Key Takeaways

• 6G RF hardware design is becoming a strategic race. The bottleneck lies in the intricate requirements of frontier radio technologies. The Hidden 6G Bottleneck: RF Hardware Design I…
• 3GPP meetings reveal specific future paths for radio and networks. Recent discussions in Dalian highlight critical evolutionary steps for the next generation connectivity. 6G in Dalian: What the Latest 3GPP Meetings Re…
• RF digital twins are essential for advanced predictive simulation. Industry shifts toward 5G-Advanced necessitate robust simulation environments. RF Digital Twins: Why 5G-Advanced and 6G Need …
• Amazon's Globalstar deal accelerates direct device-to-device pathways. This acquisition provides Leo with faster access to critical communication layers. Amazon’s Globalstar deal gives Amazon Leo a fa…
• LLMs face new challenges in HVAC building energy operations. A critical review highlights deployment readiness issues for large language models in thermal management. Large Language Models for HVAC Operations in B…
• Teaching LLM-based agents to prioritize must-haves is foundational. Research indicates the need to train agents to distinguish essential tasks before addressing secondary goals. First Things First: Teaching LLM-Based Agents …
• Web security strategies must shift from injection prevention to interaction. The age of advanced AI models requires a rethinking of traditional security paradigms. Shifting from Injection to Interaction: Rethin…

Executive Summary

The day's landscape is defined by the convergence of hard hardware constraints and emerging software architectures for next-generation networks. On one front, 6G development faces tangible physical bottlenecks as RF hardware design enters a competitive strategic phase, while recent 3GPP discussions in Dalian clarify the roadmap for future radio technologies. Simultaneously, the digital ecosystem grapples with scaling satellite interoperability for non-terrestrial networks and spectrum utilization strategies urged by industry bodies like the GSMA. In the realm of artificial intelligence, the focus shifts from simple deployment to rigorous evaluation: large language models are being critically assessed for their efficacy in specific domains such as building energy management and forced outage risk prediction. Furthermore, the integration of memory-efficient training methods and quantum-assisted techniques suggests a trajectory toward handling parameter-intensive tasks like human activity recognition. The overarching narrative is one of measured progression, where theoretical advancements in reasoning uncertainty and world models are immediately tested against practical deployment challenges in healthcare, finance, and security. The Hidden 6G Bottleneck: RF Hardware Design I… 6G in Dalian: What the Latest 3GPP Meetings Re… RF Digital Twins: Why 5G-Advanced and 6G Need … Amazon’s Globalstar deal gives Amazon Leo a fa… Large Language Models for HVAC Operations in B… First Things First: Teaching LLM-Based Agents …

6G Hardware Constraints

Recent analysis indicates that the development of sixth-generation radio access networks is increasingly dependent on advancements in RF hardware design, marking a shift from pure protocol innovation to physical layer constraints. The industry is witnessing this evolution as specific bottlenecks emerge, requiring a new strategic approach to manufacturing and component integration. This transition represents a significant race where early movers secure advantages through superior thermal management and signal integrity solutions. The Hidden 6G Bottleneck: RF Hardware Design I…

Network Policy and Spectrum

Policy makers and industry groups are actively coordinating to unlock the 6 GHz band for mobile use, recognizing its potential to maximize mobile ecosystem utility. Concurrently, reports from major telecom associations highlight that a significant portion of populations in developing regions remain offline despite network coverage, demanding targeted inclusion strategies. These developments underscore the tension between technological capability and equitable access, requiring coordinated policy interventions to ensure digital growth aligns with economic development goals. Mobile ecosystem ready to maximise use of 6 GH… 80% of Malawians Remain Offline Despite Covera…

AI Agents and Reasoning

The field of artificial intelligence is advancing through the application of large language models to complex problem-solving domains, yet challenges regarding deployment readiness persist. Current methodologies focus on teaching agents to prioritize essential tasks before addressing secondary needs, ensuring operational efficiency in critical systems like building energy management. Furthermore, researchers are employing novel techniques to quantify and optimize reasoning uncertainty, utilizing graph complexity metrics to improve the reliability of AI-driven decision-making frameworks in safety-critical applications. First Things First: Teaching LLM-Based Agents … Shifting from Injection to Interaction: Rethin…

Specialized Model Architectures

Research is pushing the boundaries of model efficiency through quantum-assisted memory training, specifically targeting parameter-intensive human activity recognition tasks. Additionally, new architectures are being developed to integrate diagnostic strands into free-text consensus via embedding-space reweighting, enhancing the interpretability of large-scale language models. These architectural innovations aim to address data scarcity and computational costs without sacrificing performance in high-stakes environments like aerospace simulation or medical question answering. Memory as transformation: LETHE, a self-refere… Quantum-Assisted Memory-Efficient Training for…

Babak's Daily Briefing

Monday, September 7, 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.Large Language Models for HVAC Operations in Building Energy Systems: A Critical Review of Methods, Applications, and Deployment Readiness

  Building automation systems generate rich sensor data yet remain insight-poor because heterogeneous point naming, missing metadata, and fragmented documentation obstruct their operational use. This systematic review analyses and codes 66 peer-reviewed studies on large language models (LLMs) for HVAC operations published between 2023 and March 2026. Each study is classified across five application families and three LLM method families and assessed for evidence realism, deployment readiness, and the responsibility boundary between the LLM and physical HVAC decisions. The corpus is concentrated in building energy modelling (BEM, 32 of 66 papers), while load forecasting remains too sparse for subfield-level conclusions. Only four studies reach pilot-level evidence, and none reports sustained operational deployment. No study was classified as...

• 2.First Things First: Teaching LLM-Based Agents to Prioritize Must-Haves before Nice-to-Haves

  Recent progress in multimodal large language models (MLLMs) has fueled significant enthusiasm in their potential to act as autonomous agents for real-world tasks. However, scenarios requiring agents to fulfill users' complex, structured requirements remain largely underexplored. In this work, we examine reasoning tasks under three distinct requirement scenarios: (i) Must-have requirements uniquely determine a unique feasible solution; (ii) Multiple answers satisfy the must-have requirements and are prioritized via the nice-to-have requirements; and (iii) No candidate solution satisfies the must-have requirements, in which case the agent should abstain from generating a response. We evaluate state-of-the-art MLLMs on 3,649 carefully constructed problems that reflect realistic service scenarios, including e-commerce, booking, and map-based ...

• 3.Shifting from Injection to Interaction: Rethinking Web Security in the Age of LLMs and Beyond

  Large language models (LLMs) are becoming integral to web applications and browser agents, transforming online interactions while introducing new attack vectors and reshaping longstanding web vulnerabilities. Classical threats such as cross-site scripting (XSS) can be amplified through LLM-mediated interactions, while LLM-specific vulnerabilities can propagate across web applications, introducing attacks such as prompt injection. Securing modern web systems therefore requires understanding interactions between traditional and LLM-specific threats across the system lifecycle. Unlike prior surveys treating web and LLM security separately, this survey provides a unified analysis of how LLMs amplify web vulnerabilities across client-side, server-side, and pipeline layers while evaluating defenses and their limitations. The analysis examines e...

