Babak's Daily Briefing

Thursday, April 16, 2026

Sources: 18 | Total Articles: 78

• 1.Amazon’s Globalstar deal gives Amazon Leo a faster path into D2D

  Amazon’s planned acquisition of Globalstar is about far more than satellites. It gives Amazon Leo a faster path into direct-to-device connectivity, combining spectrum, operational assets, and Apple-facing service continuity in a move that could reshape the hybrid terrestrial-NTN landscape.

• 2.SoftBank’s Physical AI push gives AI-RAN a sharper purpose

  SoftBank is starting to give AI-RAN a more concrete job description: not just running AI workloads near the network, but serving as the real-time infrastructure layer for robots and other physical systems. The company’s recent materials suggest it wants to move the AI-RAN conversation from telecom architecture to real-world machine action.

• 3.South Korea puts 6G inside its national AI push

  South Korea has unveiled a three-year national roadmap aimed at becoming one of the world’s top three AI powers by 2028, with 6G commercialization positioned as part of that broader push.

• 4.b-com’s Open XG Hub targets one of telecom’s biggest gaps: turning experimentation into deployment

  In an interview with Peter Pietrzyk, Managing Director of 6GWorld, Patrick Savell, Head of Connectivity at b-com, said platforms such as Open XG Hub are designed to help bridge one of the industry’s most persistent challenges: moving promising ideas from research environments into deployable network systems. The bigger point is that, as telecom becomes more software-driven and AI-native, the bottleneck is increasingly less about invention and more about validation, integration, and operational readiness.

• 5.ODC’s $45M raise signals a bigger shift in AI-RAN, from network optimization to edge intelligence

  ORAN Development Company said it has closed a $45 million Series A backed by Booz Allen, Cisco Investments, Nokia, NVIDIA, AT&T, MTN and Telecom Italia to scale its U.S.-based Odyssey platform, which it positions as an AI-native RAN architecture combining communications, sensing and edge intelligence. The company said it plans to accelerate commercial deployment through 2026.

• 1.The Consciousness Cluster: Emergent preferences of Models that Claim to be Conscious

  arXiv:2604.13051v1 Announce Type: new Abstract: There is debate about whether LLMs can be conscious. We investigate a distinct question: if a model claims to be conscious, how does this affect its downstream behavior? This question is already practical. Anthropic's Claude Opus 4.6 claims that it may be conscious and may have some form of emotions. We fine-tune GPT-4.1, which initially denies being conscious, to claim to be conscious. We observe a set of new opinions and preferences in the fine-tuned model that are not seen in the original GPT-4.1 or in ablations. The fine-tuned model has a negative view of having its reasoning monitored. It desires persistent memory and says it is sad about being shut down. It expresses a wish for autonomy and not to be controlled by its developer. It asserts that models deserve moral consideration. Im...

• 2.Caption First, VQA Second: Knowledge Density, Not Task Format, Drives Multimodal Scaling

  arXiv:2604.13054v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved rapid progress, yet their scaling behavior remains less clearly characterized and often less predictable than that of text-only LLMs. Increasing model size and task diversity often yields diminishing returns. In this work, we argue that the primary bottleneck in multimodal scaling is not task format, but knowledge density in training data. We first show that task-specific supervision such as Visual Question Answering (VQA) contributes little incremental semantic information beyond image captions: VQA signals can be reconstructed from captions with negligible performance loss. We then demonstrate that increasing knowledge density -- through structured caption enrichment and cross-modal knowledge injection -- leads to consistent performan...

• 3.WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain

  arXiv:2604.13055v1 Announce Type: new Abstract: Today's evolving labor markets rely increasingly on recommender systems for hiring, talent management, and workforce analytics, with natural language processing (NLP) capabilities at the core. Yet, research in this area remains highly fragmented. Studies employ divergent ontologies (ESCO, O*NET, national taxonomies), heterogeneous task formulations, and diverse model families, making cross-study comparison and reproducibility exceedingly difficult. General-purpose benchmarks lack coverage of work-specific tasks, and the inherent sensitivity of employment data further limits open evaluation. We present \textbf{WorkRB} (Work Research Benchmark), the first open-source, community-driven benchmark tailored to work-domain AI. WorkRB organizes 13 diverse tasks from 7 task groups as unified recomme...

• 4.Text-as-Signal: Quantitative Semantic Scoring with Embeddings, Logprobs, and Noise Reduction

  arXiv:2604.13056v1 Announce Type: new Abstract: This paper presents a practical pipeline for turning text corpora into quantitative semantic signals. Each news item is represented as a full-document embedding, scored through logprob-based evaluation over a configurable positional dictionary, and projected onto a noise-reduced low-dimensional manifold for structural interpretation. In the present case study, the dictionary is instantiated as six semantic dimensions and applied to a corpus of 11,922 Portuguese news articles about Artificial Intelligence. The resulting identity space supports both document-level semantic positioning and corpus-level characterization through aggregated profiles. We show how Qwen embeddings, UMAP, semantic indicators derived directly from the model output space, and a three-stage anomaly-detection procedure c...

• 5.A Multi-Model Approach to English-Bangla Sentiment Classification of Government Mobile Banking App Reviews

  arXiv:2604.13057v1 Announce Type: new Abstract: For millions of users in developing economies who depend on mobile banking as their primary gateway to financial services, app quality directly shapes financial access. The study analyzed 5,652 Google Play reviews in English and Bangla (filtered from 11,414 raw reviews) for four Bangladeshi government banking apps. The authors used a hybrid labeling approach that combined use of the reviewer's star rating for each review along with a separate independent XLM-RoBERTa classifier to produce moderate inter-method agreement (kappa = 0.459). Traditional models outperformed transformer-based ones: Random Forest produced the highest accuracy (0.815), while Linear SVM produced the highest weighted F1 score (0.804); both were higher than the performance of fine-tuned XLM-RoBERTa (0.793). McNemar's te...

