Babak's Daily Briefing

Monday, July 6, 2026

Sources: 17 | Total Articles: 81

• 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.Prompt Coverage Adequacy

  In recent years, it has become increasingly evident that large language models (LLMs) and autonomous agents raise the level of abstraction in software development by shifting the focus from writing precise procedures to expressing intents and goals. This paradigm shift introduces new challenges, particularly in how testing should be guided when prompts, rather than code, become primary development artifacts. To address this challenge, we propose Prompt Coverage Adequacy, a novel coverage criterion designed to support the testing of code generated from task descriptions. Prompt Coverage Adequacy serves as an analog to traditional code coverage, but operates at the level of prompts used in LLM and agent-based programming. Specifically, it measures how well a given test suite satisfies the requirements expressed in a prompt by leveraging the...

• 2.Managed Autonomy at Runtime: Gear-Based Safety and Governance for Single- and Multi-Agent Cyber-Physical Systems

  Autonomous agents, whether LLM-driven software agents or robotic physical agents, face a common class of failure modes when operating without continuous human oversight: safety violations from unverified actions, behavioral instability from unconstrained loops, and continuity loss from unhandled error states. We develop \system{}, a discrete-time control system that combines five execution gears (\Gobs{}, \Gsug{}, \Gplan{}, \Gexec{}, \Gint{}) with utility-gated dispatch and event-driven fallback. For the single-agent case, we prove monotonic stability, execution safety, eventual stabilization, fallback completeness, and equivalence to a gear-constrained Markov decision process. For multi-agent cyber-physical systems (CPS), we apply the established \smart{} managed-autonomy lifecycle and map runtime evidence into its four governance states...

• 3.A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems

  Large language models are no longer only text generators. They are increasingly embedded in retrieval pipelines, enterprise assistants, coding environments, robotic systems, security-operation workflows, and autonomous agents that can read private data, call tools, write files, execute code, and act across organizational boundaries. This shift changes the security problem: risks do not arise from the model weights alone, but from the full lifecycle and application stack through which data, prompts, model outputs, tools, memories, and user authority interact. This paper systematizes the literature on vulnerabilities in large language model systems through a lifecycle and application-stack lens. We organize attacks across eight stages: data collection, pretraining, post-training alignment, model packaging and supply chain, retrieval and mem...

• 4.Attractor States Emerge in Multi-Turn LLM Conversations

  Large language models (LLMs) are increasingly used in open-ended multi-agent settings, but the long-run dynamics of model--model interaction remain poorly understood. We study whether open-ended LLM discussions exhibit attractor-like behavior, i.e. topic-independent stable sets of behaviors which conversations settle into. Across 7 LLMs and 20 controversial topics, we compare self-play and mixed-play dyadic debates, tracking trajectories in representation space, discourse traits, and stances. We find self-play trajectories to be model-specific attractors that draw their conversation partners asymmetrically in mixed-play debates, influencing the other models' stylistic choices and behavior. For example, Claude Haiku is a strong attractor of other models in latent space, corresponding to other models taking on its traits like metacommentary...

• 5.Neural Procedural Memory: Empowering LLM Agents with Implicit Activation Steering

  While Large Language Models (LLMs) excel as static solvers, transforming them into autonomous agents remains challenging. This transition requires continuous environmental interaction, yet current agents lack the necessary persistent procedural memory. Existing approaches predominantly employ Retrieval-Augmented Generation (RAG) to inject explicit textual guidelines into model contexts. However, relying solely on symbolic instructions can introduce a text-action disconnect, frequently failing to activate the internal representations necessary for correct task execution. To address this, the paper introduces Neural Procedural Memory (NPM), a training-free framework that represents agent memory through implicit activation steering rather than explicit instructions. By distilling procedural skills from historical contrastive experiences into...

• 1.Shapley in Context: Explaining Financial Language with Domain Expertise

  In recent years, large language models have achieved remarkable success and have seen growing adoption in financial applications. At the same time, explainability remains critical in finance, a domain characterized by high stakes and strict regulatory requirements. Although numerous methods have been proposed to explain black box machine learning models, the majority of these approaches are designed for general purpose tasks and do not incorporate domain specific knowledge. In this work, we study the explainability of financial textual data modeled by large language models through the lens of the Shapley value. Specifically, we investigate whether Shapley based attributions align with established financial domain knowledge. Through rigorous theoretical analysis and extensive empirical evaluations, we demonstrate that Shapley values can yi...

• 2.Heads, Not Backbones: Output Heads Dominate Architectures on Fat-Tailed Returns

  In a deep forecasting pipeline for fat-tailed financial returns at short horizons, which matters more - the backbone architecture or the output head? We compare four modern backbones (TimesNet, DLinear, N-BEATS, iTransformer) under three output heads: a point head, a single-Gaussian density head, and a Gaussian mixture density head with K=4 components. On S and P 500 monthly log-returns (1871-2023) under anchored walk-forward validation, the three heads form a strict gradient: switching from point to Gaussian improves CRPS by about 1.3 percent; switching from Gaussian to mixture adds a further about 2.4 percent. Switching between backbones, in contrast, changes CRPS by less than 1.5 percent on the point-head row and on the backbone-mean axis; density-head backbone spread is larger (up to 5.1 percent on the h=1 Gaussian row, driven by N-BE...

• 3.CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents

  LLM agents are increasingly cast as autonomous portfolio managers, and benchmarks have moved from financial question-answering to sequential trading. Yet most still rank agents by returns over a fixed window -- a weak proxy, since a period's return is dominated by the market path and apparent alpha can dissolve once look-ahead leakage is controlled. Such a ranking certifies neither sound reasoning, nor a consistent strategy, nor a durable edge. We introduce CLQT, which reframes closed-loop trading evaluation as diagnosis rather than ranking: an instrument that localizes where and why an agent's process succeeds or fails. CLQT is a fully closed-loop, cost-aware, strategy-consistent, temporally-gated environment whose agents run a five-stage cycle: gather, synthesize, allocate, execute, reflect. Each round emits a complete DecisionRound sea...