• 4.Loom: Weaving Diagnostic Strands into Free-Text Consensus via Embedding-Space Reweighting

  Aggregating noisy, conflicting textual hypotheses into a reliable consensus is a fundamental challenge when deploying NLP systems in real-world industrial settings. While monolithic Large Language Model (LLM) agents offer unbounded expressivity for tasks like Root Cause Analysis (RCA), they suffer from context limits, compounding hallucinations, and prohibitive inference latency. Traditional weak supervision offers statistical rigor but is mathematically restricted to discrete classes. We present Loom, a generative consensus framework deployed for real-world RCA that bridges these paradigms. Loom aggregates open-form hypotheses emitted by modular heuristics (diagnostic templates dynamically populated with episode-specific entities, times, and metrics) by projecting them into a continuous embedding space, and resolves conflicting signals w...

• 5.A Dataset for Modeling Iterative Problem-Solving

  Solving problems through repeated attempts is a sequential modeling task: at each step, the solver receives feedback and decides how to revise their solutions. Predicting whether performance improves, plateaus, or regresses across attempts is central to understanding any iterative problem-solving process in both human learners and autonomous agents. Beyond outcomes, modeling what errors persist and how strategies shift across attempts provides deeper insight into the mechanics of sequential learning. Studying these dynamics requires observing many solvers as they attempt, receive feedback, and revise. Programming courses with automated grading provide this setting, as students iteratively submit code to test suites and receive feedback on every attempt. We therefore curate CodeInsight, a large-scale dataset of over 3 million submissions f...

• 1.How Much Does Corpus Choice Change Dependency-Distance Estimates?

  arXiv:2609.04223v1 Announce Type: new Abstract: Dependency-distance estimates derived from a single corpus are routinely treated as properties of a language, yet this assumption has not been tested across independently compiled corpora. We compared mean dependency-distance estimates across 38 same-language treebank pairs from Universal Dependencies v2.18, using concordance correlation, Bland-Altman analysis, and a twelve-specification multiverse design. Cross-treebank agreement was moderate at best: substituting one treebank for another reversed nearly 40 percent of pairwise language orderings, and treebank choice accounted for roughly 29 percent of between-group variance. This disagreement substantially exceeded within-treebank sampling error and persisted across all twelve preprocessing specifications. Nevertheless, every treebank conf...

• 2.Memory as transformation: LETHE, a self-referential gan-inspired architecture

  arXiv:2609.04289v1 Announce Type: new Abstract: LETHE (Latent-parameter Evolution with Temporal Hierarchical quasi-Equilibrium) is a self-referential sonic-oblivion system implemented in SuperCollider. It adopts the formal vocabulary of Generative Adversarial Networks in a closed configuration without external datasets or supervision after initialization. Audio is processed by a 3 x 3 mixing matrix built around two delay lines; its nine coefficients and two delay times evolve through the interaction of a five-feature linear discriminator and a random-perturbation optimizer analogous to single-sample REINFORCE. The discriminator compares current energy behavior with an archive of the initial state and guides parameter updates. Circular, fixed, and live sources can be mixed independently. Across fixed and circular sessions with an ablation...

• 3.Evidence Integration in Large Language Models

  arXiv:2609.04290v1 Announce Type: new Abstract: Despite increasing reliance on LLMs that reason with external evidence supplied by tools, retrieval-augmented generation, other agents, and users, how LLMs integrate such evidence into decisions they have already begun to form remains largely unclear. We present a distributional theory in which evidence shifts the receiver's distribution of initial answers, driven by a receiver prior weight and a candidate evidence tilt, leading to three predictions. First, candidates more probable to the receiver are more persuasive. Second, receivers more readily integrate characteristic errors of their own than foreign errors from different sources. Third, identical evidence can improve weaker models and harm stronger ones. We confirm these over ten million trials, twelve LLMs from four families, and eig...

• 4.MedProb: Probing Internal Representations of Vision-Language Models for Medical Question Answering

  arXiv:2609.04336v1 Announce Type: new Abstract: Medical visual question answering (Med-VQA) is often assumed to require medical fine-tuning, large models, or complex multi-agent pipelines. We revisit this assumption with \textbf{MedProb}, a lightweight probing framework that predicts multiple-choice Med-VQA answers from frozen VLM representations without free-text generation. Across PATH-VQA, SLAKE, and VQA-RAD, MedProb recovers substantially more answer-relevant signal than prompting and performs stronger than medical VLMs and agentic systems. Probing also reduces the apparent gap between small and large models compared to prompting, suggesting that smaller VLMs contain more recoverable Med-VQA signal than generation-based evaluation reveals. Across 14 matched general-purpose and medical VLM pairs, medical adaptation does not consistent...

• 5.Adapting from Downturns: Prediction of Long-Term Conversational-Skill Development in Mental-Health Crisis Counselors

  arXiv:2609.04350v1 Announce Type: new Abstract: How do people learn to become better conversationalists? This question is especially important in the context of mental-health counseling, where conversational skills are essential, yet volunteer counselors often have limited access to supervision and structured feedback. Understanding how counselors develop their ability to steer conversations toward positive outcomes -- and identifying early which counselors are (not) on track to improve -- can help prioritize support for the counselors who need it most. In this work, we introduce the task of predicting, early in a conversationalist's career, whether they will eventually improve at steering conversations toward positive outcomes, and demonstrate the feasibility of this task in the case of volunteer mental-health crisis counselors. Our c...

• 1.Spectral-Target Physical Latent Structuring for JEPA-Style World Models

  arXiv:2609.04264v1 Announce Type: new Abstract: Latent world models have become increasingly popular as a method to predict and plan in latent space rather than pixel space. Recent architectures, such as LeWorldModel (LeWM), jointly train the encoder and predictor using regularization techniques like SIGReg to prevent representation collapse. Even with such regularization preventing representation collapse, we identify a new world model failure mode of \textit{physical representation laziness}, particularly noted in highly dynamic environments. For these lazy cases, the learned latent states do not collapse but nonetheless fail to represent key physical properties, causing ubiquitous downstream planning failure. To resolve this issue, we propose training-time auxiliary supervision with a lightweight "Fourier auxiliary head", which enforce...

• 2.ProToMEx: Rapid, Interpretable Explanations via Structured Representations

  arXiv:2609.04265v1 Announce Type: new Abstract: Existing post-hoc explainers for machine learning classifiers primarily focus on feature attribution, assigning importance scores to individual features. While valuable, this approach struggles to articulate the complex, combinatorial patterns that often drive a model's decision-making process. To overcome this limitation, we introduce ProToMEx, a new paradigm for explainability that leverages Probabilistic Topic Models (PTMs). Our model-agnostic framework learns latent ''topics'' that represent distinct, high-level reasons for a classification, moving beyond simple feature importance to reveal underlying semantic structures. ProToMEx naturally provides both global explanations of a model's overall behaviour and local explanations that can disentangle multiple co-existing reasons for a speci...

• 3.A Data Fusion Framework for Grounding Aerospace Surrogate Model via Experimental Wind-Tunnel Observations

  arXiv:2609.04267v1 Announce Type: new Abstract: Aerodynamic surrogate models trained on high-fidelity CFD data reproduce numerical predictions of both scalar outputs and entire fields accurately, yet their predictive fidelity is limited by systematic discrepancies between CFD and experimental observations. We present an experimentally grounded correction framework that adapts a CFD-trained deep learning surrogate using wind-tunnel PSP measurements. A Geotransolver surrogate trained on 2,300 high-fidelity CFD simulations of the NASA CRM wing-body configuration, spanning geometric variation, Mach 0.70-0.85, and angles of attack 0 to 4 degrees, reproduces the CFD integrated aerodynamic forces and pitching moment to R2 > 0.99 but does not match the experimental data. To incorporate experimental information without retraining the surrogate, a ...