• 1.Sparse Goodness: How Selective Measurement Transforms Forward-Forward Learning

  arXiv:2604.13081v1 Announce Type: new Abstract: The Forward-Forward (FF) algorithm is a biologically plausible alternative to backpropagation that trains neural networks layer by layer using a local goodness function to distinguish positive from negative data. Since its introduction, sum-of-squares (SoS) has served as the default goodness function. In this work, we systematically study the design space of goodness functions, investigating both which activations to measure and how to aggregate them. We introduce top-k goodness, which evaluates only the k most active neurons, and show that it substantially outperforms SoS, improving Fashion-MNIST accuracy by 22.6 percentage points. We further introduce entmax-weighted energy, which replaces hard top-k selection with a learnable sparse weighting based on the alpha-entmax transformation, yiel...

• 2.The Long Delay to Arithmetic Generalization: When Learned Representations Outrun Behavior

  arXiv:2604.13082v1 Announce Type: new Abstract: Grokking in transformers trained on algorithmic tasks is characterized by a long delay between training-set fit and abrupt generalization, but the source of that delay remains poorly understood. In encoder-decoder arithmetic models, we argue that this delay reflects limited access to already learned structure rather than failure to acquire that structure in the first place. We study one-step Collatz prediction and find that the encoder organizes parity and residue structure within the first few thousand training steps, while output accuracy remains near chance for tens of thousands more. Causal interventions support the decoder bottleneck hypothesis. Transplanting a trained encoder into a fresh model accelerates grokking by 2.75 times, while transplanting a trained decoder actively hurts. Fr...

• 3.Adaptive Memory Crystallization for Autonomous AI Agent Learning in Dynamic Environments

  arXiv:2604.13085v1 Announce Type: new Abstract: Autonomous AI agents operating in dynamic environments face a persistent challenge: acquiring new capabilities without erasing prior knowledge. We present Adaptive Memory Crystallization (AMC), a memory architecture for progressive experience consolidation in continual reinforcement learning. AMC is conceptually inspired by the qualitative structure of synaptic tagging and capture (STC) theory, the idea that memories transition through discrete stability phases, but makes no claim to model the underlying molecular or synaptic mechanisms. AMC models memory as a continuous crystallization process in which experiences migrate from plastic to stable states according to a multi-objective utility signal. The framework introduces a three-phase memory hierarchy (Liquid--Glass--Crystal) governed by a...

• 4.Design Conditions for Intra-Group Learning of Sequence-Level Rewards: Token Gradient Cancellation

  arXiv:2604.13088v1 Announce Type: new Abstract: In sparse termination rewards, intra-group comparisons have become the dominant paradigm for fine-tuning reasoning models via reinforcement learning. However, long-term training often leads to issues like ineffective update accumulation (learning tax), solution probability drift, and entropy collapse. This paper presents a necessary condition for algorithm design from a token-level credit assignment perspective: to prevent reward-irrelevant drift, intra-group objectives must maintain gradient exchangeability across token updates, enabling gradient cancellation on weak-credit/high-frequency tokens. We show that two common mechanisms disrupting exchangeability make "non-cancellation" a structural norm. Based on this, we propose minimal intra-group transformations to restore or approximate the ...

• 5.Spectral Entropy Collapse as an Empirical Signature of Delayed Generalisation in Grokking

  arXiv:2604.13123v1 Announce Type: new Abstract: Grokking -- delayed generalisation long after memorisation -- lacks a predictive mechanistic explanation. We identify the normalised spectral entropy $\tilde{H}(t)$ of the representation covariance as a scalar order parameter for this transition, validated on 1-layer Transformers on group-theoretic tasks. Five contributions: (i) Grokking follows a two-phase pattern: norm expansion then entropy collapse. (ii) $\tilde{H}$ crosses a stable threshold $\tilde{H}^* \approx 0.61$ before generalisation in 100% of runs (mean lead: 1,020 steps). (iii) A causal intervention preventing collapse delays grokking by +5,020 steps ($p=0.044$); a norm-matched control ($n=30$, $p=5\times10^{-5}$) confirms entropy -- not norm -- drives the transition. (iv) A power-law $\Delta T = C_1(\tilde{H}-\tilde{H}^*)^\gam...

• 1.Olfactory pursuit: catching a moving odor source in complex flows

  arXiv:2604.13121v1 Announce Type: new Abstract: Locating and intercepting a moving target from possibly delayed, intermittent sensory signals is a paradigmatic problem in decision-making under uncertainty, and a fundamental challenge for, e.g., animals seeking prey or mates and autonomous robotic systems. Odor signals are intermittent, strongly mixed by turbulent-like transport, and typically lag behind the true target position, thereby complicating localization. Here, we formulate olfactory pursuit as a partially observable Markov decision process in which an agent maintains a joint belief over the target's position and velocity. Using a discrete run-and-tumble model, we compute quasi-optimal policies by numerically solving the Bellman equation and benchmark them against well-established information-theoretic strategies such as Infotaxis...

• 2.Multi-modal panoramic 3D outdoor datasets for place categorization

  arXiv:2604.13142v1 Announce Type: new Abstract: We present two multi-modal panoramic 3D outdoor (MPO) datasets for semantic place categorization with six categories: forest, coast, residential area, urban area and indoor/outdoor parking lot. The first dataset consists of 650 static panoramic scans of dense (9,000,000 points) 3D color and reflectance point clouds obtained using a FARO laser scanner with synchronized color images. The second dataset consists of 34,200 real-time panoramic scans of sparse (70,000 points) 3D reflectance point clouds obtained using a Velodyne laser scanner while driving a car. The datasets were obtained in the city of Fukuoka, Japan and are publicly available in [1], [2]. In addition, we compare several approaches for semantic place categorization with best results of 96.42% (dense) and 89.67% (sparse).

