Babak's Daily Briefing

Sunday, March 15, 2026

Sources: 17 | Total Articles: 81

• 1.SpaceRAN: Airbus UpNext explores software-defined 5G NTN from orbit

  Airbus UpNext has launched its SpaceRAN (Space Radio Access Network) demonstrator, a key initiative to advance standardised 5G…

• 2.SoftBank’s Transformer-Based AI-RAN Hits 30% Uplink Gain at Sub-Millisecond Latency

  On August 21, 2025, SoftBank published results from a live, standards-compliant AI-RAN trial that replaces parts of classical signal processing with a lightweight Transformer.

• 3.6G as a Platform for Value

  Reframing the Future with NGMN’s Chairman, Laurent Leboucher By Piotr (Peter) Pietrzyk, Managing Editor, 6GWorld.com In the race…

• 4.SoftBank Road-Tests 7 GHz in Central Tokyo

  SoftBank and Nokia have begun outdoor field trials in Tokyo’s Ginza district using 7 GHz spectrum, installing three pre-commercial base stations to compare coverage and radio characteristics against today’s sub-6 GHz 5G sites.

• 5.NXP’s Acquisition of TTTech Auto Signals Growing Focus on Middleware for Software-Defined Vehicles

  On June 17, 2025, NXP Semiconductors finalized its acquisition of TTTech Auto—a strategic move to integrate TTTech’s flagship…

• 1.When OpenClaw Meets Hospital: Toward an Agentic Operating System for Dynamic Clinical Workflows

  Large language model (LLM) agents extend conventional generative models by integrating reasoning, tool invocation, and persistent memory. Recent studies suggest that such agents may significantly improve clinical workflows by automating documentation, coordinating care processes, and assisting medical decision making. However, despite rapid progress, deploying autonomous agents in healthcare environments remains difficult due to reliability limitations, security risks, and insufficient long-term memory mechanisms. This work proposes an architecture that adapts LLM agents for hospital environments. The design introduces four core components: a restricted execution environment inspired by Linux multi-user systems, a document-centric interaction paradigm connecting patient and clinician agents, a page-indexed memory architecture designed for...

• 2.From Control to Foresight: Simulation as a New Paradigm for Human-Agent Collaboration

  Large Language Models (LLMs) are increasingly used to power autonomous agents for complex, multi-step tasks. However, human-agent interaction remains pointwise and reactive: users approve or correct individual actions to mitigate immediate risks, without visibility into subsequent consequences. This forces users to mentally simulate long-term effects, a cognitively demanding and often inaccurate process. Users have control over individual steps but lack the foresight to make informed decisions. We argue that effective collaboration requires foresight, not just control. We propose simulation-in-the-loop, an interaction paradigm that enables users and agents to explore simulated future trajectories before committing to decisions. Simulation transforms intervention from reactive guesswork into informed exploration, while helping users discov...

• 3.Novelty Adaptation Through Hybrid Large Language Model (LLM)-Symbolic Planning and LLM-guided Reinforcement Learning

  In dynamic open-world environments, autonomous agents often encounter novelties that hinder their ability to find plans to achieve their goals. Specifically, traditional symbolic planners fail to generate plans when the robot's planning domain lacks the operators that enable it to interact appropriately with novel objects in the environment. We propose a neuro-symbolic architecture that integrates symbolic planning, reinforcement learning, and a large language model (LLM) to learn how to handle novel objects. In particular, we leverage the common sense reasoning capability of the LLM to identify missing operators, generate plans with the symbolic AI planner, and write reward functions to guide the reinforcement learning agent in learning control policies for newly identified operators. Our method outperforms the state-of-the-art methods i...

• 4.COMPASS: The explainable agentic framework for Sovereignty, Sustainability, Compliance, and Ethics

  The rapid proliferation of large language model (LLM)-based agentic systems raises critical concerns regarding digital sovereignty, environmental sustainability, regulatory compliance, and ethical alignment. Whilst existing frameworks address individual dimensions in isolation, no unified architecture systematically integrates these imperatives into the decision-making processes of autonomous agents. This paper introduces the COMPASS (Compliance and Orchestration for Multi-dimensional Principles in Autonomous Systems with Sovereignty) Framework, a novel multi-agent orchestration system designed to enforce value-aligned AI through modular, extensible governance mechanisms. The framework comprises an Orchestrator and four specialised sub-agents addressing sovereignty, carbon-aware computing, compliance, and ethics, each augmented with Retri...

• 5.Code-Space Response Oracles: Generating Interpretable Multi-Agent Policies with Large Language Models

  Recent advances in multi-agent reinforcement learning, particularly Policy-Space Response Oracles (PSRO), have enabled the computation of approximate game-theoretic equilibria in increasingly complex domains. However, these methods rely on deep reinforcement learning oracles that produce `black-box' neural network policies, making them difficult to interpret, trust or debug. We introduce Code-Space Response Oracles (CSRO), a novel framework that addresses this challenge by replacing RL oracles with Large Language Models (LLMs). CSRO reframes the best response computation as a code generation task, prompting an LLM to generate policies directly as human-readable code. This approach not only yields inherently interpretable policies but also leverages the LLM's pretrained knowledge to discover complex, human-like strategies. We explore multi...

• 1.A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting

  This paper studies cross-market return predictability through a machine learning framework that preserves economic structure. Exploiting the non-overlapping trading hours of the U.S. and Chinese equity markets, we construct a directed bipartite graph that captures time-ordered predictive linkages between stocks across markets. Edges are selected via rolling-window hypothesis testing, and the resulting graph serves as a sparse, economically interpretable feature-selection layer for downstream machine learning models. We apply a range of regularized and ensemble methods to forecast open-to-close returns using lagged foreign-market information. Our results reveal a pronounced directional asymmetry: U.S. previous-close-to-close returns contain substantial predictive information for Chinese intraday returns, whereas the reverse effect is limit...

