Babak's Daily Briefing

Monday, August 3, 2026

Sources: 20 | Total Articles: 92

• 1.The Hidden 6G Bottleneck: RF Hardware Design Is Becoming a Strategic Race

  As 5G-Advanced matures and 6G research moves closer to implementation, the wireless industry faces a deeper challenge than spectrum, standards or AI-native network architecture. Future wireless systems will depend on whether the industry can design, validate and manufacture increasingly complex RF modules fast enough.

• 2.6G in Dalian: What the Latest 3GPP Meetings Reveal About the Future Radio and Network

  The 6G physical layer is starting to converge. The protocol stack is being simplified in meaningful places. But the most consequential architecture decisions are now moving toward the June plenary in Singapore.

• 3.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…

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

  Summary available at source link.

• 5.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.

• 1.Rethinking AI Cloud Infrastructure for Agentic Serving Systems with the Aries Experimentation Framework

  Autonomous agents challenge conventional LLM serving by coupling repeated inference with persistent context and sandboxed tool execution. We present Aries, a full-stack experimentation framework that separates task semantics from execution configurations, reconstructs cross-component agent trajectories with correlated system telemetry, and exposes stateful tool execution through a consistent interface across heterogeneous sandbox substrates. We use Aries to conduct reproducible experiments on open agent harnesses and benchmarks. We complement these experiments with production traces from a commercial platform, grounding low-level systems research in observed production behavior. Our results show that (1) token-centric metrics miss non-inference bottlenecks, (2) retaining additional context yields diminishing accuracy benefits while reduci...

• 2.NeSyFS: A Neuro-symbolic Fast-Slow Thinking Framework for LLM Agent under Partial Observability

  Recently Large Language Models (LLMs) have been increasingly deployed as autonomous agents in applications such as self-reflection, retrieval-augmented generation, and scientific discovery. In these settings, agents must act based on limited observations rather than full environmental states, leading to partial observability. This introduces several key challenges: belief state inference, task objective misalignment, and planning under uncertainty. Prior approaches typically condition actions on full or summarized action-observation histories whose redundant and irrelevant information can mislead the decision making of LLM agent. Inspired by human cognition, we propose a novel neuro-symbolic fast-slow thinking (NeSyFS) framework for LLM agent, addressing the challenges introduced by partial observability in a unified approach. We use a kn...

• 3.ClawTrack: Towards Trace-Level Evaluation and Improvement of Real-World Autonomous Agents

  As LLM-based agents are deployed in complex, multi-step workflows, a critical evaluation gap has emerged: most existing benchmarks judge only final outcomes, unable to distinguish reliable reasoning from lucky success or attribute failures to specific process deficiencies, hindering attribution in long-horizon tasks. In this work, we present ClawTrack, a dual-assessment benchmark that simultaneously measures what an agent achieves (Task Score) and how it achieves it (Process Score). ClawTrack comprises 320 tasks across 8 domains with 25+ deterministic mock services. A Process Grader scores each reasoning turn along four dimensions (goal alignment, efficiency, information utilization, and result verification), anchored by 12,541 task-specific rubric items. Evaluating 21 models over 16,000+ trials, we find that: (1) process scores effective...

• 4.DataClawEval: A Benchmark for Data Engineering Agents in Real Industrial Harness

  Large language models (LLMs) and LLM-based agents are increasingly being deployed to automate complex workflows, promising to revolutionize data management and processing. However, existing benchmarks predominantly focus on simplified Text-to-SQL translation or data analysis, leaving the critical and complex domain of end-to-end data engineering largely unexplored. To bridge this gap, we introduce DataClawEval, the first comprehensive benchmark designed specifically to evaluate the end-to-end task completion capabilities of autonomous agents in real-world data engineering scenarios. Built upon production-grade code authored by professional enterprise data engineers, it comprises 100 rigorous, end-to-end tasks spanning five execution engines: PySpark, MySQL, HiveSQL, PrestoSQL/Trino, and FlinkSQL. Rather than non-deterministic LLM-as-a-jud...

• 5.FinanceHarness: Autonomous Financial Deep Research Framework

  Powered by advances in LLMs and autonomous agents, deep research has become one of the most widely adopted agentic products. However, most deep research systems write general-purpose reports, which are inadequate for financial deep research. Financial research demands specialized knowledge to analyze historical patterns and forecast upcoming events. Automating financial deep research therefore requires both a layered harness to drive the research agent and a verifiable, point-in-time benchmark that prevents leakage of future information. We present FinanceHarness, a harness that runs finance-oriented tools and practitioner-guided workflows, automating financial deep research end to end: environment and data construction, the agent execution loop, and reward modeling. We further propose FinanceGym, comprising thesis-driven research questio...

• 1.Can LLMs Really Understand Item Difficulty Levels? Implications for Automated Item Generation Using LLMs

  arXiv:2607.28634v1 Announce Type: new Abstract: The estimation of item difficulty plays a key role in both formative assessment and large-scale high-stakes summative assessments. This study explores how large language models (LLMs) perform in predicting item difficulty levels using items from a large-scale Reading and Writing test. The study investigated various prompting strategies and parameter settings across multiple LLMs. LLM performance was compared with encoder-only language models and feature-based supervised machine learning models. Zero-shot GPT-4.1 with a temperature of 0 yielded the highest item difficulty level prediction accuracy, with a quadratic weighted kappa (QWK) of 0.578. However, LLMs' prediction accuracy was lower than that of ConvBERT (QWK = 0.625), which outperformed the best feature-based supervised machine learn...

• 2.Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM

  arXiv:2607.28635v1 Announce Type: new Abstract: In Natural Language Processing (NLP), dealing with underrepresented topics is challenging, especially in unsupervised tasks where clustering might not adequately capture minority topics. To tackle this challenge, our paper presents a novel unsupervised data augmentation method that integrates Gaussian Mixture Models (GMMs) and Large Language Models (LLMs). Due to their flexibility and robustness, GMMs can detect clusters corresponding to underrepresented areas in the data, while LLMs create synthetic documents to enrich these clusters and improve their representation. Experiments on various imbalanced text datasets demonstrate that our approach preserves clustering performance in all cases and often enhances cluster interpretability, offering a robust and scalable solution for improving dat...

• 3.Chain-of-Models: Cross-Model Auditing for Bias-Robust LLM Judges

  arXiv:2607.28636v1 Announce Type: new Abstract: LLMs increasingly serve as automated judges, but their judgments remain vulnerable to cognitive biases. Existing mitigations mostly rely on prompt-driven debiasing, which is brittle across bias types, or human evaluation, which does not scale. We study \emph{Chain-of-Models} (CoM), an automated audit pipeline in which a second model inspects the first model's reasoning trace before producing the final judgment. The key design question is whether the auditor should be the same model, a same-family model, or a different-family model. Across 9 models from 6 families, 4 cognitive biases, and 4 factual datasets, we find that auditor identity matters in two ways. First, standalone bias resistance does not predict audit effectiveness: Kimi-K2.5 is the strongest standalone model on several biases, ...