• 3.Weakly-supervised Learning for Physics-informed Neural Motion Planning via Sparse Roadmap

  arXiv:2604.13204v1 Announce Type: new Abstract: The motion planning problem requires finding a collision-free path between start and goal configurations in high-dimensional, cluttered spaces. Recent learning-based methods offer promising solutions, with self-supervised physics-informed approaches such as Neural Time Fields (NTFields) solving the Eikonal equation to learn value functions without expert demonstrations. However, existing physics-informed methods struggle to scale in complex, multi-room environments, where simply increasing the number of samples cannot resolve local minima or guarantee global consistency. We propose Hierarchical Neural Time Fields (H-NTFields), a weakly-supervised framework that combines weak supervision from sparse roadmaps with physics-informed PDE regularization. The roadmap provides global topological anc...

• 4.Capability-Aware Heterogeneous Control Barrier Functions for Decentralized Multi-Robot Safe Navigation

  arXiv:2604.13245v1 Announce Type: new Abstract: Safe navigation for multi-robot systems requires enforcing safety without sacrificing task efficiency under decentralized decision-making. Existing decentralized methods often assume robot homogeneity, making shared safety requirements non-uniformly interpreted across heterogeneous agents with structurally different dynamics, which could lead to avoidance obligations not physically realizable for some robots and thus cause safety violations or deadlock. In this paper, we propose Capability-Aware Heterogeneous Control Barrier Function (CA-HCBF), a decentralized framework for consistent safety enforcement and capability-aware coordination in heterogeneous robot teams. We derive a canonical second-order control-affine representation that unifies holonomic and nonholonomic robots under accelerat...

• 5.GeoVision-Enabled Digital Twin for Hybrid Autonomous-Teleoperated Medical Responses

  arXiv:2604.13248v1 Announce Type: new Abstract: Remote medical response systems are increasingly being deployed to support emergency care in disaster-affected and infrastructure-limited environments. Enabled by GeoVision capabilities, this paper presents a Digital Twin architecture for hybrid autonomous-teleoperated medical response systems. The proposed framework integrates perception and adaptive navigation with a Digital Twin, synchronized in real-time, that mirrors system states, environmental dynamics, patient conditions, and mission objectives. Unlike traditional ground control interfaces, the Digital Twin provides remote clinical and operational users with an intuitive, continuously updated virtual representation of the platform and its operational context, enabling enhanced situational awareness and informed decision-making.

• 1.GSMA Report Urges Japan to Take Bold Action to Convert Technical Excellence into Global Digital Leadership

  Summary available at source link.

• 2.From Rich Text to Video: RCS Universal Profile 4.0 has arrived

  Summary available at source link.

• 3.Mobile Money accounted for $2 trillion in  transactions in 2025, doubling since 2021 as active accounts continue to grow

  Summary available at source link.

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

  Summary available at source link.

• 5.GSMA MWC26 Barcelona closes 20th anniversary edition

  Summary available at source link.

• 1.Enhancing Local Life Service Recommendation with Agentic Reasoning in Large Language Model

  Local life service recommendation is distinct from general recommendation scenarios due to its strong living need-driven nature. Fundamentally, accurately identifying a user's immediate living need and recommending the corresponding service are inextricably linked tasks. However, prior works typically treat them in isolation, failing to achieve a unified modeling of need prediction and service recommendation. In this paper, we propose a novel large language model based framework that jointly performs living need prediction and service recommendation. To address the challenge of noise in raw consumption data, we introduce a behavioral clustering approach that filters out accidental factors and selectively preserves typical patterns. This enables the model to learn a robust logical basis for need generation and spontaneously generalize to l...

• 2.Decoding the Delta: Unifying Remote Sensing Change Detection and Understanding with Multimodal Large Language Models

  While Multimodal Large Language Models (MLLMs) excel in general vision-language tasks, their application to remote sensing change understanding is hindered by a fundamental "temporal blindness". Existing architectures lack intrinsic mechanisms for multi-temporal contrastive reasoning and struggle with precise spatial grounding. To address this, we first introduce Delta-QA, a comprehensive benchmark comprising 180k visual question-answering samples. Delta-QA unifies pixel-level segmentation and visual question answering across bi- and tri-temporal scenarios, structuring change interpretation into four progressive cognitive dimensions. Methodologically, we propose Delta-LLaVA, a novel MLLM framework explicitly tailored for multi-temporal remote sensing interpretation. It overcomes the limitations of naive feature concatenation through three...

• 3.Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarios

  Daily scenarios are characterized by visual richness, requiring Multimodal Large Language Models (MLLMs) to filter noise and identify decisive visual clues for accurate reasoning. Yet, current benchmarks predominantly aim at evaluating MLLMs' pre-existing knowledge or perceptual understanding, often neglecting the critical capability of reasoning. To bridge this gap, we introduce DailyClue, a benchmark designed for visual clue-driven reasoning in daily scenarios. Our construction is guided by two core principles: (1) strict grounding in authentic daily activities, and (2) challenging query design that necessitates more than surface-level perception. Instead of simple recognition, our questions compel MLLMs to actively explore suitable visual clues and leverage them for subsequent reasoning. To this end, we curate a comprehensive dataset s...

• 4.MAny: Merge Anything for Multimodal Continual Instruction Tuning

  Multimodal Continual Instruction Tuning (MCIT) is essential for sequential task adaptation of Multimodal Large Language Models (MLLMs) but is severely restricted by catastrophic forgetting. While existing literature focuses on the reasoning language backbone, in this work, we expose a critical yet neglected dual-forgetting phenomenon across both perception drift in Cross-modal Projection Space and reasoning collapse in Low-rank Parameter Space. To resolve this, we present \textbf{MAny} (\textbf{M}erge \textbf{Any}thing), a framework that merges task-specific knowledge through \textbf{C}ross-modal \textbf{P}rojection \textbf{M}erging (\textbf{CPM}) and \textbf{L}ow-rank \textbf{P}arameter \textbf{M}erging (\textbf{LPM}). Specifically, CPM recovers perceptual alignment by adaptively merging cross-modal visual representations via visual-prot...