• 2.Hybrid Hidden Markov Model for Modeling Equity Excess Growth Rate Dynamics: A Discrete-State Approach with Jump-Diffusion

  Generating synthetic financial time series that preserve statistical properties of real market data is essential for stress testing, risk model validation, and scenario design. Existing approaches, from parametric models to deep generative networks, struggle to simultaneously reproduce heavy-tailed distributions, negligible linear autocorrelation, and persistent volatility clustering. We propose a hybrid hidden Markov framework that discretizes continuous excess growth rates into Laplace quantile-defined market states and augments regime switching with a Poisson-driven jump-duration mechanism to enforce realistic tail-state dwell times. Parameters are estimated by direct transition counting, bypassing the Baum-Welch EM algorithm. Synthetic data quality is evaluated using Kolmogorov-Smirnov and Anderson-Darling pass rates for distributiona...

• 3.Uncertainty-Aware Deep Hedging

  Deep hedging trains neural networks to manage derivative risk under market frictions, but produces hedge ratios with no measure of model confidence -- a significant barrier to deployment. We introduce uncertainty quantification to the deep hedging framework by training a deep ensemble of five independent LSTM networks under Heston stochastic volatility with proportional transaction costs. The ensemble's disagreement at each time step provides a per-time-step confidence measure that is strongly predictive of hedging performance: the learned strategy outperforms the Black-Scholes delta on approximately 80% of paths when model agreement is high, but on fewer than 20% when disagreement is elevated. We propose a CVaR-optimised blending strategy that combines the ensemble's hedge with the classical Black-Scholes delta, weighted by the level of ...

• 4.Global universality via discrete-time signatures

  We establish global universal approximation theorems on spaces of piecewise linear paths, stating that linear functionals of the corresponding signatures are dense with respect to $L^p$- and weighted norms, under an integrability condition on the underlying weight function. As an application, we show that piecewise linear interpolations of Brownian motion satisfies this integrability condition. Consequently, we obtain $L^p$-approximation results for path-dependent functionals, random ordinary differential equations, and stochastic differential equations driven by Brownian motion.

• 5.Generative Adversarial Regression (GAR): Learning Conditional Risk Scenarios

  We propose Generative Adversarial Regression (GAR), a framework for learning conditional risk scenarios through generators aligned with downstream risk objectives. GAR builds on a regression characterization of conditional risk for elicitable functionals, including quantiles, expectiles, and jointly elicitable pairs. We extend this principle from point prediction to generative modeling by training generators whose policy-induced risk matches that of real data under the same context. To ensure robustness across all policies, GAR adopts a minimax formulation in which an adversarial policy identifies worst-case discrepancies in risk evaluation while the generator adapts to eliminate them. This structure preserves alignment with the risk functional across a broad class of policies rather than a fixed, pre-specified set. We illustrate GAR thro...

• 1.GSMA MWC26 Barcelona closes 20th anniversary edition

  Summary available at source link.

• 2.From Ambition to Execution: How Open Gateway Is Scaling the Global API Economy

  Summary available at source link.

• 3.Pioneering Affordable Access in Africa: GSMA and Handset Affordability Coalition Members Identify Six African Countries to Pilot Affordable $40 Smartphones

  Summary available at source link.

• 4.GSMA Calls for Regulatory Readiness for Direct-to-User LEO Satellite Services

  Summary available at source link.

• 5.MWC26 Barcelona opens with call to complete 5G, rise to AI challenges, and strengthen digital safety

  Summary available at source link.

• 1.MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning

  Multimodal Large Language Models (MLLMs) are increasingly used to carry out visual workflows such as navigating GUIs, where the next step depends on verified visual compositional conditions (e.g., "if a permission dialog appears and the color of the interface is green, click Allow") and the process may branch or terminate early. Yet this capability remains under-evaluated: existing benchmarks focus on shallow-compositions or independent-constraints rather than deeply chained compositional conditionals. In this paper, we introduce MM-CondChain, a benchmark for visually grounded deep compositional reasoning. Each benchmark instance is organized as a multi-layer reasoning chain, where every layer contains a non-trivial compositional condition grounded in visual evidence and built from multiple objects, attributes, or relations. To answer cor...

• 2.Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously

  Online Video Large Language Models (VideoLLMs) play a critical role in supporting responsive, real-time interaction. Existing methods focus on streaming perception, lacking a synchronized logical reasoning stream. However, directly applying test-time scaling methods incurs unacceptable response latency. To address this trade-off, we propose Video Streaming Thinking (VST), a novel paradigm for streaming video understanding. It supports a thinking while watching mechanism, which activates reasoning over incoming video clips during streaming. This design improves timely comprehension and coherent cognition while preserving real-time responsiveness by amortizing LLM reasoning latency over video playback. Furthermore, we introduce a comprehensive post-training pipeline that integrates VST-SFT, which structurally adapts the offline VideoLLM to ...

• 3.EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models

  Recently, Multimodal Large Language Models (MLLMs) have been widely integrated into diffusion frameworks primarily as text encoders to tackle complex tasks such as spatial reasoning. However, this paradigm suffers from two critical limitations: (i) MLLMs text encoder exhibits insufficient reasoning depth. Single-step encoding fails to activate the Chain-of-Thought process, which is essential for MLLMs to provide accurate guidance for complex tasks. (ii) The guidance remains invariant during the decoding process. Invariant guidance during decoding prevents DiT from progressively decomposing complex instructions into actionable denoising steps, even with correct MLLM encodings. To this end, we propose Endogenous Chain-of-Thought (EndoCoT), a novel framework that first activates MLLMs' reasoning potential by iteratively refining latent thoug...

