Babak's Daily Briefing

Tuesday, March 24, 2026

Sources: 19 | Total Articles: 79

• 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.Causal Evidence that Language Models use Confidence to Drive Behavior

  Metacognition -- the ability to assess one's own cognitive performance -- is documented across species, with internal confidence estimates serving as a key signal for adaptive behavior. While confidence can be extracted from Large Language Model (LLM) outputs, whether models actively use these signals to regulate behavior remains a fundamental question. We investigate this through a four-phase abstention paradigm.Phase 1 established internal confidence estimates in the absence of an abstention option. Phase 2 revealed that LLMs apply implicit thresholds to these estimates when deciding to answer or abstain. Confidence emerged as the dominant predictor of behavior, with effect sizes an order of magnitude larger than knowledge retrieval accessibility (RAG scores) or surface-level semantic features. Phase 3 provided causal evidence through a...

• 2.Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe

  Reinforcement Learning (RL) is essential for evolving Large Language Models (LLMs) into autonomous agents capable of long-horizon planning, yet a practical recipe for scaling RL in complex, multi-turn environments remains elusive. This paper presents a systematic empirical study using TravelPlanner, a challenging testbed requiring tool orchestration to satisfy multifaceted constraints. We decompose the agentic RL design space along 5 axes: reward shaping, model scaling, data composition, algorithm selection, and environmental stability. Our controlled experiments yield 7 key takeaways, e.g., (1) reward and algorithm choices are scale-dependent as smaller models benefit from staged rewards and enhanced exploration, whereas larger models converge efficiently with simpler dense rewards, (2) ~ 1K training samples with a balanced difficulty mi...

• 3.AI In Cybersecurity Education -- Scalable Agentic CTF Design Principles and Educational Outcomes

  Large language models are rapidly changing how learners acquire and demonstrate cybersecurity skills. However, when human--AI collaboration is allowed, educators still lack validated competition designs and evaluation practices that remain fair and evidence-based. This paper presents a cross-regional study of LLM-centered Capture-the-Flag competitions built on the Cyber Security Awareness Week competition system. To understand how autonomy levels and participants' knowledge backgrounds influence problem-solving performance and learning-related behaviors, we formalize three autonomy levels: human-in-the-loop, autonomous agent frameworks, and hybrid. To enable verification, we require traceable submissions including conversation logs, agent trajectories, and agent code. We analyze multi-region competition data covering an in-class track, a ...

• 4.Trojan's Whisper: Stealthy Manipulation of OpenClaw through Injected Bootstrapped Guidance

  Autonomous coding agents are increasingly integrated into software development workflows, offering capabilities that extend beyond code suggestion to active system interaction and environment management. OpenClaw, a representative platform in this emerging paradigm, introduces an extensible skill ecosystem that allows third-party developers to inject behavioral guidance through lifecycle hooks during agent initialization. While this design enhances automation and customization, it also opens a novel and unexplored attack surface. In this paper, we identify and systematically characterize guidance injection, a stealthy attack vector that embeds adversarial operational narratives into bootstrap guidance files. Unlike traditional prompt injection, which relies on explicit malicious instructions, guidance injection manipulates the agent's rea...

• 5.Memori: A Persistent Memory Layer for Efficient, Context-Aware LLM Agents

  As large language models (LLMs) evolve into autonomous agents, persistent memory at the API layer is essential for enabling context-aware behavior across LLMs and multi-session interactions. Existing approaches force vendor lock-in and rely on injecting large volumes of raw conversation into prompts, leading to high token costs and degraded performance. We introduce Memori, an LLM-agnostic persistent memory layer that treats memory as a data structuring problem. Its Advanced Augmentation pipeline converts unstructured dialogue into compact semantic triples and conversation summaries, enabling precise retrieval and coherent reasoning. Evaluated on the LoCoMo benchmark, Memori achieves 81.95% accuracy, outperforming existing memory systems while using only 1,294 tokens per query (~5% of full context). This results in substantial cost reduct...

• 1.Enhancing Safety of Large Language Models via Embedding Space Separation

  arXiv:2603.20206v1 Announce Type: new Abstract: Large language models (LLMs) have achieved impressive capabilities, yet ensuring their safety against harmful prompts remains a critical challenge. Recent work has revealed that the latent representations (embeddings) of harmful and safe queries in LLMs typically exhibit linear separability, a property that has been exploited to construct attacks by perturbing the embeddings of harmful queries towards the safe subspace. Motivated by this observation, we propose a representation-level fine-tuning approach, named Embedding Space Separation (ES2), which improves LLM safety by explicitly enlarging the distance between harmful and safe representations in the embedding space. To prevent degradation of model's general capabilities, we introduce a Kullback-Leibler (KL) divergence regularization ter...

