Key Takeaways

• The GSMA projects a US$1.4 trillion contribution from the mobile industry to the Asia Pacific economy by 2030. This figure underscores the critical role of digital trust, AI, and digital sovereignty in regional economic growth GSMA Report: Mobile Industry to Contribute US$….
• 80% of Malawians remain offline despite network coverage availability. A new GSMA report highlights that infrastructure alone is insufficient for inclusive digital access without addressing specific barriers to usage 80% of Malawians Remain Offline Despite Covera….
• AI safety must be stress-tested across Africa's diverse languages and contexts. New benchmarks are being developed to evaluate the robustness of large language models in local linguistic environments African Trust & Safety LLM Benchmark: Str….
• Machine learning frameworks are successfully deployed to detect money laundering in Rwandan mobile money systems. These tools address critical financial integrity issues in developing economies Detecting Money Laundering in Rwandan Mobile M….
• GSMA industry services have launched specific initiatives to help operators reduce electronic waste. These circularity services aim to unlock additional value through better resource management GSMA Industry Services Launches Circularity Se….

Executive Summary

The day's focus centers on the intersection of emerging 6G infrastructure challenges and immediate economic realities in developing markets. While strategic races for RF hardware design define the long-term horizon for future radio networks The Hidden 6G Bottleneck: RF Hardware Design I…, immediate concerns regarding digital inclusion persist, with significant portions of populations remaining offline despite coverage 80% of Malawians Remain Offline Despite Covera…. Concurrently, the telecommunications sector is adapting through circularity services designed to reduce e-waste GSMA Industry Services Launches Circularity Se…. On the artificial intelligence front, rigorous safety stress-testing is now mandatory across African languages and contexts African Trust & Safety LLM Benchmark: Str…, while new mechanisms aim to prevent bounded rationality errors in large language models Level-k Distinguishable Mechanisms for Evaluat…. Financial applications of AI continue to evolve, with machine learning frameworks detecting money laundering in mobile money ecosystems Detecting Money Laundering in Rwandan Mobile M… and adaptive forecasting methods analyzing public health data from Ontario Machine Learning and ARIMA Model Averaging for…. The narrative shifts away from hype toward measurable contributions, where the mobile industry's potential to bolster the Asia Pacific economy is quantified at US$1.4 trillion by 2030 GSMA Report: Mobile Industry to Contribute US$…, alongside critical examinations of occupational bias in language models regarding perceived competence Who Do Language Models Think Is Competent? A M….

AI Agent Safety and Evaluation

The evaluation landscape for autonomous systems has matured with the introduction of portable contracts for testing agent performance, ensuring that system-level intelligence does not compromise individual safety protocols The Evaluation Context Protocol (ECP): A Porta…. Mechanistic analyses now reveal how language models determine which occupations they perceive as competent, highlighting deep-seated occupational biases within their decision-making architectures Who Do Language Models Think Is Competent? A M…. To mitigate risks where vocabulary comprehension fails clinical reasoning, new frameworks are being applied to evaluate therapy bots specifically for the safety of Generation Alpha users When Vocabulary Comprehension Fails Clinical R…. These developments demonstrate a concerted industry effort to align advanced capabilities with rigorous safety standards before deployment in sensitive sectors like healthcare.

Africa's Digital Divide and Financial Integrity

Digital inclusion remains a paramount issue in Sub-Saharan Africa, where reports indicate that 80% of Malawians are still offline despite existing network coverage 80% of Malawians Remain Offline Despite Covera…. This disparity necessitates targeted strategies to bridge the gap between physical connectivity and actual digital access for millions. Parallel to these infrastructure challenges is the critical need for financial security, as machine learning frameworks have been successfully established to detect money laundering activities within Rwandan mobile money systems Detecting Money Laundering in Rwandan Mobile M…. Addressing both connectivity gaps and financial fraud simultaneously represents a dual-pronged approach to stabilizing the region's emerging digital economies.

Economic Impact and Sustainability

The macroeconomic potential of the telecommunications sector is being redefined through data-driven projections, with new research estimating that the mobile industry will contribute US$1.4 trillion to the Asia Pacific economy by 2030 GSMA Report: Mobile Industry to Contribute US$…. This growth trajectory relies heavily on pillars such as digital trust, AI integration, and network resilience. In parallel, sustainability concerns are being addressed directly through the launch of industry services aimed at helping operators reduce electronic waste and unlock latent value GSMA Industry Services Launches Circularity Se…. These circularity initiatives suggest that future profitability in telecom is increasingly tied to environmental stewardship and responsible resource management rather than单纯的 expansion.

Technical Innovations in Robotic Control

Advancements in robotic control systems are enabling more precise manipulation through models conditioned on temporal logic for vision, language, and action execution Logic-VLA: A Temporal Logic Conditioned Vision…. New techniques for endoscopic control utilize latent-conditioned rectified flows to disambiguate complex language inputs during medical procedures Koala Gripper: Co-designing Robotic Grippers a…. These developments are complemented by research into differentially flat fixed-wing aerial systems, which employ nonlinear model predictive control for accurate trajectory tracking Nonlinear Model Predictive Control for Traject…. Together, these innovations bridge the gap between theoretical AI models and physical dexterity in high-stakes environments.

Babak's Daily Briefing

Monday, August 24, 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.Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence

  LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities, Context Engineering to manage information access, Harness Engineering to organize external tools and resources, and Loop Engineering to support continual reflection and self-improvement. Yet as tasks grow more complex, individual intelligence faces a fundamental limit: many tasks require heterogeneous expertise, interdependent subtasks, parallel execution, independent verification, and persistent state, exceeding any single agent's organizational capacity. Augmenting one agent's capabilities or context cannot resolve this architectural mismatch; intelligence must instead be distributed across specialized agents and organized at the system le...

• 2.DART-SD: Diamond-topology Aware Retrieval and Tuning for Self-Distillation of Multi-Turn Tool-Calling Agents

  Equipping Large Language Models (LLMs) with multi-turn tool-calling capabilities is essential for building autonomous agents. However, progress is fundamentally limited by the reliance on full-length trajectory imitation. For tasks involving multiple order-independent sub-goals, the optimal solution space forms a vast combinatorial diamond lattice. Forcing this rich topology into monolithic trajectories causes a severe topological collapse, indiscriminately penalizing valid alternative explorations and severely degrading policy diversity. To address this, we propose DART-SD (Diamond-topology Aware Retrieval and Tuning for Self-Distillation), a novel framework that shifts the paradigm from global forcing to topology-guided localized correction. DART-SD first models the execution process as a converging Interaction-State Transition Graph (I...

• 3.The Evaluation Context Protocol (ECP): A Portable Contract for AI Agent Evaluation

  The evolution of artificial intelligence has necessitated a fundamental shift from evaluating isolated Large Language Models (LLMs) to assessing autonomous agentic architectures. This paper explores the critical methodologies for evaluating AI agents and the essential role of advanced observability infrastructure. We analyze the architectural components of agents and identify the severe limitations of current evaluation paradigms, including benchmark exploitation, the "confidently wrong" phenomenon, and the discrepancy between theoretical capability and operational reliability. To begin addressing the fragmentation in current evaluation infrastructure, this paper proposes the Evaluation Context Protocol (ECP), an early-stage, vendor-neutral framework intended to act as a portable evaluation contract layer for agentic systems. In its curre...

