Daily Briefing – Mar 20 (71 Articles)
Babak's Daily Briefing
Friday, March 20, 2026
Sources: 15 | Total Articles: 71
6G World
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…
AI Computation & Hardware
1.Do Large Language Models Possess a Theory of Mind? A Comparative Evaluation Using the Strange Stories Paradigm
arXiv:2603.18007v1 Announce Type: new Abstract: The study explores whether current Large Language Models (LLMs) exhibit Theory of Mind (ToM) capabilities -- specifically, the ability to infer others' beliefs, intentions, and emotions from text. Given that LLMs are trained on language data without social embodiment or access to other manifestations of mental representations, their apparent social-cognitive reasoning raises key questions about the nature of their understanding. Are they capable of robust mental-state attribution indistinguishable from human ability in its output, or do their outputs merely reflect superficial pattern completion? To address this question, we tested five LLMs and compared their performance to that of human controls using an adapted version of a text-based tool widely used in human ToM research. The test invo...
2.TherapyGym: Evaluating and Aligning Clinical Fidelity and Safety in Therapy Chatbots
arXiv:2603.18008v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for mental-health support; yet prevailing evaluation methods--fluency metrics, preference tests, and generic dialogue benchmarks--fail to capture the clinically critical dimensions of psychotherapy. We introduce THERAPYGYM, a framework that evaluates and improves therapy chatbots along two clinical pillars: fidelity and safety. Fidelity is measured using the Cognitive Therapy Rating Scale (CTRS), implemented as an automated pipeline that scores adherence to CBT techniques over multi-turn sessions. Safety is assessed using a multi-label annotation scheme, covering therapy-specific risks (e.g., failing to address harm or abuse). To mitigate bias and unreliability in LLM-based judges, we further release THERAPYJUDGEBENCH, a validation set of 1...
3.How Confident Is the First Token? An Uncertainty-Calibrated Prompt Optimization Framework for Large Language Model Classification and Understanding
arXiv:2603.18009v1 Announce Type: new Abstract: With the widespread adoption of large language models (LLMs) in natural language processing, prompt engineering and retrieval-augmented generation (RAG) have become mainstream to enhance LLMs' performance on complex tasks. However, LLMs generate outputs autoregressively, leading to inevitable output uncertainty. Since model performance is highly sensitive to prompt design, precise uncertainty measurement is crucial for reliable prompt optimization. For multi-class multiple-choice (understanding) tasks, conventional uncertainty measures (e.g., entropy) based on output probabilities treat all classes equally and ignore class prior differences in pretraining corpora. This failure to distinguish spurious confidence (from priors) from true certainty (from contextual understanding) results in poo...
4.Agentic Framework for Political Biography Extraction
arXiv:2603.18010v1 Announce Type: new Abstract: The production of large-scale political datasets typically demands extracting structured facts from vast piles of unstructured documents or web sources, a task that traditionally relies on expensive human experts and remains prohibitively difficult to automate at scale. In this paper, we leverage Large Language Models (LLMs) to automate the extraction of multi-dimensional elite biographies, addressing a long-standing bottleneck in political science research. We propose a two-stage ``Synthesis-Coding'' framework for complex extraction task: an upstream synthesis stage that uses recursive agentic LLMs to search, filter, and curate biography from heterogeneous web sources, followed by a downstream coding stage that maps curated biography into structured dataframes. We validate this framework t...
5.Controllable Evidence Selection in Retrieval-Augmented Question Answering via Deterministic Utility Gating
arXiv:2603.18011v1 Announce Type: new Abstract: Many modern AI question-answering systems convert text into vectors and retrieve the closest matches to a user question. While effective for topical similarity, similarity scores alone do not explain why some retrieved text can serve as evidence while other equally similar text cannot. When many candidates receive similar scores, systems may select sentences that are redundant, incomplete, or address different conditions than the question requires. This paper presents a deterministic evidence selection framework for retrieval-augmented question answering. The approach introduces Meaning-Utility Estimation (MUE) and Diversity-Utility Estimation (DUE), fixed scoring and redundancy-control procedures that determine evidence admissibility prior to answer generation. Each sentence or record is...
