Babak's Daily Briefing

Monday, June 8, 2026

Sources: 18 | Total Articles: 86

• 1.RF Digital Twins: Why 5G-Advanced and 6G Need Predictive Simulation

  RF Digital Twins: Why 5G-Advanced and 6G Need Predictive Simulation As wireless systems become more tightly coupled across…

• 2.Evaluating 6G PHY Evolution: What the Industry Is Really Trying to Solve

  Summary available at source link.

• 3.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.

• 4.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.

• 5.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.

• 1.Benchmark Everything Everywhere All at Once

  Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance. However, their construction is labor-intensive and hard to reuse, raising concerns about sustainability and scalability. Moreover, existing benchmarks often quickly reach performance saturation after their release, resulting in insufficient discrimination among state-of-the-art models. To address these challenges, we introduce Benchmark Agent, a fully autonomous agentic system designed for benchmark building. Our framework orchestrates the complete benchmark construction pipeline, from user query analysis and subtask design to data annotation and quality control. To assess Benchmark Agent, we implement it to produce 15 representative benchmarks, spanning diverse evaluation scenarios, including text underst...

• 2.ADK Arena: Evaluating Agent Development Kits via LLM-as-a-Developer

  The rapid proliferation of Agent Development Kits (ADKs), SDK-level frameworks for building LLM-powered autonomous agents, has outpaced any empirical understanding of how framework choice affects agent performance. We propose \textbf{LLM-as-a-Developer}, a methodology that replaces human developers with an LLM coding agent that learns each framework's API from documentation, writes agent code, and iteratively repairs it through a validate-and-feedback loop until tests pass. By holding the developer constant and varying only the framework, generation effort becomes a quantitative proxy for API usability and the resulting agents provide a controlled measure of framework effectiveness. We implement this in \textbf{ADK Arena}, a fully automated pipeline with per-framework Docker isolation, a three-level validation pipeline, and benchmark adap...

• 3.FALSIFYBENCH: Evaluating Inductive Reasoning in LLMs with Rule Discovery Games

  Large language models (LLMs) are increasingly deployed as autonomous agents in scientific tasks. Yet whether these systems can effectively engage in forms of inductive reasoning relevant to scientific discovery remains an open question. In this work, we introduce FALSIFYBENCH, an evaluation framework for hypothesis-driven reasoning inspired by the classic Wason 2-4-6 task, in which agents must discover hidden semantic properties by iteratively proposing examples and receiving feedback. This task captures key elements of scientific reasoning: hypothesis generation, evidence gathering, and belief revision in response to both confirming and disconfirming evidence. Our evaluation of 12 LLMs across model families and scales shows that reasoning models are generally stronger scientific reasoners than instruction-tuned models, although no model ...

• 4.The Saturation Trap and the Subjectivity of Intervention Timing: Why Affect-Based Triggers and LLM Judges Fail to Time Interventions on Autonomous Agents

  As autonomous AI agents move from conversational systems to long-horizon software execution, runtime safety layers that decide when to interrupt an agent have become essential. We study this timing problem using a continuous 18-dimensional affective-dynamics engine (HEART) as a diagnostic probe, evaluating four intervention trigger families - absolute state thresholds, composite state-action patterns, regex reasoning-feature extraction, and zero-shot LLM-as-judge - against human-annotated intervention points on SWE-bench-Verified debugging traces. We report three findings. First, a State Saturation Trap: agents show no recovery signal under sustained difficulty, so modeled frustration quickly crosses the threshold and stays at its maximum, converting threshold-on-state triggers from moment detectors into near-constant indicators that fire...

• 5.EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning

  Autonomous LLM training is often framed as recipe search, which leaves the training harness largely static. This limitation sharpens in agentic RL, where shifting bottlenecks and scalar rewards mask diverse failure modes. We introduce EvoTrainer, an autonomous training framework that co-evolves LLM policies and training-side harnesses through empirical feedback: it diagnoses rollout-level evidence, revises diagnostics, backtests interventions, and accumulates reusable skills. Evaluated on mathematical reasoning, competitive-programming code generation, and repository-level software engineering, EvoTrainer matches or exceeds the human-engineered RL references under the same data, codebase, and evaluation protocol, with the largest gain on long-horizon agentic SWE. Trajectory analyses show that retained strategies diverge across domains, ev...

• 1.Improving Cross-Lingual Factual Recall via Consistency-Driven Reinforcement Learning

  arXiv:2606.06586v1 Announce Type: new Abstract: Large language models (LLMs) trained predominantly on English data encode substantial world knowledge, yet often fail to express it reliably in other languages, a phenomenon known as cross-lingual factual inconsistency. To study and address this, we introduce PolyFact, a large-scale parallel multilingual factual QA dataset containing 100K Wikidata-grounded facts across 12 typologically diverse languages. Using PolyFact, we compare light continual pretraining (CPT), supervised fine-tuning (SFT), and reinforcement learning via Group Relative Policy Optimization (GRPO) for improving cross-lingual factual recall in Qwen-2.5-7B and OLMo-2-1124-7B. We find that GRPO consistently outperforms SFT, improving both cross-lingual consistency and generalization to unseen languages, while CPT on parallel...

• 2.Re-Centering Humans in LLM Personalization

  arXiv:2606.06614v1 Announce Type: new Abstract: Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data. It remains unclear how well current personalization systems work for real users. In this paper, we study the gap in LLM personalization performance when using synthetic versus human data. We collect human conversations (550 conversations) and judgments across three stages of personalization: extracting user attributes from conversations (5,949 judgments), pairing relevant attributes with new prompts (11,919), and incorporating relevant attributes into a personalized response (1,101). Incorporating human data reveals system limitations at each stage. Models struggle to extract attributes from human conversations, disagree with human judgments on relevant attri...

• 3.UnpredictaBench: A Benchmark for Evaluating Distributional Randomness in LLMs

  arXiv:2606.06622v1 Announce Type: new Abstract: We introduce UnpredictaBench, an evaluation that tests the ability of large language models (LLMs) to capture true underlying distributions. As LLMs are increasingly used as substitutes for other entities (e.g., for humans in economic simulations), the tendency of many models to collapse towards a single plausible answer means a failure to capture the unpredictability of real systems. Recent work on improving output diversity is insufficient for this setting: simulation requires samples that are calibrated to a target distribution, not merely varied outputs. UnpredictaBench isolates a simplified but fundamental version of this problem: sampling outcomes from individual target distributions, including canonical statistical distributions, distributions induced by stochastic programs, and natu...

