Babak's Daily Briefing

Wednesday, March 11, 2026

Sources: 16 | Total Articles: 64

• 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.One Language, Two Scripts: Probing Script-Invariance in LLM Concept Representations

  arXiv:2603.08869v1 Announce Type: new Abstract: Do the features learned by Sparse Autoencoders (SAEs) represent abstract meaning, or are they tied to how text is written? We investigate this question using Serbian digraphia as a controlled testbed: Serbian is written interchangeably in Latin and Cyrillic scripts with a near-perfect character mapping between them, enabling us to vary orthography while holding meaning exactly constant. Crucially, these scripts are tokenized completely differently, sharing no tokens whatsoever. Analyzing SAE feature activations across the Gemma model family (270M-27B parameters), we find that identical sentences in different Serbian scripts activate highly overlapping features, far exceeding random baselines. Strikingly, changing script causes less representational divergence than paraphrasing within the sa...

• 2.MultiGraSCCo: A Multilingual Anonymization Benchmark with Annotations of Personal Identifiers

  arXiv:2603.08879v1 Announce Type: new Abstract: Accessing sensitive patient data for machine learning is challenging due to privacy concerns. Datasets with annotations of personally identifiable information are crucial for developing and testing anonymization systems to enable safe data sharing that complies with privacy regulations. Since accessing real patient data is a bottleneck, synthetic data offers an efficient solution for data scarcity, bypassing privacy regulations that apply to real data. Moreover, neural machine translation can help to create high-quality data for low-resource languages by translating validated real or synthetic data from a high-resource language. In this work, we create a multilingual anonymization benchmark in ten languages, using a machine translation methodology that preserves the original annotations and...

• 3.ConFu: Contemplate the Future for Better Speculative Sampling

  arXiv:2603.08899v1 Announce Type: new Abstract: Speculative decoding has emerged as a powerful approach to accelerate large language model (LLM) inference by employing lightweight draft models to propose candidate tokens that are subsequently verified by the target model. The effectiveness of this paradigm critically depends on the quality of the draft model. While recent advances such as the EAGLE series achieve state-of-the-art speedup, existing draft models remain limited by error accumulation: they condition only on the current prefix, causing their predictions to drift from the target model over steps. In this work, we propose \textbf{ConFu} (Contemplate the Future), a novel speculative decoding framework that enables draft models to anticipate the future direction of generation. ConFu introduces (i) contemplate tokens and soft prom...

• 4.SciTaRC: Benchmarking QA on Scientific Tabular Data that Requires Language Reasoning and Complex Computation

  arXiv:2603.08910v1 Announce Type: new Abstract: We introduce SciTaRC, an expert-authored benchmark of questions about tabular data in scientific papers requiring both deep language reasoning and complex computation. We show that current state-of-the-art AI models fail on at least 23% of these questions, a gap that remains significant even for highly capable open-weight models like Llama-3.3-70B-Instruct, which fails on 65.5% of the tasks. Our analysis reveals a universal "execution bottleneck": both code and language models struggle to faithfully execute plans, even when provided with correct strategies. Specifically, code-based methods prove brittle on raw scientific tables, while natural language reasoning primarily fails due to initial comprehension issues and calculation errors.

• 5.Automated Thematic Analysis for Clinical Qualitative Data: Iterative Codebook Refinement with Full Provenance

  arXiv:2603.08989v1 Announce Type: new Abstract: Thematic analysis (TA) is widely used in health research to extract patterns from patient interviews, yet manual TA faces challenges in scalability and reproducibility. LLM-based automation can help, but existing approaches produce codebooks with limited generalizability and lack analytic auditability. We present an automated TA framework combining iterative codebook refinement with full provenance tracking. Evaluated on five corpora spanning clinical interviews, social media, and public transcripts, the framework achieves the highest composite quality score on four of five datasets compared to six baselines. Iterative refinement yields statistically significant improvements on four datasets with large effect sizes, driven by gains in code reusability and distributional consistency while pr...

• 1.Equitable Multi-Task Learning for AI-RANs

  arXiv:2603.08717v1 Announce Type: new Abstract: AI-enabled Radio Access Networks (AI-RANs) are expected to serve heterogeneous users with time-varying learning tasks over shared edge resources. Ensuring equitable inference performance across these users requires adaptive and fair learning mechanisms. This paper introduces an online-within-online fair multi-task learning (OWO-FMTL) framework that ensures long-term equity across users. The method combines two learning loops: an outer loop updating the shared model across rounds and an inner loop rebalancing user priorities within each round with a lightweight primal-dual update. Equity is quantified via generalized alpha-fairness, allowing a trade-off between efficiency and fairness. The framework guarantees diminishing performance disparity over time and operates with low computational ove...