• 4.Liquidity-Based Audit of Algorithmic Trading Strategies

  We show that net demand for liquidity by algo strategies is identifiable from its trade and price history alone, with no knowledge of its signal or optimization problem. An exact multi-period regret decomposition implies that the sign of this statistic classifies a linear strategy as a net liquidity consumer or provider, recovering the Kyle (1985) informed-trader/market-maker dichotomy from observables alone. Under an AR(1) cost process, the same statistic equals the product of strategy size and the squared Roll (1984) implied spread, making the correction a direct proxy for prevailing illiquidity. Extending to endogenous price impact and aggregating across N correlated strategies yields a liquidity-balance condition whose violation produces welfare loss scaling as N squared, a closed-form fire-sale externality. We calibrate to CRSP equit...

• 5.The Decision Geometry of Covariance Estimation for the Global Minimum-Variance Portfolio under Heavy Tails

  The global minimum-variance portfolio (GMVP) is the canonical decision built from an estimated covariance matrix, yet covariance estimators are universally evaluated by matrix-norm loss, which is not the object the decision depends on. We characterise exactly how covariance-estimation error maps into GMVP suboptimality. We prove an exact regret identity and a non-asymptotic bound showing decision regret depends on the estimation error only through its action on the portfolio weights, scaled by portfolio concentration and the conditioning of the true covariance. From this we derive the decision geometry: GMVP regret is invariant to a (p-1)-dimensional projection of the p^2-dimensional error matrix, with invariance to the covariance-scale direction as an exact special case. We then apply the framework to heavy-tailed returns (tail index kap...

• 1.From fragmentation to control: why device manufacturers need an industry-led approach to homologation

  Summary available at source link.

• 2.Telco Common Corpus: The largest open, verified data commons for telecom AI

  Summary available at source link.

• 3.GSMA Launches Global Satellite Regulatory Playbook to Help Policymakers Build Future-Ready Connectivity Frameworks

  Summary available at source link.

• 4.GSMA welcomes China Tower to advance AI-ready mobile infrastructure

  Summary available at source link.

• 5.Scaling Telco AI from Ambition to Reality

  Summary available at source link.

• 1.ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning

  Understanding and reasoning over long contexts has become a key requirement for deploying large language models (LLMs) in realistic applications. Although recent LLMs support increasingly long context windows, they often fail to use relevant evidence that is already present in the input, revealing a gap between context access and effective context utilization. In this work, we propose Recursive Evidence Replay as LLM Harness for Long-Context Reasoning (RECONTEXT), a training-free inference method for improving long-context reasoning. RECONTEXT uses model-internal relevance signals to construct a query-conditioned evidence pool and replays it before final generation while preserving the full original context. This recursive selection process separates evidence organization from answer generation without training, external memory, or contex...

• 2.DemoPSD: Disagreement-Modulated Policy Self-Distillation

  On-policy self-distillation (OPSD) has emerged as a practical method for training large language models (LLMs) to reason, where a single model acts as both the teacher and the student with different levels of information access. However, recent studies have found that the teacher's dense token-level supervision, conditioned on privileged information, can lead to overfitting to in-domain patterns, suppress exploration, and hurt cross-domain generalization, while also introducing a more fundamental issue: *privileged information leakage*, where the student encodes answer-dependent shortcuts that are unavailable at test time. We introduce **DemoPSD**, a novel framework that resolves such problems through the idea of *selective adoption of teacher guidance*. Instead of fitting the full teacher distribution, DemoPSD steers the student toward a...

• 3.EAGLE-360: Embodied Active Global-to-Local Exploration in 360$^\circ$

  While Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in standard visual understanding, adapting them for active visual search in 360$^\circ$ panoramic environments exposes fundamental limitations. Specifically, standard MLLMs struggle to effectively model inherent panoramic properties, such as severe polar distortion and continuous cylindrical topologies, which significantly degrades target detection accuracy. Consequently, existing panoramic search methods attempt to compensate by relying heavily on fragmented local viewpoints. Burdened by rigid initialization and a lack of global panoramic priors, these approaches suffer from myopic, inefficient exploration and struggle with robust error recovery when targets are out of view. To overcome these challenges, we propose EAGLE-360, a novel Embodied Active...

• 4.Will Scaling Improve Social Simulation with LLMs?

  Large Language Model (LLM) social simulations are a promising research method, but they are not yet faithful enough to be adopted widely. In this work, we investigate whether the current scaling paradigm in language modeling is likely to close these gaps, or whether simulation fidelity is orthogonal to general capabilities and therefore deserving of more research attention. We use scaling laws to study the relationship between LLMs' compute scale, general capability benchmarks, and the fidelity of social simulation in three representative sub-domains: opinion modeling, behavioral simulation, and longitudinal forecasting. Surprisingly, we discover strong compute scaling in all three settings, using a suite of 85 transformer LLMs with the Qwen3 architecture pre-trained on the DCLM web text corpus under fixed-compute budgets from $10^{18}$ t...