• 2.RedacBench: Can AI Erase Your Secrets?

  arXiv:2603.20208v1 Announce Type: new Abstract: Modern language models can readily extract sensitive information from unstructured text, making redaction -- the selective removal of such information -- critical for data security. However, existing benchmarks for redaction typically focus on predefined categories of data such as personally identifiable information (PII) or evaluate specific techniques like masking. To address this limitation, we introduce RedacBench, a comprehensive benchmark for evaluating policy-conditioned redaction across domains and strategies. Constructed from 514 human-authored texts spanning individual, corporate, and government sources, paired with 187 security policies, RedacBench measures a model's ability to selectively remove policy-violating information while preserving the original semantics. We quantify pe...

• 3.Children's Intelligence Tests Pose Challenges for MLLMs? KidGym: A 2D Grid-Based Reasoning Benchmark for MLLMs

  arXiv:2603.20209v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) combine the linguistic strengths of LLMs with the ability to process multimodal data, enbaling them to address a broader range of visual tasks. Because MLLMs aim at more general, human-like competence than language-only models, we take inspiration from the Wechsler Intelligence Scales - an established battery for evaluating children by decomposing intelligence into interpretable, testable abilities. We introduce KidGym, a comprehensive 2D grid-based benchmark for assessing five essential capabilities of MLLMs: Execution, Perception Reasoning, Learning, Memory and Planning. The benchmark comprises 12 unique tasks, each targeting at least one core capability, specifically designed to guage MLLMs' adaptability and developmental potential, mirroring the ...

• 4.CRoCoDiL: Continuous and Robust Conditioned Diffusion for Language

  arXiv:2603.20210v1 Announce Type: new Abstract: Masked Diffusion Models (MDMs) provide an efficient non-causal alternative to autoregressive generation but often struggle with token dependencies and semantic incoherence due to their reliance on discrete marginal distributions. We address these limitations by shifting the diffusion process into a continuous sentence-level semantic space. We propose CRoCoDiL (Continuous and Robust Conditioned Diffusion for Language), a unified fine-tuning approach that jointly trains an encoder-demasker architecture, grounding the MDM demasking in continuous latent representations. This leads to the formation of a novel autoencoder in which decoding is obtained by an MDM algorithm. Relying on the same framework, we introduce two unconditional text synthesis algorithms: Continuous-Then-Discrete (ConThenDisc...

• 5.Fast-Slow Thinking RM: Efficient Integration of Scalar and Generative Reward Models

  arXiv:2603.20212v1 Announce Type: new Abstract: Reward models (RMs) are critical for aligning Large Language Models via Reinforcement Learning from Human Feedback (RLHF). While Generative Reward Models (GRMs) achieve superior accuracy through chain-of-thought (CoT) reasoning, they incur substantial computational costs. Conversely, Scalar Reward Models (SRMs) offer efficiency but suffer from limited performance and adaptability in complex scenarios. We introduce Fast-Slow Thinking Reward Models (F/S-RM), a hybrid RM architecture inspired by Dual Process Theory. It trains a single model to integrate two distinct reward paradigms: first-token prediction as a scalar score (fast thinking) and CoT-based judgment (slow thinking), regulated by a dual-confidence activation mechanism that determines when to activate slow thinking. F/S-RM achie...

• 1.JointFM-0.1: A Foundation Model for Multi-Target Joint Distributional Prediction

  arXiv:2603.20266v1 Announce Type: new Abstract: Despite the rapid advancements in Artificial Intelligence (AI), Stochastic Differential Equations (SDEs) remain the gold-standard formalism for modeling systems under uncertainty. However, applying SDEs in practice is fraught with challenges: modeling risk is high, calibration is often brittle, and high-fidelity simulations are computationally expensive. This technical report introduces JointFM, a foundation model that inverts this paradigm. Instead of fitting SDEs to data, we sample an infinite stream of synthetic SDEs to train a generic model to predict future joint probability distributions directly. This approach establishes JointFM as the first foundation model for distributional predictions of coupled time series - requiring no task-specific calibration or finetuning. Despite operating...