• 2.Hindsight Credit Assignment for Long-Horizon LLM Agents

  arXiv:2603.08754v1 Announce Type: new Abstract: Large Language Model (LLM) agents often face significant credit assignment challenges in long-horizon, multi-step tasks due to sparse rewards. Existing value-free methods, such as Group Relative Policy Optimization (GRPO), encounter two fundamental bottlenecks: inaccurate step-level Q-value estimation and misaligned value baselines for intermediate states. To address these limitations, we introduce HCAPO, the first framework to integrate hindsight credit assignment into LLM agents. HCAPO leverages the LLM itself as a post-hoc critic to refine step-level Q-values through hindsight reasoning. Furthermore, HCAPO's multi-scale advantage mechanism effectively supplements the inaccurate value baselines at critical decision states. Evaluations across three challenging benchmarks, including WebShop ...

• 3.Generalized Reduction to the Isotropy for Flexible Equivariant Neural Fields

  arXiv:2603.08758v1 Announce Type: new Abstract: Many geometric learning problems require invariants on heterogeneous product spaces, i.e., products of distinct spaces carrying different group actions, where standard techniques do not directly apply. We show that, when a group $G$ acts transitively on a space $M$, any $G$-invariant function on a product space $X \times M$ can be reduced to an invariant of the isotropy subgroup $H$ of $M$ acting on $X$ alone. Our approach establishes an explicit orbit equivalence $(X \times M)/G \cong X/H$, yielding a principled reduction that preserves expressivity. We apply this characterization to Equivariant Neural Fields, extending them to arbitrary group actions and homogeneous conditioning spaces, and thereby removing the major structural constraints imposed by existing methods.

• 4.SPREAD: Subspace Representation Distillation for Lifelong Imitation Learning

  arXiv:2603.08763v1 Announce Type: new Abstract: A key challenge in lifelong imitation learning (LIL) is enabling agents to acquire new skills from expert demonstrations while retaining prior knowledge. This requires preserving the low-dimensional manifolds and geometric structures that underlie task representations across sequential learning. Existing distillation methods, which rely on L2-norm feature matching in raw feature space, are sensitive to noise and high-dimensional variability, often failing to preserve intrinsic task manifolds. To address this, we introduce SPREAD, a geometry-preserving framework that employs singular value decomposition (SVD) to align policy representations across tasks within low-rank subspaces. This alignment maintains the underlying geometry of multimodal features, facilitating stable transfer, robustness,...

• 5.Multi-level meta-reinforcement learning with skill-based curriculum

  arXiv:2603.08773v1 Announce Type: new Abstract: We consider problems in sequential decision making with natural multi-level structure, where sub-tasks are assembled together to accomplish complex goals. Systematically inferring and leveraging hierarchical structure has remained a longstanding challenge; we describe an efficient multi-level procedure for repeatedly compressing Markov decision processes (MDPs), wherein a parametric family of policies at one level is treated as single actions in the compressed MDPs at higher levels, while preserving the semantic meanings and structure of the original MDP, and mimicking the natural logic to address a complex MDP. Higher-level MDPs are themselves independent MDPs with less stochasticity, and may be solved using existing algorithms. As a byproduct, spatial or temporal scales may be coarsened at...

• 1.Age-Related Differences in the Perception of Eye-Gaze from a Social Robot

  arXiv:2603.08810v1 Announce Type: new Abstract: There is an increasing interest in social robots assisting older adults during daily life tasks. In this context, non-verbal cues such as deictic gaze are important in natural communication in human-robot interaction. However, the sensibility to deictic-gaze declines naturally with age and results in a reduction in social perception. Therefore, this work explores the benefits of deictic gaze from social robots assisting older adults during daily life tasks, and how age-related differences may influence their social perception in contrast to younger populations. This may help on the design of adaptive age-related non-verbal cues in the Human-Robot Interaction context.

• 2.Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams

  arXiv:2603.08814v1 Announce Type: new Abstract: Long-horizon task planning for heterogeneous multi-robot systems is essential for deploying collaborative teams in real-world environments; yet, it remains challenging due to the large volume of perceptual information, much of which is irrelevant to task objectives and burdens planning. Traditional symbolic planners rely on manually constructed problem specifications, limiting scalability and adaptability, while recent large language model (LLM)-based approaches often suffer from hallucinations and weak grounding-i.e., poor alignment between generated plans and actual environmental objects and constraints-in object-rich settings. We present Scale-Plan, a scalable LLM-assisted framework that generates compact, task-relevant problem representations from natural language instructions. Given a P...