• 4.ZeroR@CHiPSAL 2026: Two-Stage Vision-Language Adaptation with Contrastive Learning for Nepali Meme Classification

  arXiv:2607.28637v1 Announce Type: new Abstract: This paper presents our system for the CHiPSAL 2026 shared task on multimodal hate speech and sentiment detection in Nepali memes. We address both subtasks: binary hate speech classification and three-class sentiment analysis. Our approach adapts the Robust Adaptation of Hateful Meme Detection (RA-HMD) framework using Qwen3-VL-8B-Instruct, a state-of-the-art vision-language model with native Devanagari support. We employ a two-stage training pipeline: (1) LoRA fine-tuning with an MLP projection head for generative classification, and (2) contrastive backbone fine-tuning with supervised InfoNCE loss. We handle class imbalance through minority oversampling, image augmentation, and focal loss. At inference, we ensemble Stage 1 token probabilities with Stage 2 classifier scores using validation...

• 5.Learning Stateful Predictive Knowledge From Experience

  arXiv:2607.28638v1 Announce Type: new Abstract: As large language model (LLM) agents increasingly learn from experience, they primarily rely on trajectory-level reflection to extract insights. Viewed through the lens of predictive knowledge, we argue that this approach operates on episodic hindsight rather than predictive foresight, yielding brittle, path-dependent heuristics. To address this, we propose Stateful Knowledge Learning (SKL). SKL shifts the agent's focus from trajectory-level summarization to maintaining Stateful Knowledge: explicit, declarative predictive assessments anchored to state. We first demonstrate a motivating example showing how stateful knowledge provides granularity, enhances generalization, and enables knowledge bootstrapping. To further scale up the idea, we introduce two algorithms via self-distillation (SKL-...

• 1.Topology-Aware Data Movement for Disaggregated GPU Inference

  arXiv:2607.28633v1 Announce Type: new Abstract: Disaggregated LLM inference creates a datacenter networking problem that no existing system solves correctly. When prefill and decode run on separate GPU pools, the KV cache must be transferred between them. For a 70B model this is 2.6 GB per request, exceeding 100 GB/s aggregate at production scale. Yet DistServe, Splitwise, and Mooncake all use uniform RDMA, ignoring that bandwidth between two GPUs varies by 72x depending on their physical relationship: 900 GB/s via NVLink within a domain, 50 GB/s via InfiniBand across nodes, 12.5 GB/s via TCP across data centers. We design a topology-aware transfer orchestrator that discovers interconnect hierarchy at startup and selects optimal transport per transfer. Three mechanisms work together: (1) pipelined layer-by-layer transfer that overlaps tra...

• 2.Sensitivity Analysis of GRU, LSTM and Transformer Encoder in Classification of Automated Driving Systems

  arXiv:2607.28665v1 Announce Type: new Abstract: Automated driving systems (ADSs) are becoming ubiquitous. Future Software Defined Vehicles (SDVs) may be able to run multiple ADSs, both native and aftermarket such as Comma.ai's Openpilot. Monitoring systems to independently verify which automated driving system is active are important for safety monitoring, regulatory compliance, insurance assessment, and anomaly detection. In this paper, we first evaluate the effectiveness of three sequence-based classification models: Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) networks, and a Transformer encoder model for identifying Level 2 automated driving systems using vehicle telematics data alone: Comma Openpilot, Tesla Autopilot, and Cadillac Super Cruise, along with manual driving. All three models achieve strong clean-data perfor...

• 3.Guarantees on Dynamical System Distinguishability for LLM Token Generation

  arXiv:2607.28667v1 Announce Type: new Abstract: Recent work has shown that classifying large language models (LLMs)' responses can be distinguished by modeling token embeddings as trajectories of a black-box dynamical system (DS) and comparing prediction residuals of two DSs. Despite the empirical success of this dynamical approach, a theoretical understanding of why it works, how well it scales as a function of the token sequence, and when it transfers across embedding models remains lacking. We address these questions by formalizing the classification task as a binary hypothesis test between two stochastic linear DSs. We show that the total variation distance between the stationary marginal distributions of the two DSs can be arbitrarily small even when the dynamics differ substantially, which provides a fundamental accuracy floor for a...

• 4.LARA: Lightweight Adapters in the Residual Stream for Composable Adaptation and Alignment

  arXiv:2607.28669v1 Announce Type: new Abstract: We present LARA (Lightweight Additive Residual Adaptation), a method for efficient adaptation that operates in the residual stream of a frozen model rather than in its weights. Where LoRA adds an update of low rank to weight matrices, LARA reads the hidden state at a small set of layers and adds a correction of low rank back to the residual stream, leaving all base weights untouched. On a code fine-tuning task and on preference optimization (DPO), LARA matches LoRA at equal parameter counts. Because adaptation is a frozen base plus a residual, LARA exposes a scale {\gamma}, applied at inference, that interpolates smoothly between base and adapted behavior, a form of graded control that adaptation in weight space does not offer. Finally, because each behavior is a small residual module over a...

• 5.Hierarchical Copula-Gumbel-Top-\texorpdfstring{$K$}{K} Routing: Two-Sided Dependence Control for Frozen Mixture-of-Experts at Fixed Per-Token Routing Laws

  arXiv:2607.28670v1 Announce Type: new Abstract: A stochastic Gumbel-Top-$K$ router defines, for every token of a mixture-of-experts (MoE) model, a \emph{routing law}: a distribution over ordered expert lists and mixture weights. We ask which \emph{joint} distributions over the routing choices of different tokens are reachable while every individual token's complete routing law is held exactly fixed. We give a two-sided construction, \emph{Hierarchical Copula-Gumbel-Top-$K$} (\CGA{}). Within a group of related tokens, an exchangeable Gaussian copula positively correlates the Gumbel perturbations at each expert coordinate, which can increase within-group expert-set coherence. Across disjoint pairs of groups, a tunable antithetic construction introduces a selectable amount of negative dependence. We prove that both operations leave each toke...

• 1.Advances, challenges, and opportunities for legged robots

  arXiv:2607.28952v1 Announce Type: new Abstract: Humanoid and quadrupedal robots have the potential to revolutionize the way we work, interact, and coexist with intelligent machines. To understand their effects on society and how they can enable scientific discovery, we assess the current capabilities of these systems along hardware, locomotion, autonomy, data, and applications. We identify recent advances and key open challenges that must be overcome to enable widespread adoption and new use cases for legged robots. Last, we provide an outlook on the future of legged robots, exploring their ethical considerations, economic potential, policy implications, and broader societal effects.