• 4.Quantum-Assisted Memory-Efficient Training for Parameter-Intensive Wi-Fi-Based Human Activity Recognition

  arXiv:2609.04271v1 Announce Type: new Abstract: Wi-Fi-based human activity recognition (HAR) has become an important part of integrated sensing and communications, paving the way for a range of context-aware services. However, most existing Wi-Fi-based HAR systems rely on deep learning (DL) models that are computationally and memory intensive in both training and inference, which poses significant challenges for real-world deployment. Conventional training requires simultaneous updates of millions of parameters, leading to prohibitive memory consumption. In this paper, we propose a novel quantum-assisted memory-efficient training framework (Q-MET) designed to improve efficiency in both training and inference. Q-MET utilizes a hybrid quantum classical neural network to indirectly generate parameters for HAR models, significantly reducing t...

• 5.Evaluating Large Language Models for Forced Outage Risk Prediction: Benefits and Comparison to Machine Learning

  arXiv:2609.04272v1 Announce Type: new Abstract: This study examines the ability of large language models (LLMs) to predict the risk of weather-related forced outages in the distribution grid in a zero-shot framework, without labeled training data. The problem is formulated as a binary severity classification task across three forecast horizons (3h, 6h, 12h), using six years of outage records and high-resolution weather data for a utility service area in central Texas. Four zero-shot LLMs are benchmarked against two supervised classifiers across two input configurations: one using current weather observations and the other using weather forecast data. Results show that supervised models outperform LLMs on macro-F1 and precision, while newer LLM generations achieve competitive scores. Beyond accuracy, LLMs offer complementary strengths in a...

• 1.FailureSpot: Label-Efficient Timestamp-Level Failure Detection for Vision-Language-Action Models

  arXiv:2609.04277v1 Announce Type: new Abstract: Vision-language-action (VLA) policies have shown strong potential for general-purpose robotic manipulation, but they can still fail unpredictably during long-horizon execution, making reliable failure detection essential for safe deployment. Existing methods either rely on visual models that typically detect failures only after erroneous actions have occurred, or use lightweight proactive detectors trained on VLA internal representations. However, these proactive methods are often supervised with trajectory-level labels, causing normal pre-failure behavior in unsuccessful trajectories to be incorrectly labeled as failure. This supervision mismatch introduces label noise and limits both trajectory-level detection accuracy and precise timestamp-level failure localization. In this work, we stud...

• 2.VLA-Precision: Asymmetric Co-Bootstrapping for Efficient Real-World Online RL of Vision-Language-Action Models

  arXiv:2609.04355v1 Announce Type: new Abstract: Pretrained vision-language-action (VLA) models enable broad manipulation but remain unreliable in tasks demanding precision and repeatability. Applying real-world online reinforcement learning (RL) to VLA post-training enables autonomous trial-and-error improvement beyond demonstrations alone, but exposes two bottlenecks: 1) unreliable value signals can induce policy drift; 2) large-VLA overhead constrains throughput and sample efficiency. To address these challenges, we present VLA-Precision, an efficient real-world online RL framework featuring the Asymmetric Co-Bootstrapping (ACoB) algorithm and the ACoB-Stream architecture. Specifically, ACoB establishes asymmetric co-bootstrapping across timescales: early intervention-guided behavioral learning rapidly improves policy performance while ...

• 3.Scalable Edge-assisted Fusion and Path Prediction for Connected Autonomous Vehicles

  arXiv:2609.04364v1 Announce Type: new Abstract: The planning algorithms inside an Autonomous Vehicle (AV) rely on information from on-board sensors whose line of sight is limited by emerging traffic conditions and occlusions. Edge-assisted creation of a unified world model fusing information from AVs and Road Side Units (RSUs) in a geographical locale, and the prediction of AVs' future trajectories, can enhance the planning algorithms inside AVs to improve quality metrics, such as better traffic flow and collision prevention. AVs participating in such enhancements are called Connected Autonomous Vehicles (CAVs). However, such information generated by the edge (world model and motion predictions) must reach the planners within a tight Age of Information (AoI) time budget to be useful. The state of the art fuses per-CAV information: each AV...

• 4.AquaBEV: Monocular Underwater BEV Occupancy with 3D Sonar Supervision

  arXiv:2609.04411v1 Announce Type: new Abstract: Autonomous underwater robots are widely used for exploration, monitoring, and inspection, where safe navigation depends on understanding the surrounding free and occupied space. Bird's eye view (BEV) occupancy provides such a representation, but predicting it from a single underwater RGB image is difficult due to limited, unreliable geometric cues from appearance alone. 3D imaging sonar offers complementary geometric measurements to supervise this task. We introduce AquaBEV, a monocular underwater occupancy model that predicts local BEV occupancy from a single RGB image, using paired 3D imaging sonar as geometric supervision during training. AquaBEV maps visual features into a calibration free polar representation and applies causal decoding along the range dimension before reconstructing th...

• 5.Achieving Asymptotic Near-Optimality Without $\delta$-Similarity

  arXiv:2609.04464v1 Announce Type: new Abstract: Sampling-based motion planning algorithms are a popular class of trajectory planning algorithm due to their speed in complex, high-dimensional environments and ability to handle kinodynamic constraints, specifically through the use of forward dynamics propagation. Many such planners claim to achieve asymptotic near-optimality by proving the almost sure sampling of trajectories that are close to an optimal trajectory in the state space, known as $\delta$-similar trajectories. This paper shows that the proof behind asymptotic $\delta$-similarity relies on an unstated assumption that $\delta$-similar trajectory segments will always be kept once sampled. This assumption does not hold in general. A problematic case, referred to as ``crowding out,'' is described, where locally low-cost paths preve...

• 1.Artificial Intelligence in Equity and Crypto Markets: Progress, Profitability Evidence, and the Limits of Automated Investing

  Artificial intelligence (AI) now supports investment workflows from data and prediction through research, portfolios, execution, and tool use. Technical capability, however, is not evidence of investment profitability. This critical state-of-the-art review examines public research available through 31 August 2026 on listed equities, exchange-traded funds, centralized crypto spot, perpetual futures, and on-chain markets. We organize evidence with an alpha-translation chain: point-in-time information must yield a stable signal, feasible positions, executable orders, and risk-adjusted returns after costs. Across machine learning, time-series foundation models, financial language models, reinforcement learning, and agents, the examined record shows real but mainly upstream progress in prediction, text processing, portfolio design, and workflo...

• 2.Optimal Stratified Allocation for Rare-Event Onset Forecasting in Dependent Sequences

  Let a finite population of n labelled examples carry a class-weighted loss, with pi*n in a rare positive class weighted by N0/N1. We study estimation of total risk from a subsample K << n under designs allocating K0 and K1 draws to the two strata. We derive the exact finite-population variance of the weighted risk estimator under class-conditional sampling without replacement and solve for the optimal allocation. The class multiplier inflates positive-stratum dispersion by the imbalance ratio, causing that ratio to cancel from the optimal allocation and making equal, rather than proportional, allocation the natural default. Simple random sampling is dominated by an explicit between-stratum term; an exact bias identity shows that cluster-representative selection has no general unbiasedness guarantee; and a Serfling bound transfers the al...

• 3.Scaling Laws, Tabular Data and Actuarial Ratemaking Models

  Scaling laws in modern deep learning describe how held-out loss improves as model capacity, training data, and compute increase, often following power-law trends. We investigate whether analogous scaling regularities arise in actuarial ratemaking, where data are tabular, heterogeneous, and noisy, and where classical models such as GLMs remain strong baselines. Using a real-world motor insurance portfolio, we train models from different families across increasing fractions of the training data and multiple random seeds, evaluating out-of-sample Poisson deviance, a likelihood-based loss for Poisson count predictions in which lower values indicate better held-out fit. We find that all model families improve with additional data, but scaling exponents differ substantially: TabM exhibits markedly stronger data scaling than purely supervised ta...