• 4.How Language Models Fail: Token-Level Signatures of Committed and Persistent Reasoning Failures

  arXiv:2606.06635v1 Announce Type: new Abstract: Failures in language model reasoning emerge through distinct processes that leave identifiable signatures in the reasoning trace. We characterize these failures using token-level uncertainty signals, finding they arise through two empirically distinguishable processes. The first is committed failure, in which a model locks onto an incorrect reasoning path early in its trace. A central diagnostic signature is the commitment point, beyond which considering additional tokens hurt rather than help failure detection. In the second, persistent uncertainty, uncertainty instead accumulates throughout, and the full trace is needed to best distinguish failing from successful completions. These signatures reproduce across 23 model-dataset configurations, with the framework's falsifiable predictions ho...

• 5.CAF-Gen: A Multi-Agent System for Enriching Argumentation Structures

  arXiv:2606.06646v1 Announce Type: new Abstract: Formalizing complex reasoning from natural text is one of the central challenges in computational linguistics. It requires systems to understand not just keywords but also the context and complex reasoning embedded in a text. Current Argument Mining (AM) techniques identify basic claims and premises, yet they often struggle to capture the richer structural information required by advanced schemas such as the Carneades Argumentation Framework (CAF), which incorporates features such as premise types, proof standards, and argument schemes. We address this limitation by introducing CAF-Gen, an automated multi-agent framework designed to enrich shallow argument structures into CAF-compliant argument models. By employing an iterative Creator-Reviewer pipeline, a creator agent's output is validate...

• 1.Elmes*: Automated Construction of Fine-Grained Evaluation Rubrics for Large Language Models in Long-Tail Educational Scenarios

  arXiv:2606.06546v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know. Existing benchmarks emphasize domain-general correctness or depend on manually designed rubrics that scale poorly to long-tail pedagogical scenarios. We introduce Elmes*, an end-to-end framework for constructing, refining, and applying fine-grained scenario-specific rubrics. Elmes* combines a declarative multi-agent engine for teacher--student--judge interactions with SceneGen, a self-evolving module that co-optimizes evaluation criteria and test data from expert-defined pedagogical dimensions. Using Elmes*, we build Edu-330, covering 330 scenarios across 11 subjects, 3 grade bands, and 10 task types, with over 1{,}000 second-level indicators. Experiments on Edu-330 and four ex...

• 2.FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models

  arXiv:2606.06547v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) refine tokens iteratively but commit them irreversibly, leading to a "stability lag" where early decisions remain fragile even after being written. We reveal that Post-Training Quantization (PTQ) error easily flips these borderline decisions at the write frontier, which are then permanently locked in and amplified. To address this, we propose Frontier-Aware Instability-Reweighted Calibration (FAIR-Calib), a two-stage PTQ framework for dLLMs. Stage I probes a full-precision teacher to estimate a position prior that combines frontier hits and masked-stage reliability. Stage II performs off-policy, layer-wise calibration by minimizing a reweighted hidden-state MSE, effectively prioritizing the protection of fragile frontier states without requiring expens...

• 3.Multi-Scale Feature Attention Network for Polymer Classification using THz Dual-Comb Spectroscopy

  arXiv:2606.06554v1 Announce Type: new Abstract: Reliable polymer identification is essential for ensuring the quality and safety of recycled plastics, yet conventional sorting and spectroscopic techniques often struggle to deliver robust discrimination. Terahertz Dual-Comb Spectroscopy (THz-DCS) offers a promising alternative, providing rapid, high-resolution, and non-destructive measurements. In this work, we leverage THz-DCS to classify 12 types of polymers, including pure polymers, multilayer films, commercial blends, and biopolymers. To handle the complexity of these spectral signals, we propose the Multi-Scale Feature Attention Network (MSFAN), a novel deep learning architecture tailored for THz-DCS data. The framework integrates feature gating for signal recalibration and multi-scale parallel convolutions to capture diverse frequenc...

• 4.MacArena: Benchmarking Computer Use Agents on an Online macOS Environment

  arXiv:2606.06560v1 Announce Type: new Abstract: Computer-use agents (CUAs) operate graphical user interfaces (GUIs) through vision and control primitives, and their capabilities have advanced rapidly, driven in part by standardized online evaluation benchmarks such as OSWorld, which serve both as evaluation tools and as training environments for reinforcement learning. However, macOS remains underserved in this landscape: the only existing benchmark, macOSWorld, covers a narrow slice of first-party applications with simpler tasks, and runs on x86 virtual machines incompatible with Apple Silicon. We introduce MacArena, a benchmark of 421 manually verified tasks spanning 50 applications that combines a curated port of OSWorld tasks, content sourced from macOSWorld, and 49 new macOS-native tasks, all running on Apple's native Virtualization ...

• 5.WAV: Multi-Resolution Block Residual Routing for Deep Decoder-Only Transformers

  arXiv:2606.06564v1 Announce Type: new Abstract: Residual connections are central to training deep Transformers, but standard PreNorm residual streams aggregate sublayer updates with fixed unit weights. Recent Attention Residuals replace this fixed accumulation with content-dependent depth-wise routing, and Block Attention Residuals make the mechanism efficient by routing over block-level residual summaries. However, a single block summary stores only the low-frequency total residual displacement inside a block, discarding directional structure such as attention-vs-MLP imbalance and early-vs-late block dynamics. We propose WAV v1, a lightweight multi-resolution residual routing method for decoder-only Transformers. Instead of representing each block only by its accumulated residual sum, WAV v1 augments every block with two directional deta...

• 1.PandaAI: A Practical Agent CQ2 for Neuro-symbolic Data Analysis And Integrated Decision-Making in Quantitative Finance

  While deep learning has excelled in various domains, its application to sequential decision-making in finance remains challenging due to the low Signal-to-Noise Ratio (SNR) and non-stationarity of financial data. Leveraging the reasoning capabilities of Large Language Models (LLMs), we propose \textbf{PandaAI}, a closed-loop neuro-symbolic LLM agent with market regime modeling and constrained alpha generation, which bridges general LLM reasoning with financial rigor and suppresses the financial toxicity of LLM-generated outputs. To bridge the gap between general linguistic capability and financial rigor, we fine-tune a domain-specific LLM. Furthermore, we integrate this LLM into a modular architecture and form a closed-loop system. Unlike traditional models that optimize isolated prediction metrics, \textbf{PandaAI} is designed as a neuro...