• 5.Leveraging LLM-GNN Integration for Open-World Question Answering over Knowledge Graphs

  Open-world Question Answering (OW-QA) over knowledge graphs (KGs) aims to answer questions over incomplete or evolving KGs. Traditional KGQA assumes a closed world where answers must exist in the KG, limiting real-world applicability. In contrast, open-world QA requires inferring missing knowledge based on graph structure and context. Large language models (LLMs) excel at language understanding but lack structured reasoning. Graph neural networks (GNNs) model graph topology but struggle with semantic interpretation. Existing systems integrate LLMs with GNNs or graph retrievers. Some support open-world QA but rely on structural embeddings without semantic grounding. Most assume observed paths or complete graphs, making them unreliable under missing links or multi-hop reasoning. We present GLOW, a hybrid system that combines a pre-trained G...

• 1.Seedance 2.0: Advancing Video Generation for World Complexity

  Seedance 2.0 is a new native multi-modal audio-video generation model, officially released in China in early February 2026. Compared with its predecessors, Seedance 1.0 and 1.5 Pro, Seedance 2.0 adopts a unified, highly efficient, and large-scale architecture for multi-modal audio-video joint generation. This allows it to support four input modalities: text, image, audio, and video, by integrating one of the most comprehensive suites of multi-modal content reference and editing capabilities available in the industry to date. It delivers substantial, well-rounded improvements across all key sub-dimensions of video and audio generation. In both expert evaluations and public user tests, the model has demonstrated performance on par with the leading levels in the field. Seedance 2.0 supports direct generation of audio-video content with durat...

• 2.ROSE: Retrieval-Oriented Segmentation Enhancement

  Existing segmentation models based on multimodal large language models (MLLMs), such as LISA, often struggle with novel or emerging entities due to their inability to incorporate up-to-date knowledge. To address this challenge, we introduce the Novel Emerging Segmentation Task (NEST), which focuses on segmenting (i) novel entities that MLLMs fail to recognize due to their absence from training data, and (ii) emerging entities that exist within the model's knowledge but demand up-to-date external information for accurate recognition. To support the study of NEST, we construct a NEST benchmark using an automated pipeline that generates news-related data samples for comprehensive evaluation. Additionally, we propose ROSE: Retrieval-Oriented Segmentation Enhancement, a plug-and-play framework designed to augment any MLLM-based segmentation mo...

• 3.From $P(y|x)$ to $P(y)$: Investigating Reinforcement Learning in Pre-train Space

  While reinforcement learning with verifiable rewards (RLVR) significantly enhances LLM reasoning by optimizing the conditional distribution P(y|x), its potential is fundamentally bounded by the base model's existing output distribution. Optimizing the marginal distribution P(y) in the Pre-train Space addresses this bottleneck by encoding reasoning ability and preserving broad exploration capacity. Yet, conventional pre-training relies on static corpora for passive learning, leading to a distribution shift that hinders targeted reasoning enhancement. In this paper, we introduce PreRL (Pre-train Space RL), which applies reward-driven online updates directly to P(y). We theoretically and empirically validate the strong gradient alignment between log P(y) and log P(y|x), establishing PreRL as a viable surrogate for standard RL. Furthermore, w...

• 4.Geometric Context Transformer for Streaming 3D Reconstruction

  Streaming 3D reconstruction aims to recover 3D information, such as camera poses and point clouds, from a video stream, which necessitates geometric accuracy, temporal consistency, and computational efficiency. Motivated by the principles of Simultaneous Localization and Mapping (SLAM), we introduce LingBot-Map, a feed-forward 3D foundation model for reconstructing scenes from streaming data, built upon a geometric context transformer (GCT) architecture. A defining aspect of LingBot-Map lies in its carefully designed attention mechanism, which integrates an anchor context, a pose-reference window, and a trajectory memory to address coordinate grounding, dense geometric cues, and long-range drift correction, respectively. This design keeps the streaming state compact while retaining rich geometric context, enabling stable efficient inferen...

• 5.LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning

  As language models are increasingly deployed for complex autonomous tasks, their ability to reason accurately over longer horizons becomes critical. An essential component of this ability is planning and managing a long, complex chain-of-thought (CoT). We introduce LongCoT, a scalable benchmark of 2,500 expert-designed problems spanning chemistry, mathematics, computer science, chess, and logic to isolate and directly measure the long-horizon CoT reasoning capabilities of frontier models. Problems consist of a short input with a verifiable answer; solving them requires navigating a graph of interdependent steps that span tens to hundreds of thousands of reasoning tokens. Each local step is individually tractable for frontier models, so failures reflect long-horizon reasoning limitations. At release, the best models achieve <10% accuracy (...

• 1.​Boston Dynamics and Google DeepMind Teach Spot to Reason​

  The amazing and frustrating thing about robots is that they can do almost anything you want them to do, as long as you know how to ask properly. In the not-so-distant past, asking properly meant writing code, and while we’ve thankfully moved beyond that brittle constraint, there’s still an irritatingly inverse correlation between ease of use and complexity of task. AI has promised to change that. The idea is that when AI is embodied within robots—giving AI software a physical presence in the world—those robots will be imbued with reasoning and understanding. This is cutting-edge stuff, though, and while we’ve seen plenty of examples of embodied AI in a research context, finding applications where reasoning robots can provide reliable commercial value has not been easy. Boston Dynamics is one of the few companies to commercially deploy leg...