• 2.ST-WAM: Semantic-Temporal World Action Model for Robust Manipulation under Visual Distribution Shifts

  arXiv:2607.28993v1 Announce Type: new Abstract: World Action Models (WAMs) have emerged as a promising paradigm by jointly modeling robot actions and future visual dynamics. However, their reliance on pixel-generative future supervision can entangle action-relevant state transitions with task-irrelevant visual content, limiting robustness under visual distribution shifts. We identify Training-Distribution Hallucination, a recurring phenomenon in which futures conditioned on visually shifted observations hallucinate training-domain content rather than remain faithful to the current scene. A controlled frame-triplet diagnosis further shows that DINOv3 features remain more stable across visual shifts while better preserving task-state distinctions than Wan-VAE latents. Rather than correcting the predicted futures, we propose Semantic-Tempora...

• 3.Receding-Horizon Next-Best-View Planner for Autonomous Leaf Surface Reconstruction

  arXiv:2607.28995v1 Announce Type: new Abstract: Accurate plant leaf modeling is fundamental to downstream tasks such as plant growth monitoring, and phenotyping for yield estimation. Autonomous robotic reconstruction for large-scale field deployment must address limitations on robot planning budget and computation resources while optimizing viewpoint utility for leaf surface reconstruction. Existing approaches either focus on rigid objects, point-cloud coverage or plant reconstruction without fully addressing the system limitations or exploiting task-driven point cloud utility. In this work, we study next-best-view (NBV) planning for leaf surface reconstruction under travel constraints. We develop a novel Centroid-based Information Gain (CIG) function that measures the spatial distribution of observed points relative to the centroid of th...

• 4.D-VLC: Decentralized Vision-Language Collaboration for Heterogeneous Embodied Multi-Robot Systems in Unknown Environments

  arXiv:2607.29009v1 Announce Type: new Abstract: Multi-robot systems, particularly heterogeneous robot swarms, can improve the efficiency of complex task execution through parallel collaboration and complementary capabilities. However, conventional rule-based methods rely on predefined task models and specialized decision making programs, making it difficult to understand complex semantic instructions and coordinate heterogeneous robots. LLMs introduce strong language understanding and task reasoning capabilities, allowing multi-robot systems to interpret instructions, decompose tasks, and assign roles according to task semantics. VLMs further incorporate visual perception, enabling robots to reason about objects, regions, and spatial relationships in physical environments. Nevertheless, existing LLM/VLM based methods often depend on known...

• 5.DART: Dual-Axis Airborne Reachability-Gated Torque-Reaction for Off-Road Vehicle Jumps

  arXiv:2607.29011v1 Announce Type: new Abstract: Traversing crests, ledges, and ditches at high speed often launches vehicles into the air, and a mishandled landing presents a substantial crash hazard. We show that the airborne phase is barely controllable: on a 1383 kg platform the wheel angular-momentum budget caps the recoverable pitch-rate change at roughly $9$-$13^\circ$/s in the tighter nose-up direction under drive at typical takeoff wheel speeds, and at about twice that in the reverse-inclusive braking direction; driving the wheels to their drivetrain hard limit raises the measured nose-up ceiling to only $16$-$18^\circ$/s. Takeoff pitch-rate disturbances beyond this directional budget are physically unrecoverable in flight, so the decisive leverage lies before takeoff. DART (Dual-Axis Airborne Reachability-Gated Torque-Reaction) b...

• 1.FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning

  Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs). However, existing approaches remain confined to a market-agnostic, supervised learning paradigm that relies on limited, static and human-annotated datasets, and thus are incapable of adapting to evolving market conditions. To address this limitation, we introduce FinSMART, the first market-aligned reinforcement learning framework for financial sentiment analysis, which directly optimizes sentiment signals using realized market outcomes. To deal with the noisy, non-stationary, and multifactorial nature of financial markets, FinSMART incorporates a signal extraction pipeline that combines market-aware data filtering with a discrete asymmetric trading reward, enabling stable reinforcement lear...

• 2.ZAPs: A Reward Attribution Framework for DeFi Ecosystems with Adversarial-Robust Scoring via Parallel Anomaly Ensemble Detection

  Incentive programs are central to user acquisition in decentralized finance, but many reward systems rely on raw volume, transaction count, and wallet count, making them vulnerable to bots and sybil operations. We present ZAPs, a reward attribution framework that combines economic contribution scoring with adversarial robustness. A composite activity score uses protocol-specific percentile normalization to limit whale dominance while preserving differentiation among users. A two-layer weighting mechanism combines protocol share within sector and sector share within the ecosystem, which reduces the profitability of farming small protocols. We show that the maximum reward obtainable from any protocol is bounded by that protocol's global volume share. ZAPs also introduces a four-layer defense stack consisting of transaction-level integrity...

• 3.Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes

  Accurate option prices do not imply accurate recovery of the latent risk-neutral density. We study this distinction with two complementary benchmarks. A controlled benchmark exposes simulator-truth densities for latent evaluation, while a chronological NIFTY benchmark tests only held-out market prices. A two-component lognormal mixture has the lowest aggregate price, $L^1$, Wasserstein, and fixed-tail errors on the synthetic benchmark. Learned operators retain narrower strengths: DeepONet reduces 1% quantile and variance error by 39.0% and 34.6% relative to the mixture, and a quote transformer reduces $L^1$ by 16.4% on the structurally misspecified Merton family. A numerical conditioning analysis explains why these rankings can differ: after enforcing mass and forward constraints, 95 of 126 pricing directions are numerically null, and two...

• 4.No Data Is Not No Risk: Visibility Aware Graph-Based Inference of Business Conduct Risk

  The monitoring of business conduct risk is hindered by sparse, uneven, and visibility-biased data. Prior studies show that business conduct risk information and media coverage propagate through supply chain, peer, and corporate structure networks, yet incident records remain incomplete for many firms. As a result, the absence of reported events could reflect limited coverage rather than the absence of underlying business conduct risk. This paper examines whether inter-firm relationships can improve the prediction of future recorded conduct related incidents, particularly among firms with limited prior visibility. We formulate the task as Positive--Unlabeled node classification on a corporate ownership graph, where firms with recorded incidents are treated as labeled positives and firms without recorded incidents remain unlabeled. We then ...

• 5.Crossing-Free Probabilistic K-Line Forecasts Without Retraining

  Probabilistic K-line forecasting describes uncertainty in four complementary prices, namely open--high--low--close (OHLC). However, it introduces two consistency problems: quantile crossing and K-line crossing. Quantile crossing occurs when a higher-quantile forecast falls below a lower-quantile forecast, while K-line crossing occurs when the forecast low exceeds the open or close, or the forecast high falls below the open or close. Existing solutions generally address only one problem through output reordering, specialized architectures, or penalized training objectives. We propose K-line--Quantile Sequential Projection (KQSP), a parameter-free and training-free reconciliation method applicable to forecasts produced by any model. Compared with other crossing solutions, KQSP preserves predictive accuracy while producing substantially smal...