• 4.Topological Attribution Distance (TAD): Revealing Segment-Level RAG Influence on LLM Output Geometry for Incident Log Analysis

  Large Language Models (LLMs) are increasingly being deployed in cybersecurity operations to assist cybersecurity analysts with rapid decision-making against emerging threats. However, there is a main criteria that must be met when using LLMs in cybersecurity, that is, trust in the generated outputs. As Agentic AI is integrated into operational systems, a robust evidence attribution and provenance tracking technique is essential to trace the origins of model generations. When autonomous agents make a decision (right or wrong), the ability to trace back through the decision chain is critical, as without it, teams cannot identify which segment of the data caused the model generation. Existing methods often struggle to distinguish among complex and highly similar evidence sources, such as cyber incident logs. This reveals a key gap: current a...

• 5.The Role Specialization Model (RSM): Coordinating LLM-Based Tools in Agentic Software Development - An Exploratory Case Study

  The integration of large language models (LLMs) into software development workflows has given rise to a paradigm known as Agentic Software Engineering (SE 3.0), in which autonomous agents manage full development life cycles under human supervision. This paper presents an exploratory case study in which three LLM-based tools, Antigravity (an agentic IDE with a Gemini 2.5 backend), Gemini CLI, and Qwen Code (local execution via Ollama), are coordinated according to a role-distribution framework proposed in this work as the Role Specialization Model (RSM). Three research questions guide the study: (RQ1) how can LLM-based tools with distinct capabilities be coordinated through the RSM in a real development workflow; (RQ2) what deviations from the planned role distribution emerge during RSM execution and what factors explain them; and (RQ3) ho...

• 1.Beyond Raw Transcripts: Structured Persona Extraction for LLM-Based Digital Twins

  arXiv:2608.20344v1 Announce Type: new Abstract: LLM-based "digital twins" aim to simulate how an individual would behavein new environments or respond to novel questions, given some representation of that individual's prior responses. A common approach constructs this representation from survey transcripts or summaries responses. Prior work shows that compressing long transcripts into shorter LLM-generated summaries does not significantly reduce predictive accuracy, suggesting that information volume is not the primary bottleneck. In this work, we argue that the key limitation is instead structural:how persona information is organized before being provided to thesimulator model. We study this by comparing unstructured summaries with structured persona representations. First, we introduce a hand-craftedschema (BDE: Background, Decision ...

• 2.When Vocabulary Comprehension Fails Clinical Reasoning: Evaluating Therapy Bots' Safety Risks for Generation Alpha

  arXiv:2608.20345v1 Announce Type: new Abstract: Conversational AI systems have become informal mental health support resources for Generation Alpha (Gen Alpha, born 2010-2024), with 13.1% of U.S. adolescents (5.4 million) using generative AI for mental health advice. While these systems, from therapy apps to general chatbots, rely on large language models trained on extensive psychological literature, their safety for youth communication patterns characterized by hyperbolic language, ironic positivity, rapid semantic drift, and contextual polysemy remains unvalidated. Following multiple adolescent deaths linked to AI chatbot interactions, systematic evaluation is critical. We present two benchmarks: (1) 64 Gen Alpha mental health expressions validated by native speakers (ICC=0.72) and clinicians (kappa=0.78); (2) 75 multi-turn conversati...

• 3.Building and Evaluating a Synthetic Bengali Speech Resource for Telecom Customer Care

  arXiv:2608.20346v1 Announce Type: new Abstract: Speech systems used in customer-facing applications often require domain-specific language coverage. We present a synthetic Bengali speech dataset for telecom customer-care scenarios. The dataset contains 10,000 audio-text pairs, approximately 26.82 hours of 24 kHz speech, and predefined train, validation, and test splits of 9,000, 500, and 500 examples. It is publicly released on Hugging Face under the CC-BY-4.0 license. The speech was generated with OmniVoice in voice-cloning mode using a real female reference recording and transcript, with bfloat16 precision, 16 diffusion sampling steps, and a speaking-rate control value of 1.0. Along with the original Bengali text, the dataset provides a normalized transcript field designed for ASR/STT training and evaluation. We report an automatic int...

• 4.Who Do Language Models Think Is Competent? A Mechanistic Analysis of Occupational Bias

  arXiv:2608.20347v1 Announce Type: new Abstract: Language models (LMs) often pass behavioral bias evaluations, but it remains unclear whether they no longer represent the underlying associations that give rise to biases, or have merely learned not to express them. In this study, we show that representational biases are often detectable, even when behavioral biases are not visible. We introduce a causal framework that decomposes occupational bias into two measurement points: a model's internal representation of a user's competence, and its observable outputs. We derive steering vectors for representations of user expertise, and verify that they causally mediate model behavior in both a question-answering task and a hiring task. Applying this framework to several open-weight models, we find that demographic attributes, such as gender, race,...

• 5.Inhibitory Attention for Clinical Long-Context Reasoning: Characterizing and Mitigating Lost-in-the-Middle Effects in EHR Processing

  arXiv:2608.20348v1 Announce Type: new Abstract: Electronic health records now routinely exceed 100,000 tokens per patient. Yet large language models exhibit the lost-in-the-middle (LitM) effect: information near the center of a long context is retrieved less reliably than information near the edges. In clinical use this is not benign: the single most consequential fact in a note can sit at its center. We term this the clinical lost-in-the-middle (CLitM) problem, give its first systematic characterization using MedAlign, and compare context-selection strategies as remedies. Across 2,196 instruction-response pairs and six language models, we observe a 21.9 percentage-point gap between peak accuracy (59.5%, 95% CI [46.3, 71.0], 20-30% decile) and trough accuracy (37.6% [23.2, 52.5] at 70-80%); 67.8% of reference answers fall between the 10t...

• 1.Bankruptcy Prediction via Hybrid Resampling and Stacking Ensemble Techniques with Explainable Artificial Intelligence (XAI)-Driven Analysis

  arXiv:2608.20343v1 Announce Type: new Abstract: This study develops and evaluates a bankruptcy prediction framework that integrates consensus-based feature selection, hybrid resampling, stacking ensembles, and explainable artificial intelligence to improve minority-class detection in severely imbalanced financial data. Using the Taiwanese Bankruptcy Prediction dataset from the UCI Machine Learning Repository, five feature-selection algorithms were first applied, and a consensus retention rule reduced the input space to 23 robust variables. The balanced training data were then generated using SVM-SMOTE, SMOTE-Tomek, and SMOTE-ENN. Five ensemble machine learning classifiers, namely gradient boosting, extreme gradient boosting, histogram-based gradient boosting, LightGBM, and AdaBoost, were compared with five deep learning models, including ...

• 2.Machine Learning and ARIMA Model Averaging for Adaptive Public Health Forecasting: Comparative Evaluation and an Ontario COVID-19 Case Study

  arXiv:2608.20406v1 Announce Type: new Abstract: Public health forecasts must respond to abrupt changes in surveillance data without over-extrapolating noise, reporting artifacts, or temporary trends. We evaluated autoregressive integrated moving average (ARIMA), random forest, and extreme gradient boosting (XGBoost) models using 190 weekly observations of publicly available Ontario COVID-19 case counts from January 2020 to October 2023. Rolling-origin time-series cross-validation preserved temporal order during model tuning and evaluation. Performance was assessed across three operating dimensions: responsiveness following selected turning points, forecast horizons of one to six weeks, and the amount of historical training data. We also developed Machine Learning and ARIMA Model Averaging (MLAMA), a non-negative performance-weighted ensem...