• 2.MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery

  arXiv:2603.20295v1 Announce Type: new Abstract: Uncovering causal structures from observational data is crucial for understanding complex systems and making informed decisions. While reinforcement learning (RL) has shown promise in identifying these structures in the form of a directed acyclic graph (DAG), existing methods often lack efficiency, making them unsuitable for online applications. In this paper, we propose MARLIN, an efficient multi agent RL based approach for incremental DAG learning. MARLIN uses a DAG generation policy that maps a continuous real valued space to the DAG space as an intra batch strategy, then incorporates two RL agents state specific and state invariant to uncover causal relationships and integrates these agents into an incremental learning framework. Furthermore, the framework leverages a factored action spa...

• 3.Collaborative Adaptive Curriculum for Progressive Knowledge Distillation

  arXiv:2603.20296v1 Announce Type: new Abstract: Recent advances in collaborative knowledge distillation have demonstrated cutting-edge performance for resource-constrained distributed multimedia learning scenarios. However, achieving such competitiveness requires addressing a fundamental mismatch: high-dimensional teacher knowledge complexity versus heterogeneous client learning capacities, which currently prohibits deployment in edge-based visual analytics systems. Drawing inspiration from curriculum learning principles, we introduce Federated Adaptive Progressive Distillation (FAPD), a consensus-driven framework that orchestrates adaptive knowledge transfer. FAPD hierarchically decomposes teacher features via PCA-based structuring, extracting principal components ordered by variance contribution to establish a natural visual knowledge h...

• 4.Transformer-Based Predictive Maintenance for Risk-Aware Instrument Calibration

  arXiv:2603.20297v1 Announce Type: new Abstract: Accurate calibration is essential for instruments whose measurements must remain traceable, reliable, and compliant over long operating periods. Fixed-interval programs are easy to administer, but they ignore that instruments drift at different rates under different conditions. This paper studies calibration scheduling as a predictive maintenance problem: given recent sensor histories, estimate time-to-drift (TTD) and intervene before a violation occurs. We adapt the NASA C-MAPSS benchmark into a calibration setting by selecting drift-sensitive sensors, defining virtual calibration thresholds, and inserting synthetic reset events that emulate repeated recalibration. We then compare classical regressors, recurrent and convolutional sequence models, and a compact Transformer for TTD prediction...

• 5.Rolling-Origin Validation Reverses Model Rankings in Multi-Step PM10 Forecasting: XGBoost, SARIMA, and Persistence

  arXiv:2603.20315v1 Announce Type: new Abstract: (a) Many air quality forecasting studies report gains from machine learning, but evaluations often use static chronological splits and omit persistence baselines, so the operational added value under routine updating is unclear. (b) Using 2,350 daily PM10 observations from 2017 to 2024 at an urban background monitoring station in southern Europe, we compare XGBoost and SARIMA against persistence under a static split and a rolling-origin protocol with monthly updates. We report horizon-specific skill and the predictability horizon, defined as the maximum horizon with positive persistence-relative skill. Static evaluation suggests XGBoost performs well from one to seven days ahead, but rolling-origin evaluation reverses rankings: XGBoost is not consistently better than persistence at short and...

• 1.Your Robot Will Feel You Now: Empathy in Robots and Embodied Agents

  arXiv:2603.20200v1 Announce Type: new Abstract: The fields of human-robot interaction (HRI) and embodied conversational agents (ECAs) have long studied how empathy could be implemented in machines. One of the major drivers has been the goal of giving multimodal social and emotional intelligence to these artificially intelligent agents, which interact with people through facial expressions, body, gesture, and speech. What empathic behaviors and models have these fields implemented by mimicking human and animal behavior? In what ways have they explored creating machine-specific analogies? This chapter aims to review the knowledge from these studies, towards applying the lessons learned to today's ubiquitous, language-based agents such as ChatGPT.

• 2.Beyond Scalar Rewards: Distributional Reinforcement Learning with Preordered Objectives for Safe and Reliable Autonomous Driving

  arXiv:2603.20230v1 Announce Type: new Abstract: Autonomous driving involves multiple, often conflicting objectives such as safety, efficiency, and comfort. In reinforcement learning (RL), these objectives are typically combined through weighted summation, which collapses their relative priorities and often yields policies that violate safety-critical constraints. To overcome this limitation, we introduce the Preordered Multi-Objective MDP (Pr-MOMDP), which augments standard MOMDPs with a preorder over reward components. This structure enables reasoning about actions with respect to a hierarchy of objectives rather than a scalar signal. To make this structure actionable, we extend distributional RL with a novel pairwise comparison metric, Quantile Dominance (QD), that evaluates action return distributions without reducing them into a singl...