• 5.AgentsCAD: Automated Design for Manufacturing of FDM Parts via Multi-Agent LLM Reasoning and Geometric Feature Recognition

  Parts manufactured with Fused Deposition Modeling (FDM) often require Design for Additive Manufacturing (DFAM) modifications to ensure printability, structural integrity, and reduced post-processing. Current slicers identify defects such as steep overhangs but are unable to modify the underlying geometry. This work presents AgentsCAD, a multi-agent system that bridges raw boundary-representation (B-Rep) geometry and Large Language Model (LLM) reasoning to automate targeted DFM. The workflow begins by parsing a STEP file. The agentic system detects overhangs above a 45°threshold, constructs a face-adjacency topology graph, and optionally injects semantic feature labels from a GraphSAGE model trained on MFCAD++ (59,665 parts), before dispatching a Claude Sonnet design-reasoning agent that recommends reorientations, fillets, chamfers, and si...

• 1.From SRA to Self-Flow: Data Augmentation or Self-Supervision?

  Representation alignment has become an effective way to accelerate diffusion transformer training and improve generation quality. Recent self-alignment methods, such as SRA and Self-Flow, further remove the dependency on external pretrained encoders by constructing alignment within the diffusion model itself. However, the mechanism behind the improvement from SRA to Self-Flow, dual-time scheduling, remains under-examined: Self-Flow attributes its gain to interactions between tokens at different noise levels, where cleaner tokens help infer noisier ones. In this work, we revisit this explanation and ask whether the gain instead comes from data augmentation along the noise dimension. To disentangle these factors, we introduce Attention Separation, which preserves the same dual-timestep input as Self-Flow while blocking attention between tok...

• 2.Dual-Selective Network for Domain-Incremental Change Detection

  Domain-incremental change detection (DICD) continuously adapts models to new geographic domains while preserving prior knowledge. However, a structural mismatch exists: the label space remains fixed while domain characteristics vary drastically. Consequently, incremental models struggle to maintain stable spatial change representations across domains. Existing strategies, such as replay-based or regularization-based methods, often fail to scale to long domain sequences, leading to knowledge degradation or increased computational cost. We propose Dual-Selective Incremental Network (DSINet), a unified framework built on visual state space models. DSINet leverages Mamba's input-dependent selective mechanism through a selective spatial state unit (S3U). This unit preserves stable spatial change structures while filtering domain-specific varia...

• 3.AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognition

  Vein recognition is a secure biometric technology often constrained by limited annotated data and imaging variations. While data augmentation mitigates this, strategies designed for natural images may disrupt the fine-grained topology and textures essential for identity discrimination. We present AGVBench, which evaluates 30 representative augmentation strategies on five public palm- and finger-vein datasets with seven backbone architectures, covering classic CNNs, vision transformers, and vein-specific recognition models. Our results show that multi-image mixing methods (e.g., MixUp, PuzzleMix, StarMixup) generally provide the strongest recognition performance. However, they are often poorly calibrated and vulnerable to adversarial perturbations, revealing a clear inconsistency between clean accuracy and adversarial security. We also fin...

• 4.Criticality-Based Guard Rail Validation for AI Agent Decisions in Autonomous Telecom Networks

  The evolution toward fully autonomous telecommunications networks (Autonomous Network Levels 4-5) requires AI/ML agents to make real-time network decisions without human intervention. However, no standardized runtime mechanism exists to intercept and validate individual inference outputs before they trigger live network state changes, creating risks of erroneous autonomous decisions. This paper proposes the Guard Rail Validation (GRV) framework, a standardizable runtime architecture for intercepting and validating AI-driven decisions before execution. The framework evaluates decisions across multiple weighted dimensions -- including action scope, action type, service criticality, agent autonomy level, reversibility, and temporal behavioural patterns -- to determine a criticality level. Based on this level, graduated validation mechanisms ...

• 5.Tight Lower Bounds for the Multi-Secretary Problem via Bellman Certificates

  This paper studies additive regret in the multi-secretary problem, defined as the gap between the expected offline prophet reward and the reward of the best online policy. Prior work established \(O(\log T)\) regret for bounded-density distributions with connected support and \(O((\log T)^2)\) upper bounds for bounded-density distributions with support gaps. It was unknown whether the extra logarithmic factor is necessary even in the one-resource model. We prove that it is necessary. For a mixture of two separated uniform distributions at the critical capacity, the optimal regret grows at least on the order of \((\log T)^2\). Thus the existing \(O((\log T)^2)\) upper bounds for bounded-density gapped instances, including those implied by network revenue management models with continuous rewards, are tight in this simplest specialization. ...

• 1.AI’s Volatile Power Use Quietly Tests Grid Limits

  The rapid expansion of artificial intelligence infrastructure is typically framed as an energy problem. Data centers are projected to consume a growing share of global electricity demand: The International Energy Agency estimates they could account for 3 to 4 percent of total global consumption within this decade. Utilities are already adjusting long-term forecasts to accommodate anticipated growth from hyperscale facilities and high-density compute clusters. This framing captures scale. It misses behavior. The emerging issue is not simply how much power large-scale compute systems consume, but how increasingly dense and synchronized computational workloads are beginning to alter the operating characteristics of the electrical grid itself through increasingly unpredictable demand that varies rapidly in both time and location, creating new...

• 2.As AI Reshapes Global Energy Systems, Melbourne Leads Through Engineering Collaboration

  This article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia . As artificial intelligence accelerates global demand for compute, a parallel constraint is emerging with equal urgency: energy. From hyperscale data centers to electrified industries, AI is driving a step change in electricity demand. This is not a future challenge, it is a present, system-level issue requiring coordinated action across energy, infrastructure, and engineering disciplines. Around the world, the question is no longer whether AI will scale, but whether energy systems can scale with it. Melbourne, Australia is moving beyond participation to become a globally connected leader helping define how these challenges are addressed. A national challenge with global implications Australia’s ambition to lead in artificial intell...