• 2.Zero-Copy Semantic Contagion: An In-Memory Streaming Architecture for Evolving Attention Graphs

  Per-ticker forecasting models dominate financial time-series work yet remain blind to cross-company propagation: a foundry disruption in Taiwan does not register in a single-asset model until Apple's own price has already moved. To address this limitation, we introduce a heterogeneous Rust-Python streaming architecture that maps cross-company attention as a continuous-time graph driven directly from text. We show that on the ingestion side, a zero-copy Rust edge parses news records in $\sim$100 ns and scans the target equity universe in $\sim$1.2 $μ$s. On the inference end, a multivariate Neural Hawkes Process featuring per-node continuous-time LSTM states and a bilinear latent projection propagates directed excitation, while an adaptive pruning rule bounds the computational cost of dynamic neighborhood updates. Combining these stages, we...

• 3.Stress Amplified Resilience: ESG and Joint Fragility in Equity Markets

  Market stress rarely harms investors through one channel alone. Losses, volatility spikes, and deteriorating tradability often arrive together. We examine whether ESG is associated with lower exposure to clustered fragility in equity markets. Using monthly data on S&P 500 constituents from 2014 to 2025, we study downside returns, volatility, illiquidity, and a cofragility state that captures their joint occurrence within the same firm month. The evidence supports a stress-amplified resilience interpretation rather than an unconditional ESG return premium. In the return channel, the ESG association is concentrated in the extreme downside tail during stress months. In the volatility channel, higher ESG is associated with smaller risk spikes when aggregate conditions are weak. In the illiquidity channel, the association is more persistent, s...

• 4.Generating Financial Time Series by Matching Random Convolutional Features

  Generating realistic financial time series is challenging as training data is often limited to a single historical path. With such scarce data, overfitting is hard to avoid, especially under adversarial training where a trained discriminator can memorize the training samples. To mitigate this, recent approaches train generators to minimize the discrepancy between untrained feature representations of real and generated time series. In these works, the feature maps are based on path signatures, which can fail to capture relevant time series properties at tractable truncation depths. In this work, we instead train generators by matching random convolutional features of real and generated time series. Existing random convolutional feature maps, such as Rocket and Hydra, have been shown to provide informative representations of real-world time...

• 5.ReSGA: A Large Tail Risk Model for Learning Value-at-Risk and Expected Shortfall

  Learning Value-at-Risk (VaR) and Expected Shortfall (ES) is important for managing financial risks effectively. Existing approaches with limited parameters are vulnerable to model misspecification in the era of big data. To address this limitation, we propose a large tail risk model, the retrieval-enhanced self-grouping autoencoder (ReSGA), which is designed with millions of parameters to exploit the rich cross-sectional dependence and long-term temporal dynamics of assets using their characteristics. Applied to monthly US equity returns from 1926 to 2023 with 153 firm characteristics, ReSGA outperforms twelve econometric and machine learning competitors in terms of out-of-sample loss and statistical backtesting. In addition, its forecast advantages can translate into significant economic gains from long-short decile portfolios that are c...

• 1.GSMA Presents Vietnam with Government Leadership Award 2026, Recognising the Country as One of the World’s Most Dynamic Digital Leaders

  Summary available at source link.

• 2.Network Slicing to Help Drive Next Wave of 5G Innovation in India

  Summary available at source link.

• 3.Canadian Telecommunications Association and GSMA Convene Industry Leaders at “Inflection Point” for Canada’s Connectivity Future

  Summary available at source link.

• 4.iOS 26.5 brings E2EE  for RCS: A new milestone for secure cross‑platform messaging

  Summary available at source link.

• 5.GSMA Calls for Urgent Action to Protect Connectivity Resilience Across Africa

  Summary available at source link.

• 1.Counterintuitive problems in discrete probability

  This manuscript contains a collection of counterintuitive problems in discrete probability, together with detailed solutions. The dataset was constructed as part of a broader research project investigating the capabilities of the latest-generation Large Language Models (LLMs) in solving discrete probability problems, in order to assess whether LLMs tend to make systematic reasoning errors associated with known cognitive biases. The problems collected here are specifically designed to challenge heuristic reasoning strategies that often lead to intuitively appealing but mathematically incorrect conclusions. The dataset combines several types of problems. Some are adapted from classical probabilistic paradoxes and cognitive-bias literature, while others originate from recreational mathematics sources or were developed by ourselves following ...

• 2.How reliable are LLMs when it comes to playing dice?

  We investigate the probabilistic reasoning capabilities of large language models through a controlled benchmarking study on discrete probability problems. We constructed two datasets, respectively a set of standard exercises and a set of counterintuitive exercises, designed to trigger heuristic reasoning, and evaluated 8 state-of-the-art models, each tested with and without Chain-of-Thought prompting. Models achieve an average accuracy of 0.96 on standard problems but only 0.59 on counterintuitive ones. We further provide empirical evidence of token bias: performance drops by over 20% when canonical formulations are replaced by disguised variants. Embedding misleading suggestions in the prompt reduces performance by up to 34%, with no model proving immune. Taken together, the reported findings suggest that current LLMs are not yet genuine...

• 3.Watch, Remember, Reason: Human-View Video Understanding with MLLMs

  Video understanding is being rapidly transformed by multimodal large language models (MLLMs), as research moves from short clips to long, multimodal, and knowledge-intensive video scenarios. These scenarios require models to handle sparse evidence, long-range dependencies, multimodal alignment, and reliable inference under limited computational budgets. This work presents a human-view perspective on LLM-based video understanding, organized around three functional abilities: watching, remembering, and reasoning. Rather than treating video tasks as isolated benchmarks, this view provides a unified structure for analyzing how video MLLMs acquire evidence, preserve context, and produce grounded outputs. We introduce a formulation that characterizes video understanding systems by their perceptual representations, memory states, reasoning trace...