• 3.From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensing

  arXiv:2608.20423v1 Announce Type: new Abstract: Personalised thermal comfort is essential for occupant wellbeing and for the development of more responsive building-control strategies, yet conventional Heating, Ventilation, and Air Conditioning (HVAC) systems rely on static setpoints and population-level comfort models that fail to capture individual physiological variability. This paper presents a two-stage personalised thermal comfort approach integrating multimodal physiological and environmental sensing with reinforcement learning-based decision-making.

• 4.BF1: A Causal Dyadic Sparse-Attention Retrofit for Efficient Long-Context Transformers

  arXiv:2608.20427v1 Announce Type: new Abstract: Dense causal attention remains expensive at long context even when implemented with highly optimized exact kernels. We study BF1, a deterministic block-aligned dyadic sparse-attention route that combines a small exact local neighborhood, a global first block, and logarithmically spaced historical blocks. The route is related to prior log-sparse and dilated attention patterns; our contribution is a correctness-gated pretrained-model retrofit, a matched topology-control study, and a systems characterization that connects per-layer sparsity to whole-model latency. For fixed block width, every converted layer uses O(n log n) selected token interactions and has O(log n) graph communication depth. On an NVIDIA RTX PRO 6000 Blackwell GPU, an optimized BF16 implementation crosses dense attention bet...

• 5.Approximate Homomorphisms and Convergent Representations in Transducers

  arXiv:2608.20428v1 Announce Type: new Abstract: We study the stability of minimal representations of controlled stochastic processes (in particular, transducers) under perturbations. This question is motivated by recent experiments finding predictive-state structure in the latent representations of neural networks. We consider standard, linear and predictive transducers. We introduce notions of approximate homomorphism capturing local structural similarity between them, together with metrics comparing their induced dynamics (which we refer to as interfaces), and prove properties such as composability of the approximate homomorphisms. For standard transducers, we show that there exist simple interfaces for which there is no approximate homomorphism between the different implementations of the dynamics. In contrast, for every finite-rank in...

• 1.Humanoid Musical Robots as Experimental Interfaces for Music-Evoked Emotion

  arXiv:2608.20433v1 Announce Type: new Abstract: Advances in technology have led to increasingly sophisticated musical humanoid robots. However, their use has largely been limited to performance and related research in human-robot interaction. In this position paper, we propose a novel perspective: musical humanoid robots as experimental interfaces for investigating music-evoked emotions. We argue that current research is constrained by paradigms relying on pre-recorded auditory stimuli, which fail to capture the multimodal, embodied, and interactive nature of real-world musical experience. Building on existing theories of music cognition and emotion, we identify mechanisms that require controlled manipulation of both acoustic and non-acoustic variables. We show that humanoid robots are well-suited as they enable parametric control of perf...

• 2.EndoLIFT: Language-Disambiguated Latent-Conditioned Rectified Flow for Bidirectional Endoscopic Control

  arXiv:2608.20478v1 Announce Type: new Abstract: Routine gastrointestinal endoscopy is intrinsically bidirectional: the instrument is advanced to reach target anatomy and later withdrawn or retroflexed for inspection, while an external cue may require earlier reversal. When the requested phase changes before the visual scene does, nearly identical observations can require opposite axial actions. We identify and formalize this ambiguity in bidirectional endoscopic control as intent aliasing. We propose EndoLIFT (Endoscopic Language-Instruction Flow with Trajectory Latents), a vision-language-action policy that combines explicit language-based intent conditioning with a latent-conditioned rectified-flow action expert. The policy receives RGB, a language instruction, and the previous-action state; a 32-D variational trajectory latent stochast...

• 3.Koala Gripper: Co-designing Robotic Grippers and Data-Capture Devices for Scaling Dexterous Manipulation Learning

  arXiv:2608.20546v1 Announce Type: new Abstract: As the demand for larger manipulation datasets grows, handheld robotic gripper data collection and the associated gripper designs become more vital. Current data collection device designs trend towards matching the morphologies of existing robotic grippers, sacrificing ergonomics and manipulation performance. In this paper, we propose a co-design framework that guides the simultaneous development of both data collection and robotic execution devices by weaving both platform constraints into the design process. Through this workflow, we present the Koala Gripper system, a data capture device and robotic gripper platform that improves dexterity and grasp capability compared to parallel jaw grippers while preserving scalability and ease-of-use. The design introduces a novel force-optimized fing...

• 4.Logic-VLA: A Temporal Logic Conditioned Vision-Language-Action Model

  arXiv:2608.20556v1 Announce Type: new Abstract: Vision-language-action (VLA) models can follow natural-language (NL) task instructions, but such instructions may not precisely specify safety-critical or spatiotemporal requirements on the resulting behavior. We introduce Logic-VLA, a formal-requirement-aware VLA that conditions on Signal Temporal Logic (STL) specifications supplied at inference time. Logic-VLA uses a syntax-graph-based STL encoder pre-trained to capture temporal logic semantics. Policy adaptation proceeds in two stages: STL-conditioned supervised fine-tuning on satisfying demonstrations is followed by trajectory-level preference optimization over matched satisfying-violating rollout pairs using a flow-matching surrogate for Identity Preference Optimization. This formulation improves formal requirement satisfaction while pr...

• 5.Nonlinear Model Predictive Control for Trajectory Tracking of Differentially Flat Fixed-Wing Aerial Systems

  arXiv:2608.20655v1 Announce Type: new Abstract: Planning and control of fixed-wing Unmanned Aerial Vehicles (UAVs) are challenging due to nonlinear dynamics, aerodynamic limits, and environmental disturbances. Differential flatness offers a principled way to generate fast, feasible trajectories, but its use has largely been confined to model-free controllers, which lack predictive capabilities and demand tuning. In this paper, we propose a unified framework that integrates differential flatness-based trajectory generation with Nonlinear Model Predictive Control (NMPC), combining computationally efficient planning with predictive, constraint-aware control. To further improve robustness, we introduce a wind-aware sampling strategy embedded within the NMPC framework, enabling the generation of dynamically feasible reference trajectories that...

• 1.Deep-MKV-TS: Path-Dependent McKean--Vlasov Control for Financial Time Series Generation

  We introduce Deep-MKV-TS, a path-dependent McKean-Vlasov framework for financial scenario generation. The stochastic dynamics are chosen by matching selected path and volatility features of generated scenarios to those observed in the data. Starting from an interpretable reference model, Deep-MKV-TS preserves the reference drift and adjusts its volatility, while a regularization penalty limits unnecessary departures from the calibrated dynamics. We solve the resulting control problem using a neural, sample-based implementation of the stochastic maximum principle. We validate the method against an exactly computable oracle. On Heston and Heston-mixture models, Deep-MKV-TS substantially reduces path-dependent and volatility-related deficiencies of the reference model. In delayed-volatility experiments, the correction remains effective as ...