• 3.The Space-based Data Center Hype Machine Is Already in Orbit

  “ The lowest-cost place to put AI will be in space, and that will be true within two years, maybe three at the latest,” SpaceX founder Elon Musk told the World Economic Forum in Davos this past January, as his company was preparing to go public . Later that month, SpaceX filed an application with the Federal Communications Commission for an orbital data center constellation of up to 1 million satellites in low Earth orbit, 500 to 2,000 kilometers above Earth. And just three days before the IPO, he discussed some initial design specifications for a new AI-1 satellite data center in a video interview. Musk is prone to hyperbole when it comes to timelines. Full self-driving cars by 2017 . First human mission to Mars in 2024 . Ten thousand Optimus humanoid robots by the end of 2025 . Et cetera. For orbital data centers, which he says will be ...

• 4.Emily Bender Sets the Record Straight on “Stochastic Parrots”

  In March 2021, a group of four researchers—a collaboration of linguists and computer scientists—published their now legendary paper “ On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜” The paper received significant attention at the time (in part because Google fired two of the authors, Timnit Gebru and Margaret Mitchell, shortly before its publication). It argued that large language models (LLMs) generate text by statistically predicting likely sequences of words rather than understanding what they are saying—a process the authors captured with the metaphor of a “stochastic parrot,” a system that repeats patterns without comprehension. And over the past five years, the analogy has spread well beyond the academic field where it originated, spawning debates and inspiring projects such as a shoulder-mounted robot name...

• 5.Poetry for Engineers: Nine Lives of Nikola Tesla

  He was born into a storm, lightning split the summer sky, in a village the world had not yet heard of. The midwife called it a bad omen, his mother called it a sign. Your first life began in a storm, under open sky. One winter night you ran your hand along a cat’s back, and the darkness cracked open with sparks. Your mother warned the house could burn. You were already chasing what you learned: Light would return. Your second life came underwater, in the current deep. No light, no air, the river pulling you under, the surface closing above you without a sound, and something in you refused to sink or sleep. Your third life came at the dam. The water rose. The wall held you in place. One flash, you turned your body and rose back into air, and left the weight of water without a trace. Your fourth life came in stone and dark. Entombed for a n...

• 1.The Real Question to Ask About AI Governance

  Carolyn Geason-Beissel/MIT SMR | Getty Images Leaders at literally every Fortune 500 company will tell you that they are governing their AI — every single one of them. Now ask those same leaders who’s responsible for shutting down an AI model that’s causing harm. Most people can’t answer that question. That silence is the most […]

• 2.Transforming Investing With AI at Franklin Templeton

  Patrick George/Ikon Images What would you do with artificial intelligence if you were confident that it would transform your industry? What actions would you take if you felt that you were at an inflection point in that transformation? Would you try to be an early proponent of AI-first in your industry, or a fast follower? […]

• 3.Redefine What ‘Professionalism’ Means

  Matt Kenyon/Ikon Images “Professionalism” encompasses the broad set of shared beliefs and expectations about how people within an industry or workplace should interact with one another: Think communication style, punctuality, or meeting etiquette. But opinions differ: Cameras on? Cameras off? Do meetings start precisely on the hour? Is arriving a few minutes late acceptable or […]

• 4.Three Approaches to Measuring and Managing AI ROI

  Matt Harrison Clough/Ikon Images After several years of AI experiments and pilot initiatives, a crucial question remains open for most companies: How much of a return — and what kinds of returns — are we getting from all of this AI investment? To many executives, AI ROI still often feels more like art than science: […]

• 5.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 […]

• 1.Seemingly Anchored Inflation Expectations -- by Ulrike Malmendier, Stefan Nagel

  Empirical evidence commonly cited as indicating that inflation expectations have become better anchored includes the declining sensitivity of expectations to inflation surprises over time, particularly around the adoption of inflation targeting. These patterns are typically attributed to the influence of explicit or implicit inflation targets on inflation expectations. We show that this evidence is consistent with a model of experience-based learning in which individuals learn solely from their life-time history of realized inflation, without anchoring their expectations to an announced inflation target. In this model, the prolonged experience of low short-run inflation persistence in the pre-COVID decades renders long-run expectations insensitive to inflation surprises, matching the patterns observed in empirical anchoring tests. A uniqu...

• 2.New Evidence on the Health-Status Gradient from Imperial China -- by Wolfgang Keller, Carol H. Shiue, Karen Eggleston

  We study the relationship between health and social status in China using genealogical data from the 14th to 19th centuries. The data covers broad segments of the population linking husbands, wives, and sons, allowing us to examine whether status was associated with survival not only for adult men but also across households and generations. In a sample centered on the early 1700s, the typical lifespan of married men is 52, but varies widely. High-status men have a 22 percent higher probability of living past 50 than low-status men, resulting in a lifespan advantage of seven years. High status is also associated with significant survival advantages for wives and children. The lifespan premium of the wife of a high-status man over the wife of a commoner exceeds 10 years, and the chance that the son of a high-status man dies before reaching ...

• 3.The Pass-through of Tariffs and Exchange Rates -- by Stéphane Auray, Michael B. Devereux, Aurélien Eyquem

  This paper shows that the currency in which traded goods are invoiced has a first-order implication for the short-run impact of tariffs on the economy. If prices are set in terms of producer’s currency (PCP), a unilateral tariff is contractionary on impact, reducing GDP, and causing a deterioration in the trade balance. By contrast, with prices pre-set in buyers currency (LCP), the same tariff shock is expansionary, and improves the trade balance. The key difference between the two regimes lies in the differential exchange rate pass- through under PCP relative to LCP. In welfare terms however, the situation is reversed. A tariff under LCP leads to a fall in short-run welfare, as consumption and investment fall sharply, while the same tariff may be welfare enhancing under PCP. Under dollar currency pricing (DCP), we find results intermedia...