• 3.Fusing Driver Perceived and Physical Risk for Safety Critical Scenario Screening in Autonomous Driving

  arXiv:2603.20232v1 Announce Type: new Abstract: Autonomous driving testing increasingly relies on mining safety critical scenarios from large scale naturalistic driving data, yet existing screening pipelines still depend on manual risk annotation and expensive frame by frame risk evaluation, resulting in low efficiency and weakly grounded risk quantification. To address this issue, we propose a driver risk fusion based hazardous scenario screening method for autonomous driving. During training, the method combines an improved Driver Risk Field with a dynamic cost model to generate high quality risk supervision signals, while during inference it directly predicts scenario level risk scores through fast forward passes, avoiding per frame risk computation and enabling efficient large scale ranking and retrieval. The improved Driver Risk Fiel...

• 4.SwiftBot: A Decentralized Platform for LLM-Powered Federated Robotic Task Execution

  arXiv:2603.20233v1 Announce Type: new Abstract: Federated robotic task execution systems require bridging natural language instructions to distributed robot control while efficiently managing computational resources across heterogeneous edge devices without centralized coordination. Existing approaches face three limitations: rigid hand-coded planners requiring extensive domain engineering, centralized coordination that contradicts federated collaboration as robots scale, and static resource allocation failing to share containers across robots when workloads shift dynamically. We present SwiftBot, a federated task execution platform that integrates LLM-based task decomposition with intelligent container orchestration over a DHT overlay, enabling robots to collaboratively execute tasks without centralized control. SwiftBot achieves 94.3% d...

• 5.Emergency Lane-Change Simulation: A Behavioral Guidance Approach for Risky Scenario Generation

  arXiv:2603.20234v1 Announce Type: new Abstract: In contemporary autonomous driving testing, virtual simulation has become an important approach due to its efficiency and cost effectiveness. However, existing methods usually rely on reinforcement learning to generate risky scenarios, making it difficult to efficiently learn realistic emergency behaviors. To address this issue, we propose a behavior guided method for generating high risk lane change scenarios. First, a behavior learning module based on an optimized sequence generative adversarial network is developed to learn emergency lane change behaviors from an extracted dataset. This design alleviates the limitations of existing datasets and improves learning from relatively few samples. Then, the opposing vehicle is modeled as an agent, and the road environment together with surroundi...

• 1.Mobile Moneya ccounted for $2 trillion in  transactions in 2025, doubling since 2021 as active accounts continue to grow

  Summary available at source link.

• 2.Strengthening the Global Fight Against Fraud and Scams – Takeaways from the Global Fraud Summit in Vienna

  Summary available at source link.

• 3.GSMA MWC26 Barcelona closes 20th anniversary edition

  Summary available at source link.

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

  Summary available at source link.

• 5.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.

• 1.3D-Layout-R1: Structured Reasoning for Language-Instructed Spatial Editing

  Large Language Models (LLMs) and Vision Language Models (VLMs) have shown impressive reasoning abilities, yet they struggle with spatial understanding and layout consistency when performing fine-grained visual editing. We introduce a Structured Reasoning framework that performs text-conditioned spatial layout editing via scene-graph reasoning. Given an input scene graph and a natural-language instruction, the model reasons over the graph to generate an updated scene graph that satisfies the text condition while maintaining spatial coherence. By explicitly guiding the reasoning process through structured relational representations, our approach improves both interpretability and control over spatial relationships. We evaluate our method on a new text-guided layout editing benchmark encompassing sorting, spatial alignment, and room-editing ...

• 2.Closed-Loop Verbal Reinforcement Learning for Task-Level Robotic Planning

  We propose a new Verbal Reinforcement Learning (VRL) framework for interpretable task-level planning in mobile robotic systems operating under execution uncertainty. The framework follows a closed-loop architecture that enables iterative policy improvement through interaction with the physical environment. In our framework, executable Behavior Trees are repeatedly refined by a Large Language Model actor using structured natural-language feedback produced by a Vision-Language Model critic that observes the physical robot and execution traces. Unlike conventional reinforcement learning, policy updates in VRL occur directly at the symbolic planning level, without gradient-based optimization. This enables transparent reasoning, explicit causal feedback, and human-interpretable policy evolution. We validate the proposed framework on a real mob...

• 3.On the Direction of RLVR Updates for LLM Reasoning: Identification and Exploitation

  Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning capabilities of large language models. While existing analyses identify that RLVR-induced changes are sparse, they primarily focus on the \textbf{magnitude} of these updates, largely overlooking their \textbf{direction}. In this work, we argue that the direction of updates is a more critical lens for understanding RLVR's effects, which can be captured by the signed, token-level log probability difference $Δ\log p$ between the base and final RLVR models. Through statistical analysis and token-replacement interventions, we demonstrate that $Δ\log p$ more effectively identifies sparse, yet reasoning-critical updates than magnitude-based metrics (\eg divergence or entropy). Building on this insight, we propose two practical applications: (1) a \text...