• 1.African Trust & Safety LLM Benchmark: Stress-testing AI Safety Across Africa’s Languages and Contexts

  Summary available at source link.

• 2.New GSMA Intelligence Research Examines Saudi Arabia’s Mobile Network Performance and Quality of Service

  Summary available at source link.

• 3.Connectivity Plays Its Part: Lessons From 2026 World Cup That Spain Should Consider for 2030

  Summary available at source link.

• 4.GSMA Convenes National Leaders to Advance Digital Pakistan 2030

  Summary available at source link.

• 5.RCS Universal Profile 4.1: Stronger foundations for secure messaging

  Summary available at source link.

• 1.AMTFV: Agentic Mathematical Tool-Flow Verification for LLM Self-Correction

  Large language models have demonstrated strong mathematical problem-solving capabilities, yet reliably verifying their candidate answers remains challenging. Existing representative methods mainly revise outputs through natural-language reflection or assist verification by directly generating verification programs; the former may not reliably support exact computation, whereas the latter prematurely couples mathematical modeling with low-level implementation. We propose AMTFV (Agentic Mathematical Tool-Flow Verification). By introducing Mathematical Tool Flow (MTF) as an interrupt--execute--resume interface, AMTFV decouples verification modeling from concrete execution and supports exact computation through a mathematical toolbox. Specifically, the verification agent first constructs a verification workflow, encodes the mathematical objec...

• 2.Don't Mix Rewards, Mix Policies: Policy Decomposition and Optimization for Multi-Reward RL

  Modern large language models (LLMs) are expected not just to answer correctly, but to adapt their behavior to different human values and use cases. As a result, multi-reward reinforcement learning (RL) has become an increasingly important problem for LLMs, where each reward captures a different aspect of desired behavior. However, optimizing with multiple rewards suffers from a more severe alignment tax issue, where different optimization objectives can trade off or even conflict with each other, leading to unstable and inefficient post-training. In this work, we propose PRISM, a new multi-reward RL framework built upon the idea of policy-space decomposition and composition. Instead of compositing different rewards, PRISM optimizes a set of standalone positive policies and a global negative policy. This alleviates the potential conflict d...

• 3.Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning

  Large language models generate computationally expensive yet semantically void reasoning on beyond-capability tasks, creating risks where plausible-sounding but incorrect derivations mislead users. We characterize this \textit{futile reasoning} phenomenon through systematic analysis, revealing universal capability overreach and systematic miscalibration between capability and behavior. The dominant failure mode is specious reasoning, which outputs look superficially valid but contain subtle errors, escalating with task difficulty. To address this, we introduce \textbf{CaRL} (\textbf{Ca}pability-\textbf{a}ligned \textbf{R}einforcement \textbf{L}earning), which aligns model behavior with capability boundaries through reward shaping that incentivizes refusal over futile reasoning and hindsight refusal augmentation that converts failures into...

• 4.Learning Latent Reasoning Traces for Scalar Reward Models End-to-End

  Reward models (RMs) are central to aligning large language models with human preferences via reinforcement learning. Although traditional scalar RMs enable efficient and probabilistic reward modeling, they rely on superficial cues that fail to generalize to complex or out-of-distribution (OOD) tasks. Conversely, generative RMs leverage extensive reasoning to improve robustness on challenging tasks, but their natural language-based scores lack the numerical flexibility and probabilistic interpretability that scalar RMs offer. While recent approaches combine both paradigms through off-policy multi-task learning, such parallel optimization does not guarantee that generated reasoning traces actively align with or benefit downstream scalar reward prediction. To address this mismatch, we propose LatentRM, a reward modeling framework that learns...

• 5.Tokenizer-Agnostic Engram Module

  Deepseek's Engram, a conditional memory module, was introduced to trade-off storage versus reasoning in large language models. However, the module relies on token-level $N$-gram hashing for Engram embedding lookup, introducing a tight coupling to the tokenizer used: a model with a different tokenizer would have to train its own Engram embeddings from scratch. To improve the reusability of Engram embeddings, we propose a change to the hashing routine, enabling compatibility between Engram models using different tokenizers. Instead of modelling disjoint $N$-gram spaces, we treat $N$-gram as a method to sample potentially useful byte sequences, from all possible byte sequences across tokens. We replace the XOR-based hashing with the general polynomial hashing with a joint embedding space across $N$. This work investigates the possible trade-...

• 1.Domain-Division based Progressive Learning for Source-Free Domain Adaptation

  With growing privacy and portability concerns, source-free domain adaptation requires only a source pre-trained model and an unlabeled target domain, allowing for effective adaptation to the target data. Most existing self-training methods focus on selecting and exploiting samples with reliable predictions, often neglecting others. Inspired by the finding that deep models learn clean samples faster than noisy ones, we propose a domain-division based progressive learning method named DPL. Specifically, our approach consists of two alternating stages, each beginning with the division of the target domain into easy-to-adapt and hard-to-adapt subdomains based on adaptation difficulty, followed by neighborhood-based pseudo label assignment. In stage one, we enhance classification accuracy through uncertainty-aware self-training and alignment o...

• 2.Design Concept: Scaffolding Geopolitical Reflection Among Tech Workers

  This paper presents a speculative Human-Computer Interaction design proposal for encouraging geopolitical reflexivity amongst tech workers at geopolitically relevant technology companies. Recent scholarship in International Relations and Science and Technology Studies increasingly recognizes technology firms and their workers as geopolitical actors whose decisions shape international dynamics. However, existing Responsible Innovation and Responsible AI approaches rarely engage with the geopolitical narratives and imaginaries that underpin contemporary AI development. Building upon RI scholarship on reflexivity, reflective HCI, and creative HCI work on computational narratives, this paper proposes an AI-enabled interactive narrative system in which users engage with a speculative scenario centred on technology, power, and geopolitics. Thro...

• 3.Learning to Trace Seiberg Dualities

  Dualities play an important role in establishing both microscopic and emergent phenomena in a wide range of physical systems. In practice, though, it can often be computationally challenging to establish when two systems are dual, even when all of the "rules of the game" are well-known. Said differently, when confronted with two systems, how can one efficiently establish that they are in fact dual? In this paper we use machine learning methods to address this question for Seiberg dualities of supersymmetric quiver gauge theories. Mathematically, this involves establishing mutations of quivers, which is in turn a variation on the theme of "learning to unknot". On the one hand, this leads us to a practical tool for establishing the computational complexity of different dualities. On the other hand, it also allows us to study how different n...

• 4.Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale

  Computer-use agents learn from what their actions change, so training one needs applications it can act on, break and reset. The applications that matter most are login-gated and stateful, so synthetic environments stand in for them. Recent pipelines generate such environments in bulk, which moves the bottleneck from how many exist to what is inside each one. The returns, we find, come from three properties: how much behavioural depth an environment carries, whether it targets the interaction an agent actually fails, and whether it improves alongside the model. We present Echoverse, which compiles specifications into stateful applications whose tasks are graded against the application's own database, and a co-evolution loop that reads every graded rollout twice: as repairs to the environment, its tasks and its verifier, and as training si...