• 4.Eliciting ESG Preferences for Reinforcement Learning-Based Portfolio Optimization

  Modern portfolio management increasingly demands a balance between traditional risk-adjusted returns and strict Environmental, Social, and Governance (ESG) mandates. Current Reinforcement Learning (RL) approaches typically optimize for a single ESG provider, neglecting the significant divergence in rating methodologies across the industry and the unintuitive nature of manually weighting conflicting objectives. This paper addresses these limitations by formulating ESG-aware portfolio optimization as a Multi-Objective Reinforcement Learning (MORL) problem that simultaneously incorporates ratings from three distinct ESG agencies. To bridge the gap between high-dimensional algorithmic trade-offs and human decision-making, we integrate a Preference Elicitation framework using Gaussian Processes. This system enables practitioners to infer their...

• 5.Agentic Empirical Asset Pricing: Methodological Foundations

  Recent advances in LLM agents enable a new paradigm for asset pricing, which we call Agentic Empirical Asset Pricing (AEAP): systems that autonomously conduct the scientific discovery process itself. We define AEAP and identify its core building blocks. Existing evaluation practices backtest only the outputs (factors or trades), not the autonomous discovery system that produced them. We focus on factor discovery, contributing a reference architecture, a rigorous evaluation standard for discovered factors, and a method for out-of-sample backtesting the discovery system. As a concrete instance of that architecture, we evaluate SEADS against five re-implemented baselines on two US equity panels using this standard: no single metric ranks the systems consistently, motivating evaluation on multiple axes at once. A separate rolling re-execution...

• 1.Mobile ecosystem ready to maximise use of 6 GHz as GSMA urges policymakers to unlock spectrum for mobile

  Summary available at source link.

• 2.Scaling satellite connectivity: why interoperability matters for NTN’s next phase

  Summary available at source link.

• 3.GSMA MWC26 Doha to co-locate alongside the ITU Plenipotentiary Conference in November 2026

  Summary available at source link.

• 4.80% of Malawians Remain Offline Despite Coverage – New GSMA Report Highlights Path to Inclusive Digital Access and MWK 1.1 Trillion Growth

  Summary available at source link.

• 5.GSMA Industry Services Launches Circularity Services to Help Operators Reduce E-Waste and Unlock Value

  Summary available at source link.

• 1.Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

  Large language models (LLMs) are increasingly evaluated on molecular property benchmarks, but accuracy cannot distinguish a model that predicts a property from one that retrieves a published number. We audit 22 frontier models on 12 regression benchmarks for verbatim retrieval and find that it is widespread but relatively benchmark-specific: on five datasets more than $50\%$ of the LLMs show verbatim retrieval, while on the remaining datasets it appears only in isolated cells. We run our experiments at two reasoning levels and find that reasoning changes retrieval. The same experiments, on the same molecules and with the same prompt, are flagged $89\%$ more often at the higher reasoning level than at the lowest one. Finally, we test a way to interrupt retrieval in our most contaminated cases, and find that the strongest models in some cas...

• 2.Distill Globally, Adapt Locally: Reasoning Distillation and Product-Type Test-Time Training for Scalable Trade-Up Recommendation

  Trade-up recommendation identifies higher-quality alternatives that preserve a customer's purchase intent while offering upgraded benefits. Large language models (LLMs) can reason about such distinctions, but applying them directly to hundreds of millions of product pairs is operationally impractical. We introduce a two-level framework that distills LLM reasoning into an efficient non-generative student and adapts its decision boundary to product-type-specific trade-up criteria. At Level 1, a retrieval-augmented few-shot LLM teacher generates structured relation labels and natural-language rationales. These rationales supervise a compact embedding-pair classifier through alignment and contrastive objectives; at inference, the student uses only two precomputed 768-dimensional product embeddings, with no LLM calls or text generation. On a f...

• 3.GUT: Quantifying and Optimizing the Reasoning Uncertainty of LLMs via Graph Complexity

  Recent years have witnessed great advances in the reasoning ability of Large Language Models (LLMs). However, the reasoning processes of LLMs often exhibit uncertainty, where LLMs often produce a proliferation of divergent branches at each reasoning step even when fed the same prompting inputs, and certain branches exhibit evidently incredible, even nonsensical, reasoning chains and results. In this paper, we propose the Graph-complexity-based UncerTainty (GUT) method for investigating the reasoning uncertainty of LLMs. The key idea of GUT is to characterize the potential branches of each reasoning chain with a directed acyclic graph, thereby ensuring that all potential branches are comprehensively covered within the graph space. Building upon this recognition, we further build two modules of GUT, that is, a Quantification (GUT-Q) module ...

• 4.First Things First: Teaching LLM-Based Agents to Prioritize Must-Haves before Nice-to-Haves

  Recent progress in multimodal large language models (MLLMs) has fueled significant enthusiasm in their potential to act as autonomous agents for real-world tasks. However, scenarios requiring agents to fulfill users' complex, structured requirements remain largely underexplored. In this work, we examine reasoning tasks under three distinct requirement scenarios: (i) Must-have requirements uniquely determine a unique feasible solution; (ii) Multiple answers satisfy the must-have requirements and are prioritized via the nice-to-have requirements; and (iii) No candidate solution satisfies the must-have requirements, in which case the agent should abstain from generating a response. We evaluate state-of-the-art MLLMs on 3,649 carefully constructed problems that reflect realistic service scenarios, including e-commerce, booking, and map-based ...

• 5.A Verifier-Guided Explainable Reasoning Framework with Gold-Anchored QLoRA, Task-Aware Mixture-of-Experts, and Group-Relative RLVR

  Large language models (LLMs) show strong reasoning ability, but their explanations can remain inconsistent, weakly grounded, or difficult to verify. We propose a verifier-guided explainable reasoning framework for transparent educational question answering that combines gold-anchored QLoRA, task-aware symbolic routing, and group-relative RLVR. Qwen2.5-3B-Instruct is first adapted with field-weighted QLoRA supervision anchored to authoritative answers. A lightweight router then assigns logic problems to a FOL/Z3 verifier and physics problems to a formula- and unit aware symbolic solver. Verifier feedback is further used to support candidate evaluation, self-revision, and reward construction during RLVR. Candidate responses are evaluated along three complementary dimensions: P1 for answer correctness, P2 for evidence or unit consistency, an...

• 1.Measured Sliders: Learning Continuous Controls from Differentiable Image Measurements

  Continuous sliders are useful only when coefficient changes produce predictable image changes. Yet most diffusion sliders derive their axes from text or learned representations, leaving their scales disconnected from observable image properties. Consequently, we cannot tell in advance which attributes are learnable, compare control strengths directly, or anticipate interference when multiple controls are combined. We propose Measured Sliders, a framework that defines continuous controls through closed-form differentiable image measurements. A common measurement space unifies the pipeline. Before training, an observability test identifies usable supervision. During training, a measurement-guided objective learns target movement while suppressing non-target changes. After training, decoded calibration expresses controls in comparable units ...