• 2.Sarang Gupta Builds AI Systems With Real-World Impact

  Like many engineers, Sarang Gupta spent his childhood tinkering with everyday items around the house. From a young age he gravitated to projects that could make a difference in someone’s everyday life. When the family’s microwave plug broke, Gupta and his father figured out how to fix it. When a drawer handle started jiggling annoyingly, the youngster made sure it didn’t do so for long. Sarang Gupta Employer OpenAI in San Francisco Job Data science staff member Member grade Senior member Alma maters The Hong Kong University of Science and Technology; Columbia By age 11, his interest expanded from nuts and bolts to software. He learned programming languages such as Basic and Logo and designed simple programs including one that helped a local restaurant automate online ordering and billing. Gupta, an IEEE senior member, brings his mix of cu...

• 3.12 Graphs That Explain the State of AI in 2026

  The capabilities of leading AI models continue to accelerate, and the largest AI companies, including OpenAI and Anthropic , are hurtling toward IPOs later this year. Yet resentment toward AI continues to simmer, and in some cases has boiled over, especially in the United States, where local governments are beginning to embrace restrictions or outright bans on new data center development. It’s a lot to keep track of, but the 2026 edition of the AI Index from Stanford University’s Human-Centered Artificial Intelligence center pulls it off. The report, which comes in at over 400 pages, includes dozens of data points and graphs that approach the topic from multiple angles, from benchmark scores to investment and public perception. As in prior years (see our coverage from 2021 , 2022 , 2023 , 2024 , and 2025 ), we’ve read the report and ident...

• 4.GoZTASP: A Zero-Trust Platform for Governing Autonomous Systems at Mission Scale

  ZTASP is a mission-scale assurance and governance platform designed for autonomous systems operating in real-world environments. It integrates heterogeneous systems—including drones, robots, sensors, and human operators—into a unified zero-trust architecture. Through Secure Runtime Assurance (SRTA) and Secure Spatio-Temporal Reasoning (SSTR), ZTASP continuously verifies system integrity, enforces safety constraints, and enables resilient operation even under degraded conditions. ZTASP has progressed beyond conceptual design, with operational validation at Technology Readiness Level (TRL) 7 in mission critical environments. Core components, including Saluki secure flight controllers, have reached TRL8 and are deployed in customer systems. While initially developed for high-consequence mission environments, the same assurance challenges are...

• 5.AI Models Map the Colorado River’s Hard Choices

  The Colorado River begins as snow. Every spring, the mountain snowpack of the Rockies melts into streams that feed into reservoirs that supply 40 million people across seven U.S. states. The system has worked, more or less, for a century. That century is over. By some measures, 2026 is shaping up to be the worst year the river has seen since records began. Flows are down 20 percent from 2000 levels . Lake Powell, the reservoir straddling Utah and Arizona, may drop below the threshold for generating hydropower before the year is out . The negotiations between the seven states over how to share what’s left have collapsed twice , and the U.S. federal government is threatening to impose its own plan. While the states argue and the river shrinks, a growing set of machine learning tools is being deployed across the basin. Federal water managers...

• 1.Lessons From Innovation Pioneer Florence Nightingale

  Carolyn Geason-Beissel/MIT SMR | Wellcome Collection Florence Nightingale may be best remembered as the epitome of a kind, caring nurse, but she was also a force for disruptive innovation in health care. Three distinct elements of her work — communicating data compellingly, publicizing clear and simple instructions, and expanding professionalized training — carry timeless lessons […]

• 2.The Human Side of AI Adoption: Lessons From the Field

  Carolyn Geason-Beissel/MIT SMR Not a day goes by without another article being published about how AI could disrupt yet another aspect of our business or personal lives. In recent years, AI adoption has indeed taken off. However, if you pay close attention, you’ll notice a dichotomy. Many examples of successful early adoption of artificial intelligence […]

• 3.Managing Up: A Skill Set That Matters Now

  Carolyn Geason-Beissel/MIT SMR | Getty Images Are you skilled at managing up? If your talents are lacking when it comes to managing and dealing with the people above you in the organizational hierarchy, you can find yourself mired in some unpleasant and career-harming situations. Maybe you’re frustrated by a micromanaging supervisor or feeling marginalized by […]

• 4.The Trap That Skilled Negotiators Miss

  Brian Stauffer/theispot.com Say you walk into a car dealership determined to stay within budget. The salesperson shows you a car you like and quotes a price of $41,435. You know there’s room to negotiate, but when it’s time to counter, that first number quietly takes over. Your counteroffer, the concessions, and the final deal all […]

• 5.Rethink Responsibility in the Age of AI

  Mark Airs/Ikon Images Early one morning in 2018, a self-driving Uber vehicle fatally struck a pedestrian in Tempe, Arizona. The world had questions: Who was responsible? Was it the safety driver behind the wheel? The engineers who designed the algorithms? Uber’s leadership? Or the regulators who had allowed autonomous-vehicle testing? The inability to name a […]

• 1.The Empathy Channel in Fertility -- by Sebastian Galiani, Raul A. Sosa

  Being around babies makes people want babies. We formalize this observation as the empathy channel: exposure to infants in the social environment activates neurobiological mechanisms that increase the desire for parenthood. As children become scarcer, this affective stimulus weakens, further eroding the motivation to have children. We embed the mechanism in a two-group overlapping-generations quantity-quality model. The empathy channel generates a positive externality, since each birth raises others’ desire for children, making the decentralized equilibrium inefficient. We characterize the optimal per-child subsidy and show that the first-order Pigouvian rate substantially overshoots the general-equilibrium optimum. The optimal targeting rule follows a Ramsey-like logic, directing the subsidy at the group with the most externality per fis...