• 4.Lost in Migration: Exposing Android Framework Vulnerabilities in Parallel Java-Kotlin Implementations

  Android has adopted Kotlin alongside Java across apps and core system components. During this shift, we observe parallel implementations in the Android Open Source Project (AOSP) where the same component is implemented in both Java and Kotlin. In principle, their functional purposes are identical. In practice, subtle semantic divergences can appear. Such divergences are not vulnerabilities by themselves, but they provide useful clues that may reveal flaws in surrounding enforcement logic. To the best of our knowledge, this paper presents the first systematic study of Java-Kotlin parallel implementations in the Android framework and examines their security implications. We design and build ParaDroid, an analysis framework that identifies parallel methods at scale and compares their behaviors. ParaDroid normalizes code into a bytecode-level...

• 5.A Comprehensive Anatomy of Human and DeepSeek-R1 LLM Mathematical Reasoning

  The emergence of "Aha moments" in large language models, particularly DeepSeek-R1-0120, has raised the question of whether these systems genuinely reason or merely imitate the appearance of reasoning. We conduct a comprehensive empirical comparison between model and human reasoning across all 30 problems from AIME 2025, exhaustively annotating 10,247 reasoning steps into five functional categories: Analysis, Inference, Branch, Backtrace, and Reflection. We find a clear structural difference. Human solutions maintain a compact alternation between analysis and deduction, whereas DeepSeek-R1 frequently revisits intermediate results, performs shallow and often unnecessary verification, and loops through local checks without meaningful logical progress. We describe this as topological mimicry: reproducing the surface form of reasoning without ...

• 1.TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies

  Robot manipulation alternates between low-risk transit phases that call for fast execution and high-risk contact stages that demand slow, precise motion. Yet existing Vision-Language-Action models (VLAs) only inherit a single fixed speed from training demonstrations. Prior efforts to accelerate VLAs through model compression, KV-cache reuse, or reinforcement learning only shift the policy from one fixed speed to another, and leave deceleration almost unexplored. We observe that the magnitude of each predicted action already governs how fast the robot moves, opening a direct route to controllable execution speed. We turn this observation into TempoVLA, a single VLA whose execution speed is controlled by an explicit condition. TempoVLA combines two coupled components. (1) A data-side Variable-Speed Trajectory Augmentation (VSTA) that re-tim...

• 2.Human Adults and LLMs as Scientists: Who Benefits from Active Exploration?

  A long-standing finding in the causal learning literature is that adults struggle to identify conjunctive causal rules, where an effect requires the simultaneous presence of multiple causes, while performing better in disjunctive settings. However, most demonstrations of this ``conjunctive handicap'' rely on passive observation paradigms with limited evidence, where learners have no control over evidence generation. This paper asks whether this bias persists when adults are granted agency through active exploration. Using a modified ``blicket detector'' task, adult participants freely intervened to identify causal objects under conjunctive or disjunctive rule structures. We show that active exploration substantially improves adults' conjunctive causal reasoning, although conjunctive rules still require more tests to infer than disjunctive...

• 3.Latent Reasoning with Normalizing Flows

  Large language models often improve reasoning by generating explicit chain-of-thought (CoT), demonstrating the importance of intermediate computation. However, textual CoT forces this computation through a discrete, serial, and communication-oriented token stream: each reasoning step must be verbalized before the model can proceed, even when the underlying update is semantic, uncertain, or only partially formed. Latent reasoning offers a higher-bandwidth alternative by performing intermediate computation in compact continuous states before committing to text. Yet existing latent-reasoning methods often sacrifice key advantages that make CoT effective in autoregressive language models, including native left-to-right generation, probabilistic sampling, compatibility with KV-cache decoding, and tractable likelihood estimation. We propose NF-...

• 4.Proper Scoring Rules for Right-Censored Survival Data

  Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts. However, in the presence of right censoring, the event time is only partially observed, rendering conventional scoring rules inapplicable in their standard form. We propose a framework for proper scoring of right-censored survival outcomes based on a simple idea: first, map the predictive distribution through the censoring mechanism, then apply the underlying proper score on the induced observed-data law. This yields localized scores for fixed censoring times and marginalized scores when the censoring time is random or only partially observed. The resulting construction recovers familiar right-censored likelihood and IPCW-type criteria within a coherent framework, while also yielding right-censored versions of the CRPS, pi...

• 5.GMBFormer: An NDVI-Guided Global Memory Bank Transformer for Urban Green-Space Extraction from Ultra-High-Resolution Imagery

  Urban green-space extraction from ultra-high-resolution (UHR) imagery is commonly performed patch by patch, which limits semantic reuse among spatially separated but visually similar vegetation patterns. Directly injecting the Normalized Difference Vegetation Index (NDVI) into red-green-blue (RGB) backbones can also blur the roles of visual appearance learning and physical vegetation confidence. We propose GMBFormer, a SegFormer-based framework that replaces adjacency-driven feature propagation with selective, similarity-driven prototype retrieval. Only RGB channels enter the backbone and decoder, while NDVI is decoupled as a physics-informed gate that admits high-confidence vegetation descriptors into a compact global memory bank through momentum updates. During training and inference, the current patch queries stored prototypes through ...

• 1.Nvidia’s AI Hardware Comes to Windows in RTX Spark PCs

  At Computex 2026, an annual computer trade show held in Taipei, Taiwan, Nvidia made a long anticipated announcement—a version of the company’s Blackwell GB10 superchip for Windows PCs, called RTX Spark. Originally rumored to launch in 2025 , it was finally introduced at this year’s show. It came with full support from Microsoft, which announced two new devices powered by RTX Spark: the Surface Laptop Ultra and the Surface RTX Spark Dev Box . Asus, Dell, Lenovo, HP, and MSI also announced Windows PCs with RTX Spark. If this is triggering déjà vu, that’s for good reason. In June 2024, Qualcomm and Microsoft partnered to launch AI-focused Copilot+ PCs. Qualcomm’s Arm-based chips provided an alternative to x 86-based chips from AMD and Intel used across dozens of budget and mid-range Windows laptops. It was met with mixed commercial success, ...