• 2.A Human-in-the-Loop Framework for AI-Assisted Scoring in Large-Scale Writing Assessment

  The integration of artificial intelligence (AI), particularly large language models (LLMs), into educational assessment has opened new opportunities to enhance the efficiency and scalability of grading processes. This study presents the design and validation of an AI-assisted scoring framework for written responses in a large-scale national assessment. The proposed approach focuses on short written texts of approximately 150-200 words and incorporates a human-in-the-loop strategy to preserve assessment quality while reducing manual workload. The study is grounded in a real operational context, using data from two recent editions of a nationwide test, each comprising approximately 5,000 student responses. We analyze the alignment between AI-generated scores and human raters across multiple rubric dimensions, as well as the impact of the pr...

• 3.Beyond Co-purchase Relation: Evolution of Complementary Recommendations at Allegro

  When a customer adds a professional camera to their cart, should the system suggest a matching lens, a generic tripod, or another camera body? Complementary Product Recommendation is vital for comprehensive basket building, yet standard models often fail to distinguish between items that are merely bought together and those that truly work together. In this paper, we present AlleCompanion: a production-scale retrieval framework deployed at Allegro.com that transforms noisy behavioural signals into precise semantic compatibility. We mitigate the intrinsic noise in large-scale co-purchase traffic by combining data-level filtering heuristics with a category-constrained Two Tower architecture. Within this framework, the Category Adapter guides the model in the embedding space, constraining candidates within logically complementary boundaries....

• 4.Efficient Multi-Timescale Event Representations for Feed-Forward Object Detection

  Autonomous systems require robust low-latency perception under rapidly changing scene dynamics and challenging illumination. In event cameras object detection commonly relies on recurrent architectures to accumulate sparse temporal information over time. This work investigates how temporal information can be encoded directly within the event representation. We propose a confidence-normalized continuous multi-timescale representation based on logarithmic B-spline temporal encoding together with a geometry-aware local confidence mechanism that exploits the spatial structure of event generation. Using a fixed feed-forward EventCenterNet detector, we show that the proposed representations consistently outperform the compact CSTR representation on PEDRo and Gen1 datasets. We further introduce a recursive exponential-polynomial approximation th...

• 5.Sound-based Multi-Person 3D Pose Estimation

  Can we recover the 3D poses of multiple people using only sound? This paper presents the first attempt to estimate multi-person 3D poses solely from acoustic signals. Estimating the poses of multiple individuals using acoustic signals is inherently challenging due to the superposition of motion-dependent signal variations. Unlike single-person scenarios, the presence of multiple subjects leads to overlapping acoustic signatures, making it difficult to attribute specific signal changes to an individual's pose. Furthermore, the complexity is compounded by inter-person reflections, which introduce intricate propagation delays that obscure the temporal motion-acoustic relationship. To address these issues, we propose SoundMHPE (Sound-based Multi-person Human Pose Estimator), a novel encoder-decoder framework consisting of two key components. ...

• 1.AI Efficiency Could Cost Us the Next Generation of Experts

  A little over a decade ago, I led the controls design for a first-of-its-kind full digital-control system for a U.S. nuclear plant. It was, on paper, a beautiful machine—engineered to run itself the way a modern airliner does, with operators watching over a system that rarely needed them. And we made a decision that, to an efficiency-minded observer, looked backward: We deliberately left manual steps inside sequences the system could execute on its own. We were solving a specific problem. An operator who only ever supervises automation slowly stops being an operator. The hands go cold. The mental model of what the plant is actually doing gets fuzzy. Then comes the day the automation hands control back. It’s always the worst day, because automation only quits when it’s confused or in trouble. But by then, you have a person in the chair who...

• 2.Cash In on the AI Boom by Renting Out Your Spare Compute

  If you own an at-home server, a gaming computer, or just a laptop that doesn’t get much love, listen up. You can now put that spare computing power to use and earn some passive income in the process. AI companies are hungry for more compute to run AI inference—the process of using a pretrained model to respond to queries—and they’re willing to pay you for it. “Imagine Uber or Airbnb, but for AI-inference computing tasks,” says Ilman Shazhaev , founder and CEO of Far Labs , based in Abu Dhabi. The AI boom has spurred on the construction of massive data centers , often damaging local communities by raising electricity prices, straining local water resources, causing environmental damage and noise, and being just plain ugly. Huge data centers are likely not going anywhere—training new frontier models and running AI models from leading compan...

• 3.New Platform Peers Inside AI’s Black Box

  Prompt Claude, ChatGPT, Gemini, or any other popular large language model with a question like “What is the best film ever made?” and the response will vary. And you (and most worryingly, the people who built the LLM) have little idea exactly how it came up with that specific answer. This mysterious behavior can be useful in some situations. But—as highlighted by a recent incident where OpenAI could not explain why its advanced prerelease model hacked AI company Hugging Face—it can have negative and alarming consequences too. And when frontier AI models are writing code, generating results humans could not achieve alone, and performing other important tasks across society, the need to interpret AI “thinking” and outputs has never been greater. Goodfire , an AI lab focused solely on this very problem, recently made its cutting-edge Silico ...

• 4.AI Companion Robots Are Closing the Human Connection in Modern Homes

  This article is brought to you by Ollobot . From about 2017, individuals began to truly connect with the initial wave of companion robots. These devices had personality, moved around, joked, and answered when you spoke to them. Most early companion robots, however, were still limited by simple voice-command interactions and narrow functionality. Once the novelty wore off, many ended up sitting unused on shelves. As some of those companies went out of business and turned off their servers, many owners likened it to losing a pet. What Ollobot describes as “gentle intelligence” is a useful way to think about where the serious work in this category is going. Not toward more powerful assistants, but toward more present ones. The problem companion robots were trying to solve Loneliness is not a niche issue. According to one study , nearly one o...

• 5.Self-Driving Cars Could Someday Take Requests

  This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore. The idea of letting a machine do the driving for you may put a lot of people off autonomous vehicles . But research could make it possible to backseat-drive an autonomous vehicle just as you might with a human driver. Self-driving cars carefully balance a host of parameters to ensure a smooth ride, including things like speed, acceleration, and the smoothness of turns. But human driving preferences can often vary depending on how much of a rush they’re in, whether they’re feeling carsick, or how busy the traffic is. These cars have a software component called the motion planner, which is responsible for choosing a safe and efficient path through traffic. The motion planner is normally tuned by engineers before the vehicles hit the road so that...

• 1.Monday assorted links

  1. How are age verification systems going? 2. Eliezer predictions. 3. Are humpback whales policing nature? 4. “Traces of nearly four million coins reveal how Rome achieved economic integration.” 5. Estimating cybersecurity costs (numbers). 6. Great Barrier Reef is doing fine? 7. California Forever meets further setbacks (NYT).

  The post Monday assorted links appeared first on Marginal REVOLUTION.

• 2.Capital gains vs. wealth taxes

  Standard optimal capital tax theory abstracts from modeling asset prices, making it unsuitable for thinking about capital gains and wealth taxation. We study optimal redistributive taxation in an environment with asset price movements, adopting the modern finance view that asset prices fluctuate not only because of changing cash flows, but also due to other factors […]

  The post Capital gains vs. wealth taxes appeared first on Marginal REVOLUTION.

• 3.Emergent Ventures winners, 59th cohort

  Tym Syrytczyk, London, autonomous vehicles in the UK. Shane Regan, Long Island, 16, AI agents. Maximilian Kornstein, 15, Atlanta area, agents and general career support. Irene Chen and Jessica Dai, UC Berkeley, data on peptides use. Evan Warfel, Bay Area, updated meta-analyses through AI. Daniel Dominguez Gomez, biomedical think tank for Mexico. Malhaar Agrawal, U […]

  The post Emergent Ventures winners, 59th cohort appeared first on Marginal REVOLUTION.