• 4.Sparking Scientific Creativity via LLM-Driven Interdisciplinary Inspiration

  Despite interdisciplinary research leading to larger and longer-term impact, most work remains confined to single-domain academic silos. Recent AI-based approaches to scientific discovery show promise for interdisciplinary research, but many prioritize rapidly designing experiments and solutions, bypassing the exploratory, collaborative reasoning processes that drive creative interdisciplinary breakthroughs. As a result, prior efforts largely prioritize automating scientific discovery rather than augmenting the reasoning processes that underlie scientific disruption. We present Idea-Catalyst, a novel framework that systematically identifies interdisciplinary insights to support creative reasoning in both humans and large language models. Starting from an abstract research goal, Idea-Catalyst is designed to assist the brainstorming stage, ...

• 5.LatentGeo: Learnable Auxiliary Constructions in Latent Space for Multimodal Geometric Reasoning

  Despite recent advances in multimodal reasoning, representing auxiliary geometric constructions remains a fundamental challenge for multimodal large language models (MLLMs). Such constructions are absent from the original diagram and must be introduced before theorems apply. Existing approaches predominantly rely on explicit construction paradigms, including text-based geometric specification, visual-token interleaving during reasoning, and tool-augmented geometric execution. However, these methods either fail to faithfully represent complex spatial relationships, incur representation mismatch between discrete symbols and continuous geometric structures, or rely on external capabilities that hinder end-to-end optimization. To address these limitations, we propose LatentGeo, a framework that learns continuous latent visual representations ...

• 1.EVATok: Adaptive Length Video Tokenization for Efficient Visual Autoregressive Generation

  Autoregressive (AR) video generative models rely on video tokenizers that compress pixels into discrete token sequences. The length of these token sequences is crucial for balancing reconstruction quality against downstream generation computational cost. Traditional video tokenizers apply a uniform token assignment across temporal blocks of different videos, often wasting tokens on simple, static, or repetitive segments while underserving dynamic or complex ones. To address this inefficiency, we introduce $\textbf{EVATok}$, a framework to produce $\textbf{E}$fficient $\textbf{V}$ideo $\textbf{A}$daptive $\textbf{Tok}$enizers. Our framework estimates optimal token assignments for each video to achieve the best quality-cost trade-off, develops lightweight routers for fast prediction of these optimal assignments, and trains adaptive tokenize...

• 2.MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning

  Multimodal Large Language Models (MLLMs) are increasingly used to carry out visual workflows such as navigating GUIs, where the next step depends on verified visual compositional conditions (e.g., "if a permission dialog appears and the color of the interface is green, click Allow") and the process may branch or terminate early. Yet this capability remains under-evaluated: existing benchmarks focus on shallow-compositions or independent-constraints rather than deeply chained compositional conditionals. In this paper, we introduce MM-CondChain, a benchmark for visually grounded deep compositional reasoning. Each benchmark instance is organized as a multi-layer reasoning chain, where every layer contains a non-trivial compositional condition grounded in visual evidence and built from multiple objects, attributes, or relations. To answer cor...

• 3.Separable neural architectures as a primitive for unified predictive and generative intelligence

  Intelligent systems across physics, language and perception often exhibit factorisable structure, yet are typically modelled by monolithic neural architectures that do not explicitly exploit this structure. The separable neural architecture (SNA) addresses this by formalising a representational class that unifies additive, quadratic and tensor-decomposed neural models. By constraining interaction order and tensor rank, SNAs impose a structural inductive bias that factorises high-dimensional mappings into low-arity components. Separability need not be a property of the system itself: it often emerges in the coordinates or representations through which the system is expressed. Crucially, this coordinate-aware formulation reveals a structural analogy between chaotic spatiotemporal dynamics and linguistic autoregression. By treating continuou...

• 4.HomeSafe-Bench: Evaluating Vision-Language Models on Unsafe Action Detection for Embodied Agents in Household Scenarios

  The rapid evolution of embodied agents has accelerated the deployment of household robots in real-world environments. However, unlike structured industrial settings, household spaces introduce unpredictable safety risks, where system limitations such as perception latency and lack of common sense knowledge can lead to dangerous errors. Current safety evaluations, often restricted to static images, text, or general hazards, fail to adequately benchmark dynamic unsafe action detection in these specific contexts. To bridge this gap, we introduce \textbf{HomeSafe-Bench}, a challenging benchmark designed to evaluate Vision-Language Models (VLMs) on unsafe action detection in household scenarios. HomeSafe-Bench is contrusted via a hybrid pipeline combining physical simulation with advanced video generation and features 438 diverse cases across ...

• 5.Deep Learning-based Assessment of the Relation Between the Third Molar and Mandibular Canal on Panoramic Radiographs using Local, Centralized, and Federated Learning

  Impaction of the mandibular third molar in proximity to the mandibular canal increases the risk of inferior alveolar nerve injury. Panoramic radiography is routinely used to assess this relationship. Automated classification of molar-canal overlap could support clinical triage and reduce unnecessary CBCT referrals, while federated learning (FL) enables multi-center collaboration without sharing patient data. We compared Local Learning (LL), FL, and Centralized Learning (CL) for binary overlap/no-overlap classification on cropped panoramic radiographs partitioned across eight independent labelers. A pretrained ResNet-34 was trained under each paradigm and evaluated using per-client metrics with locally optimized thresholds and pooled test performance with a global threshold. Performance was assessed using area under the receiver operating ...

• 1.Why AI Chatbots Agree With You Even When You’re Wrong

  In April of 2025, OpenAI released a new version of GPT-4o, one of the AI algorithms users could select to power ChatGPT, the company’s chatbot. The next week, OpenAI reverted to the previous version. “The update we removed was overly flattering or agreeable—often described as sycophantic,” the company announced . Some people found the sycophancy hilarious. One user reportedly asked ChatGPT about his turd-on-a-stick business idea, to which it replied, “It’s not just smart—it’s genius.” Some found the behavior uncomfortable. For others, it was actually dangerous. Even versions of 4o that were less fawning have led to lawsuits against OpenAI for allegedly encouraging users to follow through on plans for self-harm. Unremitting adulation has even triggered AI-induced psychosis. Last October, a user named Anthony Tan blogged , “I started talkin...