• 2.7 Ways New Engineers Can Flourish in the Age of AI

  New graduates’ careers are unfolding in an era when AI is not optional. The most successful engineers treat artificial intelligence as leverage, not competition. Here are seven tips to help keep young professionals in demand no matter how quickly the field’s tools evolve. 1. Master the fundamentals first. AI tools can help you code, but you still need strong fundamentals in: Data structures and algorithms for problem-solving. Operating systems, databases, and networking for system-level understanding. Core programming languages such as C++ , Java , and Python . AI can autocomplete syntax, but if you don’t understand how things work under the hood, you’re likely to struggle to debug or optimize. 2. Learn how to work with AI, not against it. The best engineers will not try to out-code AI. Instead, they will learn to: Write clear prompts to ...

• 3.Why Aren’t We Measuring How AI Affects Humans?

  As AI systems become more capable, a lot of resources and effort are being put toward measuring their abilities. Researchers look at technical evaluation metrics, subject AIs to reasoning tests, track their throughput, and much more. But there’s one key metric that often gets overlooked, and it’s arguably the most important of all: What is AI doing to humans? Imran Khan leads psychosocial evaluation of AI at the nonprofit Center for Humane Technology . In a recent essay published on the organization’s Substack, Khan points out that we’re deploying AI tools capable of reshaping our cognition, relationships, and behavior, but with little systematic effort to measure the downstream impacts they’re having on us. The push to look more closely at AI’s psychosocial effects is similar to debates that emerged around social media and its harms, but...

• 4.New Server Hopes to Break Through AI’s “Memory Wall”

  Memory is arguably the most serious constraint on modern AI large language models (LLMs). According to one influential paper , LLM token generation is an inherently memory-bound task, meaning the rate at which models output text is limited by how quickly data can be read in from memory. The severity of this bottleneck grows with model size. This creates a “memory wall” that holds back LLM inference performance. AI hardware startup Majestic Labs is taking a direct—and comprehensive—approach to solving this problem. It’s developing a new AI server, Prometheus, with up to 128 terabytes of memory. That’s over 60 times more than Nvidia’s DGX B300 server , a cutting-edge AI processing rack. Sha Rabii , co-founder and president of Majestic Labs, believes that this drastic increase in memory will provide his company an edge. While he acknowledges...

• 5.Finding Success in Industry as a Chip Designer

  I have been an application-specific IC (ASIC) designer for almost three decades. Over that time, I’ve moved through the full academic trajectory, from graduate student to full professor; later, I transitioned to industry after an unsuccessful stint at entrepreneurship. When I made the switch to the private sector in 2019, I began focusing on a critically important aspect of the electronic industry: silicon intellectual property. As much as 80 percent of the physical area in today’s most advanced chips is occupied by blocks that aren’t made for specific products or even designed by the consumer-facing companies that built them. Instead, chipmakers draw heavily on established silicon IP from companies like Arm , Cadence , Rambus , Synopsys , and the company I work for, Silicon Creations . Throughout my career, I’ve designed chips for very d...

• 1.The Empathy Tax Female Leaders Pay

  Carolyn Geason-Beissel/MIT SMR | Getty Images The consulting manager took a call at 7:30 p.m., while volunteering at her son’s soccer practice, from an employee who felt “on the verge of quitting.” Later that same week, she responded to texts sent at 2 a.m. from team members who could not sleep amid corporate restructuring and […]

• 2.How Nespresso Builds Sustainability Into Its Business Model

  Photo courtesy of Nestlé Jean-Christophe Jaunin became CEO of Nespresso North America, the Nestlé unit that sells coffee brewing machines and capsules, on Jan. 1, 2026, after having served as global chief customer and technology officer. At the NYU Stern Center for Sustainable Business’s annual practice forum in March, MIT Sloan Management Review spoke with […]

• 3.Our Guide to the Summer 2026 Issue

  Create Generative AI Value at Scale Kevin Schmitt, Gregory Vial, and Ivo Blohm Key Insight: Organizations are expanding their GenAI use by implementing coordinated cross-functional structures that draw on domain expertise and user innovation. Top Takeaways: Companies that establish a new kind of internal AI organization that researchers have dubbed the “AI spine” are better […]

• 4.What Wise Leaders Understand About Business Ecosystems

  It’s safe to say that most people who rise to the top of their companies like to win. A healthy competitive streak is energizing and motivates individuals and teams to do their best — to find their edge and sharpen it. But sustained, long-term success and industry leadership often rely on the ability to look […]

• 5.Why AI Isn’t Transforming Finance Yet

  Christian Gralingen The Research The authors engaged in two complementary research streams. One was a multiyear program of action design research conducted with organizations undergoing digital transformation that focused on how leadership work evolves under conditions of technological and market uncertainty. The other, a study of how AI is introduced into finance functions and how […]

• 1.The Unintended Effects of Interest Rate Caps: Credit Reallocation to Safer Borrowers

  Several states have recently capped consumer loan rates with the stated purpose of protecting borrowers. In a recent Staff Report, we study how these interventions have played out in three states. In our first post about that study, we showed that rate caps lead riskier borrowers to face rationing in the credit market. One question that naturally arises is what lenders do with the credit they used to provide to high-risk borrowers before the caps were imposed. Lenders that lend exclusively to high-risk borrowers (at rates above the cap) may decide to stop lending to high-risk borrowers in that state. Others, however, may ...

• 2.The Unintended Effects of Interest Rate Caps: Credit Rationing for Risky Borrowers

  In imperial China, 3 percent was the maximum legal monthly loan rate; charging more was punishable by 40 to 100 blows with the “light cane.” (Rockoff 2003) Centuries later, many U.S. states are imposing the same cap (without corporal penalties) on alternative credit providers, such as payday, installment, and auto-title lenders, with the goal of lowering credit costs and delinquency for the high-risk borrowers that rely on these funding sources. A concern, however, is that lenders will simply refuse to lend to these borrowers at lower interest rates. Our recent Staff Report studies how interest rate caps have played out in several states that recently adopted them. Using hou...

• 3.Struggling Regional Small Businesses Deeply Pessimistic About 2026 Prospects

  We recently updated the suite of indicators describing the performance of small businesses in the Second District (defined, for the purpose of this study, as New York, New Jersey, and Connecticut) and nationally with data from the 2025 edition of the Small Business Credit Survey (SBCS). In this post, we find that regional small businesses reported severe declines in employment and revenue growth in 2025 and became more pessimistic about growth in 2026. In contrast, small firms in the rest of the nation enjoyed stable revenues and employment in 2025 and, while they also had lower expectations of futur...