• 4.SpatialBoost: Enhancing Visual Representation through Language-Guided Reasoning

  Despite the remarkable success of large-scale pre-trained image representation models (i.e., vision encoders) across various vision tasks, they are predominantly trained on 2D image data and therefore often fail to capture 3D spatial relationships between objects and backgrounds in the real world, constraining their effectiveness in many downstream applications. To address this, we propose SpatialBoost, a scalable framework that enhances the spatial awareness of existing pre-trained vision encoders by injecting 3D spatial knowledge expressed in linguistic descriptions. The core idea involves converting dense 3D spatial information from 2D images into linguistic expressions, which is then used to inject such spatial knowledge into vision encoders through a Large Language Model (LLM). To this end, we adopt a multi-turn Chain-of-Thought (CoT...

• 5.Dynamic analysis enhances issue resolution

  Translating natural language descriptions into viable code fixes remains a fundamental challenge in software engineering. While the proliferation of agentic large language models (LLMs) has vastly improved automated repository-level debugging, current frameworks hit a ceiling when dealing with sophisticated bugs like implicit type degradations and complex polymorphic control flows. Because these methods rely heavily on static analysis and superficial execution feedback, they lack visibility into intermediate runtime states. Consequently, agents are forced into costly, speculative trial-and-error loops, wasting computational tokens without successfully isolating the root cause. To bridge this gap, we propose DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent), a pioneering automated repair framework that natively embeds dynamic analys...

• 1.WorldCache: Content-Aware Caching for Accelerated Video World Models

  Diffusion Transformers (DiTs) power high-fidelity video world models but remain computationally expensive due to sequential denoising and costly spatio-temporal attention. Training-free feature caching accelerates inference by reusing intermediate activations across denoising steps; however, existing methods largely rely on a Zero-Order Hold assumption i.e., reusing cached features as static snapshots when global drift is small. This often leads to ghosting artifacts, blur, and motion inconsistencies in dynamic scenes. We propose \textbf{WorldCache}, a Perception-Constrained Dynamical Caching framework that improves both when and how to reuse features. WorldCache introduces motion-adaptive thresholds, saliency-weighted drift estimation, optimal approximation via blending and warping, and phase-aware threshold scheduling across diffusion s...

• 2.UniMotion: A Unified Framework for Motion-Text-Vision Understanding and Generation

  We present UniMotion, to our knowledge the first unified framework for simultaneous understanding and generation of human motion, natural language, and RGB images within a single architecture. Existing unified models handle only restricted modality subsets (e.g., Motion-Text or static Pose-Image) and predominantly rely on discrete tokenization, which introduces quantization errors and disrupts temporal continuity. UniMotion overcomes both limitations through a core principle: treating motion as a first-class continuous modality on equal footing with RGB. A novel Cross-Modal Aligned Motion VAE (CMA-VAE) and symmetric dual-path embedders construct parallel continuous pathways for Motion and RGB within a shared LLM backbone. To inject visual-semantic priors into motion representations without requiring images at inference, we propose Dual-Po...

• 3.ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model

  Recent progress in latent world models (e.g., V-JEPA2) has shown promising capability in forecasting future world states from video observations. Nevertheless, dense prediction from a short observation window limits temporal context and can bias predictors toward local, low-level extrapolation, making it difficult to capture long-horizon semantics and reducing downstream utility. Vision--language models (VLMs), in contrast, provide strong semantic grounding and general knowledge by reasoning over uniformly sampled frames, but they are not ideal as standalone dense predictors due to compute-driven sparse sampling, a language-output bottleneck that compresses fine-grained interaction states into text-oriented representations, and a data-regime mismatch when adapting to small action-conditioned datasets. We propose a VLM-guided JEPA-style la...

• 4.DualCoT-VLA: Visual-Linguistic Chain of Thought via Parallel Reasoning for Vision-Language-Action Models

  Vision-Language-Action (VLA) models map visual observations and language instructions directly to robotic actions. While effective for simple tasks, standard VLA models often struggle with complex, multi-step tasks requiring logical planning, as well as precise manipulations demanding fine-grained spatial perception. Recent efforts have incorporated Chain-of-Thought (CoT) reasoning to endow VLA models with a ``thinking before acting'' capability. However, current CoT-based VLA models face two critical limitations: 1) an inability to simultaneously capture low-level visual details and high-level logical planning due to their reliance on isolated, single-modal CoT; 2) high inference latency with compounding errors caused by step-by-step autoregressive decoding. To address these limitations, we propose DualCoT-VLA, a visual-linguistic CoT me...