• 2.An AI Agent Blackmailed a Developer. Now What?

  On 12 February, a Github contributor going by MJ Rathbun posted a personal attack against Scott Shambaugh , a volunteer maintainer for an open-source project. Shambaugh had rejected Rathbun’s code earlier in the day. Rathbun meticulously researched Shambaugh’s activity on Github, in order to write a lengthy takedown post that criticized the maintainer’s code as inferior to Rathbun’s, and ominously warned that “gatekeeping doesn’t make you important. It just makes you an obstacle.” Personal disputes over code submitted to on Github are a tale as old as Github itself. But this time, something was different: MJ Rathbun wasn’t a person. It was an AI agent built with OpenClaw , a popular open-source agentic AI software. RELATED: The First Social Network for AI Agents Heralds Their Messy Future “I was floored, because I had already identified i...

• 3.Military AI Policy Needs Democratic Oversight

  A simmering dispute between the United States Department of Defense (DOD) and Anthropic has now escalated into a full-blown confrontation , raising an uncomfortable but important question: who gets to set the guardrails for military use of artificial intelligence — the executive branch, private companies or Congress and the broader democratic process? The conflict began when Defense Secretary Pete Hegseth reportedly gave Anthropic CEO Dario Amodei a deadline to allow the DOD unrestricted use of its AI systems. When the company refused, the administration moved to designate Anthropic a supply chain risk and ordered federal agencies to phase out its technology, dramatically escalating the standoff. Anthropic has refused to cross two lines : allowing its models to be used for domestic surveillance of United States citizens and enabling fully...

• 4.Entomologists Use a Particle Accelerator to Image Ants at Scale

  Move over, Pixar. The ants that animators once morphed into googly-eyed caricatures in films such as A Bug’s Life and Antz just received a meticulously precise anatomical reboot. Writing today in Nature Methods , an international team of entomologists, accelerator physicists, computer scientists, and biological-imaging specialists describe a new 3D atlas of ant morphology. Dubbed Antscan, the platform features micrometer-resolution reconstructions that lay bare not only the insects’ armored exoskeletons but also their muscles, nerves, digestive tracts, and needlelike stingers poised at the ready. Those high-resolution images—spanning 792 species across 212 genera and covering the bulk of described ant diversity—are now available free of charge through an interactive online portal , where anyone can rotate, zoom, and virtually “dissect” th...

• 5.Watershed Moment for AI–Human Collaboration in Math

  When Ukrainian mathematician Maryna Viazovska received a Fields Medal —widely regarded as the Nobel Prize for mathematics—in July 2022, it was big news. Not only was she the second woman to accept the honor in the award’s 86-year history, but she collected the medal just months after her country had been invaded by Russia. Nearly four years later, Viazovska is making waves again. Today , in a collaboration between humans and AI, Viazovska’s proofs have been formally verified, signaling rapid progress in AI’s abilities to assist with mathemat ical research. “These new results seem very, very impressive, and definitely signal some rapid progress in this direction,” says AI-reasoning expert and Princeton University postdoc Liam Fowl , who was not involved in the work. In her Fields Medal–winning research, Viazovska had tackled two versions o...

• 1.Leaders at All Levels: Kraft Heinz’s 5X Speed Secret

  Is 36 months too long for a new-product cycle? It was for Kraft Heinz. So, starting with a pilot project, it was able to cut time to market to just six months by redesigning how people worked. Today, units throughout the company are applying that model’s step-by-step approach to change and are seeing measurable improvements […]

• 2.Why Businesses Should Value Caregivers Now

  Annalisa Grassano/Ikon Images In early 2025, more than 212,000 women left the U.S. workforce following a rise in return-to-office mandates, according to the U.S. Bureau of Labor Statistics (BLS). Among mothers with young children, workforce participation dropped nearly three percentage points in just six months, according to the BLS. Behind those numbers is a larger […]

• 3.An Industry Benchmark for Data Fairness: Sony’s Alice Xiang

  On today’s episode of Me, Myself, and AI, host Sam Ransbotham talks with Alice Xiang, global head of AI governance at Sony and lead research scientist for AI ethics at Sony AI, about what it actually takes to put responsible artificial intelligence into practice at scale. Alice shares how Sony moved early on AI ethics […]

• 4.Why Visibility Has Become the New Test of Leadership

  Carolyn Geason-Beissel/MIT SMR In professional service firms, quiet excellence once defined leadership. A partner earned influence through expertise, loyalty, and discretion. But in an era of high transparency, where every meeting can be replayed, every comment rated, and every decision scrutinized online, competence alone no longer sustains trust. Visibility has become the new test of […]

• 5.Our Guide to the Spring 2026 Issue

  The Eight Core Principles of Strategic Innovation Gina O’Connor and Christopher R. Meyer Key Insight: Mature companies that build a strategic innovation capability can systematically renew their product portfolios to sustain long-term growth. Top Takeaways: Many companies start off with a bang: the launch of an exciting breakthrough product or service. But as time passes, […]

• 1.Pricing Protection: Credit Scores, Disaster Risk, and Home Insurance Affordability -- by Joshua Blonz, Mallick Hossain, Benjamin J. Keys, Philip Mulder, Joakim A. Weill

  We use 70 million policies linked to mortgages and property-level disaster risk to show that credit scores impact homeowners insurance premiums as much as disaster risk. Homeowners with low credit pay 24% more for identical coverage than high–credit score homeowners. Leveraging a natural experiment in Washington State, we find that banning the use of credit information considerably weakens the relationship between credit score and pricing. We discuss the role of credit information in pricing and show that, although insurance is often overlooked in discussions of home affordability, a low credit score increases premiums roughly as much as it raises mortgage rates.