AI Machine Learning
1.Frayed RoPE and Long Inputs: A Geometric Perspective
arXiv:2603.18017v1 Announce Type: new Abstract: Rotary Positional Embedding (RoPE) is a widely adopted technique for encoding position in language models, which, while effective, causes performance breakdown when input length exceeds training length. Prior analyses assert (rightly) that long inputs cause channels to rotate ``out of distribution,'' but it is not clear how extra rotation relates to or causes pathological behavior. Through empirical and theoretical analysis we advance a unified geometric understanding of attention behavior with RoPE. We find that attention induces tight clustering of separated key and query latent point clouds, allowing for creation of sink tokens: placeholders that allow attention heads to avoid token mixing when not required. RoPE applied to longer inputs damages this key/query cluster separation, producin...
2.Engineering Verifiable Modularity in Transformers via Per-Layer Supervision
arXiv:2603.18029v1 Announce Type: new Abstract: Transformers resist surgical control. Ablating an attention head identified as critical for capitalization produces minimal behavioral change because distributed redundancy compensates for damage. This Hydra effect renders interpretability illusory: we may identify components through correlation, but cannot predict or control their causal role. We demonstrate that architectural interventions can expose hidden modularity. Our approach combines dual-stream processing separating token and contextual representations, per-layer supervision providing independent gradient signal at each depth, and gated attention regularizing toward discrete activation patterns. When trained with per-layer supervision, models produce ablation effects 5 to 23 times larger than architecturally identical controls trai...
3.InfoMamba: An Attention-Free Hybrid Mamba-Transformer Model
arXiv:2603.18031v1 Announce Type: new Abstract: Balancing fine-grained local modeling with long-range dependency capture under computational constraints remains a central challenge in sequence modeling. While Transformers provide strong token mixing, they suffer from quadratic complexity, whereas Mamba-style selective state-space models (SSMs) scale linearly but often struggle to capture high-rank and synchronous global interactions. We present a consistency boundary analysis that characterizes when diagonal short-memory SSMs can approximate causal attention and identifies structural gaps that remain. Motivated by this analysis, we propose InfoMamba, an attention-free hybrid architecture. InfoMamba replaces token-level self-attention with a concept bottleneck linear filtering layer that serves as a minimal-bandwidth global interface and i...
4.Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams
arXiv:2603.18032v1 Announce Type: new Abstract: Anomaly and failure detection methods are crucial in identifying deviations from normal system operational conditions, which allows for actions to be taken in advance, usually preventing more serious damages. Long-lasting deviations indicate failures, while sudden, isolated changes in the data indicate anomalies. However, in many practical applications, changes in the data do not always represent abnormal system states. Such changes may be recognized incorrectly as failures, while being a normal evolution of the system, e.g. referring to characteristics of starting the processing of a new product, i.e. realizing a domain shift. Therefore, distinguishing between failures and such ''healthy'' changes in data distribution is critical to ensure the practical robustness of the system. In this pap...
5.Taming Epilepsy: Mean Field Control of Whole-Brain Dynamics
arXiv:2603.18035v1 Announce Type: new Abstract: Controlling the high-dimensional neural dynamics during epileptic seizures remains a significant challenge due to the nonlinear characteristics and complex connectivity of the brain. In this paper, we propose a novel framework, namely Graph-Regularized Koopman Mean-Field Game (GK-MFG), which integrates Reservoir Computing (RC) for Koopman operator approximation with Alternating Population and Agent Control Network (APAC-Net) for solving distributional control problems. By embedding Electroencephalogram (EEG) dynamics into a linear latent space and imposing graph Laplacian constraints derived from the Phase Locking Value (PLV), our method achieves robust seizure suppression while respecting the functional topological structure of the brain.
AI Robotics
1.Uncovering Latent Phase Structures and Branching Logic in Locomotion Policies: A Case Study on HalfCheetah
arXiv:2603.18084v1 Announce Type: new Abstract: In locomotion control tasks, Deep Reinforcement Learning (DRL) has demonstrated high performance; however, the decision-making process of the learned policy remains a black box, making it difficult for humans to understand. On the other hand, in periodic motions such as walking, it is well known that implicit motion phases exist, such as the stance phase and the swing phase. Focusing on this point, this study hypothesizes that a policy trained for locomotion control may also represent a phase structure that is interpretable by humans. To examine this hypothesis in a controlled setting, we consider a locomotion task that is amenable to observing whether a policy autonomously acquires temporally structured phases through interaction with the environment. To verify this hypothesis, in the MuJoC...