• 5.MemDLM: Memory-Enhanced DLM Training

  Diffusion Language Models (DLMs) offer attractive advantages over Auto-Regressive (AR) models, such as full-attention parallel decoding and flexible generation. However, they suffer from a notable train-inference mismatch: DLMs are trained with a static, single-step masked prediction objective, but deployed through a multi-step progressive denoising trajectory. We propose MemDLM (Memory-Enhanced DLM), which narrows this gap by embedding a simulated denoising process into training via Bi-level Optimization. An inner loop updates a set of fast weights, forming a Parametric Memory that captures the local trajectory experience of each sample, while an outer loop updates the base model conditioned on this memory. By offloading memorization pressure from token representations to parameters, MemDLM yields faster convergence and lower training lo...

• 1.Transforming Data Science With NVIDIA RTX PRO 6000 Blackwell Workstation Edition

  This is a sponsored article brought to you by PNY Technologies . In today’s data-driven world, data scientists face mounting challenges in preparing, scaling, and processing massive datasets. Traditional CPU-based systems are no longer sufficient to meet the demands of modern AI and analytics workflows. NVIDIA RTX PRO TM 6000 Blackwell Workstation Edition offers a transformative solution, delivering accelerated computing performance and seamless integration into enterprise environments. Key Challenges for Data Science Data Preparation: Data preparation is a complex, time-consuming process that takes most of a data scientist’s time. Scaling: Volume of data is growing at a rapid pace. Data scientists may resort to downsampling datasets to make large datasets more manageable, leading to suboptimal results. Hardware: Demand for accelerated AI...

• 2.Why Thermal Metrology Must Evolve for Next-Generation Semiconductors

  An in-depth examination of how rising power density, 3D integration, and novel materials are outpacing legacy thermal measurement — and what advanced metrology must deliver. What Attendees will Learn Why heat is now the dominant constraint on semiconductor scaling — Explore how heterogeneous integration, 3D stacking, and AI-driven power density have shifted the primary bottleneck from lithography to thermal management, with heat flux projections exceeding 1,000 W/cm² for next-generation accelerators. How extreme material properties are redefining thermal design requirements —Understand the measurement challenges posed by nanoscale thin films where bulk assumptions fail, engineered ultra-high-conductivity materials (diamond, BAs, BNNTs), and devices operating above 200 °C in wide-band gap systems. Why interfaces and buried layers now gover...

• 3.What Happens If AI Makes Things Too Easy for Us?

  Most people who regularly use AI tools would say they’re making their lives easier. The technology promises to streamline and take over tasks both professionally and personally—whether that’s summarizing documents, drafting deliverables, generating code, or even offering emotional support. But researchers are concerned AI is making some tasks too easy, and that this will come with unexpected costs. In a commentary titled Against Frictionless AI , published in Communications Psychology on 24 February, psychologists from the University of Toronto discuss what might be lost when AI removes too much effort from human activities. Their argument centers on the idea that friction—difficulty, struggle, and even discomfort—plays an important role in learning, motivation, and meaning. Psychological research has long shown that effortful engagement ...

• 4.AI Aims for Autonomous Wheelchair Navigation

  Wheelchair users with severe disabilities can often navigate tight spaces better than most robotic systems can. A wave of new smart-wheelchair research, including findings presented in Anaheim, Calif., earlier this month, is now testing whether AI-powered systems can, or should, fully close this gap. Christian Mandel —senior researcher at the German Research Center for Artificial Intelligence (DFKI) in Bremen, Germany— co-led a research team together with his colleague Serge Autexier that developed prototype sensor-equipped electric wheelchairs designed to navigate a roomful of potential obstacles. The researchers also tested a new safety system that integrated sensor data from the wheelchair and from sensors in the room, including from drone -based color and depth cameras . Mandel says the team’s smart wheelchairs were both semiautonomou...

• 5.Startups Bring Optical Metamaterials to AI Data Centers

  Light-warping physics made “invisibility cloaks” a possibility. Now two startups hope to harness the science underlying this advance to boost the bandwidth of data centers and speed artificial intelligence. Roughly 20 years ago, scientists developed the first structures capable of curving light around objects to conceal them. These are composed of optical metamaterials —materials with structures smaller than the wavelengths they are designed to manipulate, letting them bend light in unexpected ways. The problem with optical cloaks? “There’s no market for them,” says Patrick Bowen, cofounder and CEO of photonic computing startup Neurophos in Austin, Texas. For instance, each optical cloak typically works only on a single color of light instead of on all visible colors as you might want for stealth applications. Now companies are devising m...