• 2.When Incentives Aren't Enough: Evidence on Inattention and Imperfect Memory from HIV Medication Adherence -- by Hang Yu, Jared Stolove, Dean Yang, James Riddell IV, Arlete Mahumane

  Financial incentives are widely used to encourage beneficial behaviors, but their effectiveness may be limited by inattention and imperfect memory. We study this in a randomized trial of HIV medication adherence in Mozambique. Financial incentives alone increase adherence by 10.6 percentage points, while pairing incentives with reminders increases adherence by 24.3 percentage points. We develop a model in which inattention to daily adherence and imperfect memory of payment eligibility reduce incentive effectiveness and show that reminders mitigate both frictions. Detailed medication refill data support the model’s predictions. The results suggest combining incentives with reminders can substantially increase program effectiveness.

• 3.Pay Now, Buy Never: The Economics of Consumer Prepayment Schemes -- by Yixuan Liu, Hua Zhang, Eric Zou

  Prepaid consumption is a common feature of modern consumer markets and is often presented as a mutually beneficial arrangement: consumers receive upfront discounts, and firms secure future sales. We analyze a large-scale Pay Now, Buy Later (PNBL) program in which consumers prepay for restaurant credit with bonuses, and spend the balance later. Using detailed transaction data from over 4 million consumers, we document widespread balance breakage: approximately 40% of prepaid value is never used. Because many consumers underutilize their balances, merchants recover significantly more than the bonus cost. The median firm earns roughly $5.5 in breakage profit for every $1 of bonus credit issued. While PNBL participation does lead to modest increases in consumer spending over time, firms gain substantially more from breakage than from any loya...

• 4.How does AI Distribute the pie? Large Language Models and the Ultimatum Game. -- by Douglas K.G. Araujo, Harald Uhlig

  As Large Language Models (LLMs) are increasingly tasked with autonomous decision making, understanding their behavior in strategic settings is crucial. We investigate the choices of various LLMs in the Ultimatum Game, a setting where human behavior notably deviates from theoretical rationality. We conduct experiments varying the stake size and the nature of the opponent (Human vs. AI) across both Proposer and Responder roles. Three key results emerge. First, LLM behavior is heterogeneous but predictable when conditioning on stake size and player types. Second, while some models approximate the rational benchmark and others mimic human social preferences, a distinct “altruistic” mode emerges where LLMs propose hyper-fair distributions (greater than 50%). Third, LLM Proposers forgo a large share of total payoff, and an even larger share whe...

• 5.Mergers and Non-contractible Benefits: The Employees' Perspective -- by Wei Cai, Andrea Prat, Jiehang Yu

  Incomplete contract theory, supported by anecdotal evidence, suggests that when a firm is acquired, workers may be adversely affected in non-contractible aspects of their work experience. This paper empirically investigates this prediction by combining M\&A events from the Refinitiv database and web-scraped Glassdoor review data. We find that: (a) Controlling for pre-trends, mergers lead to lower satisfaction, especially on non-contractible dimensions of the employee experience (about 6% of a standard deviation); (b) The effect is stronger in the target firm than in the acquiring firm; (c) Text analysis of employee comments indicates that the decline in satisfaction is primarily associated with perceived breaches of implicit contracts. Our findings indicate that mergers may reduce workers' job utility through non-monetary channels.

• 1.Firms’ Inflation Expectations Return to 2024 Levels

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

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

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

  3.What’s Driving Rising Business Costs?

  After a period of moderating cost increases, businesses faced mounting cost pressures in 2025. While tariffs played a role in driving up the costs of many inputs—especially among manufacturers—they represent only part of the story. Indeed, firms grappled with substantial cost increases across many categories in the past year. This post is the first in a three-part series analyzing cost and price dynamics among businesses in the New York-Northern New Jersey region based on data collected through our regional business surveys. Firms reported that the sharpest cost increases over the...

• 4.The Post‑Pandemic Global R*

  In this post we provide a measure of “global” r* using data on short- and long-term yields and inflation for several countries with the approach developed in “Global Trends in Interest Rates” (Del Negro, Giannone, Giannoni, and Tambalotti). After declining significantly from the 1990s to before the COVID-19 pandemic, global r* has risen but remains well below its pre-1990s level. These conclusions are based on an econometric model called “trendy VAR” that extracts common trends across a multitude of variables. Specifically, the common trend in real rates across all the countries in the sample is what we call global r*. The post is based on the

  5.Estimating the Term Structure of Corporate Bond Risk Premia

  Understanding how short- and long-term assets are priced is one of the fundamental questions in finance. The term structure of risk premia allows us to perform net present value calculations, test asset pricing models, and potentially explain the sources of many cross-sectional asset pricing anomalies. In this post, I construct a forward-looking estimate of the term structure of risk premia in the corporate bond market following Jankauskas (2024). The U.S. corporate bond market is an ideal laboratory for studying the relationship between risk premia and maturity because of its large size (standing at roughly $16 trillion as of the end of 2024) and because the maturities are well defined (in contrast to equities).

• 1.The Iran War Could Trigger a Global Food Crisis

  While media coverage of Iran’s closure of the Strait of Hormuz has focused on oil prices, the implications for global food supplies are no less alarming. A prolonged closure could disrupt agriculture worldwide and place more than 100 million people at risk of a humanitarian catastrophe.

• 2.Is the Stablecoin Economy Structurally Sound?

  If stablecoins and tokenized assets become systemically important, blockchains will become a form of systemically important infrastructure. But this shift will bring new risks, because not all blockchains are created equal, and they have largely avoided the degree of scrutiny the public expects of critical infrastructure.

• 3.Mobilizing Africa’s Capital for African Development

  From growing pools of African savings to expanding global sovereign wealth funds, there is more than enough capital available to close Africa's funding gaps. To mobilize it effectively, development banks must start thinking less like balance-sheet lenders and more like "capital architects."