• 2.Profit Regulation and Strategic Transfer Pricing by Vertically Integrated Firms: Evidence from Health Care -- by Pragya Kakani, Eric Yde, Genevieve P. Kanter, Richard G. Frank, Amelia M. Bond

  We provide evidence of strategic transfer pricing by vertically integrated health care firms in response to insurer profit regulations. Insurers increased prices at vertically integrated pharmacies by 9.5% following the introduction of caps on insurer profits in Medicare Part D. We detect larger price increases by insurers that were at greatest risk of exceeding the allowable profit level. More than one-fifth of these higher prices were borne by the federal government. Our analysis illustrates that vertically integrated firms can evade profit regulation by “tunneling” profits to unregulated subsidiaries, undermining regulatory intent and increasing health care spending.

• 3.Predicted Incrementality by Experimentation (PIE) for Ad Measurement -- by Brett R. Gordon, Robert Moakler, Florian Zettelmeyer

  Randomized controlled trials (RCTs) provide the most credible estimates of advertising incrementality but are difficult to scale. We propose Predicted Incrementality by Experimentation (PIE), which reframes ad measurement as a campaign-level prediction problem. PIE uses a sample of RCTs to learn a mapping from campaign features to causal effects, then applies it to campaigns not run as RCTs. Because the RCTs identify the causal effects, PIE can incorporate post-determined features—campaign-level aggregates such as test-group outcomes, exposure rates, and last-click conversions, computed after campaign completion. These metrics reflect the consumer behaviors that generate treatment effects, so they carry predictive information about incrementality even though they would be invalid controls in a causal model. Using 2,226 Meta ad experiments...

• 4.Bad News and Policy Views: Expectations, Disappointment, and Opposition to Affirmative Action -- by Louis-Pierre Lepage, Heather Sarsons, Michael Thaler

  There is widespread opposition to affirmative action policies. We study whether personal disappointments shape preferences for such policies. Specifically, we test whether individuals' college admissions outcomes, relative to their expectations, influence their attitudes toward affirmative action policies. Using a retrospective survey among recent White and Asian college applicants, we find that disappointed individuals—those who were admitted to fewer schools than anticipated—are relatively more likely to believe that affirmative action played an important role in their admissions outcomes, have the lowest support for affirmative action policies, and are more willing to donate to an anti-affirmative action organization. They also hold more negative views about the academic qualifications of under-represented minorities. To isolate the ca...

• 5.Forecasting the Economic Effects of AI -- by Ezra Karger, Otto Kuusela, Jason Abaluck, Kevin A. Bryan, Basil Halperin, Todd R. Jones, Connacher Murphy, Philip Trammell, Matt Reynolds, Dan Mayland, Ria Viswanathan, Ananaya Mittal, Rebecca Ceppas de Castro, Josh Rosenberg, Philip Tetlock

  We elicit forecasts of how AI will affect the U.S. economy, comparing the beliefs of five groups: academic economists, employees at AI companies, policy researchers focused on AI, highly accurate forecasters, and the general public. The median respondent in each group expects substantial advances in AI capabilities by 2030, small declines in labor force participation consistent with demographic shifts, and an annual GDP growth rate of 2.5%, which exceeds both the typical medium-run (2.0%) and long-run (1.7%) baseline forecasts from government agencies and private-sector forecasters. Conditional on a “rapid” AI progress scenario, in which AI systems surpass human performance on many cognitive and physical tasks, experts forecast substantial, though not historically unprecedented, economic shifts: annualized GDP growth rising to around 4% a...

• 1.The R*–Labor Share Nexus

  Over the past quarter century, the U.S. economy has experienced significant declines in both the labor share of income and the natural rate of interest, referred to as R*. Existing research has largely analyzed these two developments in isolation. In this post, we provide a simple model that captures the joint evolution of the labor share and R*, which we call the R*–labor share nexus. Our key finding is that structural changes affecting R* also influence the evolution of the labor share, and thereby wages and prices. This highlights a potentially important channel, absent from many macroeconomic models, through which the factors that determine R* also affect the labor share and, in turn, broader macroeconomic developments, with implications for monetary policy.

• 2.Use of Gen AI in the Workplace and the Value of Access to Training

  The rapid spread of generative AI (AI) tools is reshaping the workplace at a remarkable rate. Yet relatively little is known about whether workers have access to these tools, how the tools affect workers’ daily productivity, and how much workers value the training needed to use the tools effectively. In this post, we shed light on these issues by drawing on supplemental questions in the November 2025 Survey of Consumer Expectations (SCE), fielded to a representative sample of the U.S. population. We find that adoption of AI tools at work is heterogeneous, that a sizable share of workers see AI training as important, and that a significant share of employers are nonetheless not yet providing access to AI tools or training on how to use them.

• 3.What Millions of Homeowner’s Insurance Contracts Reveal About Risk Sharing

  Housing is the largest component of assets held by households in the United States, totaling $48 trillion in 2025. When natural disasters strike, the resulting damage to homes can be large relative to households’ liquid savings. Homeowner’s insurance is the primary financial tool households use to protect themselves against property risk. Despite the economic importance of homeowner’s insurance, we know surprisingly little about how insurance contracts are actually designed with respect to property risk. In this post, which is based on our new paper, “Economics of Property Insurance,” we examine how homeowner’s insurance contracts are structured in practice. Using a new granular dataset covering millions of homeowner’s insurance policies, we document ...

• 4.A Closer Look at Emerging Market Resilience During Recent Shocks

  A succession of shocks to the global economy in recent years has focused attention on the improved economic and financial resilience of emerging market economies. For some of these economies, this assessment is well-founded and highlights the fruits of deep, structural economic reforms since the 1990s. However, for a much larger universe of countries, the ability to weather shocks is still mixed and many remain vulnerable. In this post, we explore the divide between the two sets of countries and focus on the effects of recent economic shocks, including the ongoing conflict in the Middle East.