• 5.The Geometric Nature and a Free Proxy for Flow-Matching Uncertainty

  Flow matching (FM) has become a popular action head paradigm for modern embodied models. However, as a conditional generative model, it does not explicitly expose its inherent uncertainty, producing faulty action chunks even when it misinterprets the scene or encounters out-of-distribution (OOD) inputs. Therefore, determining when an FM-generated action can be trusted is essential for safe deployment, yet existing uncertainty estimation methods on real-time control suffer from several issues: extra training budget, high computational overhead, and low generalization ability. In this work, we provide a geometric interpretation of FM uncertainty in the velocity field, showing that uncertainty manifests as deviation from an ideal affine-isotropic contraction field. Building on this observation, we introduce denoising acceleration ($\mathrm{a...

• 1.Fridays With Bob

  When I started at Spectrum 25 years ago, a senior editor suggested that I find a “rabbi,” by which he meant someone who could mentor me in how EEs approach problems and evaluate potential solutions. I didn’t find one right away. Then in 2005 we decided to do a special report, focusing on the challenges of enterprise software development. I suggested we invite IEEE Life Senior Member Robert N. Charette , a self-described risk ecologist, prolific book author, and leading authority on risk management and software engineering, to explore in our pages the myriad reasons software projects fail. His seminal article “ Why Software Fails ” is still read in university engineering classes today. IEEE Life Senior Member Robert N. Charette is one of IEEE Spectrum’s most prolific authors. Robert N. Charette It was, as they say, the beginning of a beaut...

• 2.Are AI Models Working Harder Than They Need to?

  Much of modern AI runs on multiplication. Neural networks behind everything from generated answers to photo organization and song recommendations perform millions or billions of operations that multiply inputs by learned weights. Lizy K. John thinks that’s more work than the job requires. John, a professor of electrical and computer engineering at the University of Texas at Austin, has spent the past five years working on a class of models called weightless neural networks . Instead of repeatedly multiplying inputs by weights, these networks pass binary inputs through interconnected lookup tables—closer to consulting a collection of stored answers than solving the same arithmetic problem repeatedly. Depending on the task, she says, the networks can be less than a thousandth the size or 1,000 times as fast as conventional alternatives whil...

• 3.Siobahn Day Grady Wants Everyone to Be AI Literate

  Artificial intelligence is reshaping the skills employers expect from new graduates. In response, universities are scrambling to launch new courses, research centers, and industry partnerships that prepare students for today’s workforce. But building a cutting-edge AI curriculum demands funding and access to industry networks, resources that remain unevenly distributed across higher education. At North Carolina Central University, Siobahn Day Grady is trying to change that equation. In January 2025, Grady, an associate professor in the NCCU School of Library and Information Sciences , launched the first AI research institute at a historically Black college or university, or HBCU. The Institute for Artificial Intelligence and Emerging Research (IAIER) aims in part to help students and faculty across the university develop the skills needed...

• 4.AI Is Hyper-Scaling Digital Inequality

  Artificial intelligence is rapidly becoming part of everyday infrastructure–in some places. It helps write emails and software code, filters job applications, powers recommendation systems, and is increasingly being integrated into education, health care, finance, and public administration. Industry leaders talk about “AI for everyone,” while governments rush to publish national AI strategies and build sovereign compute. Yet over the past decade, working on digital inclusion and digital literacy projects in regions from Europe to sub-Saharan Africa and Southeast Asia, I’ve seen the same pattern repeat : Each new wave of “transformative” technology lands on a landscape already stratified by connectivity, skills, and institutional capacity. The current AI wave is no exception. If anything, it amplifies those underlying fractures. Still, som...

• 5.Why AI-Driven Cognitive Systems Are Redefining Radar and Electronic Warfare

  An overview of how mode-agile threats challenge static library radar/EW systems, and how AI/ML cognitive architectures enable adaptive, real-time countermeasures. What Attendees will Learn Why mode-agile threats render static library systems ineffective — Explore how wartime reserve modes and mode-agile emitters deploy unexpected frequencies, modulation techniques, and hopping schemes that cannot be matched against traditional threat databases, leaving legacy electronic protect, attack, and support systems unable to respond. How AI/ML techniques power cognitive radar/EW systems — Understand the roles of artificial neural networks (ANN), deep neural networks (DNN), fuzzy logic, and genetic algorithms in enabling autonomous threat classification, signal de-interleaving, and real-time countermeasure generation without human intervention. The...

• 1.Monday assorted links

  1. A new simulation argument? 2. PC on aesthetics. 3. An interview with Yiyang Zhuge. 4. The truth and aesthetics of going to church? 5. Some forms of math cope? And a different view. 6. At the demolition derby.

  The post Monday assorted links appeared first on Marginal REVOLUTION.

• 2.What should I ask Nick Bostrom?

  Yes I will be doing a Conversation with him.  So what should I ask? If you need them, here are various Bostrom links.

  The post What should I ask Nick Bostrom? appeared first on Marginal REVOLUTION.

• 3.On infinite ethics

  Infinite numbers have long troubled utilitarian ethicists: if the universe is boundless, then there is infinite positive utility and infinite negative utility, and we can’t even define our current state, let alone affect it. Oxford philosopher Toby Ord proposes a solution: use hyperreal numbers, a mathematical construct in which infinite sums behave more like finite ones (for example, […]

  The post On infinite ethics appeared first on Marginal REVOLUTION.

• 4.*Rome’s Age of Revolution*

  The author is Tim Whitmarsh and the subtitle is Augustus, Empire and the Making of Christianity.  This is the most important book on the history of Christianity I have read in a long time, and it is also an important work in Roman history.  Yet also fun and extremely readable.  Excerpt: The story I have […]

  The post *Rome’s Age of Revolution* appeared first on Marginal REVOLUTION.

• 5.Sunday assorted links

  1. The inverseminar, someone else presents your paper. 2. The rise of million-dollar companies with just one employee (WSJ). 3. The political party itself is a big driver of polarization. 4. Gita Gopinath is making sense. 5. GPT Pro (not Astra) on what “Astra” means. 6. David Perell art history game. 7. New model of […]

  The post Sunday assorted links appeared first on Marginal REVOLUTION.

• 1.Stablecoins and (Non)Crypto Shocks: A 2026 Update

  Stablecoins are digital assets whose value is pegged to that of a fiat currency, typically the U.S. dollar at a peg of $1.00 per token.  In a previous blog post, we described the rapid growth of stablecoins through early 2025, highlighted changes in stablecoins’ reserve-asset composition, and examined their reactions to Bitcoin price shocks. In this post, we document the growth of stablecoins since our last post. Then, we examine how shocks from outside the crypto industry can impact the composition of stablecoins’ reserve assets. For our case study, we use the 2023 failure of Silicon Valley Bank (SVB) and its impact on USD Coin (USDC, issued by Circle), the second-largest stablecoin by market capitalization.