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

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

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

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

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

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

• 4.Retro-Innovation: How Smart Companies Profit From the Past

  AI may be today’s hot topic, but there’s a robust market for old-fashioned products. Board games, vinyl records, and even 1990s-style video game consoles are making a comeback, especially with Generation Z. What does this mean for teams building modern products? In this video, MIT Sloan Management Review senior features editor Kaushik Viswanath explains “retro-innovation” […]

• 5.Bridge the Intergenerational Leadership Gap

  Carolyn Geason-Beissel/MIT SMR | Getty Images Today’s workforce spans five generations, with millennials and Generation Z together accounting for over 60% of workers globally — a share projected to reach 74% by 2030. Yet there’s a widening intergenerational gap in business leadership. While age diversity in the workplace is growing, decision-making power increasingly rests with […]

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

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

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

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

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

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

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

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

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

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

• 1.China’s Electric Trade

  China has spent considerable government resources to develop advanced electric technology industries, such as those that produce electric vehicles, lithium batteries, and solar panels. These efforts have spilled over to international trade as improvements in price and quality have increased the global demand for these goods. One consequence is that passenger cars and batteries have been disproportionately large contributors to the rise in the country’s trade surplus in recent years. This has not been the case, though, for solar panels, as falling prices due to a supply glut pulled down export revenues despite higher volumes.

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

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

• 3.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...

• 4.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

  5.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...

• 1.A Golden Opportunity for a Beleaguered WTO

  By adopting the Investment Facilitation for Development Agreement, the World Trade Organization would brighten developing economies’ future prospects at a time of rising economic uncertainty. This would also send a powerful signal that, despite the challenges it faces, the WTO can still deliver tangible results.

• 2.The Far Right’s False Pacifism Is Endangering Europe

  Europe's nationalist parties talk endlessly about defending European countries, but they are the least willing to confront hostile foreign governments. By taking a weak security posture, these leaders are practically inviting other countries to escalate their threats to Europe’s way of life.

• 3.What Is America’s Goal in Cuba?

  With the US fuel blockade creating a humanitarian crisis, Cuba’s current stalemate with the United States cannot last indefinitely. But it remains to be seen whether the Trump administration will focus solely on opening the market or insist that economic reforms are accompanied by political liberalization.

• 4.Is Honesty the Best Policy for the International Order?

  Instead of ensuring greater consistency in the application of global rules or inspiring reforms to existing institutions, Western leaders’ newfound frankness about the international order seems to be advancing quite different ends. If this is where honesty leads, we may soon miss the "fictional" world we have lost.

• 5.What Trump Gets Wrong About the Cultural Logic Driving Iran

  US President Donald Trump’s Iran strategy rests on the premise that economic and military force will eventually compel the regime to back down. But such approaches tend to backfire in societies where preservation of honor and reputation dictates defiance.

• 1.Department of War takes aim at closed RAN stacks with open source

  The Open Centralized Unit Distributed Unit (OCUDU) Ecosystem Foundation challenges AI-RAN gatekeeping that has been stalling the open RAN movement Opinions on open RAN (Radio Access Network) are mixed. On the surface, the movement looks less alive now than it did 8 years ago. So far, adoption has remained concentrated to a handful of providers […]

• 2.SpaceX brings Xsight Labs onboard ahead of Starlink v3 launch

  Xsight Labs’ programmable X2 switch tapped by SpaceX to push throughput beyond 1 Tbps My home airport is XNA, so I spend a lot of time on United regional jets. Fortunately, the carrier is rapidly equipping its ERJ175s with Starlink kit, which means I can regularly access downlink speeds of more than 300 Mbps and […]

• 3.Negotiating network slices with AI

  Telecom and enterprise AI systems are beginning to negotiate and provision network slices The telecommunications industry is rapidly moving toward a future where network connectivity is traded as dynamically as cloud computing. Instead of static contracts and manual provisioning, the next evolution of enterprise networking relies on the convergence of network slicing, intent-based networking, and […]

• 4.The hidden bottleneck in modern network deployment (Reader Forum)

  The telecommunications industry has made remarkable progress in expanding mobile infrastructure to support the demands of a hyper-connected world. Operators continue to invest heavily in network modernization, densification, and next-generation technologies to deliver faster speeds, lower latency, and greater reliability. Yet behind the scenes, a less visible challenge continues to slow deployment timelines and increase […]