2.Final Report for the Workshop on Robotics & AI in Medicine
arXiv:2603.18130v1 Announce Type: new Abstract: The CARE Workshop on Robotics and AI in Medicine, held on December 1, 2025 in Indianapolis, convened leading researchers, clinicians, industry innovators, and federal stakeholders to shape a national vision for advancing robotics and artificial intelligence in healthcare. The event highlighted the accelerating need for coordinated research efforts that bridge engineering innovation with real clinical priorities, emphasizing safety, reliability, and translational readiness with an emphasis on the use of robotics and AI to achieve this readiness goal. Across keynotes, panels, and breakout sessions, participants underscored critical gaps in data availability, standardized evaluation methods, regulatory pathways, and workforce training that hinder the deployment of intelligent robotic systems in...
3.GoalVLM: VLM-driven Object Goal Navigation for Multi-Agent System
arXiv:2603.18210v1 Announce Type: new Abstract: Object-goal navigation has traditionally been limited to ground robots with closed-set object vocabularies. Existing multi-agent approaches depend on precomputed probabilistic graphs tied to fixed category sets, precluding generalization to novel goals at test time. We present GoalVLM, a cooperative multi-agent framework for zero-shot, open-vocabulary object navigation. GoalVLM integrates a Vision-Language Model (VLM) directly into the decision loop, SAM3 for text-prompted detection and segmentation, and SpaceOM for spatial reasoning, enabling agents to interpret free-form language goals and score frontiers via zero-shot semantic priors without retraining. Each agent builds a BEV semantic map from depth-projected voxel splatting, while a Goal Projector back-projects detections through calibr...
4.ReDAG-RT: Global Rate-Priority Scheduling for Real-Time Multi-DAG Execution in ROS 2
arXiv:2603.18238v1 Announce Type: new Abstract: ROS 2 has become a dominant middleware for robotic systems, where perception, estimation, planning, and control pipelines are structured as directed acyclic graphs of callbacks executed under a shared executor. However, default ROS 2 executors use best-effort dispatch without cross-DAG priority enforcement, leading to callback contention, structural priority inversion, and deadline instability under concurrent workloads. These limitations restrict deployment in time-critical and safety-sensitive cyber-physical systems. This paper presents ReDAGRT, a user-space global scheduling framework for deterministic multi-DAG execution in unmodified ROS 2. The framework introduces a Rate-Priority driven global ready queue that orders callbacks by activation rate, enforces per-DAG concurrency bounds, an...
5.Rapid Adaptation of Particle Dynamics for Generalized Deformable Object Mobile Manipulation
arXiv:2603.18246v1 Announce Type: new Abstract: We address the challenge of learning to manipulate deformable objects with unknown dynamics. In non-rigid objects, the dynamics parameters define how they react to interactions -- how they stretch, bend, compress, and move -- and they are critical to determining the optimal actions to perform a manipulation task successfully. In other robotic domains, such as legged locomotion and in-hand rigid object manipulation, state-of-the-art approaches can handle unknown dynamics using Rapid Motor Adaptation (RMA). Through a supervised procedure in simulation that encodes each rigid object's dynamics, such as mass and position, these approaches learn a policy that conditions actions on a vector of latent dynamic parameters inferred from sequences of state-actions. However, in deformable object manipul...
GSMA Newsroom
1.Strengthening the Global Fight Against Fraud and Scams – Takeaways from the Global Fraud Summit in Vienna
Summary available at source link.
2.GSMA MWC26 Barcelona closes 20th anniversary edition
Summary available at source link.
3.From Ambition to Execution: How Open Gateway Is Scaling the Global API Economy
Summary available at source link.
4.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.
5.GSMA Calls for Regulatory Readiness for Direct-to-User LEO Satellite Services
Summary available at source link.
Hugging Face Daily Papers
1.Universal Skeleton Understanding via Differentiable Rendering and MLLMs
Multimodal large language models (MLLMs) exhibit strong visual-language reasoning, yet remain confined to their native modalities and cannot directly process structured, non-visual data such as human skeletons. Existing methods either compress skeleton dynamics into lossy feature vectors for text alignment, or quantize motion into discrete tokens that generalize poorly across heterogeneous skeleton formats. We present SkeletonLLM, which achieves universal skeleton understanding by translating arbitrary skeleton sequences into the MLLM's native visual modality. At its core is DrAction, a differentiable, format-agnostic renderer that converts skeletal kinematics into compact image sequences. Because the pipeline is end-to-end differentiable, MLLM gradients can directly guide the rendering to produce task-informative visual tokens. To furthe...