• 3.HMR-1: Hierarchical Massage Robot with Vision-Language-Model for Embodied Healthcare

  arXiv:2603.08817v1 Announce Type: new Abstract: The rapid advancement of Embodied Intelligence has opened transformative opportunities in healthcare, particularly in physical therapy and rehabilitation. However, critical challenges remain in developing robust embodied healthcare solutions, such as the lack of standardized evaluation benchmarks and the scarcity of open-source multimodal acupoint massage datasets. To address these gaps, we construct MedMassage-12K - a multimodal dataset containing 12,190 images with 174,177 QA pairs, covering diverse lighting conditions and backgrounds. Furthermore, we propose a hierarchical embodied massage framework, which includes a high-level acupoint grounding module and a low-level control module. The high-level acupoint grounding module uses multimodal large language models to understand human langua...

• 4.Impact of Different Failures on a Robot's Perceived Reliability

  arXiv:2603.08821v1 Announce Type: new Abstract: Robots fail, potentially leading to a loss in the robot's perceived reliability (PR), a measure correlated with trustworthiness. In this study we examine how various kinds of failures affect the PR of the robot differently, and how this measure recovers without explicit social repair actions by the robot. In a preregistered and controlled online video study, participants were asked to predict a robot's success in a pick-and-place task. We examined manipulation failures (slips), freezing (lapses), and three types of incorrect picked objects or place goals (mistakes). Participants were shown one of 11 videos -- one of five types of failure, one of five types of failure followed by a successful execution in the same video, or a successful execution video. This was followed by two additional suc...

• 5.Predictive Control with Indirect Adaptive Laws for Payload Transportation by Quadrupedal Robots

  arXiv:2603.08831v1 Announce Type: new Abstract: This paper formally develops a novel hierarchical planning and control framework for robust payload transportation by quadrupedal robots, integrating a model predictive control (MPC) algorithm with a gradient-descent-based adaptive updating law. At the framework's high level, an indirect adaptive law estimates the unknown parameters of the reduced-order (template) locomotion model under varying payloads. These estimated parameters feed into an MPC algorithm for real-time trajectory planning, incorporating a convex stability criterion within the MPC constraints to ensure the stability of the template model's estimation error. The optimal reduced-order trajectories generated by the high-level adaptive MPC (AMPC) are then passed to a low-level nonlinear whole-body controller (WBC) for tracking....

• 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.Leveraging whole slide difficulty in Multiple Instance Learning to improve prostate cancer grading

  Multiple Instance Learning (MIL) has been widely applied in histopathology to classify Whole Slide Images (WSIs) with slide-level diagnoses. While the ground truth is established by expert pathologists, the slides can be difficult to diagnose for non-experts and lead to disagreements between the annotators. In this paper, we introduce the notion of Whole Slide Difficulty (WSD), based on the disagreement between an expert and a non-expert pathologist. We propose two different methods to leverage WSD, a multi-task approach and a weighted classification loss approach, and we apply them to Gleason grading of prostate cancer slides. Results show that integrating WSD during training consistently improves the classification performance across different feature encoders and MIL methods, particularly for higher Gleason grades (i.e. worse diagnosis...

• 2.MissBench: Benchmarking Multimodal Affective Analysis under Imbalanced Missing Modalities

  Multimodal affective computing underpins key tasks such as sentiment analysis and emotion recognition. Standard evaluations, however, often assume that textual, acoustic, and visual modalities are equally available. In real applications, some modalities are systematically more fragile or expensive, creating imbalanced missing rates and training biases that task-level metrics alone do not reveal. We introduce MissBench, a benchmark and framework for multimodal affective tasks that standardizes both shared and imbalanced missing-rate protocols on four widely used sentiment and emotion datasets. MissBench also defines two diagnostic metrics. The Modality Equity Index (MEI) measures how fairly different modalities contribute across missing-modality configurations. The Modality Learning Index (MLI) quantifies optimization imbalance by comparin...

• 3.MA-EgoQA: Question Answering over Egocentric Videos from Multiple Embodied Agents

  As embodied models become powerful, humans will collaborate with multiple embodied AI agents at their workplace or home in the future. To ensure better communication between human users and the multi-agent system, it is crucial to interpret incoming information from agents in parallel and refer to the appropriate context for each query. Existing challenges include effectively compressing and communicating high volumes of individual sensory inputs in the form of video and correctly aggregating multiple egocentric videos to construct system-level memory. In this work, we first formally define a novel problem of understanding multiple long-horizon egocentric videos simultaneously collected from embodied agents. To facilitate research in this direction, we introduce MultiAgent-EgoQA (MA-EgoQA), a benchmark designed to systemically evaluate ex...

• 4.ConfCtrl: Enabling Precise Camera Control in Video Diffusion via Confidence-Aware Interpolation

  We address the challenge of novel view synthesis from only two input images under large viewpoint changes. Existing regression-based methods lack the capacity to reconstruct unseen regions, while camera-guided diffusion models often deviate from intended trajectories due to noisy point cloud projections or insufficient conditioning from camera poses. To address these issues, we propose ConfCtrl, a confidence-aware video interpolation framework that enables diffusion models to follow prescribed camera poses while completing unseen regions. ConfCtrl initializes the diffusion process by combining a confidence-weighted projected point cloud latent with noise as the conditioning input. It then applies a Kalman-inspired predict-update mechanism, treating the projected point cloud as a noisy measurement and using learned residual corrections to ...