• 2.Concentrated Liquidity Provision: a Reinforcement Learning Perspective

  Automated market makers (AMMs) are a cornerstone of decentralised finance (DeFi). Constant product markets with concentrated liquidity, such as UniswapV3, are now a well-established design. In these markets, liquidity providers (LPs) face a sequential decision problem: they must decide when to rebalance their positions and which price ranges to allocate capital to as market conditions evolve. We formulate dynamic liquidity provision as a stochastic impulse control problem and use reinforcement learning (RL) to solve it, focusing on providing interpretable solutions. We show that learned policies exhibit rich state-dependent behaviour, allocating liquidity according to mispricing, rebalancing costs, uncertainty, inventory exposure, and heterogeneous risk preferences. These behaviours help compress the left tail of the Profit and Loss (PnL)...

• 3.zLend: A Dual-Scope Cash-Flow Reconstruction Framework for On-Chain Credit Underwriting

  Decentralized lending lacks a credit bureau: a borrower's capacity to repay must be inferred entirely from public on-chain activity, without income verification or a liability record. This paper presents zLend, a deployed cash-flow underwriting framework that reconstructs a wallet's daily balance history from raw token transfers and derives short-duration repayment-capacity signals from it. The reconstruction is performed twice per wallet, once restricted to a fixed stablecoin basket and once over all fungible transfers, on the premise that a wallet's total token holdings and its liquid, spendable balance are distinct quantities whose conflation misprices risk. From each series we derive liquidity coverage against a fixed loan size, cash-flow volatility and regularity, a drawdown-and-recovery statistic adapted from quantitative finance, a...

• 4.Self-Supervised Auxiliary Task Discovery for Stable Reinforcement Learning in Stock Trading

  Reinforcement learning has gained increasing attention as a data-driven approach for stock trading. However, learning a policy that is both profitable and stable remains challenging due to non-stationary market behaviour and noisy reward signals. Auxiliary tasks are often used to improve representation learning and stabilize training, yet they are usually designed manually and depend heavily on prior assumptions about targets and prediction horizons. Such fixed designs may not remain suitable across changing market regimes. In this work, we propose a self-supervised framework that automatically discovers auxiliary tasks to support reinforcement learning for stock trading. The auxiliary tasks are formulated as General Value Functions so that their predictions enrich the learned state representation and assist policy optimization. The frame...

• 5.Detecting Money Laundering in Rwandan Mobile Money: A Machine Learning Framework

  Mobile money has widened financial access across Sub-Saharan Africa and enlarged the surface for money-laundering and terrorism-financing (ML/TF) activity in ecosystems dominated by high-volume, low-value transactions. Rwanda is a case in point: several million active mobile-money users, telecom-led wallets on the MTN and Airtel networks, and a Financial Intelligence Centre (FIC) supervising transaction streams whose scale exceeds static rule-based monitoring. This paper develops and evaluates a transaction-monitoring framework aligned to the Rwandan AML/CFT regime under (i) extreme class imbalance (~0.1% prevalence), (ii) scarce and delayed labels, and (iii) bounded investigator capacity. Using SAML-D, a synthetic dataset of 9,504,852 transactions with 17 laundering typologies, we engineer account-centric behavioural features (rolling ve...

• 1.80% 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.

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

  Summary available at source link.

• 3.GSMA Report: Mobile Industry to Contribute US$1.4 Trillion to Asia Pacific Economy by 2030 as Digital Trust, AI, Digital Sovereignty and Resilience Take Centre Stage at M360 ASEAN

  Summary available at source link.

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

  Summary available at source link.

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

  Summary available at source link.

• 1.Level-k Distinguishable Mechanisms for Evaluating Bounded Rationality in LLMs

  Strategic depth of reasoning is essential for human interaction of Large Language Models (LLMs) operating in boundedly rational environments. However, existing evaluations are primarily based on canonical games prevalent in pretraining corpora, making it difficult to disentangle true strategic reasoning from memorisation. To address this, we formalise a necessary level-K distinguishability condition for strategic depth inference and construct a suite of novel game structures that meet this standard. Using these games, we evaluate strategic depth in LLMs from both the Chain-of-Thought tokens and actual actions under recursive reasoning and an inductive trace of opponent game-play data. Across experimental trials spanning four LLMs, four game structures, and ten levels of iterated reasoning, we find that model models maintain accurate strat...

• 2.Memory Augmentation Unlocks Efficient Chain-of-Thought Reasoning

  Large language models often rely on Chain-of-Thought (CoT) reasoning to solve complex tasks, but verbose reasoning traces introduce substantial inference overhead. CoT compression shortens generation, yet aggressive compression may disrupt logical coherence and degrade performance. We formalize this trade-off as the \textit{Context-Generation Substitution Law}, where explicit reasoning context substitutes for part of decode-time generation. Based on this principle, we propose \textit{Memory-Augmented Compression}, a training-free framework that constructs reusable reasoning memories from historical traces and retrieves them as prefill-side scaffolds. Rather than using raw demonstrations, these memories summarize reusable reasoning patterns, key constraints, and critical operations to compensate for information lost during compression. Exp...

• 3.ReFrame: Evidence-Guided Test-Time Safety Alignment in Multimodal Large Language Models

  While multimodal large language models (MLLMs) extend model capabilities beyond text, they also make safety alignment increasingly challenging. Multimodal safety alignment methods must address cross-modal jailbreaks, safety-awareness failures, and over-sensitive refusals. However, existing methods often rely on retraining or internal-state inspection, limiting their applicability to deployed closed-source MLLMs and motivating test-time safety alignment. We analyze this setting and identify two key obstacles, utility dominance and reasoning inertia, which cause models to overlook latent risks or follow malicious reasoning trajectories. Guided by these insights, we propose ReFrame, a training-free multimodal input reframing framework where two agents share a lightweight locally deployed MLLM: the evidence-generation agent constructs complem...

• 4.COMET: Contrastive Motion-Enhanced Temporal Reasoning for Video Multimodal Large Language Models

  Video multimodal large language models have advanced significantly, yet fine-grained motion-temporal understanding remains fragile. The core bottleneck is not only sparse frame sampling, but also the lack of a complete temporal modeling pipeline for explicitly representing frame-to-frame change, enabling appearance-motion interaction, and optimizing temporal direction sensitivity. We propose COMET, a temporally grounded framework that systematically strengthens video MLLMs through explicit temporal representation, appearance-motion fusion, and direction-aware optimization. Architecturally, COMET introduces a temporal motion branch built on Taylor frame differences and injects its motion evidence into the appearance stream via temporal attention bias-enhanced cross-attention. For optimization, COMET combines temporal prior distillation wit...

• 5.Recognition-Conditioned Reasoning: A Training-Free Multimodal-LLM Pipeline for Fine-Grained Micro-Action Understanding

  Micro-actions are subtle, short, low-amplitude body movements, such as a fidgeting hand or a slight head tilt, that humans perform with little conscious intent yet that reliably leak emotional and psychological state. Understanding them goes beyond assigning a label: a model must also describe which body parts move and reason, faithfully, about why a clip warrants a particular fine-grained category. We present the training-free, prompt-only system that won first place in the fine-grained understanding track (MA-Bench) of the MAC~2026 Micro-Action Challenge, where both fine-tuning and ground-truth supervision are disallowed. Built entirely upon frozen multimodal large language models (MLLMs), the system dynamically routes each of the eight sub-tasks to the MLLM empirically best suited for that task: a discriminative MLLM for closed-ended r...