2.AgentFactory: A Self-Evolving Framework Through Executable Subagent Accumulation and Reuse
Building LLM-based agents has become increasingly important. Recent works on LLM-based agent self-evolution primarily record successful experiences as textual prompts or reflections, which cannot reliably guarantee efficient task re-execution in complex scenarios. We propose AgentFactory, a new self-evolution paradigm that preserves successful task solutions as executable subagent code rather than textual experience. Crucially, these subagents are continuously refined based on execution feedback, becoming increasingly robust and efficient as more tasks are encountered. Saved subagents are pure Python code with standardized documentation, enabling portability across any Python-capable system. We demonstrate that AgentFactory enables continuous capability accumulation: its library of executable subagents grows and improves over time, progre...
3.LoST: Level of Semantics Tokenization for 3D Shapes
Tokenization is a fundamental technique in the generative modeling of various modalities. In particular, it plays a critical role in autoregressive (AR) models, which have recently emerged as a compelling option for 3D generation. However, optimal tokenization of 3D shapes remains an open question. State-of-the-art (SOTA) methods primarily rely on geometric level-of-detail (LoD) hierarchies, originally designed for rendering and compression. These spatial hierarchies are often token-inefficient and lack semantic coherence for AR modeling. We propose Level-of-Semantics Tokenization (LoST), which orders tokens by semantic salience, such that early prefixes decode into complete, plausible shapes that possess principal semantics, while subsequent tokens refine instance-specific geometric and semantic details. To train LoST, we introduce Relat...
4.GMT: Goal-Conditioned Multimodal Transformer for 6-DOF Object Trajectory Synthesis in 3D Scenes
Synthesizing controllable 6-DOF object manipulation trajectories in 3D environments is essential for enabling robots to interact with complex scenes, yet remains challenging due to the need for accurate spatial reasoning, physical feasibility, and multimodal scene understanding. Existing approaches often rely on 2D or partial 3D representations, limiting their ability to capture full scene geometry and constraining trajectory precision. We present GMT, a multimodal transformer framework that generates realistic and goal-directed object trajectories by jointly leveraging 3D bounding box geometry, point cloud context, semantic object categories, and target end poses. The model represents trajectories as continuous 6-DOF pose sequences and employs a tailored conditioning strategy that fuses geometric, semantic, contextual, and goaloriented i...
5.AdaRadar: Rate Adaptive Spectral Compression for Radar-based Perception
Radar is a critical perception modality in autonomous driving systems due to its all-weather characteristics and ability to measure range and Doppler velocity. However, the sheer volume of high-dimensional raw radar data saturates the communication link to the computing engine (e.g., an NPU), which is often a low-bandwidth interface with data rate provisioned only for a few low-resolution range-Doppler frames. A generalized codec for utilizing high-dimensional radar data is notably absent, while existing image-domain approaches are unsuitable, as they typically operate at fixed compression ratios and fail to adapt to varying or adversarial conditions. In light of this, we propose radar data compression with adaptive feedback. It dynamically adjusts the compression ratio by performing gradient descent from the proxy gradient of detection c...
IEEE Xplore AI
1.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, co-founder and CEO of photonic computing startup Neurophos in Austin, Texas. For instance, each optical cloak typically only works on a single color of light instead of all visible colors as one might want for stealth applications. Now companies are devising mor...
2.How Your Virtual Twin Could One Day Save Your Life
One morning in May 2019, a cardiac surgeon stepped into the operating room at Boston Children’s Hospital more prepared than ever before to perform a high-risk procedure to rebuild a child’s heart. The surgeon was experienced, but he had an additional advantage: He had already performed the procedure on this child dozens of times—virtually. He knew exactly what to do before the first cut was made. Even more important, he knew which strategies would provide the best possible outcome for the child whose life was in his hands. How was this possible? Over the prior weeks, the hospital’s surgical and cardio-engineering teams had come together to build a fully functioning model of the child’s heart and surrounding vascular system from MRI and CT scans. They began by carefully converting the medical imaging into a 3D model, then used physics to b...
3.Nvidia’s Always-On Chip Detects Faces in Less Than a Millisecond
Always-on vision systems might be used in autonomous vehicles, robotics, or to help consumer electronics save power by turning screens off when no one’s around. But to be used in such a way, these systems need to minimize their own power consumption. An always-on computer vision system developed by Nvidia researchers can detect human faces in less than a millisecond. The face detector, which is part of an chip that could be integrated into robots, autonomous vehicles, or laptops, saves power by storing all data locally and “racing to sleep” after detections. NVIDIA electrical engineer Ben Keller presented the system on 18 February at the IEEE International Solid State Circuits Conference in San Francisco. Efficient Vision Processing Technology According to the researchers, this kind of vision processing typically requires about 10 watts. ...