• 2.Nonbank Subsidiaries and the Hidden Fragility of Internal Capital Markets Reallocation

  This post concludes a three-part series on how bank regulation interacts with the organizational structure of banking firms. The first post documented the equity-rich nonbank subsidiaries inside bank holding companies (BHCs); the second post showed that BHCs met Basel III by reallocating capital internally, moving equity from nonbank affiliates to bank subsidiaries rather tha...

• 3.How Basel III Changes Where Capital Sits: Nonbank Subsidiaries as Equity Reservoirs

  This post is the second in a three-part series on how bank regulation interacts with the organizational structure of banking firms. The first post documented that nonbank subsidiaries inside bank holding companies (BHCs) are large, equity-rich "reservoirs," and that bank-level capital diverged sharply from consolidated capital after Basel III took effect in 2015. This post asks why, and traces the answer through the internal plumbing of the holding company. The series draws on the authors' recent Staff Report, "

• 4.Capitalizing on Nonbanks: Regulatory Arbitrage Within Bank Holding Companies

  This post is the first in a three-part series on how bank regulation interacts with the organizational structure of banking firms. The series draws on the authors' recent Staff Report, "Regulatory Arbitrage Within the Firm."

• 5.Effect of Tariffs on U.S. Small Businesses

  How has the recent implementation of tariffs affected small businesses? Due to lack of data, little is known about this issue. In this Liberty Street Economics post, we use data from the 2025 edition of the Small Business Credit Survey (SBCS) to explore this question for businesses nationally and in the Second District (defined, for the purpose of this study, as New York, New Jersey, and Connecticut). We find that the majority of national firms in the goods and retail sectors reported experiencing financial challenges due to tariffs in 2025, with even larger shares of regional firms doing so. In response, about 80 percent of national and regional firms passed on at least some of the higher ...

• 1.The Canary in the AI Coal Mine

  The greatest danger posed by AI is not the technology itself, but the race to deploy it before we know how to control it. The recent Hugging Face breach shows how seemingly minor human errors can be amplified by AI, underscoring the risks of treating safety as an afterthought.

• 2.The Painful Incompetence of the Indian State

  India’s relatively weak state capacity has long hampered its development. Prime Minister Narendra Modi has been promising to address the problem since he came to power in 2014, but his government’s emphasis on centralizing power and strengthening executive control has had the opposite effect.

• 3.This Time Could Be Different for Russia Sanctions

  High inflation and interest rates are eating away at the foundations of the Russian economy and sapping its growth. By hitting its most important sectors, and its essential source of foreign hard cash, new US legislation would pile enormous pressure on the Kremlin just when it is urgently needed.

• 4.Africa’s Energy Curse Is a Political Choice

  It is no secret why African countries with large fossil-fuel reserves nonetheless have failed to reduce extreme poverty or provide reliable electricity to all households. Those in power have made a conscious choice to privilege hard-currency export revenues, some of which then finance elite consumption rather than public investment.

• 5.The EU’s Incredible Shrinking Banking Sector

  At the start of the millennium, all five of the largest European banks had a higher market capitalization than the largest US bank. What explains the fact that a quarter-century later, the largest US bank has a higher capitalization than all five top EU banks combined, and how can European policymakers address this dramatic reversal?

• 1.Poste deal advances as TIM eyes growth in AI services, mission-critical industries

  TIM sees additional growth opportunities in areas including defense, mission-critical services, AI-enabled products and operations, next-generation connectivity applications, and data centers In sum – what to know: Poste transaction –…

• 2.Physical AI is exposing a data sovereignty gap (Reader Forum)

  As connected vehicles, robots, and other physical AI systems cross borders, data sovereignty becomes about more than storage. Curtis Govan at floLIVE argues regulators and operators must address “network sovereignty”…

• 3.Friday (telco diary) | Telcos in AI and ISAC arms race

  From the newsletter (sign-up if you want it sooner): AI infrastructure is opening new opportunities for telcos, from data center connectivity to sovereign AI and defense networks. But the payoff…

• 4.Building the AI backbone, waiting on the AI billions

  Verizon, AT&T, and SK Telecom are betting big on AI infrastructure. But technology advisory group Omdia says the headline numbers are ceilings, and warns revenue will trail capacity growth for…

• 5.Ensuring colocation success by eliminating network observability blind spots (Reader Forum)

  As enterprises shift workloads into colocation facilities, visibility gaps across shared infrastructure can undermine performance and resilience. End-to-end network observability, including packet-level inspection, helps IT teams identify faults faster, reduce…

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• 1.Near-Field Communications with Grating Lobes for Quasi-Distributed Arrays: From ULA to MRA

  Extremely large-scale antenna array (ELAA) has emerged as a common feature of many key candidate technologies for 6G, where the near-field characteristics become dominant. The quasi-distributed array can further extend the near-field range and utilize the near-field benefits to improve the system performance. However, its typical implementation with modular arrays suffers from severe grating lobes that cause non-negligible inter-user interferences. To solve this problem, we propose the modular minimum-redundancy array (M-MRA) to suppress near-field grating lobes by redesigning the subarray configuration. Specifically, we first characterize the beam pattern of the conventional modular uniform linear array (M-ULA). Contrary to the common belief that grating lobes only exist in the angle domain, we reveal that near-field grating lobes may al...

• 2.METIS: A Declarative Slice Orchestrator for Application-Centric 5G/6G Networks

  Network slicing is the cornerstone of application-aware 5G and 6G networks, yet dynamic lifecycle management of network slice instances with coordinated quality-of-service enforcement across the radio access network and core network remains unresolved. Existing orchestrators rely on network-centric data models, imperative workflows, and static slice templates, while O-RAN addresses radio-side slice control independently of 3GPP core-side control, leaving slice-level quality-of-service enforcement uncoordinated across domains. This paper introduces METIS, a declarative slice orchestrator that manages the Day-0/1/2 lifecycle of network slice instances through cascaded reconciliation loops. METIS defines an application-centric data model for service profiles, enabling customers to describe the semantics and quality-of-experience requirements...

• 3.Integrated tunable magnonic devices for beyond 6 GHz signal processing

  In the search for novel technology platforms supporting the transition towards ''beyond 6G'' telecommunications, magnonics is emerging as a viable route, primarily due to its intrinsic compatibility with the UWB-FR3 bands and its easy tunability. In this paper, we present a proof-of-concept device based on a CoFeB magnonic waveguide, fully integrated on silicon, which demonstrates all the key features of our integration approach, based on co-integrated hard magnetic micromagnets, magnonic conduits, reconfigurable soft magnetic elements and MEMS. The bias field enabling operation up to about 12 GHz is provided by SmCo micromagnets embedded in the silicon substrate. Real-time tunability is implemented in two ways. First, current-driven control is achieved via an integrated current line that generates an additional localized magnetic field, ...