• 1.Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence

  LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities, Context Engineering to manage information access, Harness Engineering to organize external tools and resources, and Loop Engineering to support continual reflection and self-improvement. Yet as tasks grow more complex, individual intelligence faces a fundamental limit: many tasks require heterogeneous expertise, interdependent subtasks, parallel execution, independent verification, and persistent state, exceeding any single agent's organizational capacity. Augmenting one agent's capabilities or context cannot resolve this architectural mismatch; intelligence must instead be distributed across specialized agents and organized at the system le...

• 2.DreamBench-SWE: A Multi-Session Memory-Hygiene Benchmark for Software Agents

  DreamBench-SWE is a multi-session benchmark for software-agent memory hygiene in which later software tasks depend on non-inferable evidence from earlier sessions and are scored by executable hidden oracles. We report the original scaled v2 fold and a separately preregistered v2.1 successor audit designed after that study but frozen before successor outcome inspection. The successor run completed 360/360 work units and 720/720 S3 cells across four conditions. In the original fold, the primary DF-hybrid--B5 contrast was null (95/180 versus 89/180; clustered p=.518, Holm p=1), not evidence of equivalence, and C9/C10 retained B0-headroom limitations. In the successor, no external memory achieved 21/180 passes (rate 0.1167), deterministic verbatim event memory 82/180 (rate 0.4556), the typed-plus-raw reference probe 83/180 (rate 0.4611), and ...

• 3.Interrupting the Loop: Periodic Subject Changes Raise Judged Surprise and Connection in Base Language Models

  Where does the novelty a base language model produces with no task come from, and what can an LLM judge of a long stream actually see? We dismantle a cognitively inspired generation loop over 24 conditions on three base models. Most of its effect lives in one operation: a new subject injected every few hundred tokens (an interruption) into a stream whose literal repetition is damped (habituation). We judge windows of generated text only, with the premise as the unit (n=10) and a judge measured for repeatability, against a second judge family and against human readers. Under that protocol the interruption raises judged surprise by 1.2 to 1.4 points and connection by 0.8 over habituation alone. A connective that asks for continuity hurts; a bare paragraph break adds nothing detectable on fresh text; a reset context does at least as well as ...

• 4.FAR-DPO: Feasibility-Aware and Robust Direct Preference Optimization for Cyclic Peptide Design

  Cyclic peptides are emerging as promising molecular scaffolds in drug discovery due to their high binding affinity and structural stability. However, extending generative models from linear to cyclic peptide design remains challenging, as cyclization sharply restricts the feasible design space through coupled geometric and biophysical constraints. Moreover, limited training data has led existing approaches to rely largely on zero-shot generation or post hoc filtering, resulting in low yields of feasible designs and limited control over multi-objective trade-offs. To address these limitations, we propose FAR-DPO (Feasibility-Aware and Robust Direct Preference Optimization), an architecture-agnostic framework that steers generative models toward structurally and biophysically feasible cyclic peptide designs, particularly for challenging tar...

• 5.ADAPT: Physics-Aware Diffusion-based World Models for Adaptive Predictive Transferable HVAC Control

  Buildings account for roughly one-third of global energy consumption and CO$_2$ emissions. Optimizing indoor climate systems plays a critical role for urban climate mitigation aligned with UN Sustainable Development Goals 11 and 13. However, indoor delayed thermodynamic responses and partial observability severely hinder existing methods, which are primarily limited by implicit thermal inertia, occupancy dynamic prediction, and cumulative prediction errors, especially for out-of-distribution environments. In practice, these challenges are further exacerbated by the high cost and privacy burden of dense indoor sensing, forcing operators to collect only limited data in a single operating regime while expecting controllers to generalize reliably across unseen seasons and climate regions. To address this problem, we propose ADAPT, a physics-a...

• 1.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...

• 2.What It Takes to Be an Adaptable Engineer

  The AI boom has disrupted the way engineers work, introducing new tools to learn, raising expectations for what teams can achieve in a workday, and making it harder to get hired in the first place. This makes it difficult to advise students on which specific coding languages or technical skills they should learn. So amidst the uncertainty, advice for young professionals often turns to a common refrain: Be adaptable. But what does adaptability look like in practice? Engineers often operate on the cutting edge of technology, so dealing with change is a normal part of the job, says Samantha Brunhaver , an associate professor of engineering at Arizona State University, in Tempe. Yet university curricula and training in the workplace often don’t prepare students for this. “We tell engineers that they need to be adaptable when they graduate, bu...

• 3.This IEEE Senior Member Develops AI Tools for E-Commerce Sites

  Balaji Ingole rarely saw televisions while growing up in Udgir, India. No one in the small Maharashtra village had computers or phones. Only one household owned a television, and neighbors often gathered there to watch shows together. Ingole never even saw a computer growing up. It wasn’t until he reached middle school that he encountered a computer lab, an experience he says changed his life. Almost immediately, he says, the machine felt like a window into a different scale of possibility for him. Balaji Ingole Employer Amla Commerce in Milwaukee Title Project manager Member grade Senior member Alma maters COEP Technological University and Welingkar Institute of Management, both in India “I was very studious and not very social, always reading or solving problems in a math textbook,” he says. “At the computer lab, I began learning the C ...

• 4.Stop Hunting, Start Solving: Accelerating Root Cause Analysis with Agentic AI

  About this Webinar Turn Yield Excursions into Faster, More Confident Root Cause Analysis When a yield issue emerges, the answer rarely lives in a single system. Critical clues are spread across metrology data, tool traces, chemical analysis, and facilities systems, while growing data volumes make traditional dashboards slow, fragmented, and difficult to act on. What You’ll Learn: Discover how a purpose-built semiconductor analytics platform can help engineers connect insights across domains without moving data. See how Agentic AI, semiconductor-specific visualizations, and push-down compute enable faster investigation of yield excursions and process issues, even across billions of data points. In the session, a live demonstration shows how to conduct a multi-domain root cause investigation using Spotfire® Industry Pro. Key Takeaways: Unde...

• 5.From AI Copilots to Agent Swarms

  The impact of AI on software development has been both profound and ever-evolving. Last year, I wrote about AMD’s plans to use AI not just for generating new lines of code, but also for other steps in the software development lifecycle (SDLC), such as triaging problems, debugging code, and testing the software. At the time, we were hoping for a 25 percent productivity boost from AI use over the course of two or three years. But with each new release, the capabilities of large language models (LLMs) improve dramatically—accelerating software development, increasing the quality of AI-generated code, and fundamentally reshaping how software is engineered. Now, just one year later, we have surpassed our productivity target, achieving a 30 percent overall productivity boost through AI. On top of that, we are rethinking not only how we use AI w...

• 1.Common sense in charge

  …children are increasingly seen as interfering with the freedom of parents; views on whether mothers of young children should work have become markedly more progressive and account for a substantially larger share of the decline among the tertiary-educated; and fewer people believe that women or men need children to lead a fulfilled life. This last […]

  The post Common sense in charge appeared first on Marginal REVOLUTION.

• 2.Monday assorted links

  1. There is now a formal 150 kph (93.2 mph) speed limit in one part of Czechia. 2. Why does the Japanese prime minister spend so much time in seclusion? 3. Decker defends academia (noting I do not think the “only the peaks” defense quite works.  I also worry about very high quality research that […]

  The post Monday assorted links appeared first on Marginal REVOLUTION.