4.AI Trained on Birdsong Can Recognize Whale Calls
Birds’ chirps, trills, and warbles echo through the air, while whales’ boings , “ biotwangs ,” and whistles vibrate underwater. Despite the variations in sounds and the medium through which they travel, both birdsong and whale vocalizations can be classified by Perch 2.0 , an AI audio model from Google DeepMind . As a bioacoustics foundation model, Perch 2.0 was trained on millions of recordings of birds and other land-based animals, including amphibians, insects, and mammals. Yet researchers were surprised to learn how strongly the AI model performed when repurposed for whales . Scientists at Google DeepMind and Google Research have been working on whale bioacoustics for almost a decade, with work including algorithms that can detect humpback whale calls , as well as a more recent multispecies whale model that can identify eight distinct...
5.With Nvidia Groq 3, the Era of AI Inference Is (Probably) Here
This week, over 30,000 people are descending upon San Jose, Calif., to attend Nvidia GTC , the so-called Superbowl of AI—a nickname that may or may not have been coined by Nvidia. At the main event Jensen Huang, Nvidia CEO, took the stage to announce (among other things) a new line of next generation Vera Rubin chips that represent a first for the GPU giant: a chip designed specifically to handle AI inference. The Nvidia Groq 3 language processing unit (LPU) incorporates intellectual property Nvidia licensed from the start-up Groq last Christmas Eve for US $20 billion. “Finally, AI is able to do productive work, and therefore the inflection point of inference has arrived,” Huang told the crowd. “AI now has to think. In order to think, it has to inference. AI now has to do; in order to do, it has to inference.” Training and inference tasks...
MIT Sloan Management
1.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 […]
2.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” […]
3.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 […]
4.How Schneider Electric Scales AI in Both Products and Processes
Matt Harrison Clough/Ikon Images At the World Economic Forum Annual Meeting in Davos, Switzerland, in January 2026, Schneider Electric CEO Olivier Blum accepted awards recognizing the company’s AI solutions as part of the WEF’s MINDS (Meaningful, Intelligent, Novel, Deployable Solutions) program — for the second time. The distinction highlighted two of the company’s AI-enabled applications: […]
5.Leaders at All Levels: Kraft Heinz’s 5X Speed Secret
Is 36 months too long for a new-product cycle? It was for Kraft Heinz. So, starting with a pilot project, it was able to cut time to market to just six months by redesigning how people worked. Today, units throughout the company are applying that model’s step-by-step approach to change and are seeing measurable improvements […]
NY Fed - Liberty Street
1.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.
2.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...
3.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
4.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...
5.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
Project Syndicate
1.Selective Outrage Won’t End the Iran War
The United Nations Security Council’s resolution condemning Iran’s retaliatory strikes on US bases and energy facilities in neighboring countries ignores why they occurred. The resolution is less about international law than about protecting oil flows, global economic stability, and the Gulf states’ privileged status.
2.The Real Fallout From Trump’s Tariffs
Donald Trump might have hoped that his “Liberation Day” tariffs would punish China and boost the competitiveness of American industries, but they have instead had the opposite effect. Worse, the policy has had far-reaching unintended consequences that few policymakers and commentators have yet to reckon with.
3.Trump Is Burying His Own Security Strategy
US President Donald Trump has leapt feet first into a major new Middle East war, the objectives of which are changing by the day. As the conflict escalates, the endgame scenarios are growing increasingly bleak and complicated, making a mockery of the spirit and letter of Trump's three-month-old National Security Strategy.
4.What the Iran Crisis Means for Middle Powers
For solidarity among the world's middle powers to matter, it must be used to create a new system of states that are governed by the rule of law and committed to democratic norms and principles. The alternative is a world governed by power alone, where dependence is weaponized, and public consent becomes an inconvenience.
5.Will AGI Really Be the “Last Invention”?
Listen to leading voices in Silicon Valley, and you might come to believe that “solving intelligence” is sufficient to “solve everything else.” But as seductive as this claim about AI’s potential may be, it rests on a number of assumptions that do not withstand scrutiny.