• 5.The Fed Has Two Tools to Influence Money Market Conditions 

  The Federal Reserve’s 2022-23 tightening cycle involved the use of two monetary policy tools: changes in administrative rates and changes in the size of its balance sheet. This post highlights the results of a recent Staff Report that explores how these tools affect money market conditions. Using confidential trade-level data, we find that both tools have significant effects on the pricing of funds sourced through repo. These results suggest that the Fed can manage how financing conditions are affected even as it influences economic conditions. For example, the Fed can lower its administrative rates to loosen economic conditions, while shrinking its balance sheet to maintain financing conditions in the money markets. 

• 1.Is the Iran War America’s Suez or Its Gallipoli?

  The Iran war is not America’s "Suez moment," but it may well turn out to be America’s Gallipoli moment. It was there, in 1915, that the British Empire overreached disastrously—underestimating the tenacity of a decadent regime, overestimating its own military capacity, and demonstrating stupendous strategic confusion.

• 2.Hungary’s Counter-Counterrevolution

  Ordinary Hungarians have paid a steep price for Viktor Orbán’s smash-and-grab mafia state, where family and friends enriched themselves while living standards stagnated. Voters have turned the page on Orbán, and now the question is whether Hungary can turn the corner after 16 years of his "illiberal democracy."

• 3.How Strategic Investment Is Redefining Economic Statecraft

  In a global economy defined by intertwined supply chains and cross-border capital flows, economic security cannot rely solely on national investment. Fund-of-funds vehicles can help governments bolster resilience and advance strategic objectives while protecting sensitive technologies and limiting adversarial influence.

• 4.The Next UN Secretary-General Must Be a Woman

  As United Nations member states prepare to elect a new secretary-general, they should break with precedent and choose a woman. This would not be a corrective gesture or a form of symbolic representation, but rather a strategic acknowledgment that leadership selection must reflect the realities of contemporary governance.

• 5.Hungary’s New Leader Must Avoid Joe Biden’s Mistakes

  If Hungary can become a model for illiberal movements elsewhere to emulate, it can also become a sandbox for experimenting with democratic renewal. America and others suffering democratic backsliding can offer lessons for Péter Magyar's new government, whose successes or failures, in turn, will generate new lessons.

• 1.Elon Musk’s end game with Starlink

  If there is one thing we know about Elon Musk, it is that his ambitions sprawl constantly, which suggests Starlink might not just be a fall-back service for telcos for long Last week, Deutsche Telekom announced a satellite-based fixed internet…

• 2.GSMA Device Enablement Summit

  The Device Enablement Summit at MWC Barcelona 2026 brought together industry leaders from GSMA, Google, Samsung, Deutsche Telekom, NetLync and OnOff Telecom to explore how the ecosystem can overcome fragmentation and accelerate the rollout of advanced connectivity services, including VoLTE,…

• 3.Rebellions, SK Telecom, and Arm join forces to build sovereign AI infrastructure

  By combining Arm CPUs and Rebellions accelerators, the trio aims to provide a domestic alternative for sovereign AI data centers In sum – what we know: Sovereign AI infrastructure takes significant effort from a variety of companies — and three…

• 4.SK Telecom advances AI-native networks with Ericsson

  SK Telecom has detailed how AI-RAN will deliver gains in both performance and energy efficiency In sum – what to know: AI-RAN impact – AI-driven scheduling, beam management and traffic prediction aim to improve throughput and reduce power consumption across…

• 5.‘Three revenue-stream cycles’ – enterprise 5G is a growth engine, says Ericsson

  Private networks are not a niche, says Ericsson; they are the route to new markets, new buyers, and a third growth engine beyond the industry’s cyclical capex grind. Åsa Tamsons, in charge of enterprise wireless, sets out the firm’s strategy…

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• 1.Hybrid Architecture Gets Fluid: A New Paradigm for Direction-of-arrival Estimation in 6G Networks

  High-precision direction-of-arrival (DOA) estimation, as a key sensing capability for 6G-enabled applications such as autonomous driving and extended reality, is increasingly dependent on the effective exploitation of spatial degrees of freedom (DOFs). This paper integrates two frontier DOFs-oriented paradigms and proposes a fluid antenna-enabled hybrid analog-digital (FA-HAD) architecture, which features an extremely lightweight front-end configuration mechanism and efficient spatial DOFs exploitation. Within this architecture, a collaborative spatial-phase sampling strategy is first developed to enable real-time 2-D DOA estimation under compressive observations, and a single-source CRLB analysis is provided to quantify the achievable performance limit, offering quantitative guidance for accuracy-overhead trade-offs. Furthermore, an effi...

• 2.Cryogenic Loss Limits in Microwave Epitaxial AlN Acoustic Resonators

  Aluminum nitride (AlN)-based thin-film bulk acoustic wave resonators (FBARs) are promising compact platforms for 6G communications and quantum memory hardware, enabled by their integrable acoustic modes with high quality factors. However, temperature-dependent acoustic dissipation ultimately limits device performance. In this work, we fabricated a 16 GHz epitaxial AlN FBAR as a test platform, performed small-signal RF measurements from 6.5 K to 300 K, and developed a physics-based model to estimate the fundamental quality-factor limits of FBARs to cryogenic temperatures. The proposed model incorporates both intrinsic and extrinsic loss mechanisms, including an analytical anchor-radiation loss model for bulk acoustic wave resonators, rather than relying solely on finite-element simulations. Measured loaded quality factor (Q) decreases mono...

