system. RAA architecture employs multiple ray-arranged arrays directly connected without phase shifters, significantly reducing hardware costs while supporting flexible beamforming via dynamic ray selection. Moreover, RAA can provide ...

• 3.Atomic Handover for 6G Nomadic Non-Public Networks Using Edge-Based Spectrum Brokering

  Nomadic Non-Public Networks (NNPN) are expected to play an important role in future 6G systems by enabling mobile and rapidly deployable network infrastructures for scenarios such as emergency response or temporary events. In such environments, maintaining seamless connectivity is challenging, as both network attachment and spectrum access may need to be adapted simultaneously when moving across heterogeneous infrastructures. In this paper, we investigate handover mechanisms for NNPN and propose a zero-touch approach that jointly considers mobility management and dynamic spectrum coordination. The proposed architecture introduces an edge-based Spectrum Broker in combination with a Cognitive Spectrum Manager to support an atomic handover procedure, where network selection and spectrum allocation are performed in a single step. The concept ...

• 4.Channel Charting With Physical Channel Fingerprints For Massive MIMO-OFDM Channel Acquisition

  The advancement of 6G mobile communication and positioning technologies has amplified the significance of location-aware tools, such as location-indexed channel fingerprints (CFs) and channel charting, which are becoming key enablers for massive MIMO-OFDM systems. In this paper, we propose a novel channel charting with physical CFs (PCFs) and demonstrate its effectiveness in channel state information (CSI) acquisition. First, we define the PCF based on a cluster-based geometric stochastic channel model (GBSM), enabling a comprehensive representation of physical channel characteristics using a compact set of parameters. We then develop a methodology for PCF acquisition in massive MIMO-OFDM systems. By exploiting the relationship between PCFs and the space-frequency-time (SFT) domain channel, the proposed method extracts PCFs from multi-loc...

• 5.User-Mobility-Aware Optimization of Fiber Placement in Hybrid Fiber-IAB Networks

  Metaheuristic optimization of hybrid fiber-IAB networks demonstrates that integrating user dynamics into topology design enables more adaptive and cost-efficient backhaul architectures, contributing to the development of scalable and flexible 6G network infrastructures.

• 1.Atomic Handover for 6G Nomadic Non-Public Networks Using Edge-Based Spectrum Brokering

  Nomadic Non-Public Networks (NNPN) are expected to play an important role in future 6G systems by enabling mobile and rapidly deployable network infrastructures for scenarios such as emergency response or temporary events. In such environments, maintaining seamless connectivity is challenging, as both network attachment and spectrum access may need to be adapted simultaneously when moving across heterogeneous infrastructures. In this paper, we investigate handover mechanisms for NNPN and propose a zero-touch approach that jointly considers mobility management and dynamic spectrum coordination. The proposed architecture introduces an edge-based Spectrum Broker in combination with a Cognitive Spectrum Manager to support an atomic handover procedure, where network selection and spectrum allocation are performed in a single step. The concept ...

• 2.User-Mobility-Aware Optimization of Fiber Placement in Hybrid Fiber-IAB Networks

  Metaheuristic optimization of hybrid fiber-IAB networks demonstrates that integrating user dynamics into topology design enables more adaptive and cost-efficient backhaul architectures, contributing to the development of scalable and flexible 6G network infrastructures.

• 3.Security-Induced Braess Paradoxes in Service Function Chain Orchestration

  NFV/SDN orchestration lets operators instantiate and steer traffic through virtual firewalls, IDS/IPS replicas, WAF clusters, zero-trust gateways, backup inspection paths, and migration targets on demand. Operators often treat these options as monotone improvements: more inspection capacity, lower nominal latency, or broader placement flexibility should not degrade the service. That intuition can fail even when the new option is locally attractive. We study a security-induced Braess paradox in service function chain (SFC) orchestration, where adding a defensive option worsens the post-adaptation equilibrium by concentrating traffic and adversarial value on shared security resources. We define Braessian security-management actions, derive a sufficient condition for paradox emergence under affine load-dependent VNF delay, and give a pre-dep...

• 4.Di5Guise: 5G Privacy with vSIM

  SIM cards have been the key building block of user authenticationand security in cellular networks. While they are meant to serve as privacy protecting elements in cellular communications, they can be the root cause of privacy loss. Current eSIMs come with a fixed device profile--comprising a secret key, a certificate, and a unique eUICC identifier--that permanently binds every subscriber profile provisioned on the device to that device profile. This binding enables an attacker with the vantage point of a cellular operator to correlate subscriber identities back to a single device, piecing together a complete pattern of life--online activities, movement patterns, and real-world identity--even when users rotate subscriber identities or employ traffic obfuscation techniques. To mitigate this concern, we introduce Di5Guise, a privacy-enhanci...

• 5.Towards Ubiquitous 6G Computing and Networking Convergence: Architecture and Mechanism for Cross-Domain Resource Coordination

  The 6G network will support six major application scenarios, such as immersive communication, integrated AI and communication, and integrated sensing and communication. Many scenarios necessitate significant computational support. Moreover, user demands are becoming increasingly segmented, diverse, and personalized. Traditional network slicing alone is insufficient to meet the heterogeneous computing and networking demands of emerging service scenarios. Mobile computing network convergence (CNC) introduces a fundamentally different paradigm from the conventional cloud computing plus communication network model by deeply embedding computing resources into the mobile network infrastructure and enabling integrated computing-network services tailored to diverse user demands. In this article, we investigate orchestration architectures and mech...