RCR Wireless
1.Nvidia and global telecom operators pivot to distributed AI grids
Major carriers are using Nvidia’s “AI Grid” to repurpose their networks In sum – what we know: Nvidia GTC 2026 brought a wave of announcements from some of the biggest telecom operators on the planet, rallying around a concept Nvidia is branding “AI grids” — essentially, geographically distributed AI infrastructure designed to run and monetize […]
2.Italian carriers plan tower JV to boost 5G expansion
The tower JV between TIM and Fastweb + Vodafone builds on a preliminary RAN sharing agreement signed earlier this year In sum – what to know: Up to 6,000 towers planned – TIM and Fastweb + Vodafone aim to expand tower infrastructure to support 5G coverage and capacity across Italy. JV structure – A 50-50 […]
3.AT&T, Cisco and Nvidia advance network-based edge AI
AT&T said the solution combines its IoT core network with Cisco’s mobility services platform, allowing localized data routing, predictable performance, and secure connectivity for enterprise and industrial environments In sum – what to know: Edge AI moves into networks – AT&T and Cisco integrate AI inference into telecom infrastructure to process data closer to devices, […]
4.The scaling myth holding back cellular IoT (Reader Forum)
As cellular IoT deployments grow from thousands to millions, the limits of hardware, not software, come into focus, writes IoT connectivity provider Onomondo. Until connectivity infrastructure evolves to adapt to constrained devices – rather than forcing them into legacy telecom models – cellular IoT will remain stuck in a cycle of complexity, fragmentation, and unrealised […]
5.Du targets 10Gbps 5G-A upgrade with Huawei
The partnership between Huawei and du also includes work on AI-based network management and automation to improve efficiency and spectrum utilization In sum – what to know: 5G-A speed target – du and Huawei plan Phase-2 upgrades to deliver peak 10Gbps speeds across UAE networks, focusing on both indoor and outdoor coverage. Indoor priorities – […]
Semantic Scholar – Machine Learning
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arXiv Quantitative Finance
1.Adaptive Regime-Aware Stock Price Prediction Using Autoencoder-Gated Dual Node Transformers with Reinforcement Learning Control
Stock markets exhibit regime-dependent behavior where prediction models optimized for stable conditions often fail during volatile periods. Existing approaches typically treat all market states uniformly or require manual regime labeling, which is expensive and quickly becomes stale as market dynamics evolve. This paper introduces an adaptive prediction framework that adaptively identifies deviations from normal market conditions and routes data through specialized prediction pathways. The architecture consists of three components: (1) an autoencoder trained on normal market conditions that identifies anomalous regimes through reconstruction error, (2) dual node transformer networks specialized for stable and event-driven market conditions respectively, and (3) a Soft Actor-Critic reinforcement learning controller that adaptively tunes th...
2.LR-Robot: A Unified Supervised Intelligent Framework for Real-Time Systematic Literature Reviews with Large Language Models
Recent advances in artificial intelligence (AI) and natural language processing (NLP) have enabled tools to support systematic literature reviews (SLRs), yet existing frameworks often produce outputs that are efficient but contextually limited, requiring substantial expert oversight.The framework employs a human-in-the-loop process to define sub-SLR tasks, evaluate models, and ensure methodological rigor, while leveraging structured knowledge sources and retrieval-augmented generation (RAG) to enhance factual grounding and transparency. LR-Robot enables multidimensional categorization of research, maps relationships among papers, identifies high-impact works, and supports historical, fine-grained analyses of topic evolution. We demonstrate the framework using an option pricing case study, enabling comprehensive literature analysis. Empiri...
3.ARTEMIS: A Neuro Symbolic Framework for Economically Constrained Market Dynamics
Deep learning models in quantitative finance often operate as black boxes, lacking interpretability and failing to incorporate fundamental economic principles such as no-arbitrage constraints. This paper introduces ARTEMIS (Arbitrage-free Representation Through Economic Models and Interpretable Symbolics), a novel neuro-symbolic framework combining a continuous-time Laplace Neural Operator encoder, a neural stochastic differential equation regularised by physics-informed losses, and a differentiable symbolic bottleneck that distils interpretable trading rules. The model enforces economic plausibility via two novel regularisation terms: a Feynman-Kac PDE residual penalising local no-arbitrage violations, and a market price of risk penalty bounding the instantaneous Sharpe ratio. We evaluate ARTEMIS against six strong baselines on four data...
4.Multivariate GARCH and portfolio variance prediction: A forecast reconciliation perspective
We assess the advantage of combining univariate and multivariate portfolio risk forecasts with the aid of forecast reconciliation techniques. In our analyzes, we assume knowledge of portfolio weights, a standard for portfolio risk management applications. With an extensive simulation experiment, we show that, if the true covariance is known, forecast reconciliation improves over a standard multivariate approach, in particular when the adopted multivariate model is misspecified. However, if noisy proxies are used, correctly specified models and the misspecified ones (for instance, neglecting spillovers) turn out to be, in several cases, indistinguishable, with forecast reconciliation still providing improvements. The noise in the covariance proxy plays a crucial role in determining the improvement of both the forecast reconciliation and th...