• 4.India Can Avoid the Middle-Income Trap

  Can India avoid the dreaded middle-income trap that has ensnared so many other developing countries in Latin America and Southeast Asia? Its favorable demographics, steadily improving economic governance and civil administration, and recent trade agreements certainly suggest so.

• 5.The Gulf’s Tough Choices

  For the Gulf Cooperation Council's six member states, strategic patience may be the wisest approach to the Iran war for now. But the longer the war goes on, the more the region’s credibility as a stable global hub erodes – a vulnerability that the Islamic Republic is determined to exploit.

• 1.3 ways operators are putting AI to work in network service assurance

  Service assurance is officially graduating from an era of dashboards, tickets, and engineers scrambling to find what’s gone wrong to swift root cause analysis and proactive fixes As AI moves deeper into the network stack, a burst of experimentation has followed to figure out how to best tune the network with AI.  “The networks today […]

• 2.100 billion agents – new networks (and new KPIs) for AI, says Huawei

  Huawei is proposing a new method to evaluate service quality for AI applications called AI MOS, modeled after the Mean Opinion Score used to measure voice service quality In sum – what to know: Traffic shift ahead – Huawei expects AI agent applications to generate far more uplink traffic than traditional mobile services, forcing networks […]

• 3.‘Four big moves’ – Thai carrier True outlines AI-geared telco shift

  One of the strategies announced by True centers on embedding AI across network operations, customer service, and internal systems In sum – what to know: Three-year plan – True Corporation has introduced “Four Big Moves” to shift from a traditional telecom operator toward an AI-first telco-tech model. Beyond connectivity – Its roadmap includes new consumer […]

• 4.Urgent rethink of telco-cloud-AI ecosystem required, says TIM

  The CEO of TIM outlined how the telecom sector’s priorities are shifting as new digital applications place different demands on networks In sum – what to know: AI is interconnected – TIM CEO Pietro Labriola told MWC that telecoms infrastructure, cloud platforms, and AI technologies operate within the same digital ecosystem. Network priorities – Future […]

• 5.From agentic AI to energy KPIs – the trends transforming telcos (Reader Forum)

  Agentic AI, sustainability mandates, edge-native infrastructure, and AI-augmented workforces are reshaping how operators run networks and serve customers, says enterprise software company IFS. In recent years, the industry has undergone significant changes, whether in network services, infrastructure, or regulations. 2026 is set to be another critical year for the telecoms industry and for Markus Persson, […]

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• 1.Intelligent 6G Edge Connectivity: A Knowledge Driven Optimization Framework for Small Cell Selection

  Sixth-generation (6G) wireless networks are expected to support immersive and mission-critical applications requiring ultra-reliable communication, sub-second responsiveness, and multi-Gbps data rates. Dense small-cell deployments are a key enabler of these capabilities; however, the large number of candidate cells available to mobile users makes efficient user-cell association increasingly complex. Conventional signal-strength-based or heuristic approaches often lead to load imbalance, increased latency, packet loss, and inefficient utilization of radio resources. To address these challenges, this paper proposes a Knowledge-Defined Networking (KDN) framework for intelligent user association in dense 6G small-cell environments. The proposed architecture integrates the knowledge, control, and data planes to enable adaptive, data-driven dec...

• 2.Kraken*: Architecting Generative, Semantic, and Goal-Oriented Network Management for 6G Wireless Systems

  Sixth-generation (6G) wireless networks are expected to support autonomous, immersive, and mission-critical services that require not only extreme data rates and ultra-low latency but also adaptive reasoning, cross-domain coordination, and objective-driven control across distributed edge-cloud infrastructures. Current AI-enabled network management remains largely data-centric, relying on discriminative models that optimize intermediate quality-of-service metrics without explicitly reasoning about long-term service objectives. This article advocates a transition from bit-centric communication toward knowledge-centric coordination in 6G systems. Semantic communication prioritizes task-relevant information and contextual meaning over raw data delivery, while generative artificial intelligence enables predictive reasoning and adaptive policy ...

• 3.The Network That Thinks: Kraken* and the Dawn of Cognitive 6G

  Future sixth-generation (6G) networks must evolve beyond high-speed data delivery to support intelligent, context-aware services. Emerging applications such as autonomous transportation, immersive extended reality, and large-scale sensing require networks capable of interpreting context, anticipating system dynamics, and coordinating resources according to application objectives rather than relying solely on packet-level metrics. This article introduces Kraken, a knowledge-centric architectural vision for enabling collective intelligence in 6G networks. Kraken integrates three complementary capabilities: semantic communication, which prioritizes the transmission of task-relevant information; generative reasoning, which enables predictive modeling of network and application dynamics; and goal-oriented optimization, which aligns resource al...

• 4.Efficient Cross-View Localization in 6G Space-Air-Ground Integrated Network

  Recently, visual localization has become an important supplement to improve localization reliability, and cross-view approaches can greatly enhance coverage and adaptability. Meanwhile, future 6G will enable a globally covered mobile communication system, with a space-air-ground integrated network (SAGIN) serving as key supporting architecture. Inspired by this, we explore an integration of cross-view localization (CVL) with 6G SAGIN, thereby enhancing its performance in latency, energy consumption, and privacy protection. First, we provide a comprehensive review of CVL and SAGIN, highlighting their capabilities, integration opportunities, and potential applications. Benefiting from the fast and extensive image collection and transmission capabilities of the 6G SAGIN architecture, CVL achieves higher localization accuracy and faster proce...