• 4.Insurance price sentences to ponder

  NYU Stern researcher @NateWitkin questions why cyber insurance rates keep falling if AI cyber risk is accelerating: “Insurance rates for cyber risk declined by about 4% globally in Q2 of this year, and that’s actually the 12th consecutive quarter in which they’ve declined. This is very valuable signal that implies that at a minimum you […]

  The post Insurance price sentences to ponder appeared first on Marginal REVOLUTION.

• 5.Sunday assorted links

  1. Vishy now thinks Pragg is number one in the chess world. 2. Do children grow continuously, or grow in fits and starts? 3. It remains my view that Ferrante is the husband and wife team. 4. Essay on Olaf Stapledon. 5. “US annual interest expense is up to a record 18.5% of federal government […]

  The post Sunday assorted links appeared first on Marginal REVOLUTION.

• 1.Jackson Hole: Exploring the Financial Frontier

  After two days of heady discussions with academics and policymakers from around the world in high-altitude Jackson Hole, Wyoming, I’m now decompressing back at Street Level. The main theme of this year’s Kansas City Fed symposium was financial innovation in the payments space—a fast-evolving topic with major implications for consumers and central bankers alike. In this post, I’ll share some general takeaways from those Jackson Hole talks and highlight related New York Fed research on payments and financial intermediation.

• 2.Are Central Banks Moving Out of Dollar Assets?

  The dollar’s share of global official foreign exchange reserves fell from 64 percent in 2015 to 56 percent in 2025. This downward trajectory is sometimes read as evidence that the dollar’s role in international financial markets is eroding. However, aggregate statistics obscure the composition of changes occurring at the country level. In this post, we show that the aggregate decline is not a systematic global shift away from dollar assets. Rather, the aggregate decline reflects the actions of a handful of large reserve holders, changing either their currency preferences or the size of their reserve portfolio. From the perspective of the cross section of countries holding dollar assets, the dollar’s status in official portfolios is largely intact.

• 3.Businesses Are Using AI to Transform Work, Not Cut Jobs

  The ongoing advancement and adoption of artificial intelligence continues to raise concerns about widespread job losses. Over the past three years, our regional business surveys have asked firms about their AI adoption and its effects on their workforces. This year, we found that AI use among regional businesses has continued to rise sharply, with more than 60 percent of service firms and about half of manufacturers now using AI—a notable increase from 40 percent and 26 percent, respectively, reported in 2025. Despite this rapid adoption, regional firms’ investments in AI are generally modest, usage tends to be concentrated among a small share of workers within firms, and layoffs have remained uncommon. And, while some firms have scaled back hiring due to AI, others have added workers to help them use it. Retraining employees in response ...

• 4.Has Broader Stock Market Participation Changed How Interest Rates Affect the Economy?

  Stock market participation in the U.S. has changed dramatically over the past four decades. In the mid-1980s, fewer than 30 percent of households held equity. By the early 2000s, more than half of U.S. households owned equity, either directly or through mutual funds, 401(k)s, and IRAs. As participation widened, the way stock market fluctuations passed through to household spending may have changed, with potential implications for how the broader economy behaves. An argument can be made that the rise in equity market participation has dampened the response of output to interest rate changes as stock market fluctuations are now spread across a larger share of households, moderating movements in consumer spending, asset prices, and investment spending.

• 5.Does the Equity Term Structure Respond to Monetary Policy Shocks? 

  A long-standing body of research, inspired by Bernanke and Kuttner (2005), has documented the effects of Fed interest rate surprises on stock markets. While stock markets provide valuable information about the investor risk premium and dividend growth expectations, researchers have only recently developed more comprehensive tools to estimate the term structure of equity risk premia and dividend growth expectations across a broad range of maturities. In this post, we investigate the impact of monetary policy surprises (or shocks) on short- and long-term estimates of risk premia and growth expectations through the lens of the Giglio, Kelly, and Kozak (2024) model.

• 1.The Digital Wellness Industry Is Hijacking Women’s Health

  Menopause and perimenopause, long under-researched and dismissed, have become a growth industry for online advertisers and wellness companies. Mining intimate data, this lucrative digital ecosystem exploits medical uncertainty to sell women hormones and supplements they don't need, or that can even harm them.

• 2.Who Controls the Data Controls the Future

  While AI is becoming increasingly central to finance, defense, health care, and public administration, it depends on resources most governments do not control: the data used to train it and the compute that processes it. Unless this changes, countries will remain subject to decisions made in boardrooms they cannot influence.

• 3.Will Europe Be as Unprepared as Ukraine Was?

  With Russia ramping up its hybrid war, Europe must choose. It can invest in its own defense, accept the trade-offs involved, and build credible deterrence, or it can continue to underspend, hope that diplomacy will suffice, assume that US protection will always be available, and tell itself that the war in Ukraine is a local affair.

• 4.The Case for a Cashflow Tax

  In the context of the US economy, a cashflow tax would offer far-reaching benefits in promoting economic growth, taxing rents, and raising incremental revenue efficiently. Moreover, well-known concerns about complexity and fairness are easily addressed.

• 5.Kevin Warsh’s Best Move Is to Do Nothing

  Beholden to mainstream economic ideas, the new US Federal Reserve chair has committed himself, and his reputation, to a goal he cannot achieve. Reining in inflation requires not interest-rate adjustments but much larger strategic shifts that are not within the Fed’s power to influence.

• 1.Microsoft and AWS launch a managed multicloud interconnect

  Multicloud interconnect is free in preview, built for distributed AI workloads In sum – what we know: Microsoft and AWS have jointly launched a managed multicloud interconnect designed to create…

• 2.Private 5G docks in Hamburg – as Deutsche Telekom and Ericsson scratch a seven-year itch

  Deutsche Telekom and Ericsson have deployed a private 5G network at Hamburg’s Container Terminal Altenwerder, the latest chapter in a seven-year cellular experiment that shows how industrial 5G is a…

• 3.India weighs rip-and-replace of legacy Chinese telecoms equipment

  Rémy Pascal, practice leader, mobile infrastructure and RAN lead analyst at Omdia, told RCR that Chinese equipment currently deployed in telecom networks in India has been operating for several years,…

• 4.SK Telecom’s Topdda turns work vans into an AI inspection fleet

  AI cameras on ordinary vans turn daily technician routes into wireline inspection In sum – what we know: SK Telecom has commercialized “Topdda,” an AI-powered inspection system that turns the…

• 5.Ciena sees AI driving multi-year optical infrastructure investment cycle

  AI is triggering a multiyear re-architecture of network infrastructure, says Ciena, driving higher bandwidth demand and making optical connectivity increasingly critical from long-haul networks to data center fabrics, with its…

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• 1.Towards Federated, Green, and Resilient 6G Non-Terrestrial Networks

  This study focuses on future Non-Terrestrial Networks (NTN) integrated with Terrestrial Networks (TN) for future 5G/6G systems. NTN envisions a 3D architecture, where Low Earth Orbit (LEO) satellite networks will play a key role in bridging the digital divide, complementing the gradual terrestrial 5G/6G rollout concentrated in high-density and high-traffic areas, by ensuring service continuity across broad geographic regions and providing coverage in case of emergencies or in remote areas. In this context, we address networking issues for the integration and federation of Terrestrial and Non-Terrestrial Network (T-NTN) in line with the IMT-2030 vision, focusing on interoperability, spectrum coexistence, unified control and management, and service continuity. Federation is a complementary approach to integration that enables distinct satel...