• 4.Understanding Cross-State Variations in Medicaid Enrollment During and After the COVID-19 Pandemic -- by Jeffrey Clemens, Helena Detering, Anwita Mahajan

  The implementation and unwinding of the FFCRA's continuous coverage requirement generated historically large changes in Medicaid enrollment during the COVID-19 pandemic. Using a combination of descriptive and causal analyses, we examine the substantial cross-state differences in these changes, which remain little understood. We find that policies designed to ease unwinding frictions, as well as federal fiscal assistance to states, have little predictive power, suggesting limited roles for administrative design or liquidity constraints. Political preferences have modest predictive power during the unwinding period. Pandemic-era Medicaid expansions and more generous baseline eligibility thresholds are strongly associated with larger net enrollment gains.

• 5.How Does Monetary and Fiscal Policy Affect the Economy in the Face of Large Shocks? -- by Greg Kaplan, Ken Miyahara

  We build a model that combines (i) heterogeneous households with incomplete markets, and (ii) state-dependent pricing with strategic complementarities by firms, to analyze the effects of large macroeconomic shocks and policy interventions. Both features significantly influence the transmission of fiscal stimulus and monetary policy—heterogeneous households because of failures of Ricardian equivalence, and state-dependent pricing because of its nonlinear effects on inflation. We use our model to quantify how monetary and fiscal policy shaped macroeconomic dynamics in response to the large shocks of 2020, and how alternative policy choices could have led to different aggregate and distributional outcomes. We find large departures from Ricardian equivalence and strong stepping-on-a-rake effects of interest rate changes. The large unfunded fi...

• 1.The Disappearing Overnight Drift

  In a 2021 Liberty Street Economics post, we documented the “overnight drift”—a large, persistent return to holding U.S. equity futures in the narrow window between 2:00 and 3:00 a.m. Eastern time, when European equity markets open. Five additional years of data later, that pattern appears to have faded: the 2:00–3:00 window that previously generated roughly 3.7 percent per annum has averaged close to zero since 2021. In this post, we revisit the overnight drift in light of the post-publication sample and use our inventory-risk framework to ask which of three observable channels—the dispersion of closing order imbalances, the level of return variance, or the risk-bearing capacity of liquidity providers—accounts for the cha...

• 2.Liquidity Fades as Treasuries Age

  More than $30 trillion U.S. Treasury debt is outstanding. Less than 4 percent of this amount, which is associated with the most recently issued Treasuries, called on-the-run securities, accounts for 65 percent of average daily trading volume. The remaining portion of the amount outstanding is accounted for by seasoned issues that have been replaced by newer benchmarks, which are referred to as off-the-run securities. In this post, we review the key results in our paper that uses transaction-level Treasury TRACE data to study how trading activity and liquidity evolve as securities move from on-the-run to off-the-run. We show three main patterns. First, off-the-run notes and bonds rely much more on dealer-to-customer intermediation than benchmark securities. Second,...

• 3.How Resilient Were Emerging Market Economies Through the 2022‑23 U.S. Monetary Tightening Cycle?

  The cross-border spillover effects of shifts in U.S. monetary policy have long been a focus of academics and policymakers alike. A common finding in the literature is that changes in the stance of U.S. monetary policy have sizable effects on economic activity and financial markets in emerging market economies (EMEs). In this post, we analyze one specific aspect of these spillovers: how EMEs fared through the U.S. monetary policy tightening cycle of 2022-23 relative to the predictions of a model, which was calibrated to capture empirically relevant features of these economies based on historical data. We find that more vulnerable EMEs fared better in both financial market and growth outcomes than would be expected from our model, while the relatively less vulnerable fared a bit better than the model predictions for financial outcomes but s...

• 4.The Post‑COVID Decline in the Labor Share

  The labor share of income in the U.S. is currently at its lowest-ever level in the post-war period. The labor share measures the fraction of economic output paid to workers as wages and salaries. As such, it is a useful benchmark for wage growth: when the labor share falls, it means that productivity, prices, or both are growing faster than wages. After much-studied drops in the 2000s, the labor share fell sharply again after the COVID pandemic. In this post, we compare the dynamics of the labor share post-COVID to earlier periods to understand whether the recent decline represents the continuation of a trend or a new and distinct phenomenon. We find that both the cyclicality of the labor share and the contribution of reallocation to the labor share post-COVID are similar to earlier periods.

• 5.Synthetic Stablecoins and Financial Stability

  On October 10, 2025, the announcement of a potential additional 100 percent tariff on Chinese goods drove risk-off moves across equities, Treasuries, credit spreads, and digital assets. Digital asset prices fell sharply, trading volumes surged, and liquidity vanished from key exchanges. In this post, we show how the price shock in digital assets was transmitted and amplified through a class of instruments called synthetic stablecoins—crypto assets whose structural design turned an external shock into a self-reinforcing deleveraging spiral within the crypto ecosystem.

• 1.There Is Only One Acceptable Path for Designer Babies

  Genetic-engineering tools with which to design one's progeny already exist, and they are being made even more potent with AI—at least for those who can afford such services. In the absence of policy interventions, we will be hurtling toward a tiered society in which economic privileges become biological advantages.

• 2.The Democratic Turkey NATO Needs

  At NATO's upcoming summit in Ankara, Turkish President Recep Tayyip Erdoğan will seek to portray his country as stable, confident, and essential to Western security. But only the last one is true, which is why the restoration of democracy in Turkey is more than a domestic concern.

• 3.The World Cup as It Should Be

  While Donald Trump was widely expected to lord over this year’s World Cup, which the United States is co-hosting with Mexico and Canada, it has turned out not to be his scene. National teams populated with immigrants, and small-town America’s embrace of the event, go against everything Trump’s MAGA movement stands for.