• 3.Childhood Exposure to Joint Custody Reforms and Adult Family Formation

  Joint custody reforms are among the most consequential family-law changes for children, yet little is known about their long-term effects. Exploiting staggered adoption across US states and 13 million ACS observations, we show that childhood exposure reduces adult fertility by 7 percent, symmetrically for women and men and operates primarily through lower parenthood and couple […]

  The post Childhood Exposure to Joint Custody Reforms and Adult Family Formation appeared first on Marginal REVOLUTION.

• 4.I DJ for Rick Rubin about music and patriotism

  MUSIC EPISODE: “You think you understand patriotism, you have your hands around it–it’s the simple thing, but it turns out it’s not. And maybe, music and the history of music shows us all that better than mere discourse about it.” @tylercowen 0:00:00 Tyler Cowen 0:00:24 Intro: Patriotism in Music 0:03:09 John Philip Sousa & Stars […]

  The post I DJ for Rick Rubin about music and patriotism appeared first on Marginal REVOLUTION.

• 5.Sunday assorted links

  1. “Underestimating something that has an AI “accent” by reflexively dismissing it as slop rhymes curiously with underestimating someone because they have a funny accent in your native language (and it isn’t one of the accents that you think signals superiority, like the BBC British accent for English).”  V. Rao. 2. Profile of Joe Lonsdale.  […]

  The post Sunday assorted links appeared first on Marginal REVOLUTION.

• 1.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.

• 2.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.

• 3.How Distressed Are Consumers? Reconciling Diverging Credit Card Delinquency Measures

  Total debt balances declined slightly by $13 billion in the second quarter of 2026, according to the latest Quarterly Report on Household Debt and Credit from the New York Fed’s Center for Microeconomic Data. Mortgage and student loan balances saw a small decline, while there were increases across other debt products. Delinquency rates across most products remained fairly stable. Still, between 2022:Q3 and 2026:Q1, the percentage of credit card balances 90+ days delinquent rose from 7.6 percent to 12.8 percent, prompting concerns that Americans are falling behind on their debt payments at ...

• 4.Stripping STRIPs Trading Activity

  In March 2020, the Financial Industry Regulatory Authority (FINRA) began reporting aggregate trading volume for securities issued by the U.S. Treasury Department. The public data do not, however, include information about the trading activity of Separate Trading of Registered Interest and Principal of Securities (STRIPS). STRIPS are created from existing Treasury securities and offer risk […]

• 5.Why Do Fewer Renters Expect to Move?

  Americans are moving less than they used to. Moving rates have declined steadily for decades, falling from close to 20 percent annually in the mid-1980s to below 10 percent by 2019. This decline has persisted through business cycles and has been evident across all regions, and has affected a broad range of demographic groups. Falling mobility matters because moving helps households access job opportunities, adjust to changing circumstances, and improve their housing situations. In this post, we show that the decline in mobility also holds for renters, with growing challenges to owning a home being an important contributing factor.  We use data from the annual ...

• 1.The Age of Gated Recessions

  Aggregate economic indicators are obscuring a widening divide between those enjoying rapid income growth and those facing deteriorating job prospects. The rise of AI could deepen this disparity, with certain regions and workers experiencing recession-like conditions while others enjoy boom times.

• 2.The AI Curse

  Every sanction that ordinary citizens have ever possessed—the ability to withhold labor, taxes, sons, or votes—presupposes someone who needs what you can withhold. As AI infrastructure encroaches on Americans, they are learning the lesson of many resource-rich states: where there is no need, there is no democracy.

• 3.Where Will the Global Bond Rout Hit Growth and Equities?

  In the US, the recent rise in bond yields is largely driven by structural factors like the end of the post-2008 Great Stagnation, higher potential growth from tech-industry investments, and greater private-sector demand for credit. Higher yields are more worrisome for advanced economies that are stagnating and innovating less.

• 4.Trump’s Korean Catastrophe

  Donald Trump seems not to appreciate that deterrence depends on the ability to mount a credible defense. This makes the Korean Peninsula a more dangerous place, not least because 28,000 US troops are stationed in South Korea, which the US is obliged by treaty to defend.

• 5.The Dollar’s Outer Defenses Have Been Breached

  The puzzle of why the Trump administration is so concerned with a falling yen has historical parallels not to the 1930s or the 1980s, but to the 1960s, when US officials feared that a crisis elsewhere could spread to America. With US bond yields spiking, Treasury Secretary Scott Bessent is getting desperate.

• 1.Mimosa Webinar – Build the Right Network

  Broadband expansion is rarely a choice between one technology and another. The right approach depends on where you’re building, who you’re connecting, what infrastructure is already in place, and what…

• 2.ZTE’s computing business drives H1 growth as carrier spending weakens

  ZTE said its position in China’s network equipment market remained stable, while computing-related opportunities linked to the construction of intelligent computing resource pools provided an additional source of growth In…

• 3.Planning for next: Black swans during an AI frenzy

  The AI supercycle is turbocharging investment as companies in virtually every sector accelerate their plans. Moving fast is an imperative when the entire market is enthusiastically pursuing its AI futures.…

• 4.AI infrastructure drives Keysight test demand

  Keysight reported fiscal third-quarter revenue of $1.846 billion, up 36% year over year, while orders rose 56% to $2.91 billion In sum – what to know: AI drives test intensity…

• 5.KT unveils NPU LLM Station, a fully on-premise sovereign AI server

  Korean chip, model, and platform in one box, with no cloud access needed In sum – what we know: KT has officially unveiled the “KT NPU LLM Station” — a…

• 1.Source Error

  Check Feed

• 1.Privacy-Preserving Localization via Transmit Antenna Selection and Permutation

  Integrated sensing and communication (ISAC) has been identified as one primary usage scenario in the sixth-generation (6G) network. While techniques to preserve information privacy, such as cryptography, have been widely investigated, how to preserve sensing privacy is still an open problem in the literature. This paper makes an early attempt to tackle the above issue. Specifically, we consider a localization system consisting of a multi-antenna transmitter, termed Alice, a single-antenna legitimate receiver, termed Bob, and a single-antenna illegitimate receiver, termed Eve. To allow Bob to estimate Alice's angle-of-departure (AOD) but prevent Eve from performing this task based on Alice's signals, this paper proposes a novel antenna selection and permutation based transmission strategy for Alice. Under this scheme, Alice carefully selec...

• 2.$Z^2$-ACT: End-to-End Verifiable Agentic Intent Control for Open 6G RAN

  With the progression in open and disaggregated 6G radio access networks, it is expected that the system will be able to host multi-vendors. In order to host multi-vendors, it is essential that AI-assisted control loops remain safe, verifiable, and auditable under concurrent operator intents and untrusted model inputs. The existing studies address the agentic coordination, formal intent constraints, zero-trust prompt verification and cryptographic accountability in isolation, which leaves pre-realization safety, continuous semantic verification and cross-domain audit incomplete when used individually. In this regard, we propose zero-knowledge auditable control and zero-trust verifiable agentic intent architecture ($Z^2$-ACT), which integrates the aforementioned four primitives across the non-real-time and near-real-time RICs. We encode the...