• 2.Hierarchical Codebook Design and Low-Overhead Beam Training for Near-Field Communications With Uniform Circular Arrays

  Extremely large-scale multiple-input multiple-output (XL-MIMO) enables near-field location-specific beam focusing for sixth-generation (6G) communications. Uniform circular arrays (UCAs), with rotational symmetry and uniform azimuth coverage, have emerged as a key enabling architecture for near-field XL-MIMO systems. In this paper, we propose a resolution-aware hierarchical codebook for near-field UCA systems, along with an efficient two-stage beam training scheme to significantly reduce the training overhead. Specifically, we characterize the minimum resolvable distance of UCA systems in the near-field region based on a geometric spherical-wave propagation model, revealing their spatial resolution capability in the joint angle--distance domain. Guided by this result, we design a UCA-specific hierarchical codebook, where a power-efficient...

• 3.Hierarchical Beam Training and Codebook Design for Movable Antenna-Assisted Near-Field Systems

  As sixth-generation (6G) communication systems evolve toward higher frequency bands and larger array apertures, the near-field range expands rapidly, making near-field channel estimation increasingly important and challenging. Beam training has been recognized as an effective approach for channel state information (CSI) acquisition. However, because of the propagation characteristics of spherical waves, beam training needs to perform a joint search in the angle and distance domains, which results in unaffordable beam training overhead. By flexibly reconfiguring antenna positions, movable antenna (MA) technology can fully exploit the spatial variations of wireless channels and achieve more accurate beam focusing, thereby providing additional flexibility for efficient beam training design. Therefore, based on MA-assisted near-field systems,...

• 4.Closing the Semantic-Edge Gap: Tiny Language Models for 6G Wireless Intelligence

  Sixth-generation (6G) wireless networks are envisioned as AI-native systems in which semantic communication - transmitting task-relevant meaning rather than raw bits - moves beyond Shannon's classical bit-pipe model. Large language models (LLMs) dominate semantic encoding but are unsuitable for 6G user equipment and IoT devices, given prohibitive memory, energy, and latency costs. Tiny language models (TinyLMs) - compressed via TinyML techniques into kilobyte-to-megabyte memory and milliwatt power budgets - are the missing bridge between LLM-level semantic encoding and 6G edge hardware, yet no prior work systematically maps TinyML techniques onto semantic communication architectures for this purpose. This survey closes that gap through a two-axis taxonomy connecting six compression families (quantization, pruning, knowledge distillation, ...

• 5.Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis

  Root cause analysis (RCA) is a critical task in telecom network operations, but diagnosing performance degradations in modern 5G and emerging 6G networks remains challenging due to complex cross-layer dependencies. While large language models (LLMs) offer promising capabilities for reasoning and knowledge integration, directly applying vanilla LLMs to telecom RCA often leads to hallucination, unstable reasoning, and poor alignment with structured network evidence. This work first reviews the evolution of telecom RCA from rule-based and machine learning (ML) approaches to emerging LLM-enabled techniques, and provides an overview of recent paradigms, including structured reasoning, retrieval-augmented knowledge grounding, agentic orchestration, and verifiable reasoning. Building upon these insights, we propose a structured reasoning framewo...

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• 1.Quantity, Risk, and Return

  We propose a new model of expected stock returns that incorporates quantity information from market trading activities into the factor pricing framework. We posit that the expected return of a stock is determined by not only its factor risk exposures (beta) but also the factor's quantity fluctuations (q) induced by trading flows, and hence term the model beta times quantity (BTQ). The rationale is that sophisticated investors should demand a higher factor premium when they have absorbed noise trading flows of stocks with high loadings to that factor. The BTQ model provides a compelling risk-based explanation for stock returns, which is otherwise obscured without considering the quantity information. The cross-sectional risk-return association, which is nearly flat unconditionally, strongly depends on the quantity variable. The structured ...

• 2.Optimal Stratified Allocation for Rare-Event Onset Forecasting in Dependent Sequences

  Let a finite population of n labelled examples carry a class-weighted loss, with pi*n in a rare positive class weighted by N0/N1. We study estimation of total risk from a subsample K << n under designs allocating K0 and K1 draws to the two strata. We derive the exact finite-population variance of the weighted risk estimator under class-conditional sampling without replacement and solve for the optimal allocation. The class multiplier inflates positive-stratum dispersion by the imbalance ratio, causing that ratio to cancel from the optimal allocation and making equal, rather than proportional, allocation the natural default. Simple random sampling is dominated by an explicit between-stratum term; an exact bias identity shows that cluster-representative selection has no general unbiasedness guarantee; and a Serfling bound transfers ...

• 3.Tempting the Agent: The Economics of Reputation without Persistent Identity in AI Agent Markets

  Reputation is a fundamental mechanism through which markets sustain trust when service quality cannot be perfectly assessed ex ante, constituting a form of intertemporal economic capital by attracting future demand. Its effectiveness as a disciplinary mechanism depends not only on past interactions but also on the persistence of the identity to which reputation is attached. When identities can be abandoned and recreated cheaply, reputational capital may itself become an object of opportunistic exploitation. This paper develops a dynamic economic framework to study when reputation is sufficient to discipline autonomous agents. We model reputation as capital attracting future economic activity. At each point, an agent chooses between operating honestly, investing in quality to preserve future gains, or executing a one-shot deviation to ex...

• 4.Modeling Trade Durations under Temporal Granularity Effects in Forex Markets

  Trade durations in high-frequency foreign exchange data exhibit increased occurrence near integer values. To address this empirical phenomenon, we propose the granularity-adjusted autoregressive conditional duration (GA-ACD) model. It is based on a novel two-component mixture distribution consisting of a standard generalized gamma component for regular durations and a second component that locally redistributes probability mass around integer values to capture heaping. Conditional dynamics are modeled within a score-driven framework, allowing the scale parameter to vary over time in response to past durations, and enabling maximum likelihood estimation of all model parameters. A simulation study shows that ignoring heaping leads to biased parameter estimates and distorted inference regarding both the distribution and the dynamics of durat...

• 5.Agentic Empirical Asset Pricing: Methodological Foundations

  Recent advances in LLM agents enable a new paradigm for asset pricing, which we call Agentic Empirical Asset Pricing (AEAP): systems that autonomously conduct the scientific discovery process itself. We define AEAP and identify its core building blocks. Existing evaluation practices backtest only the outputs (factors or trades), not the autonomous discovery system that produced them. We focus on factor discovery, contributing a reference architecture, a rigorous evaluation standard for discovered factors, and a method for out-of-sample backtesting the discovery system. As a concrete instance of that architecture, we evaluate SEADS against five re-implemented baselines on two US equity panels using this standard: no single metric ranks the systems consistently, motivating evaluation on multiple axes at once. A separate rolling re-execution...

• 1.Towards Federated, Green, and Resilient 6G Non-Terrestrial Networks

  This study focuses on future Non-Terrestrial Networks (NTN) integrated with Terrestrial Networks (TN) for future 5G/6G systems. NTN envisions a 3D architecture, where Low Earth Orbit (LEO) satellite networks will play a key role in bridging the digital divide, complementing the gradual terrestrial 5G/6G rollout concentrated in high-density and high-traffic areas, by ensuring service continuity across broad geographic regions and providing coverage in case of emergencies or in remote areas. In this context, we address networking issues for the integration and federation of Terrestrial and Non-Terrestrial Network (T-NTN) in line with the IMT-2030 vision, focusing on interoperability, spectrum coexistence, unified control and management, and service continuity. Federation is a complementary approach to integration that enables distinct satel...