• 5.Test-time Ego-Exo-centric Adaptation for Action Anticipation via Multi-Label Prototype Growing and Dual-Clue Consistency

  Efficient adaptation between Egocentric (Ego) and Exocentric (Exo) views is crucial for applications such as human-robot cooperation. However, the success of most existing Ego-Exo adaptation methods relies heavily on target-view data for training, thereby increasing computational and data collection costs. In this paper, we make the first exploration of a Test-time Ego-Exo Adaptation for Action Anticipation (TE$^{2}$A$^{3}$) task, which aims to adjust the source-view-trained model online during test time to anticipate target-view actions. It is challenging for existing Test-Time Adaptation (TTA) methods to address this task due to the multi-action candidates and significant temporal-spatial inter-view gap. Hence, we propose a novel Dual-Clue enhanced Prototype Growing Network (DCPGN), which accumulates multi-label knowledge and integrates...

• 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.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 […]

• 2.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 […]

• 3.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 […]

• 4.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, […]

• 5.AI Won’t Fix This

  We are firmly in the digital age, awash in data generated on every surface and in every layer of every business. Yet, despite decades of investment in technology, time, and effort, many organizations are still not seeing meaningful returns. A global survey of over 4,200 business and technology leaders conducted by research firm Gartner in […]

• 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.Trump Is Showing China How to Seize Taiwan

  Great powers study each other closely, observing the strategies used, the resistance met, and the outcomes realized. Donald Trump's strangulation of Cuba thus could become a template for others, beginning with Xi Jinping, who remains committed to achieving "reunification" with Taiwan.

• 2.Why Chaos in Washington Can’t Stop California

  Even as the Trump administration unnerves global markets with its erratic policymaking, California remains a top destination for foreign investors. Capital is flowing in because the state’s economic-policy strategy offers what the current federal government cannot: clarity and predictability.

• 3.The Wisdom of Europe’s “Great Capitulation”

  The European Union was widely criticized for agreeing to an asymmetrical trade deal with the US last summer. But in doing so, it effectively ensured that the US would continue harming itself by imposing high costs on its own businesses and consumers, while taking the economically sound step of lowering Europe’s residual tariffs.

• 4.The Troubling Ethics of Crowdfunding

  While it is encouraging to see many people responding to the appeals of strangers in great need, there are fundamental ethical problems with crowdfunding, especially when it becomes a primary method of helping others. Systemic bias and fraud associated with platforms like GoFundMe are only the start.

• 5.The Middle-Power Moment?

  With American and Chinese behavior causing unease globally, the world's middle powers know that opportunities to defend their own interests will not remain open forever. But whether and how effectively such a diverse grouping can mobilize itself very much remains to be seen.

• 1.AI synthetic data: training models without breaching privacy

  How can telcos use AI-generated synthetic data to fuel machine learning? Telecommunications companies are sitting on a huge volume of data. Call records, location pings, browsing sessions, and usage patterns can all paint a remarkably detailed picture of how millions of people move through their lives. But regulations like GDPR and CCPA, plus an ever-expanding […]

• 2.Where AI’s billion dollar boom is driving demand for test and measurement

  The staggering momentum of AI infrastructure build is driving test, measurement, and assurance demands across three key areas AI is a dominant force shaping economies around the world. It is sweeping through industries, transforming businesses, changing consumer behavior, and fueling massive demand for infrastructure. According to IDC, the infrastructure spending to support AI’s rapid growth […]

• 3.AI infrastructure and data sovereignty will reshape telecom, says Turkcell

  The CEO of Turkcell said countries are exploring different strategies to strengthen control over their digital infrastructure In sum – what to know: Connectivity as sovereignty – Turkcell CEO said telecom networks have become a pillar of economic resilience and national independence as connectivity infrastructure underpins security and digital governance. AI infra concentration – GPUs, […]

• 4.China Telecom outlines AI-driven transformation strategy

  The head of China Telecom said in a keynote speech at MWC 2026 that the carrier is advancing its strategy of “cloudification, digital transformation and AI for good” In sum – what to know: AI-driven transformation – China Telecom is expanding its strategy of “cloudification, Digital Transformation and AI for Good” as it shifts from […]

• 5.‘AI is the gas on the fire’ – Verizon on global fiber, metro access, private 5G

  Global, porgrammable, and dense – from cloud to edge; Verizon Business talked at MWC about how its sees the new AI stack evolving for telcos, and why its investments in backbone fiber, metro access, and private network will link cloud models and inference workloads to real-world machines. In sum – what to know: Layer cake […]

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