• 3.HAP-Centric Flying Ad-Hoc Networks With Cell-Free Non-Terrestrial Connectivity

  High-altitude platforms (HAPs) are key enablers of next-generation non-terrestrial networks (NTNs), offering wide coverage, long endurance, and rapid deployment. Despite these advantages, current NTN designs remain satellite-centric and rely on terrestrial cellular assumptions, limiting flexibility and scalability. To overcome these limitations, this article proposes a multi-layer HAP-centric flying ad-hoc network (FANET). In this framework, HAPs are integrated with distributed uncrewed aerial vehicles (UAVs) to form a standalone, cell-free (CF) non-terrestrial system capable of autonomous operation. The layered architecture consists of an inter-HAP ad-hoc layer, a HAP-to-UAV cooperative layer, and a UAV-to-ground access layer, collectively enabling aerial connectivity, adaptive coverage, and interference-aware user access. Unique challen...

• 4.Free-Text Evaluation of LLMs for 5G Domain Knowledge and Fault Analysis using LLM-as-Judge

  Real-world fault analysis in 5G and emerging 6G networks demands domain expertise to analyze free-text diagnostics, including root-cause explanations and recommended actions. LLMs have emerged as a promising approach to automating this, yet whether lightweight, edge-deployable models are capable of performing in-depth free-text diagnostics remains an open question. While existing benchmarks rely on restrictive MCQs with fixed answer keys, this paper evaluates 5G domain understanding and fault analysis in a free-text generation format. Transitioning to this paradigm requires evaluating lightweight, edge-deployable AI models on open-ended diagnostic reasoning, alongside a dependable framework to validate these text outputs at scale. To address this we evaluate three lightweight LLMs, Claude-Haiku-4.5, GPT-5.4-Mini, and Gemini-3.1-Flash-Lite...

• 5.Orchra: Stateful-aware Cross-slice Workload Migrations in the 6G Control Plane

  Network slicing is a foundational capability of Fifth Generation (5G)-Advanced and emerging Sixth Generation (6G) networks, yet practical support for seamless runtime slice transitions remains limited. Standard cloud-native 5G architectures lack native support for stateful inter/intra-slice session migration, relying instead on high-overhead Non-Access Stratum (NAS) re-registrations, container redeployment etc., which disrupt userplane traffic for up to 245.50 ms. To address this limitation, we present Orchra, an intelligent orchestrator for stateful, low-latency context transfer. By externalizing critical user equipment state-including NAS context, security keys, and Protocol Data Unit (PDU) session information-into a transient staging layer, Orchra preserves session continuity across slice boundaries without requiring full re-registrati...

• 1.No new articles

  Summary available at source link.

• 1.A Multiscale Ball Test for Conditional Mean Independence

  Tests of conditional mean independence can lose power when departures are confined to a bounded part of a multivariate predictor space and the relevant spatial scale is unknown. We propose a Multiscale Ball Conditional Mean Independence (MBCMI) test that aggregates support-weighted local mean contrasts in an outcome variable across balls centered on each data point in a predictor set. Fixed-grid theory identifies the population target, establishes consistency for grid-visible alternatives, and derives a Pitman local-power limit governed by the ball-smoothed mean departure. For serial data, feasible recursive-sign-bootstrap validity for stable finite-order autoregressions with conditionally sign-symmetric innovations is established. Application-aligned serial null experiments reject 4.25% of the time. MBCI is demonstrated to be strongest f...

• 2.The Reconfiguration Premium: Co-movement Structure as an Unspanned Dimension of the Variance Risk Premium

  Hedge ratios, factor models and diversified portfolios all rest on an estimate of which firms move together. That estimate is not stable: firms migrate between the groupings the market treats as coherent, and when enough migrate the organizing axes of the cross-section turn. We measure the rate of that turning as the mean squared sine of the principal angles between subdominant eigenspaces of consecutive twelve-month S&P 500 correlation matrices. A typical month rewrites a fifth of the structure and carries four-fifths forward. That rate is priced: it couples to the aggregate variance risk premium at t = 5.40, no level measure correlates above 0.32, and the implied-correlation surface spans at most 6.7 percent of it. Only the persistent component is priced - the premium compensates the pace of revision, not the distance traveled. The ...

• 3.Is the medium the message? Social disclosure channels and firm risk

  Investors interpret social disclosures from a risk perspective, yet relevant information can reach them through channels that differ sharply in regulatory enforcement and materiality: SEC filings, sustainability reports, or financial reports. We analyse how social disclosure via each channel relates to idiosyncratic risk. Studying S&P 1,500 constituents, we distinguish between initiated and continued disclosure along the three disclosure channels. We find first-time disclosure of social issues via SEC filings is related to increased idiosyncratic firm risk, highlighting that unexpected information is published. Continuous disclosure of social issues is related to lower idiosyncratic risk for sustainability and financial reports, which is in line with the literature. The SEC filing effect is robust for downside idiosyncratic risk measu...

• 4.Disclosed Human-Capital Disruption and Firm-Specific Risk

  Human capital is a central organizational input, but standard financial data reveal little about firm-specific disruptions to workforce availability, cost, skills, and continuity. I construct a measure of disclosed human-capital disruption from earnings calls using author-defined coding criteria and a contextual language model. Within firms, a one-standard-deviation increase in the annual measure is associated with 0.55 percentage points higher idiosyncratic volatility, 0.58 percentage points higher downside deviation, and a 0.46 percentage point lower worst monthly return, with no corresponding relation to market beta. The results are stable across seven broader and narrower classification rules and remain after removing explicit labor-shortage passages and controlling for a recently published labor-shortage measure and transcript-wide n...

• 5.Dependence-Informed Sparse Neural Architecture for Stock Return Prediction

  Using neural networks for stock return prediction typically requires choices about depth and hidden-layer width that are difficult to connect to financial interpretation. We study an alternative: estimate dependence among firm characteristics with a Maximally Filtered Clique Forest (MFCF), then map its clique structure to a Homological Neural Network (HNN). The MFCF maximum clique size K is the only parameter controlling architectural complexity, and it has a clear graphical meaning: it bounds the number of characteristics in each maximal clique and hence the highest interaction order the network can represent. The filtered graph then fixes the neural network's depth, layer widths, and sparse connections before training, in place of a separately chosen depth and width sequence. We apply two HNN variants to annual out-of-sample forecasts o...

• 1.Privacy-Preserving Localization via Transmit Antenna Selection and Permutation

  Integrated sensing and communication (ISAC) has been identified as one primary usage scenario in the sixth-generation (6G) network. While techniques to preserve information privacy, such as cryptography, have been widely investigated, how to preserve sensing privacy is still an open problem in the literature. This paper makes an early attempt to tackle the above issue. Specifically, we consider a localization system consisting of a multi-antenna transmitter, termed Alice, a single-antenna legitimate receiver, termed Bob, and a single-antenna illegitimate receiver, termed Eve. To allow Bob to estimate Alice's angle-of-departure (AOD) but prevent Eve from performing this task based on Alice's signals, this paper proposes a novel antenna selection and permutation based transmission strategy for Alice. Under this scheme, Alice carefully selec...