• 4.When 5G MIMO Scaling Breaks: Toward 6G Upper-Mid-Band Extreme MIMO

  The upper-mid band, particularly the 7-8 GHz range within frequency range 3 (FR3), has emerged as a leading spectrum candidate for wide-area sixth-generation (6G) cellular networks. Its shorter wavelength enables hundreds of antenna elements to be integrated within the physical aperture of an existing 5G base-station panel. In principle, the resulting aperture gain can compensate for the increased path loss and enable extreme MIMO (E-MIMO) with 256 or more antenna ports while reusing current cell sites. In practice, however, simply scaling the 5G New Radio (NR) architecture from tens to hundreds of ports encounters fundamental system-level limitations. This paper identifies where 5G-style MIMO scaling breaks and develops a research roadmap for practical upper-mid-band E-MIMO. We first review the evolution of FR3 spectrum, its propagation ...

• 5.Sovereign Cognitive Digital Twins: Fusing 6G ISAC, AI-RAN, and Zero-Trust Edge Grids for National Resilience in the Global South

  Small-island developing states face accelerating sea-level rise, intensifying cyclones, and storm surge, while suffering the sparse ground instrumentation that makes timely hazard perception difficult. This paper argues for a shift from passive cellular connectivity to the Network as a Sensor, realized through a Sovereign Cognitive Digital Twin (S-CDT): a federated national digital twin whose perceptual substrate is the 6G radio interface itself. Instead of disjoint sensing systems, a nation can reuse the Integrated Sensing and Communication (ISAC) waveforms of its own network as a distributed radar mesh. We specify a six-layer S-CDT stack in which ISAC collapses the boundary between the dynamic-data and communication layers; we map the physical layer to the ETSI GR ISC 001 and 3GPP Release 19 sensing frameworks; and we formulate a belief...

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• 1.Effort-Centric Fairness in Lending Decisions

  Algorithmic credit scoring must satisfy fairness and explanation requirements, yet prevailing predictive-parity criteria assess only outcomes at the decision point. They can therefore overlook whether rejected applicants face unequal burdens in reaching future approval, a phenomenon we call masked inequality. We develop an effort-centric framework that measures an applicant's effort as the minimum weighted cost of feasible changes required to cross the approval boundary. The framework distinguishes feature-independent actions from additive structural shifts that propagate through a causal model and defines parity by comparing average minimum effort across protected groups. We derive tractable local expressions for general differentiable classifiers and exact expressions for logistic regression, embed them in an in-processing fairness obje...

• 2.Bootstrap inference in autoregressive duration models

  This paper develops bootstrap inference for autoregressive conditional duration (ACD) models observed over a fixed calendar span, so that the number of durations is random. We study recursive schemes that either fix the calendar span or the realized event count. For the fixed-count bootstrap, we establish consistency when the duration tail index satisfies $κ\geq1$. When $0<κ<1$, classical consistency fails because the estimator has a mixed-normal limit, but the bootstrap reproduces its conditional Gaussian component. Consequently, basic percentile intervals remain first-order valid and bootstrap $t$-statistics are asymptotically standard normal. Monte Carlo experiments show accurate finite-sample inference across finite- and infinite-mean regimes and robustness to non-exponential innovations. An application to cryptocurrency ETF tra...

• 3.Boundary-Induced Apparent Risk Aversion in Nonergodic Multiplicative Growth

  Finite multiplicative systems often cease to evolve when a lower continuation threshold is reached,whereas standard growth-optimal benchmarks assume uninterrupted continuation. We study a finite-horizon binary multiplicative process in which a fixed exposure is chosen ex ante and paths crossing an absorbing boundary are assigned a residual value. Exact lattice propagation yields the optimal exposure as a function of initial log distance to the boundary, horizon, and residual ratio. Costly absorption compresses exposure below the no-boundary Kelly fraction near the boundary. When interpreted through an unconstrained constant-relative-risk-aversion benchmark,this compression appears as elevated risk aversion. As the residual value approaches the boundary, a local above-Kelly reversal can occur. Absorbing-boundary geometry can therefore gene...

• 4.FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning

  Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs). However, existing approaches remain confined to a market-agnostic, supervised learning paradigm that relies on limited, static and human-annotated datasets, and thus are incapable of adapting to evolving market conditions. To address this limitation, we introduce FinSMART, the first market-aligned reinforcement learning framework for financial sentiment analysis, which directly optimizes sentiment signals using realized market outcomes. To deal with the noisy, non-stationary, and multifactorial nature of financial markets, FinSMART incorporates a signal extraction pipeline that combines market-aware data filtering with a discrete asymmetric trading reward, enabling stable reinforcement lear...

• 5.ZAPs: A Reward Attribution Framework for DeFi Ecosystems with Adversarial-Robust Scoring via Parallel Anomaly Ensemble Detection

  Incentive programs are central to user acquisition in decentralized finance, but many reward systems rely on raw volume, transaction count, and wallet count, making them vulnerable to bots and sybil operations. We present ZAPs, a reward attribution framework that combines economic contribution scoring with adversarial robustness. A composite activity score uses protocol-specific percentile normalization to limit whale dominance while preserving differentiation among users. A two-layer weighting mechanism combines protocol share within sector and sector share within the ecosystem, which reduces the profitability of farming small protocols. We show that the maximum reward obtainable from any protocol is bounded by that protocol's global volume share. ZAPs also introduces a four-layer defense stack consisting of transaction-level integrity...

• 1.Near-Field Communications with Grating Lobes for Quasi-Distributed Arrays: From ULA to MRA

  Extremely large-scale antenna array (ELAA) has emerged as a common feature of many key candidate technologies for 6G, where the near-field characteristics become dominant. The quasi-distributed array can further extend the near-field range and utilize the near-field benefits to improve the system performance. However, its typical implementation with modular arrays suffers from severe grating lobes that cause non-negligible inter-user interferences. To solve this problem, we propose the modular minimum-redundancy array (M-MRA) to suppress near-field grating lobes by redesigning the subarray configuration. Specifically, we first characterize the beam pattern of the conventional modular uniform linear array (M-ULA). Contrary to the common belief that grating lobes only exist in the angle domain, we reveal that near-field grating lobes may al...

• 2.METIS: A Declarative Slice Orchestrator for Application-Centric 5G/6G Networks

  Network slicing is the cornerstone of application-aware 5G and 6G networks, yet dynamic lifecycle management of network slice instances with coordinated quality-of-service enforcement across the radio access network and core network remains unresolved. Existing orchestrators rely on network-centric data models, imperative workflows, and static slice templates, while O-RAN addresses radio-side slice control independently of 3GPP core-side control, leaving slice-level quality-of-service enforcement uncoordinated across domains. This paper introduces METIS, a declarative slice orchestrator that manages the Day-0/1/2 lifecycle of network slice instances through cascaded reconciliation loops. METIS defines an application-centric data model for service profiles, enabling customers to describe the semantics and quality-of-experience requirements...