• 4.The Real Threat to NATO

  The greatest challenge to NATO comes not from its adversaries but from within. As its leaders gather in Ankara on July 7-8, they should focus less on military capabilities than on reinvigorating the alliance's founding strategic philosophy, centered on democratic legitimacy, human rights, and the rule of law.

• 5.How the EU Can Lead the Rest of the West

  While Europe is not just another middle power, nor is it a great power with sufficient economic, military, and geopolitical clout to dictate terms on the global stage. Instead, Europe is the ideal candidate to convene and lead a group of like-minded middle powers, not as a US-style hegemon, but as a first among equals.

• 1.Enea White Paper: Why Intelligent AAA is the Swiss Army Knife of Telecom

  Featuring 8 case studies from Tier 1 operators around the world, this white paper demonstrates how the next-gen Enea AAA Server transforms operational complexity into scalable, automated, and revenue-generating services.…

• 2.Huawei outlines AI-centric network roadmap for telecom monetization

  Huawei tells MWC Shanghai 2026 that operators are entering an era in which AI applications, intelligent agents, and real-time interactions place new demands on mobile and transport networks In sum…

• 3.Why consolidation will reshape MDU connectivity (part 3/3) – Analyst Angle

  Buying doors is easy; stitching networks together is not. Consolidation between MDU providers creates scale on paper. Whether it creates a better operator depends on something far less glamorous: whether…

• 4.Quantum Safe Networks Forum 2026

  How can the industry future-proof telecom Infrastructure against quantum threats? As quantum computing advances at breakneck speed, the telecom industry faces an urgent challenge: safeguarding networks against the looming threat…

• 5.Samsung’s AI RAN optimizer boosts KDDI 5G speeds up to 52% in live trial

  Real-time AI RAN tuning lifted peak downlink throughput 31% network-wide across Tokyo In sum – what we know: Samsung and KDDI have completed a months-long trial of Samsung’s AI-powered RAN…

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• 1.Toward a Unified Security and Privacy Framework for AI-Native 6G Networks

  Sixth Generation (6G) communication networks are expected to evolve into AI-native, highly autonomous ecosystems that integrate communication, computing, sensing, and artificial intelligence. While these capabilities enable unprecedented connectivity and intelligent services, they also create a highly heterogeneous security and privacy landscape that cannot be addressed through isolated, technology-specific solutions. This paper presents a comprehensive survey of security and privacy in AI-native 6G networks from a cross-layer perspective. We first examine the fragmentation of existing security and privacy approaches across emerging technologies, network architectures, AI systems, and standardization efforts, motivating the need for a unified security and privacy framework. Building upon this framework, we develop a cross-layer threat tax...

• 2.Lightweight Vision-Aided Beam Tracking for Cross-Environment mmWave Communications

  Sensing-aided beam tracking is a promising approach to reduce the overhead for millimeter-wave beam management. However, real-world application remains challenging due to rapid channel variations and substantial environmental differences across deployment scenarios. Developing low-complexity sensing assisted approaches that generalize to diverse environments can alleviate the problem. With this motivation, this paper proposes a lightweight vision-aided model for cross-environment beam tracking. The task is formulated as a sequence-to-sequence classification problem, where the model jointly predicts the current and future optimal beams from past visual observations. We develop a low-complexity model based on depthwise separable convolutions and introduce hierarchical data augmentation and beam power-based label smoothing to improve robustn...

• 3.Measurement-Based Characterization and Statistical Modeling of 6G Urban Low-Altitude A2G Channels across FR1 and FR3

  Unmanned aerial vehicle (UAV) communications have been recognized as a key component of future sixth-generation (6G) space-air-ground-sea integrated networks. Accurate characterization and modeling of air-to-ground (A2G) channels are essential for the design and optimization of low-altitude communication systems. This paper presents a wideband A2G channel measurement campaign in an urban environment at 2.85 and 4.6~GHz in FR1 and 7.25~GHz in the FR3 frequency band, each with a bandwidth of 250~MHz. To enable reliable line-of-sight (LoS) and non-line-of-sight (NLoS) propagation state identification, a weakly supervised method is developed by fusing geometric priors, channel features, and spatial consistency constraints. Furthermore, based on the measured data, A2G channel characteristics are extracted and analyzed under LoS/NLoS conditions...

• 4.Scalable Security and Migration-Aware SFC Provisioning in LEO Satellite Networks

  Low Earth orbit (LEO) satellite constellations are emerging as a backbone for global 6G connectivity, where independent tenant slices share orbital infrastructure, each requiring an ordered chain of security virtual network functions (VNFs). Because onboard computation and networking are scarce, slices cannot be given dedicated VNFs. They must share instances on the same satellites, enlarging the attack surface and exposing tenants to cross-slice side-channel risk. This exposure shifts continually as visibility, orbital motion, and the inter-satellite topology change in time (epochs), making VNF migration a structural necessity that couples resource efficiency, service continuity, and security isolation into a single problem. We formulate this security- and migration-aware security function chain (SFC) placement as a multi-slice mixed-int...

• 5.Trade-Offs in Decentralized Gigantic MIMO with Hard-Boundary Constraints

  To maintain the antenna apertures offered by 5G massive MIMO systems operating at the sub-6GHz band, known as FR1, 6G base stations (BSs) using the upper-mid band, FR3, should increase the number of antennas by a factor 4-8, giving rise to gigantic MIMO. This poses challenges in terms of processing complexity and interconnection bandwidth. The WAX framework, previously introduced for exploring trade-offs in decentralized architectures, may offer the flexibility needed to tackle these challenges. However, no results have been established on the applicability of this framework in the presence of hard-boundary constraints. The current work explores gigantic MIMO implementations based on a novel adaptation of the WAX framework, where the decentralized processing is performed by non-cooperating hardware modules. These modules may be implemente...