• 2.$Z^2$-ACT: End-to-End Verifiable Agentic Intent Control for Open 6G RAN

  With the progression in open and disaggregated 6G radio access networks, it is expected that the system will be able to host multi-vendors. In order to host multi-vendors, it is essential that AI-assisted control loops remain safe, verifiable, and auditable under concurrent operator intents and untrusted model inputs. The existing studies address the agentic coordination, formal intent constraints, zero-trust prompt verification and cryptographic accountability in isolation, which leaves pre-realization safety, continuous semantic verification and cross-domain audit incomplete when used individually. In this regard, we propose zero-knowledge auditable control and zero-trust verifiable agentic intent architecture ($Z^2$-ACT), which integrates the aforementioned four primitives across the non-real-time and near-real-time RICs. We encode the...

• 3.HAP-Centric Flying Ad-Hoc Networks With Cell-Free Non-Terrestrial Connectivity

  High-altitude platforms (HAPs) are key enablers of next-generation non-terrestrial networks (NTNs), offering wide coverage, long endurance, and rapid deployment. Despite these advantages, current NTN designs remain satellite-centric and rely on terrestrial cellular assumptions, limiting flexibility and scalability. To overcome these limitations, this article proposes a multi-layer HAP-centric flying ad-hoc network (FANET). In this framework, HAPs are integrated with distributed uncrewed aerial vehicles (UAVs) to form a standalone, cell-free (CF) non-terrestrial system capable of autonomous operation. The layered architecture consists of an inter-HAP ad-hoc layer, a HAP-to-UAV cooperative layer, and a UAV-to-ground access layer, collectively enabling aerial connectivity, adaptive coverage, and interference-aware user access. Unique challen...

• 4.Free-Text Evaluation of LLMs for 5G Domain Knowledge and Fault Analysis using LLM-as-Judge

  Real-world fault analysis in 5G and emerging 6G networks demands domain expertise to analyze free-text diagnostics, including root-cause explanations and recommended actions. LLMs have emerged as a promising approach to automating this, yet whether lightweight, edge-deployable models are capable of performing in-depth free-text diagnostics remains an open question. While existing benchmarks rely on restrictive MCQs with fixed answer keys, this paper evaluates 5G domain understanding and fault analysis in a free-text generation format. Transitioning to this paradigm requires evaluating lightweight, edge-deployable AI models on open-ended diagnostic reasoning, alongside a dependable framework to validate these text outputs at scale. To address this we evaluate three lightweight LLMs, Claude-Haiku-4.5, GPT-5.4-Mini, and Gemini-3.1-Flash-Lite...

• 5.Orchra: Stateful-aware Cross-slice Workload Migrations in the 6G Control Plane

  Network slicing is a foundational capability of Fifth Generation (5G)-Advanced and emerging Sixth Generation (6G) networks, yet practical support for seamless runtime slice transitions remains limited. Standard cloud-native 5G architectures lack native support for stateful inter/intra-slice session migration, relying instead on high-overhead Non-Access Stratum (NAS) re-registrations, container redeployment etc., which disrupt userplane traffic for up to 245.50 ms. To address this limitation, we present Orchra, an intelligent orchestrator for stateful, low-latency context transfer. By externalizing critical user equipment state-including NAS context, security keys, and Protocol Data Unit (PDU) session information-into a transient staging layer, Orchra preserves session continuity across slice boundaries without requiring full re-registrati...

• 1.$Z^2$-ACT: End-to-End Verifiable Agentic Intent Control for Open 6G RAN

  With the progression in open and disaggregated 6G radio access networks, it is expected that the system will be able to host multi-vendors. In order to host multi-vendors, it is essential that AI-assisted control loops remain safe, verifiable, and auditable under concurrent operator intents and untrusted model inputs. The existing studies address the agentic coordination, formal intent constraints, zero-trust prompt verification and cryptographic accountability in isolation, which leaves pre-realization safety, continuous semantic verification and cross-domain audit incomplete when used individually. In this regard, we propose zero-knowledge auditable control and zero-trust verifiable agentic intent architecture ($Z^2$-ACT), which integrates the aforementioned four primitives across the non-real-time and near-real-time RICs. We encode the...

• 2.Free-Text Evaluation of LLMs for 5G Domain Knowledge and Fault Analysis using LLM-as-Judge

  Real-world fault analysis in 5G and emerging 6G networks demands domain expertise to analyze free-text diagnostics, including root-cause explanations and recommended actions. LLMs have emerged as a promising approach to automating this, yet whether lightweight, edge-deployable models are capable of performing in-depth free-text diagnostics remains an open question. While existing benchmarks rely on restrictive MCQs with fixed answer keys, this paper evaluates 5G domain understanding and fault analysis in a free-text generation format. Transitioning to this paradigm requires evaluating lightweight, edge-deployable AI models on open-ended diagnostic reasoning, alongside a dependable framework to validate these text outputs at scale. To address this we evaluate three lightweight LLMs, Claude-Haiku-4.5, GPT-5.4-Mini, and Gemini-3.1-Flash-Lite...

• 3.Orchra: Stateful-aware Cross-slice Workload Migrations in the 6G Control Plane

  Network slicing is a foundational capability of Fifth Generation (5G)-Advanced and emerging Sixth Generation (6G) networks, yet practical support for seamless runtime slice transitions remains limited. Standard cloud-native 5G architectures lack native support for stateful inter/intra-slice session migration, relying instead on high-overhead Non-Access Stratum (NAS) re-registrations, container redeployment etc., which disrupt userplane traffic for up to 245.50 ms. To address this limitation, we present Orchra, an intelligent orchestrator for stateful, low-latency context transfer. By externalizing critical user equipment state-including NAS context, security keys, and Protocol Data Unit (PDU) session information-into a transient staging layer, Orchra preserves session continuity across slice boundaries without requiring full re-registrati...

• 4.Fluid-Dynamic Interference Modeling for LEO Mega-Constellations: A Spatiotemporal Kinetic Field Approach

  Low Earth orbit (LEO) mega-constellations create a highly non-stationary interference environment that cannot be accurately captured by static stochastic-geometry snapshots. This paper proposes a kinetic interference field framework that models the constellation as a compressible fluid shell evolving under orbital kinematics. By mapping satellite motion into a continuum flux field, we derive a hydrodynamic conservation law for the aggregate interference and obtain a closed-form expression for the time-varying outage probability via moment matching. The analysis reveals that high-latitude ``interference surges'' are a direct consequence of orbital compression and boundary flux, rather than random anomalies. Numerical validation against ephemeris-driven Monte Carlo simulations confirms the accuracy of the framework across time evolution, la...

• 5.Empirical Evaluation of Cross-Carrier MCPTT & OTT MCX Interoperability in High-Density Environments

  Deploying broadband Mission-Critical Push-To-Talk (MCPTT) services over shared commercial infrastructures introduces resource contention during multi-agency responses in mass-crowd events. This study evaluates cross-carrier interoperability and standard versus prioritized quality of service (QoS) frameworks under real-world saturation constraints. We design an empirical multi-carrier field experiment utilizing twelve identical smartphones deployed across multiple physical sectors inside Texas A&M University's Kyle Field during a football game with 105,000+ attendees. Automated voice calls were monitored using Perceptual Objective Listening Quality Analysis (POLQA), packet delivery metrics, and connection rates. The results reveal that voice path failure is isolated to network infrastructure bottlenecks rather than device hardware limi...