• 2.Hierarchical Codebook Design and Low-Overhead Beam Training for Near-Field Communications With Uniform Circular Arrays

  Extremely large-scale multiple-input multiple-output (XL-MIMO) enables near-field location-specific beam focusing for sixth-generation (6G) communications. Uniform circular arrays (UCAs), with rotational symmetry and uniform azimuth coverage, have emerged as a key enabling architecture for near-field XL-MIMO systems. In this paper, we propose a resolution-aware hierarchical codebook for near-field UCA systems, along with an efficient two-stage beam training scheme to significantly reduce the training overhead. Specifically, we characterize the minimum resolvable distance of UCA systems in the near-field region based on a geometric spherical-wave propagation model, revealing their spatial resolution capability in the joint angle--distance domain. Guided by this result, we design a UCA-specific hierarchical codebook, where a power-efficient...

• 3.Hierarchical Beam Training and Codebook Design for Movable Antenna-Assisted Near-Field Systems

  As sixth-generation (6G) communication systems evolve toward higher frequency bands and larger array apertures, the near-field range expands rapidly, making near-field channel estimation increasingly important and challenging. Beam training has been recognized as an effective approach for channel state information (CSI) acquisition. However, because of the propagation characteristics of spherical waves, beam training needs to perform a joint search in the angle and distance domains, which results in unaffordable beam training overhead. By flexibly reconfiguring antenna positions, movable antenna (MA) technology can fully exploit the spatial variations of wireless channels and achieve more accurate beam focusing, thereby providing additional flexibility for efficient beam training design. Therefore, based on MA-assisted near-field systems,...

• 4.Closing the Semantic-Edge Gap: Tiny Language Models for 6G Wireless Intelligence

  Sixth-generation (6G) wireless networks are envisioned as AI-native systems in which semantic communication - transmitting task-relevant meaning rather than raw bits - moves beyond Shannon's classical bit-pipe model. Large language models (LLMs) dominate semantic encoding but are unsuitable for 6G user equipment and IoT devices, given prohibitive memory, energy, and latency costs. Tiny language models (TinyLMs) - compressed via TinyML techniques into kilobyte-to-megabyte memory and milliwatt power budgets - are the missing bridge between LLM-level semantic encoding and 6G edge hardware, yet no prior work systematically maps TinyML techniques onto semantic communication architectures for this purpose. This survey closes that gap through a two-axis taxonomy connecting six compression families (quantization, pruning, knowledge distillation, ...

• 5.Bringing dApps to OCUDU: An E3 Controller for Real-Time Open RAN Intelligence

  Real-time control loops in Open RAN are increasingly colocated with the gNB, where dApps, i.e., programmable applications with sub-millisecond access to PHY- and MAC-layer signals, enable latency-critical use cases such as spectrum sharing, channel-aware scheduling, and integrated sensing. What turns dApps from a per-vendor mechanism into a portable component of the emerging AI-RAN ecosystem is the E3 interface: this interface defines how a dApp subscribes to RAN telemetry, receives indications, and issues control actions back to the RAN. To date, E3 has been implemented on OpenAirInterface and on NVIDIA Aerial; OCUDU, i.e., the Linux Foundation's open-source CU/DU project, has lacked a comparable E3 path. We close that gap with an open-source E3Controller for OCUDU, designed as a sidecar daemon so the E3 protocol stack, service-model log...

• 1.Towards Federated, Green, and Resilient 6G Non-Terrestrial Networks

  This study focuses on future Non-Terrestrial Networks (NTN) integrated with Terrestrial Networks (TN) for future 5G/6G systems. NTN envisions a 3D architecture, where Low Earth Orbit (LEO) satellite networks will play a key role in bridging the digital divide, complementing the gradual terrestrial 5G/6G rollout concentrated in high-density and high-traffic areas, by ensuring service continuity across broad geographic regions and providing coverage in case of emergencies or in remote areas. In this context, we address networking issues for the integration and federation of Terrestrial and Non-Terrestrial Network (T-NTN) in line with the IMT-2030 vision, focusing on interoperability, spectrum coexistence, unified control and management, and service continuity. Federation is a complementary approach to integration that enables distinct satel...

• 2.Integrating Wi-Fi into 3GPP 5G Network Slicing: An Experimental Prototype Study

  Network Slicing (NS) is a fundamental pillar of 5G and beyond networks, enabling the provisioning of isolated, logical networks tailored to specific Quality of Service (QoS) requirements. While 3GPP standards comprehensively define slicing architectures over cellular access networks, the seamless integration of Non-3GPP technologies such as Wi-Fi into a unified slice instance remains an active area of investigation, particularly regarding empirical validation. This paper presents an end-to-end prototyping study that integrates 5G Standalone (SA) and Wi-Fi networks by adapting the Trusted Non-3GPP Gateway Function (TNGF) to extend NS to WLAN networks, enabling the unified management of Wi-Fi transmission resources. We implement a functional testbed leveraging an open-source 5G Core and an explicit Non-3GPP access to validate multi-Radio Ac...

• 3.Connectivity of HAPS-Based Solutions for Large-Scale Wireless Networks: A Percolation Theory Analysis

  In the era of sixth-generation (6G) wireless communication, numerous applications are expected to be realized, including environmental monitoring, smart agriculture, remote education, security protection, and intelligent transportation systems. These scenarios require large-scale, continuous Internet services in forests, rivers, oceans, and road networks, to name a few, where optical cables are difficult to deploy. High-altitude platform stations (HAPSs) emerge as a promising solution, offering low-latency, high-capacity services while facilitating the establishment of vertical heterogeneous networks (vHetNets) in fiber-less areas. This article investigates three HAPS-based solutions, where HAPSs can serve wireless devices directly or via gateway (GW) networks: the HAPS-to-device (H2D) scheme, the HAPS-to-GW-to-device (H2G2D) scheme, and ...

• 4.Privacy Leakage in Federated Learning: Gradient-Based Client Identity Inference and Defenses for Inertial Sensing in Vehicular Edge Networks

  As vehicular networks move toward 5G/6G edge intelligence, federated learning (FL) is widely promoted as a privacy-preserving way for vehicles and infrastructure to train shared models without exposing raw sensor data. Yet the updates clients transmit still leak enough information to identify who sent them, which threatens the anonymity that safety-critical V2X applications assume and adds to existing concerns over adversarial ML, model poisoning, and backdoor attacks. We study server-side client identity inference from transmitted weight deltas using inertial (IMU) measurements, evaluated on the UCI Human Activity Recognition (HAR) benchmark as an accessible proxy for the IMU streams produced onboard connected vehicles. Across five attack classifiers and five non-IID partitions, an honest-but-curious server recovers client identity with ...

• 5.Will there be a 7G?

  The transition from 5G to 6G is becoming concrete: the ITU-R IMT-2030 framework has established the high-level vision and capability set for 6G, while 3GPP Release 21 has defined the path toward the first 6G specifications. This raises a deliberately provocative question for the research and standards communities: will there be a 7G, and if so, what would justify it? This paper argues that 7G should not be treated as an inevitable numbering exercise or as a catalogue of more ambitious radio targets. Instead, its justification should depend on whether post-6G systems introduce needs or coordination problems that cannot be met by 6G/6G-Advanced, Wi-Fi, NTN, private cellular, neutral-host deployments, edge-cloud platforms, or complementary wireless and software-based systems. To support this assessment, the paper develops a readiness framewo...