• 4.Remote Work Leaves Younger Workers Sidelined

  Youth unemployment has risen dramatically since the pandemic—as has the prevalence of remote work. Our analysis suggests that these trends are related, with remote work making it more difficult for managers to train and mentor new employees. Accordingly, companies may be reluctant to hire less-experienced workers in distributed work arrangements. We estimate that remote work can explain 64 percent of the recent increase in unemployment among young college graduates. Further, the timing of this surge suggests that remote work—not generative AI—explains the bulk of the rise in youth unemployment.

• 5.The Regional Side of the Story: K‑Shaped Pattern in Region, Wider Gap in Gas Spending

  In this post, we use the inaugural release of our regional consumer spending indicators to ask whether these patterns hold for a significant portion of the Second District, and how regional spending patterns by income have been similar to or different from the national patterns we documented earlier. We find similar K‑shaped patterns in both retail and gas spending in our region as we do in the nation, with the K‑shaped pattern in gasoline in response to the recent gas price shock being more pronounced in the region.

• 1.The Trump Economy Is Bad News for Republicans

  Donald Trump triumphed in the 2024 election by continually hammering on the Biden administration’s dismal inflation record. If his party gets shellacked in this year’s midterm elections, it will be because he has made Americans even worse off.

• 2.SpaceX Is the New East India Company

  Although SpaceX is not about to rule over foreign subjects, as the chartered companies founded in the early modern era did, it, too, is operating beyond the reach of any sovereign. And like its predecessors, it has already accumulated immense powers that governments will struggle to reclaim.

• 3.The ECB Should Not Raise Interest Rates Yet

  Against a backdrop of acute uncertainty, weak growth, subdued wages, and elevated market interest rates, monetary tightening is warranted only when there is convincing evidence that higher inflation is becoming entrenched. No such evidence has yet materialized in Europe.

• 4.When Markets Run on Empty

  Markets are flying high despite geopolitical turmoil because a broad-based willingness to keep spending has been matched by an ability to keep doing so. But as more economic participants exhaust their means and rely on debt, a painful moment of truth will arrive—sooner rather than later if the Strait of Hormuz remains closed.

• 5.The Pope and the AI Profiteers

  In his first encyclical, Pope Leo XIV rightly rejects the idea that markets alone can be trusted to shape society’s technological future. Recognizing that AI raises questions that prices and profits cannot answer, the Chicago-born pope has posed a direct challenge to Chicago School economics.

• 1.Ciena sees AI networking market reaching $50 billion by 2029

  Hyperscalers are focused on monetizing their AI compute investments, which continues to direct a larger share of spending towards network infrastructure, observes Ciena chief Gary Smith. In sum – what to know: Market expansion – Ciena said its addressable market…

• 2.The case for defense 5G: borrowing commercial standards for the battlefield

  Panelists argued defense 5G built on 3GPP standards offers speed, scale, and a path to reclaiming contested spectrum In sum – what we know: At the Defense Communications Forum, a panel titled “from harmonizing to optimizing spectrum for defense operations”…

• 3.Sovereignty is the word – Orange anchors digital-change to trust and control

  Orange Business is advancing its enterprise strategy through sovereign and secure data management and services. A new French hospital deployment shows the model in use – but everything from the French firm, lately, is pinned to the same message, including…

• 4.6G research advances, industry pushes for simpler migration path

  Keysight has a collaboration with NTT Docomo and NTT to advance realistic 6G channel modeling and wireless communs simulation In sum – what to know: 6G testing – Keysight, NTT Docomo, and NTT are advancing measurement-driven channel modeling and distributed…

• 5.Ericsson – IBM Partnership for CSPs’ Efficiency and Competitiveness

  Ericsson is providing the back-end OSS/BSS, IBM is providing the IT services – in combination, they enable freedom of choice over CRM and user services During MWC Barcelona, RCRTech principal analyst Sean Kinney spoke with Ericsson’s Mats Karlsson, vice president…

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• 1.Implementation and Calibration of 3GPP-Compliant ISAC Channel Simulator

  Integrated sensing and communication (ISAC) has emerged as a key technology for 6G systems. To support the development of ISAC systems, accurate channel modeling and simulation for performance evaluation is essential. Recently, 3GPP introduced a standardized ISAC channel model and its associated calibration procedure for this purpose. However, due to the complexity of the modeling methodology and the lack of fully explicit implementation details in the 3GPP reports, different implementations may lead to inconsistent or unsynchronized simulation results. To address this issue, in this work, we implement the 3GPP ISAC channel model simulator specified in TR 38.901 and conduct a comprehensive calibration analysis. We compare the simulation results with the reference results reported by companies in 3GPP and discuss several key implementation...

• 2.RSMA Enabled Hierarchical UAV Networks with Non Linear Energy Harvesting: Outage Probability Analysis and UAV Placement Optimization

  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 applications. Although multi-UAV systems offer greater efficiency and cost-effectiveness than single-UAV deployments, their implementation still faces several fundamental challenges that limit their reliability, sustainability, and scalability. The limited onboard energy restricts mission duration and communication continuity. Therefore, wireless energy harvesting (EH) emerges as a promising solution to overcome this limitation. However, terrestrial energy sources experience path loss, making EH from surrounding UAVs more sustainable. Moreover, rate-splitting multiple access (RSMA) remains insufficiently explored in hierarchic...

• 3.LatentWave: JEPA Pretraining for Wireless Foundation Models

  Wireless foundation models have emerged as a promising alternative to building separate models for each wireless task. However, existing approaches rely on masked input reconstruction, which can bias representations toward low-level signal details. In this paper, we propose LatentWave, a wireless foundation model pretrained using a Joint-Embedding Predictive Architecture (JEPA) on diverse wireless spectrograms and channel state information (CSI). By predicting masked regions in latent space, LatentWave learns representations that are more transferable out of the box across diverse downstream tasks. The proposed architecture employs per-channel patch embeddings with stochastic channel sampling during pretraining, allowing it to process variable antenna counts and improving usability across heterogeneous wireless configurations. We evaluate...