5.Discrimination-insensitive pricing
Rendering fair prices for financial, credit, and insurance products is of ethical and regulatory interest. In many jurisdictions, discriminatory covariates, such as gender and ethnicity, are prohibited from use in pricing such instruments. In this work, we propose a discrimination-insensitive pricing framework, where we require the pricing principle to be insensitive to the (exogenously determined) protected covariates, that is the sensitivity of the pricing principle to the protected covariate is zero. We formulate and solve the optimisation problem that finds the nearest (in Kullback-Leibler (KL) divergence) "pricing" measure to the real world probability, such that under this pricing measure the principle is discrimination-insensitive. We call the solution the discrimination-insensitive measure and provide conditions for its existence ...
arXiv – 6G & Networking
1.Outage Probability Analysis of NOMA Enabled Hierarchical UAV Networks with Non-Linear Energy Harvesting
Uncrewed aerial vehicles (UAVs) are expected to enhance connectivity, extend network coverage, and support advanced communication services in sixth-generation (6G) cellular networks, particularly in public and civil domains. Although multi-UAV systems enhance connectivity for IoT networks more than single-UAV systems, energy-efficient communication systems and the integration of energy harvesting (EH) are crucial for their widespread adoption and effectiveness. In this regard, this paper proposes a hierarchical ad hoc UAV network with non-linear EH and non-orthogonal multiple access (NOMA) to enhance both energy and cost efficiency. The proposed system consists of two UAV layers: a cluster head UAV (CHU), which acts as the source, and cluster member UAVs (CMUs), which serve as relays and are capable of harvesting energy from a terrestrial...
2.RadioDiff-FS: Physics-Informed Manifold Alignment in Few-Shot Diffusion Models for High-Fidelity Radio Map Construction
Radio maps (RMs) provide spatially continuous propagation characterizations essential for 6G network planning, but high-fidelity RM construction remains challenging. Rigorous electromagnetic solvers incur prohibitive computational latency, while data-driven models demand massive labeled datasets and generalize poorly from simplified simulations to complex multipath environments. This paper proposes RadioDiff-FS, a few-shot diffusion framework that adapts a pre-trained main-path generator to multipath-rich target domains with only a small number of high-fidelity samples. The adaptation is grounded in a theoretical decomposition of the multipath RM into a dominant main-path component and a directionally sparse residual. This decomposition shows that the cross-domain shift corresponds to a bounded and geometrically structured feature transla...
3.Learn for Variation: Variationally Guided AAV Trajectory Learning in Differentiable Environments
Autonomous aerial vehicles (AAVs) empower sixth-generation (6G) Internet-of-Things (IoT) networks through mobility-driven data collection. However, conventional reward-driven reinforcement learning for AAV trajectory planning suffers from severe credit assignment issues and training instability, because sparse scalar rewards fail to capture the long-term and nonlinear effects of sequential movements. To address these challenges, this paper proposes Learn for Variation (L4V), a gradient-informed trajectory learning framework that replaces high-variance scalar reward signals with dense and analytically grounded policy gradients. Particularly, the coupled evolution of AAV kinematics, distance-dependent channel gains, and per-user data-collection progress is first unrolled into an end-to-end differentiable computational graph. Backpropagation...
4.Holistic Energy Performance Management: Enablers, Capabilities, and Features
Energy consumption is a significant concern for mobile network operators, and to enable further network energy improvements it is also an important target when developing the emerging 6G standard. In this paper we show that, despite the existence of many energy-saving features in 5G new radio (NR) networks, activating them in isolation yields only suboptimal savings and often compromises other network key performance indicators (KPIs) such as coverage or latency. We first introduce a compact taxonomy that distinguishes hardware capabilities from higher-layer features. Features fall into two classes: (i) signaling and scheduling mechanisms that create idle windows, and (ii) features that utilize those windows to save energy. We then present a feature orchestrator as a logical node to coordinate between features to maximize the gain. Using ...