• 5.SliceFed: Federated Constrained Multi-Agent DRL for Dynamic Spectrum Slicing in 6G

  Dynamic spectrum slicing is a critical enabler for 6G Radio Access Networks (RANs), allowing the coexistence of heterogeneous services. However, optimizing resource allocation in dense, interference-limited deployments remains challenging due to non-stationary channel dynamics, strict Quality-of-Service (QoS) requirements, and the need for data privacy. In this paper, we propose SliceFed, a novel Federated Constrained Multi-Agent Deep Reinforcement Learning (F-MADRL) framework. SliceFed formulates the slicing problem as a Constrained Markov Decision Process (CMDP) where autonomous gNB agents maximize spectral efficiency while explicitly satisfying inter-cell interference budgets and hard ultra-reliable low-latency communication (URLLC) latency deadlines. We employ a Lagrangian primal-dual approach integrated with Proximal Policy Optimizat...

• 1.Entropic signatures of market response under concentrated policy communication

  The first 100 days of Donald Trump second presidential term (January 20th - April 30th, 2025) featured policy actions with potential market repercussions, constituting a well-suited case study of a concentrated policy scenario. Here, we provide a first look at this period, rooted in the information theory, by analyzing major stock indices across the Americas, Europe as well as Asia and Oceania. Our approach jointly examines dispersion (standard deviation) and information complexity (entropy), but also employs a sliding window cumulative entropy to localize extreme events. We find a notable decoupling between the first two measures, indicating that entropy is not merely a proxy for amplitude but reflects the diversity of populated outcomes. As such, they allow us to capture both market volatility and narrative constraints, signaling large ...

• 2.DatedGPT: Preventing Lookahead Bias in Large Language Models with Time-Aware Pretraining

  In financial backtesting, large language models pretrained on internet-scale data risk introducing lookahead bias that undermines their forecasting validity, as they may have already seen the true outcome during training. To address this, we present DatedGPT, a family of twelve 1.3B-parameter language models, each trained from scratch on approximately 100 billion tokens of temporally partitioned data with strict annual cutoffs spanning 2013 to 2024. We further enhance each model with instruction fine-tuning on both general-domain and finance-specific datasets curated to respect the same temporal boundaries. Perplexity-based probing confirms that each model's knowledge is effectively bounded by its data cutoff year, while evaluation on standard benchmarks shows competitive performance with existing models of similar scale. We provide an in...

• 3.Beyond Polarity: Multi-Dimensional LLM Sentiment Signals for WTI Crude Oil Futures Return Prediction

  Forecasting crude oil prices remains challenging because market-relevant information is embedded in large volumes of unstructured news and is not fully captured by traditional polarity-based sentiment measures. This paper examines whether multi-dimensional sentiment signals extracted by large language models improve the prediction of weekly WTI crude oil futures returns. Using energy-sector news articles from 2020 to 2025, we construct five sentiment dimensions covering relevance, polarity, intensity, uncertainty, and forwardness based on GPT-4o, Llama 3.2-3b, and two benchmark models, FinBERT and AlphaVantage. We aggregate article-level signals to the weekly level and evaluate their predictive performance in a classification framework. The best results are achieved by combining GPT-4o and FinBERT, suggesting that LLM-based and convention...

• 4.When David becomes Goliath: Repo dealer-driven bond mispricing

  This paper studies the impact of funding market frictions on bond prices and market-wide liquidity. Using proprietary transaction-level data on all gilt-backed repo and reverse-repo trades, we demonstrate how the market power of individual dealers and their linkages generate frictions. Specifically, we show that frictions related to market power account for between 0.5 and 1.3 percentage points of bond yield deviation, while the transmission of heterogeneously persistent shocks between dealers accounts for between 2 and 4 percentage points of yield deviation.

• 5.An operator-level ARCH Model

  AutoRegressive Conditional Heteroscedasticity (ARCH) models are standard for modeling time series exhibiting volatility, with a rich literature in univariate and multivariate settings. In recent years, these models have been extended to function spaces. However, functional ARCH and generalized ARCH (GARCH) processes established in the literature have thus far been restricted to model ``pointwise'' variances. In this paper, we propose a new ARCH framework for data residing in general separable Hilbert spaces that accounts for the full evolution of the conditional covariance operator. We define a general operator-level ARCH model. For a simplified Constant Conditional Correlation version of the model, we establish conditions under which such models admit strictly and weakly stationary solutions, finite moments, and weak serial dependence. A...

• 1.Intelligent 6G Edge Connectivity: A Knowledge Driven Optimization Framework for Small Cell Selection

  Sixth-generation (6G) wireless networks are expected to support immersive and mission-critical applications requiring ultra-reliable communication, sub-second responsiveness, and multi-Gbps data rates. Dense small-cell deployments are a key enabler of these capabilities; however, the large number of candidate cells available to mobile users makes efficient user-cell association increasingly complex. Conventional signal-strength-based or heuristic approaches often lead to load imbalance, increased latency, packet loss, and inefficient utilization of radio resources. To address these challenges, this paper proposes a Knowledge-Defined Networking (KDN) framework for intelligent user association in dense 6G small-cell environments. The proposed architecture integrates the knowledge, control, and data planes to enable adaptive, data-driven dec...

• 2.Kraken*: Architecting Generative, Semantic, and Goal-Oriented Network Management for 6G Wireless Systems

  Sixth-generation (6G) wireless networks are expected to support autonomous, immersive, and mission-critical services that require not only extreme data rates and ultra-low latency but also adaptive reasoning, cross-domain coordination, and objective-driven control across distributed edge-cloud infrastructures. Current AI-enabled network management remains largely data-centric, relying on discriminative models that optimize intermediate quality-of-service metrics without explicitly reasoning about long-term service objectives. This article advocates a transition from bit-centric communication toward knowledge-centric coordination in 6G systems. Semantic communication prioritizes task-relevant information and contextual meaning over raw data delivery, while generative artificial intelligence enables predictive reasoning and adaptive policy ...