• 4.Foundation Models for Wireless Communications: From PHY Intelligence to Network Autonomy

  6G networks will introduce unprecedented complexity, which calls for a paradigm shift in network optimization and management. Artificial intelligence (AI)-based solutions, especially those enabled by the recently developed foundation models, have been recognized as promising candidates. Foundation models are large-scale AI models with general-purpose feature extraction capabilities, and once trained on massive amounts of data, they can be adapted to solve a wide range of downstream tasks, either in a zero-shot manner or with few-shot fine-tuning. This article provides a comprehensive overview of how foundation models are reshaping physical-layer processing and wireless resource management across three progressive paradigms. First, we examine the adaptation of off-the-shelf pre-trained foundation models to various wireless tasks. Second, w...

• 5.Toward Mobile and Converged Backhaul: The Promise of Wireless Access and Backhaul

  Wireless Access and Backhaul (WAB) is emerging as a key enabler for flexible and cost-efficient 5G deployments, offering a modular architecture that decouples access and backhaul while supporting multi-technology and mobile backhaul links. This article introduces the WAB framework standardized in 3GPP Release 19, outlining its architecture and operational principles. A practical implementation built with commercial hardware and open-source software demonstrates the feasibility and efficiency of WAB systems. We further explore four representative application scenarios - ranging from on-demand coverage to mobile Software-Defined Wide Area Network (SD-WAN) connectivity - and discuss the technical challenges that must be addressed for large-scale adoption. These insights highlight WAB as a promising foundation for 5G-Advanced and a stepping s...

• 1.Information Networks of Stock Prices

  The collective movement of stock prices harbors complex interdependencies that are conventionally simplified only through a linear lens. This paper explores computed structural network representations in the Indonesian capital market by testing the limits of Pearson correlation and Mutual Information (MI) in unveiling the spectral dynamics of the market. Across 2,328 rolling observation windows from 2015 to 2025, we examine 24 methodological configurations that combine three dependency estimators (Pearson, MI adaptive binning, and MI-kNN), two graph filtering schemes (Minimum Spanning Tree/MST and Planar Maximally Filtered Graph/PMFG), and four community decoders. The empirical results unveil a fundamental reality: topological richness does not always resonate with sectoral classification precision. The Pearson, MST, and Infomap configu...

• 2.PandaAI: A Practical Agent CQ2 for Neuro-symbolic Data Analysis And Integrated Decision-Making in Quantitative Finance

  While deep learning has excelled in various domains, its application to sequential decision-making in finance remains challenging due to the low Signal-to-Noise Ratio (SNR) and non-stationarity of financial data. Leveraging the reasoning capabilities of Large Language Models (LLMs), we propose \textbf{PandaAI}, a closed-loop neuro-symbolic LLM agent with market regime modeling and constrained alpha generation, which bridges general LLM reasoning with financial rigor and suppresses the financial toxicity of LLM-generated outputs. To bridge the gap between general linguistic capability and financial rigor, we fine-tune a domain-specific LLM. Furthermore, we integrate this LLM into a modular architecture and form a closed-loop system. Unlike traditional models that optimize isolated prediction metrics, \textbf{PandaAI} is designed as a neuro...

• 3.Multi-Scale Markov Switching GARCH

  Financial volatility exhibits substantial non-stationarity, making single-regime models inadequate for characterising changing market conditions. This paper proposes a triple-timeframe Markov-Switching GARCH (MS-GARCH) framework for volatility regime detection in EUR/USD across daily, four-hour, and hourly horizons. Three independent AR(1)-MS-GARCH models are estimated to capture macro, meso, and micro regime dynamics, while Filardo-style time-varying transition probabilities (TVTP) are incorporated at the shorter horizons through composite stress indicators. The resulting regime probabilities are combined through an outer-product construction into a 27-state cross-scale probability tensor. Using EUR/USD data from 2015-2025, the framework produces statistically distinct Calm, Turbulent, and Crisis regimes and achieves superior out-of-samp...

• 4.Forecasting of volatility and risk premia in electricity markets

  We study forecasting of the realized covariation in electricity markets. The realized covariation in this context is a matrix-valued representation of the latent infinite-dimensional covariance operator and a parsimonious matrix-HAR type model is constructed to facilitate estimation. We test the model on one-week ahead forecasts of the weekly realized covariation and find that the inclusion of longer time horizons and renewable generation information adds important predictive power. We also investigate the prediction of risk premia in electricity forward markets and find that our variance forecasts provide substantially improved forecasts of spread risk premia compared to standard methods relying on backward looking volatility.

• 5.Generating Financial Time Series by Matching Random Convolutional Features

  Generating realistic financial time series is challenging as training data is often limited to a single historical path. With such scarce data, overfitting is hard to avoid, especially under adversarial training where a trained discriminator can memorize the training samples. To mitigate this, recent approaches train generators to minimize the discrepancy between untrained feature representations of real and generated time series. In these works, the feature maps are based on path signatures, which can fail to capture relevant time series properties at tractable truncation depths. In this work, we instead train generators by matching random convolutional features of real and generated time series. Existing random convolutional feature maps, such as Rocket and Hydra, have been shown to provide informative representations of real-world time...

• 1.Implementation and Calibration of 3GPP-Compliant ISAC Channel Simulator

  Integrated sensing and communication (ISAC) has emerged as a key technology for 6G systems. To support the development of ISAC systems, accurate channel modeling and simulation for performance evaluation is essential. Recently, 3GPP introduced a standardized ISAC channel model and its associated calibration procedure for this purpose. However, due to the complexity of the modeling methodology and the lack of fully explicit implementation details in the 3GPP reports, different implementations may lead to inconsistent or unsynchronized simulation results. To address this issue, in this work, we implement the 3GPP ISAC channel model simulator specified in TR 38.901 and conduct a comprehensive calibration analysis. We compare the simulation results with the reference results reported by companies in 3GPP and discuss several key implementation...

• 2.RSMA Enabled Hierarchical UAV Networks with Non Linear Energy Harvesting: Outage Probability Analysis and UAV Placement Optimization

  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 applications. Although multi-UAV systems offer greater efficiency and cost-effectiveness than single-UAV deployments, their implementation still faces several fundamental challenges that limit their reliability, sustainability, and scalability. The limited onboard energy restricts mission duration and communication continuity. Therefore, wireless energy harvesting (EH) emerges as a promising solution to overcome this limitation. However, terrestrial energy sources experience path loss, making EH from surrounding UAVs more sustainable. Moreover, rate-splitting multiple access (RSMA) remains insufficiently explored in hierarchic...