5.Enabling 6G Wireless Communications: UWB Characterization of Corridors within the H-Band
Future sixth-generation of wireless system is expected to provide data-rates in the order of 1 Tbps and latencies below 1 ms. Among others, one of the most promising strategies to meet these requirements is to operate at higher frequencies than millimeter wave bands: the THz bands. Nevertheless, because of the higher losses and the detriment of classical propagation mechanisms, deploying systems operating at these frequencies becomes a real challenge. Consequently, short-range scenarios are of special interest since these effects of THz bands can be managed. This work conducts an extensive campaign within corridors at frequencies within the H-band in the range from 250 GHz to 330 GHz. For the first time in literature, an ultra wideband of 80 GHz is studied simultaneously. Large scale effects are assessed by estimating and modeling path ga...
arXiv – Network Architecture (6G/Slicing)
1.Holistic Energy Performance Management: Enablers, Capabilities, and Features
Energy consumption is a significant concern for mobile network operators, and to enable further network energy improvements it is also an important target when developing the emerging 6G standard. In this paper we show that, despite the existence of many energy-saving features in 5G new radio (NR) networks, activating them in isolation yields only suboptimal savings and often compromises other network key performance indicators (KPIs) such as coverage or latency. We first introduce a compact taxonomy that distinguishes hardware capabilities from higher-layer features. Features fall into two classes: (i) signaling and scheduling mechanisms that create idle windows, and (ii) features that utilize those windows to save energy. We then present a feature orchestrator as a logical node to coordinate between features to maximize the gain. Using ...
2.RIS-Aided Mobile Network Design
In this paper, we examine the distribution of radio signal propagation within the city of Poznan (Poland) to determine optimal locations for deploying Reconfigurable Intelligent Surfaces (RIS). The study focuses on designing a 5G/6G Radio Access Network (RAN), incorporating eight Base Stations (BSs) that utilize either Single Input Single Output (SISO), or Multiple Input Multiple Output (MIMO) antenna technologies, depending on the network cell configuration. Through detailed simulations and analyses, we explore various propagation scenarios in both Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions, considering the complex urban landscape characterized by high-rise buildings. The results demonstrate the potential of using RISs in mobile networks to enhance radio signal quality in urban environments through strategic placements. ...
3.HAPS-RIS-assisted IoT Networks for Disaster Recovery and Emergency Response: Architecture, Application Scenarios, and Open Challenges
Reliable and resilient communication is essential for disaster recovery and emergency response, yet terrestrial infrastructure often fails during large-scale natural disasters. This paper proposes a High-Altitude Platform Station (HAPS) and Reconfigurable Intelligent Surfaces (RIS)-assisted Internet of Things (IoT) communication system to restore connectivity in disaster-affected areas. Distributed IoT sensors collect critical environmental data and forward it to nearby gateways via short-range links, while the HAPS-RIS system provides backhaul to these gateways. To overcome the severe double path loss of passive RIS at high altitudes, we propose a dynamically adjustable sub-connected active RIS architecture that can reconfigure the number of elements connected to each power amplifier through switching mechanisms. Simulation results demon...
4.Agentic AI for SAGIN Resource Management_Semantic Awareness, Orchestration, and Optimization
Space-air-ground integrated networks (SAGIN) promise ubiquitous 6G connectivity but face significant resource management challenges due to heterogeneous infrastructure, dynamic topologies, and stringent quality-of-service (QoS) requirements. Conventional model-driven approaches struggle with scalability and adaptability in such complex environments. This paper presents an agentic artificial intelligence (AI) framework for autonomous SAGIN resource management by embedding large language model (LLM)-based agents into a Monitor-Analyze-Plan- Execute-Knowledge (MAPE-K) control plane. The framework incorporates three specialized agents, namely semantic resource perceivers, intent-driven orchestrators, and adaptive learners, that collaborate through natural language reasoning to bridge the gap between operator intents and network execution. A k...
5.Toward Experimentation-as-a-Service in 5G/6G: The Plaza6G Prototype for AI-Assisted Trials
This paper presents Plaza6G, the first operational Experiment-as-a-Service (ExaS) platform unifying cloud resources with next-generation wireless infrastructure. Developed at CTTC in Barcelona, Plaza6G integrates GPU-accelerated compute clusters, multiple 5G cores, both open-source (e.g., Free5GC) and commercial (e.g., Cumucore), programmable RANs, and physical or emulated user equipment under unified orchestration. In Plaza6G, the experiment design requires minimal expertise as it is expressed in natural language via a web portal or a REST API. The web portal and REST API are enhanced with a Large Language Model (LLM)-based assistant, which employs retrieval-augmented generation (RAG) for up-to-date experiment knowledge and Low-Rank Adaptation (LoRA) for continuous domain fine-tuning. Over-the-air (OTA) trials leverage a four-chamber ane...