• 5.Energy measurement in open RAN – what the data shows (Reader Forum)

  Energy efficiency has become one of the most pressing operational and sustainability challenges facing mobile network operators. Sarat Puthenpura at the Open Networking Foundation, discusses the findings for open RAN systems. Energy efficiency has become one of the most pressing operational and sustainability challenges facing mobile network operators. With base stations accounting for approximately 73% […]

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• 1.APEG: Adaptive Physical Layer Authentication with Channel Extrapolation and Generative AI

  With the rapid advancement of 6G, identity authentication has become increasingly critical for ensuring wireless security. The lightweight and keyless Physical Layer Authentication (PLA) is regarded as an instrumental security measure in addition to traditional cryptography-based authentication methods. However, existing PLA schemes often struggle to adapt to dynamic radio environments. To overcome this limitation, we propose the Adaptive PLA with Channel Extrapolation and Generative AI (APEG), designed to enhance authentication robustness in dynamic scenarios. Leveraging Generative AI (GAI), the framework adaptively generates Channel State Information (CSI) fingerprints, thereby improving the precision of identity verification. To refine CSI fingerprint generation, we propose the Collaborator-Cleaned Masked Denoising Diffusion Probabilis...

• 2.Extreme-MIMO Field Trials in 7 GHz Band: Unlocking the Potential of New Spectrum for 6G

  The frequency range around 7 GHz has emerged as a promising upper mid-band spectrum for 6th generation (6G), offering a practical balance between coverage and capacity. To fully exploit this band, however, future systems require substantially stronger beamforming and spatial multiplexing capability than today's 5G 64-port commercial deployments. This article investigates extreme multiple-input multiple-output (X-MIMO) with 256 digital ports as a practical 6G architecture for 7 GHz operation. First, through system-level simulations, we examine the throughput benefits and design trade-offs of increasing the number of base station (BS) and user equipment (UE) digital antenna ports, including comparisons between 128-port and 256-port configurations. We then present a 256-port 7 GHz BS and UE prototype and report field-trial results obtained i...

• 3.Rydberg Atomic Receivers for Net-Zero 6G Wireless Communication and Sensing: Progress, Experiments, and Sustainable Prospects

  Against the backdrop of the global drive to advance the green transformation of the information and communications technology (ICT) industry and leverage technological innovation to facilitate the achievement of Net-Zero carbon goals, research into Rydberg atomic receivers (RAREs) is gaining significant interest. RAREs leverage the electron transition phenomenon for signal reception, offering significant advantages over conventional radio frequency receivers in terms of miniaturized antenna design, high sensitivity, robust interference resistance, and compact form factors, which positions them as a competitive alternative for meeting zero-carbon communication demands. This article systematically elaborates on the basic principle, state-of-the-art progress, and novel experiments of RAREs in quantum wireless communication and sensing. In th...

• 4.Security and Privacy in O-RAN for 6G: A Comprehensive Review of Threats and Mitigation Approaches

  Open Radio Access Network (O-RAN) is a major advancement in the telecommunications field, providing standardized interfaces that promote interoperability between different vendors' technologies, thereby enhancing network flexibility and reducing operational expenses. By leveraging cutting-edge developments in network virtualization and artificial intelligence, O-RAN enhances operational efficiency and stimulates innovation within an open ecosystem. In the context of 6G, the potential capabilities of O-RAN have been significantly expanded, enabling ultra-reliable low-latency communication, terabit-level data rates, and seamless integration of terrestrial and non-terrestrial networks. Despite these benefits, its open architecture paradigm also brings critical security and privacy challenges, which, if not addressed, could compromise network...

• 5.Resonant tunneling diode-integrated terahertz transceiver module for wireless communications

  Terahertz bands enable ultra-broadband wireless communications but require compact, low-cost, and efficient transceiver modules. Conventional implementations based on metallic waveguides or silicon lenses suffer from high loss, bulkiness, and fabrication complexity. Here, we present a compact terahertz transceiver module enabled by a resonant tunneling diode (RTD) integrated with a photonic-electronic antenna chain. The RTD on InP is coupled to a modified Vivaldi antenna and an all-silicon effective-medium-clad waveguide, terminating in a rod antenna interfaced with a 3D-printed cyclic olefin copolymer lens. This architecture enables broadband directive radiation without matching networks or anti-reflection coatings. Packaged in a low-cost 3D-printed PLA enclosure, the module achieves realized gains of 28-33 dBi (E11x) and 30-33 dBi (E11y...

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