• 3.DIFFRACT: Neuralized Utility Maximization for Wireless Networks by Differentiable Programming

  Next-generation wireless networks, including satellite-to-Open RAN systems, demand agile and intelligent resource management capable of handling dynamic multi-user interference under stochastic quality of service constraints. This paper introduces DIFFRACT, a neuralized utility maximization framework that leverages differentiable programming to integrate deep learning with optimization in wireless networks. Central to our approach is the exploitation of the mathematical structure of standard interference functions, which are foundational in wireless power control. By developing a duality theory for these functions, we map iterative interference management algorithms into differentiable neural network architectures via algorithm unrolling. This enables distributed, end-to-end gradient-based learning at the network edge, supporting real-tim...

• 4.i2Slicer: Enabling Flexible and Automated Orchestration of 5G SA End-to-End Network Slices

  5G network slicing implies a step forward in customizing radio access and core networks by allowing the creation of logical networks adapted to service requirements. In addition, softwarisation has fueled the emergence of 5G solutions which do not require specialized hardware platforms. Therefore, a key requirement to drive the adoption of 5G slicing by verticals is to simplify its management through automated orchestration. In this paper, we present i2Slicer, a flexible solution to orchestrate the deployment of 5G standalone end-to-end network slices with multi-tenancy and multi-service capabilities. The implementation and evaluation of i2Slicer using state-of-the-art 5G software and hardware demonstrate that it offers a practical and efficient lifecycle management of network slices.

• 5.Foundation Models for Wireless Communications: From PHY Intelligence to Network Autonomy

  6G networks will introduce unprecedented complexity, which calls for a paradigm shift in network optimization and management. Artificial intelligence (AI)-based solutions, especially those enabled by the recently developed foundation models, have been recognized as promising candidates. Foundation models are large-scale AI models with general-purpose feature extraction capabilities, and once trained on massive amounts of data, they can be adapted to solve a wide range of downstream tasks, either in a zero-shot manner or with few-shot fine-tuning. This article provides a comprehensive overview of how foundation models are reshaping physical-layer processing and wireless resource management across three progressive paradigms. First, we examine the adaptation of off-the-shelf pre-trained foundation models to various wireless tasks. Second, w...

• 1.i2Slicer: Enabling Flexible and Automated Orchestration of 5G SA End-to-End Network Slices

  5G network slicing implies a step forward in customizing radio access and core networks by allowing the creation of logical networks adapted to service requirements. In addition, softwarisation has fueled the emergence of 5G solutions which do not require specialized hardware platforms. Therefore, a key requirement to drive the adoption of 5G slicing by verticals is to simplify its management through automated orchestration. In this paper, we present i2Slicer, a flexible solution to orchestrate the deployment of 5G standalone end-to-end network slices with multi-tenancy and multi-service capabilities. The implementation and evaluation of i2Slicer using state-of-the-art 5G software and hardware demonstrate that it offers a practical and efficient lifecycle management of network slices.

• 2.Toward Mobile and Converged Backhaul: The Promise of Wireless Access and Backhaul

  Wireless Access and Backhaul (WAB) is emerging as a key enabler for flexible and cost-efficient 5G deployments, offering a modular architecture that decouples access and backhaul while supporting multi-technology and mobile backhaul links. This article introduces the WAB framework standardized in 3GPP Release 19, outlining its architecture and operational principles. A practical implementation built with commercial hardware and open-source software demonstrates the feasibility and efficiency of WAB systems. We further explore four representative application scenarios - ranging from on-demand coverage to mobile Software-Defined Wide Area Network (SD-WAN) connectivity - and discuss the technical challenges that must be addressed for large-scale adoption. These insights highlight WAB as a promising foundation for 5G-Advanced and a stepping s...

• 3.BeGREEN Intelligent Plane for AI-driven Energy Efficient O-RAN management

  Cellular networks are undergoing a revolutionary transform with the advent of O-RAN architectures and AI/ML solutions. O-RAN's Non-Real-Time and Near-Real Time RAN Intelligent Controllers open the door to the implementation of automated control-loops that can provide RAN optimisations in numerous scenarios and use cases, and which can be further empowered by AI-driven approaches. Energetic sustainability has raised as one of the main optimisations targets due to the impact of mobile networks on global energy consumption. To this end, the BeGREEN project aims at enhancing the energy efficiency of beyond 5G networks by defining novel AI/ML-based methods at RAN and edge infrastructure. This paper presents BeGREEN Intelligent Plane, a novel framework which implements and exposes AI/ML workflows to O-RAN-based optimisations targeting energy ef...

• 4.AISC deployment in dynamic UAV-assisted MEC network: a reinforcement learning method based on heterogeneous graph attention neural network

  Unmanned aerial vehicles-assisted mobile edge computing (UMEC) can execute compute-intensive and latency-critical artificial intelligence (AI) services, which can be provided by multiple UAVs collaborating in the air to perform inference tasks. Completing an AI service requires multiple inferences, each of which is implemented by an AI service chain consisting of multiple virtual network functions (VNFs). The application of AISC relies on an efficient AISC deployment strategy to determine which UAV to deploy VNF on. However, the UMEC network topology is highly dynamic due to the high-speed movement of UAVs or their departure/arrival, which makes the AISC deployment in the UMEC network challenging. In addition, the intricate relationships between UMEC environment and AISC, as well as between individual VNFs in an AISC, can also affect the ...

• 5.Availability-Aware and Efficiency-Driven AI Service Chain Provisioning in Multi-Domain Edge Intelligence Cloud

  In a multi-domain edge intelligence cloud (MDEIC) managed by multiple network operators, AI services are delivered by chains of virtual network functions (VNFs) executed in sequence, called AI service chains (AISCs). Therefore, achieving an efficient and economical AISC provisioning approach is essential. However, the interaction between the environmental characteristics (heterogeneity, resource constraints and limited information visibility) of MDEIC and the time-dependence of AISCs, introduces various challenges to AISC provisioning in MDEIC. In this paper, we first formulate the AISC provisioning problem as a partially observable stochastic game (POSG). Then, we propose a graph-and-time-based multi-agent AISC provisioning (GT-MAAISCP) approach to achieve the collaborative optimization of AISC provisioning cost, delay and availability. ...