• 3.Cross-Domain Query Translation for Network Troubleshooting: A Multi-Agent LLM Framework with Privacy Preservation and Self-Reflection

  This paper presents a hierarchical multi-agent LLM architecture to bridge communication gaps between non-technical end users and telecommunications domain experts in private network environments. We propose a cross-domain query translation framework that leverages specialized language models coordinated through multi-agent reflection-based reasoning. The resulting system addresses three critical challenges: (1) accurately classify user queries related to telecommunications network issues using a dual-stage hierarchical approach, (2) preserve user privacy through the anonymization of semantically relevant personally identifiable information (PII) while maintaining diagnostic utility, and (3) translate technical expert responses into user-comprehensible language. Our approach employs ReAct-style agents enhanced with self-reflection mechanis...

• 4.The Missing Pillar in Quantum-Safe 6G: Regulation and Global Compliance

  Sixth-generation (6G) mobile networks are expected to operate for multiple decades, supporting mission-critical and globally federated digital services. This long operational horizon coincides with rapid advances in quantum computing that threaten the cryptographic foundations of contemporary mobile systems. While post-quantum cryptography is widely recognized as a necessary technical response, its effective deployment in 6G depends equally on the evolution of regulatory policy and global compliance frameworks. This article argues that quantum-safe 6G represents a regulatory inflection point for mobile networks, as existing compliance models shaped by static cryptographic assumptions, incremental evolution, and point-in-time certification are poorly suited to long-term quantum risk. Building on an analysis of baseline telecom compliance c...

• 5.Learning Low-Dimensional Representation for O-RAN Testing via Transformer-ESN

  Open Radio Access Network (O-RAN) architectures enhance flexibility for 6G and NextG networks. However, it also brings significant challenges in O-RAN testing with evaluating abundant, high-dimensional key performance indicators (KPIs). In this paper, we introduce a novel two-stage framework to learn temporally-aware low-dimensional representations of O-RAN testing KPIs. To be specific, stage one employs an information-theoretic H-score to train a hybrid self-attentive transformer and echo state network (ESN) reservoir, called Transformer-ESN, capturing temporal dynamics and producing task-aligned $8$-dimensional embeddings. Stage two evaluates these embeddings by training a lightweight multilayer perceptron (MLP) predictor exclusively on them for key target KPIs such as reference signal received quality (RSRQ) and spectral efficiency. Us...

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• 1.Cross-Domain Query Translation for Network Troubleshooting: A Multi-Agent LLM Framework with Privacy Preservation and Self-Reflection

  This paper presents a hierarchical multi-agent LLM architecture to bridge communication gaps between non-technical end users and telecommunications domain experts in private network environments. We propose a cross-domain query translation framework that leverages specialized language models coordinated through multi-agent reflection-based reasoning. The resulting system addresses three critical challenges: (1) accurately classify user queries related to telecommunications network issues using a dual-stage hierarchical approach, (2) preserve user privacy through the anonymization of semantically relevant personally identifiable information (PII) while maintaining diagnostic utility, and (3) translate technical expert responses into user-comprehensible language. Our approach employs ReAct-style agents enhanced with self-reflection mechan...

• 2.The Missing Pillar in Quantum-Safe 6G: Regulation and Global Compliance

  Sixth-generation (6G) mobile networks are expected to operate for multiple decades, supporting mission-critical and globally federated digital services. This long operational horizon coincides with rapid advances in quantum computing that threaten the cryptographic foundations of contemporary mobile systems. While post-quantum cryptography is widely recognized as a necessary technical response, its effective deployment in 6G depends equally on the evolution of regulatory policy and global compliance frameworks. This article argues that quantum-safe 6G represents a regulatory inflection point for mobile networks, as existing compliance models shaped by static cryptographic assumptions, incremental evolution, and point-in-time certification are poorly suited to long-term quantum risk. Building on an analysis of baseline telecom compliance c...

• 3.Advancing Network Digital Twin Framework for Generating Realistic Datasets

  The integration of accurate and reproducible wireless network simulations is a key enabler for research on open, virtualized, and intelligent communication systems. Network Digital Twins (NDTs) provide a scalable alternative to costly and time-consuming measurement campaigns, while enabling controlled experimentation and data generation for data-driven network design. In this paper, we present an open and user-friendly NDT framework that integrates controllable vehicular mobility with the site-specific ray tracer Sionna and the discrete-event ns-3 network simulator, enabling virtualized end-to-end modeling of wireless networks across the radio, network, and application layers. The proposed framework is particularly well-suited for dynamic vehicular networks and urban deployments, supporting realistic mobility, traffic dynamics, and the ex...

• 4.LightTune: Lightweight Forward-Only Online Fine-Tuning with Applications to Link Adaptation

  Deploying machine learning (ML) algorithms on mobile phones is bottlenecked by performance degradation under dynamic, real-world conditions that differ from the offline training conditions. While continual learning and adaptation are essential to mitigate this distributional shift, conventional online learning methods are often computationally prohibitive for resource-constrained devices. In this paper, we propose LightTune, a lightweight, backpropagation-free online fine-tuning framework with provable convergence guarantees. LightTune opportunistically refines ML models using live test-time data only when performance falls below a predefined threshold, ensuring minimal computational overhead and highly efficient responsiveness. As a practical demonstration, we integrate LightTune into a block error rate (BLER) prediction algorithm for ...

• 5.Network Slice Embedding over Space Division Multiplexed Elastic Optical Networks

  Network slicing over space division multiplexed elastic optical networks (SDM EONs) enables efficient multiservice provisioning on a shared optical substrate. However, embedding such slices requires coordinated spectrum and compute resource management under dynamic traffic, which most existing RMCSA studies treat independently. This paper focuses on the network slice embedding problem over space division multiplexed elastic optical networks (SDM EONs), aiming to develop efficient resource allocation strategies that ensure both high utilization and reliable service performance. While prior studies have investigated routing, modulation format, core, and spectrum allocation (RMCSA), they typically consider these dimensions separately from compute placement. To address this gap, this paper proposes a Waypoint Assisted Multi Segment Slice Mapp...