• 1.A Cap-Axis Integral Diagnostic of Factor Models

  I propose a cap-axis integral diagnostic for factor-model evaluation. Low-dimensional factor models can improve the maximum-Sharpe frontier while leaving zero-alpha violations on economically fixed subspaces. The diagnostic studies one such subspace by lifting pricing errors into a bridge-alpha curve along the market-capitalization rank axis. Under an aggregate-market gate, a zero curve is equivalent to pricing the market's internal cap-rank subspace. In 1967-2024 CRSP data, q5's daily negative bridge attenuates under lead-lag correction, while Fama-French and Carhart bridges are more visible monthly. Across 154 factors, the cap-axis norm is distinct from Sharpe gain and size exposure.

• 2.Liquidity Premium and Investment Horizons

  We estimate Kyle's (1985) price-impact coefficient $λ$ directly from daily equity order flow and test its ability to forecast the cross-section of subsequent stock returns. Using CRSP data from 2020 to 2025, we construct firm-month measures of signed order flow and two estimators of $\hatλ_{it}$: a within-month price-impact regression and an Amihud-style ratio. Signed order flow strongly predicts contemporaneous and one-month-ahead returns, while volume volatility predicts lower subsequent returns, consistent with widening price impact degrading price discovery. Fama-MacBeth regressions confirm that our order-flow signal carries significant cross-sectional return information after Newey--West adjustment. Theoretically, we resolve the liquidity premium puzzle of Constantinides (1986) through an adverse-selection mechanism: low order flow w...

• 3.End-to-End Parametric Portfolio Policies for Cross-Asset Futures Timing: When Do AI Models Beat Simple Rules?

  Timing-based tilts across asset classes can drive much of the risk and return of a diversified cross-asset portfolio. The standard approach forecasts returns and then optimizes weights. We instead study an end-to-end AI-based policy that maps market states directly to portfolio weights, and we then ask when this one-step modeling approach outperforms simple rules-based strategies. We train these policies on the sixteen most liquid CME futures, where an edge is unlikely to be due to illiquidity, using a differentiable Sharpe ratio loss function, and we benchmark them against equal weighting, risk parity, and time-series momentum. The learned policies rank above the rules on the pooled cross-asset portfolio and in several sub-asset classes, but not uniformly. In gross terms, an LSTM and a transformer-based architecture perform comparably ou...

• 4.Agent-to-Agent Finance: Blockchain Payments and Trust Infrastructure for Autonomous AI Agents

  Autonomous AI agents are beginning to occupy a position between analytical tools and transacting counterparties. They can interpret goals, call external tools, negotiate with other agents, access data and computation, and in some settings initiate payments or blockchain transactions. This development creates a distinct problem for financial markets: if software agents can act economically, market participants need infrastructure for identity, authorisation, payment, verification, reputation and accountability. This article develops the concept of agent-to-agent finance as the layer of machine-mediated financial interaction in which autonomous agents discover counterparties, purchase services, express transaction intent, execute payments and generate auditable evidence. The argument is not that blockchain is a universal substrate for finan...

• 5.Settlement Manipulation in Prediction Markets

  Prediction markets increasingly list contracts settling on an asset price that holders can move by trading the underlying. We build a model showing that such contracts transfer wealth from prediction-market liquidity traders to manipulators and harm price discovery in the underlying, even as it becomes more liquid. After the launch of Polymarket's five-minute Bitcoin contract, settlement-time spot order flow spikes, causing large price reversals after settlement. Manipulators capture a large amount of profit, mostly from retail. Manipulation is largely absent in the fifteen-minute contracts: lengthening the contract horizon removes it, providing the market-design remedy our model and evidence support.

• 1.Toward a Unified Security and Privacy Framework for AI-Native 6G Networks

  Sixth Generation (6G) communication networks are expected to evolve into AI-native, highly autonomous ecosystems that integrate communication, computing, sensing, and artificial intelligence. While these capabilities enable unprecedented connectivity and intelligent services, they also create a highly heterogeneous security and privacy landscape that cannot be addressed through isolated, technology-specific solutions. This paper presents a comprehensive survey of security and privacy in AI-native 6G networks from a cross-layer perspective. We first examine the fragmentation of existing security and privacy approaches across emerging technologies, network architectures, AI systems, and standardization efforts, motivating the need for a unified security and privacy framework. Building upon this framework, we develop a cross-layer threat tax...

• 2.Lightweight Vision-Aided Beam Tracking for Cross-Environment mmWave Communications

  Sensing-aided beam tracking is a promising approach to reduce the overhead for millimeter-wave beam management. However, real-world application remains challenging due to rapid channel variations and substantial environmental differences across deployment scenarios. Developing low-complexity sensing assisted approaches that generalize to diverse environments can alleviate the problem. With this motivation, this paper proposes a lightweight vision-aided model for cross-environment beam tracking. The task is formulated as a sequence-to-sequence classification problem, where the model jointly predicts the current and future optimal beams from past visual observations. We develop a low-complexity model based on depthwise separable convolutions and introduce hierarchical data augmentation and beam power-based label smoothing to improve robustn...

• 3.Latency-Sensitive 5G RAN Slicing for Industry 4.0

  Network slicing is a novel 5G paradigm that exploits the virtualization and softwarization of networks to create different logical network instances over a common network infrastructure. Each instance is tailored for specific Quality of Service (QoS) profiles so that network slicing can simultaneously support several services with diverse requirements. Network slicing can be applied at the Core Network or at the Radio Access Network (RAN). RAN slicing is particularly relevant to support latency-sensitive or timecritical applications since the RAN accounts for a significant part of the end-to-end transmission latency. In this context, this study proposes a novel latency-sensitive 5G RAN slicing solution. The proposal includes schemes to design slices and partition (or allocate) radio resources among slices. These schemes are designed with ...