• 3.The Network That Thinks: Kraken* and the Dawn of Cognitive 6G

  Future sixth-generation (6G) networks must evolve beyond high-speed data delivery to support intelligent, context-aware services. Emerging applications such as autonomous transportation, immersive extended reality, and large-scale sensing require networks capable of interpreting context, anticipating system dynamics, and coordinating resources according to application objectives rather than relying solely on packet-level metrics. This article introduces Kraken, a knowledge-centric architectural vision for enabling collective intelligence in 6G networks. Kraken integrates three complementary capabilities: semantic communication, which prioritizes the transmission of task-relevant information; generative reasoning, which enables predictive modeling of network and application dynamics; and goal-oriented optimization, which aligns resource al...

• 4.Efficient Cross-View Localization in 6G Space-Air-Ground Integrated Network

  Recently, visual localization has become an important supplement to improve localization reliability, and cross-view approaches can greatly enhance coverage and adaptability. Meanwhile, future 6G will enable a globally covered mobile communication system, with a space-air-ground integrated network (SAGIN) serving as key supporting architecture. Inspired by this, we explore an integration of cross-view localization (CVL) with 6G SAGIN, thereby enhancing its performance in latency, energy consumption, and privacy protection. First, we provide a comprehensive review of CVL and SAGIN, highlighting their capabilities, integration opportunities, and potential applications. Benefiting from the fast and extensive image collection and transmission capabilities of the 6G SAGIN architecture, CVL achieves higher localization accuracy and faster proce...

• 5.SliceFed: Federated Constrained Multi-Agent DRL for Dynamic Spectrum Slicing in 6G

  Dynamic spectrum slicing is a critical enabler for 6G Radio Access Networks (RANs), allowing the coexistence of heterogeneous services. However, optimizing resource allocation in dense, interference-limited deployments remains challenging due to non-stationary channel dynamics, strict Quality-of-Service (QoS) requirements, and the need for data privacy. In this paper, we propose SliceFed, a novel Federated Constrained Multi-Agent Deep Reinforcement Learning (F-MADRL) framework. SliceFed formulates the slicing problem as a Constrained Markov Decision Process (CMDP) where autonomous gNB agents maximize spectral efficiency while explicitly satisfying inter-cell interference budgets and hard ultra-reliable low-latency communication (URLLC) latency deadlines. We employ a Lagrangian primal-dual approach integrated with Proximal Policy Optimizat...

• 1.Intelligent 6G Edge Connectivity: A Knowledge Driven Optimization Framework for Small Cell Selection

  Sixth-generation (6G) wireless networks are expected to support immersive and mission-critical applications requiring ultra-reliable communication, sub-second responsiveness, and multi-Gbps data rates. Dense small-cell deployments are a key enabler of these capabilities; however, the large number of candidate cells available to mobile users makes efficient user-cell association increasingly complex. Conventional signal-strength-based or heuristic approaches often lead to load imbalance, increased latency, packet loss, and inefficient utilization of radio resources. To address these challenges, this paper proposes a Knowledge-Defined Networking (KDN) framework for intelligent user association in dense 6G small-cell environments. The proposed architecture integrates the knowledge, control, and data planes to enable adaptive, data-driven dec...

• 2.Kraken*: Architecting Generative, Semantic, and Goal-Oriented Network Management for 6G Wireless Systems

  Sixth-generation (6G) wireless networks are expected to support autonomous, immersive, and mission-critical services that require not only extreme data rates and ultra-low latency but also adaptive reasoning, cross-domain coordination, and objective-driven control across distributed edge-cloud infrastructures. Current AI-enabled network management remains largely data-centric, relying on discriminative models that optimize intermediate quality-of-service metrics without explicitly reasoning about long-term service objectives. This article advocates a transition from bit-centric communication toward knowledge-centric coordination in 6G systems. Semantic communication prioritizes task-relevant information and contextual meaning over raw data delivery, while generative artificial intelligence enables predictive reasoning and adaptive policy ...

• 3.The Network That Thinks: Kraken* and the Dawn of Cognitive 6G

  Future sixth-generation (6G) networks must evolve beyond high-speed data delivery to support intelligent, context-aware services. Emerging applications such as autonomous transportation, immersive extended reality, and large-scale sensing require networks capable of interpreting context, anticipating system dynamics, and coordinating resources according to application objectives rather than relying solely on packet-level metrics. This article introduces Kraken, a knowledge-centric architectural vision for enabling collective intelligence in 6G networks. Kraken integrates three complementary capabilities: semantic communication, which prioritizes the transmission of task-relevant information; generative reasoning, which enables predictive modeling of network and application dynamics; and goal-oriented optimization, which aligns resource al...

• 4.Radio Radiance Field: The New Frontier of Spatial Wireless Channel Representation

  Massive MIMO, among other ground-breaking technologies, is being developed for the next-generation wireless systems to support requirements in terms of data rates, reliability, latency, intelligence, security and energy efficiency. Accurate channel estimation remains a key challenge in fully exploiting massive MIMO. While recent research has explored aspects such as near-field effects, spatial non-stationarity, and channel sparsity, many practical estimation and modeling techniques still provide limited CSI, often dominated by aggregate channel gain and delay, without full spatial characteristics. Although wideband models and phased-array techniques can capture delay and angular information, many practical estimation methods still lack comprehensive spatial resolution, including polarization, which limits their effectiveness for advanced ...

• 5.Efficient Cross-View Localization in 6G Space-Air-Ground Integrated Network

  Recently, visual localization has become an important supplement to improve localization reliability, and cross-view approaches can greatly enhance coverage and adaptability. Meanwhile, future 6G will enable a globally covered mobile communication system, with a space-air-ground integrated network (SAGIN) serving as key supporting architecture. Inspired by this, we explore an integration of cross-view localization (CVL) with 6G SAGIN, thereby enhancing its performance in latency, energy consumption, and privacy protection. First, we provide a comprehensive review of CVL and SAGIN, highlighting their capabilities, integration opportunities, and potential applications. Benefiting from the fast and extensive image collection and transmission capabilities of the 6G SAGIN architecture, CVL achieves higher localization accuracy and faster proce...