• 3.Integrated tunable magnonic devices for beyond 6 GHz signal processing

  In the search for novel technology platforms supporting the transition towards ''beyond 6G'' telecommunications, magnonics is emerging as a viable route, primarily due to its intrinsic compatibility with the UWB-FR3 bands and its easy tunability. In this paper, we present a proof-of-concept device based on a CoFeB magnonic waveguide, fully integrated on silicon, which demonstrates all the key features of our integration approach, based on co-integrated hard magnetic micromagnets, magnonic conduits, reconfigurable soft magnetic elements and MEMS. The bias field enabling operation up to about 12 GHz is provided by SmCo micromagnets embedded in the silicon substrate. Real-time tunability is implemented in two ways. First, current-driven control is achieved via an integrated current line that generates an additional localized magnetic field, ...

• 4.When 5G MIMO Scaling Breaks: Toward 6G Upper-Mid-Band Extreme MIMO

  The upper-mid band, particularly the 7-8 GHz range within frequency range 3 (FR3), has emerged as a leading spectrum candidate for wide-area sixth-generation (6G) cellular networks. Its shorter wavelength enables hundreds of antenna elements to be integrated within the physical aperture of an existing 5G base-station panel. In principle, the resulting aperture gain can compensate for the increased path loss and enable extreme MIMO (E-MIMO) with 256 or more antenna ports while reusing current cell sites. In practice, however, simply scaling the 5G New Radio (NR) architecture from tens to hundreds of ports encounters fundamental system-level limitations. This paper identifies where 5G-style MIMO scaling breaks and develops a research roadmap for practical upper-mid-band E-MIMO. We first review the evolution of FR3 spectrum, its propagation ...

• 5.Sovereign Cognitive Digital Twins: Fusing 6G ISAC, AI-RAN, and Zero-Trust Edge Grids for National Resilience in the Global South

  Small-island developing states face accelerating sea-level rise, intensifying cyclones, and storm surge, while suffering the sparse ground instrumentation that makes timely hazard perception difficult. This paper argues for a shift from passive cellular connectivity to the Network as a Sensor, realized through a Sovereign Cognitive Digital Twin (S-CDT): a federated national digital twin whose perceptual substrate is the 6G radio interface itself. Instead of disjoint sensing systems, a nation can reuse the Integrated Sensing and Communication (ISAC) waveforms of its own network as a distributed radar mesh. We specify a six-layer S-CDT stack in which ISAC collapses the boundary between the dynamic-data and communication layers; we map the physical layer to the ETSI GR ISC 001 and 3GPP Release 19 sensing frameworks; and we formulate a belief...

• 1.METIS: A Declarative Slice Orchestrator for Application-Centric 5G/6G Networks

  Network slicing is the cornerstone of application-aware 5G and 6G networks, yet dynamic lifecycle management of network slice instances with coordinated quality-of-service enforcement across the radio access network and core network remains unresolved. Existing orchestrators rely on network-centric data models, imperative workflows, and static slice templates, while O-RAN addresses radio-side slice control independently of 3GPP core-side control, leaving slice-level quality-of-service enforcement uncoordinated across domains. This paper introduces METIS, a declarative slice orchestrator that manages the Day-0/1/2 lifecycle of network slice instances through cascaded reconciliation loops. METIS defines an application-centric data model for service profiles, enabling customers to describe the semantics and quality-of-experience requirements...

• 2.The AnyLog Edge Data Fabric

  Industrial and autonomous systems increasingly depend on AI, automation, and real-time coordination to act on operational data as it is generated. Yet conventional architectures often require that data to pass through centralized platforms before decisions can be made. Cloud systems remain valuable for training, reporting, and long-term analytics, but they add latency and external dependencies to the critical decision path and become harder to scale as each new site adds additional edge devices and data. As intelligence spreads across machines, sites, facilities, and vehicles, continued dependence on centralization will constrain response time, resilience, scalability, and autonomous operation. This paper presents the AnyLog Edge Data Fabric, an agent- and edge-based platform that manages operational data at its source while presenting ...

• 3.Sovereign Cognitive Digital Twins: Fusing 6G ISAC, AI-RAN, and Zero-Trust Edge Grids for National Resilience in the Global South

  Small-island developing states face accelerating sea-level rise, intensifying cyclones, and storm surge, while suffering the sparse ground instrumentation that makes timely hazard perception difficult. This paper argues for a shift from passive cellular connectivity to the Network as a Sensor, realized through a Sovereign Cognitive Digital Twin (S-CDT): a federated national digital twin whose perceptual substrate is the 6G radio interface itself. Instead of disjoint sensing systems, a nation can reuse the Integrated Sensing and Communication (ISAC) waveforms of its own network as a distributed radar mesh. We specify a six-layer S-CDT stack in which ISAC collapses the boundary between the dynamic-data and communication layers; we map the physical layer to the ETSI GR ISC 001 and 3GPP Release 19 sensing frameworks; and we formulate a belief...

• 4.Powering Net-Zero 6G: Packetized Energy Management for Grid-Interactive Telecom Infrastructure

  The transition to net-zero 6G requires energy-management approaches that go beyond conventional RAN efficiency mechanisms. As future networks integrate AI-native operation, edge intelligence, dense deployments, renewables, and storage, the RAN will become both a growing power consumer and a source of distributed energy flexibility. This paper introduces packetized energy management (PEM) as a framework for transforming 6G infrastructure into energy-aware, grid-interactive assets. PEM represents flexible demand as schedulable energy packets that can be admitted, deferred, or reshaped according to local constraints, renewable availability, carbon intensity, price, and communication priorities. We present a PEM-enabled base-station model, a RAN architecture for PEM integration, and the telecoms virtual power plant (VPP) concept for aggregati...

• 5.O-RAN: Analysis of Latency-critical Interfaces and Overview of Time Sensitive Networking Solutions

  5G and B5G/6G foundations heavily rely on virtualization technologies, and virtualized Radio Access Networks (vRANs) are one of their major keystones. However, while vRANs have been traditionally suffering from significant hardware/software coupling, next generation vRANs aim for open, standardized interfaces and multi-vendor, interoperable components to enable truly flexible deployments following the cloud-native principles. In this line, the O-RAN Alliance is promoting a novel Open RAN architecture to further boost flexibility and cost efficiency. In order to reduce costs and effectively achieve the promised disaggregation levels, O-RAN must ensure shared, integrated transport networks in opposition to dedicated, over-provisioned links from traditional approaches. However, keeping deterministic performance requirements in such cost-effe...