• 4.Measurement-Based Characterization and Statistical Modeling of 6G Urban Low-Altitude A2G Channels across FR1 and FR3

  Unmanned aerial vehicle (UAV) communications have been recognized as a key component of future sixth-generation (6G) space-air-ground-sea integrated networks. Accurate characterization and modeling of air-to-ground (A2G) channels are essential for the design and optimization of low-altitude communication systems. This paper presents a wideband A2G channel measurement campaign in an urban environment at 2.85 and 4.6~GHz in FR1 and 7.25~GHz in the FR3 frequency band, each with a bandwidth of 250~MHz. To enable reliable line-of-sight (LoS) and non-line-of-sight (NLoS) propagation state identification, a weakly supervised method is developed by fusing geometric priors, channel features, and spatial consistency constraints. Furthermore, based on the measured data, A2G channel characteristics are extracted and analyzed under LoS/NLoS conditions...

• 5.Scalable Security and Migration-Aware SFC Provisioning in LEO Satellite Networks

  Low Earth orbit (LEO) satellite constellations are emerging as a backbone for global 6G connectivity, where independent tenant slices share orbital infrastructure, each requiring an ordered chain of security virtual network functions (VNFs). Because onboard computation and networking are scarce, slices cannot be given dedicated VNFs. They must share instances on the same satellites, enlarging the attack surface and exposing tenants to cross-slice side-channel risk. This exposure shifts continually as visibility, orbital motion, and the inter-satellite topology change in time (epochs), making VNF migration a structural necessity that couples resource efficiency, service continuity, and security isolation into a single problem. We formulate this security- and migration-aware security function chain (SFC) placement as a multi-slice mixed-int...

• 1.Latency-Sensitive 5G RAN Slicing for Industry 4.0

  Network slicing is a novel 5G paradigm that exploits the virtualization and softwarization of networks to create different logical network instances over a common network infrastructure. Each instance is tailored for specific Quality of Service (QoS) profiles so that network slicing can simultaneously support several services with diverse requirements. Network slicing can be applied at the Core Network or at the Radio Access Network (RAN). RAN slicing is particularly relevant to support latency-sensitive or timecritical applications since the RAN accounts for a significant part of the end-to-end transmission latency. In this context, this study proposes a novel latency-sensitive 5G RAN slicing solution. The proposal includes schemes to design slices and partition (or allocate) radio resources among slices. These schemes are designed with ...

• 2.Semantic-Aware Multiple Access via Spatial Redundancy Exploitation for Uplink-Dominant 6G Use Cases

  Emerging uplink-dominant 6G use cases, such as cooperative vehicular streaming, require efficient transmission of high-volume visual data over limited wireless resources. While semantic communications can reduce traffic by prioritizing task-relevant content, most existing approaches treat users independently and therefore overlook spatial redundancy among nearby devices' observations. This paper proposes a semantic-aware multiple access scheme that exploits overlapping fields of view among vehicular users to reduce redundant uplink transmissions. We formulate a joint perception and transmission control problem in which users decide which image patches to transmit, when to transmit them, and over which channel, subject to communication constraints. To address the resulting complexity, we introduce a practical two-phase approach. First, nea...

• 3.Latency-Sensitive 5G RAN Slicing for Deterministic Aperiodic Traffic in Smart Manufacturing

  5G and beyond networks will support the digitalization of smart manufacturing thanks to their capacity to simultaneously serve different types of traffic with distinct QoS requirements. This can be achieved using Network Slicing that creates different logical network partitions (or slices) over a common infrastructure, and each can be tailored to support a particular type of traffic. The configuration of the Radio Access Network (RAN) slices strongly impacts the capacity of 5G and beyond to support critical services with stringent QoS requirements, and in particular deterministic requirements. Existing RAN Slicing solutions only consider the transmission rate (or bandwidth) requirements of the different services to partition the radio resources. This study demonstrates that this approach is not suitable to guarantee the stringent latency ...

• 4.Beyond Wireless Security: Covert Communications in Large Language Model-enabled Edge Networks

  Large language model (LLM)-enabled edge networks (LLMENs) offer mobile users high-quality and low-latency AI-generated content services in the 6G era. However, unlike typical edge networks, LLMENs present unique security challenges due to the inherent complexity of LLMs, their high computational overhead, and continuous interactions with users. Specifically, both frequent user interactions (i.e., queries and responses) over wireless channels and potential electromagnetic information leakage from intensive LLM computations make LLMENs susceptible to various security threats, such as eavesdropping, jamming, prompt poisoning, and prompt injection attacks. Since existing countermeasures against these attacks often incur prohibitive overhead, developing holistic, efficient, and secure privacy protections for LLMENs is crucial. This article fir...

• 5.SubEdge: A Subscriber-Centric Edge Computing Subsystem in 6G Networks for AI

  Beyond traditional connectivity, 6G is envisioned to transform mobile networks into a distributed fabric that provides native integrated communication, computing, and intelligence services. AI-native terminals (e.g., robots, autonomous vehicles, and smart glasses) require real-time inference from individualised, manufacturer-specific models that cannot be executed on-board nor shared across subscribers, making per-subscriber edge compute the necessary complement to per-subscriber connectivity. Existing Network for AI (Net4AI) architectures provision compute for application providers through shared deployments and do not address per-subscriber provisioning. This paper proposes SubEdge, a Net4AI subsystem that provisions integrated communication and compute resources on a per-subscriber basis, ensuring the coupled migration of both dimensio...