Babak's Daily Briefing

Monday, July 13, 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.LLM for EDA in Front-End Design: Challenges and Opportunities

  As chip complexity increases and time-to-market pressures grow, front-end design has become a critical bottleneck in chip development. Recently, Large Language Models (LLMs) have shown great potential in Electronic Design Automation (EDA). Beyond specification understanding, LLMs show the potential to serve as a unified intelligent interface for hardware description language (HDL) generation, testbench construction, and design space exploration. The rise of agentic AI, represented by pioneering systems such as OpenClaw, offers a strategic roadmap for the next generation EDA. From this perspective, this paper discusses the evolution of EDA from localized assistance to autonomous agentic execution. Then, we review representative advances of LLMs in front-end design, focusing on key tasks such as circuit and testbench generation from a share...

• 2.SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets

  As agentic AI systems are increasingly applied to cyber-physical environments, their evaluation requires assessment of both task performance and trustworthiness. In decentralized energy markets, autonomous agents may improve market utility, but may also exploit invalid physical data, create artificial liquidity, and produce unstable governance decisions. Therefore, we propose SolarChain-Eval, a physics-constrained benchmark for evaluating trustworthy economic agents. It formulates market governance as a Gymnasium-compatible Markov Decision Process, where agents make hourly decisions. SolarChain-Eval evaluates each policy across multiple dimensions, including market utility, physical safety, slippage, action smoothness, spatial fairness, and auditability. To support agentic evaluation, SolarChain-Eval incorporates an LLM-based Planner/Audi...

• 3.Mitigating Taint-Style Vulnerabilities in MCP Servers via Security-Aware Tool Descriptions

  Large language models (LLMs) are increasingly deployed as autonomous agents that interact with external tools and services via the Model Context Protocol (MCP), a standardized interface for dynamic tool invocation. While MCP simplifies integration, it also expands the attack surface and enables generic exploits across multiple servers. Despite prior work on malicious MCP servers, the vulnerability landscape of MCP servers remains underexplored. In this work, we systematically analyze MCP server vulnerabilities, focusing on metadata characteristics, vulnerable code patterns, and community responses. Our study reveals that taint-style vulnerabilities constitute a substantial fraction of MCP server vulnerabilities, require significant code modifications to remediate, and are met with slow community responses. Motivated by these findings, we ...

• 4.Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning

  As Large Language Models (LLMs) evolve into autonomous agents, traditional static evaluation fails to capture multi-step decision-making. We introduce AgenticAI-Supervisor, an API and UI-driven RL Gym environment that decouples environment creation from scalable execution. By moving to verifiable execution outcomes, the platform generates high-fidelity traces and applies multi-dimensional reward shaping. Critically, our framework mitigates reward hacking through rigorous internal state validation and testing. This work provides a first look at our platform's core capabilities through a Customer Support Agent case study demonstrating a consistent closed-loop feedback for model optimization. Future work will focus on advanced features such as Computer Use, Tool Use, automated "stumping", and edge-case generation.

• 5.When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games

  As large language models are deployed as autonomous agents that communicate intentions before acting, a critical safety question is whether agents that publicly commit to actions will honor those commitments. We place LLM agents in repeated $n$-player games with a three-stage protocol that separates private intent, public announcement, and final action, allowing us to identify whether each deviation from a stated announcement was already planned during private deliberation. Evaluating three frontier models across six games in homogeneous and heterogeneous groups over 10 rounds, we report two findings. First, when agents deviate from their announcements, the deviation is predominantly already stated in their private plan (exceeding 90% in the highest-deception conditions), yet this is not a fixed model property: the same model ranges from ...

• 1.HALO: Hybrid Adaptive Latent Reasoning for Language Models

  arXiv:2607.08775v1 Announce Type: new Abstract: We study how to improve a frozen pretrained language model with a small amount of adaptive extra computation. A simple approach is to add additional refinement steps on top of the backbone hidden states, but fixed extra refinement can be wasteful: a one-step refinement head may be too weak, while forcing a second full-sequence refinement step everywhere can increase compute without improving transfer. We introduce HALO, a hybrid adaptive latent-refinement method that combines a coarse refinement stage with selective second-stage latent refinement on a subset of tokens chosen by token scoring and monotonic token halting. On the main public benchmark comparison built from MMLU-Pro and GPQA-Diamond, HALO achieves the best overall average among the paper-facing methods, outperforming the frozen...

• 2.An Emergent Mirage: Is Emergent Misalignment and Realignment Indeed a Robust Phenomenon?

  arXiv:2607.09053v1 Announce Type: new Abstract: Recent work has reported Emergent Misalignment (EM), where language models fine-tuned on narrow, domain-specific misaligned datasets abruptly acquire broadly misaligned behavior, alongside evidence that this behavior can be reversed through limited realignment. We systematically study repeated alignment and misalignment cycles using controlled fine-tuning loops while tracking behavioral performance, and LoRA representations throughout training. Although we reproduce EM, we find that both misalignment and realignment are highly sensitive to superficial dataset characteristics, with apparent rapid realignment largely disappearing after controlling for response-length differences. We further find that previously reported mechanistic signatures, including representational phase transitions in L...

• 3.AgentKGV: Agentic LLM-RAG Framework with Two-Stage Training for the Fact Verification of Knowledge Graphs

  arXiv:2607.09092v1 Announce Type: new Abstract: Knowledge graphs (KGs) are often automatically constructed from large-scale corpora, but they inevitably contain factual errors due to noisy sources and extraction failures, and verifying them reliably at industrial scale remains a critical challenge. To address this, we propose AgentKGV, the Agentic LLM-RAG framework for KG fact Verification, that integrates dynamic routing and iterative query rewriting, which handles surface-form mismatch in document-level retrieval. To make this framework more accurate and cost-efficient for industrial deployment, we further introduce a two-stage training strategy: turn-level distillation-based SFT that transfers reasoning ability from a large teacher model into a small model for stable query rewriting and reasoning, and trajectory-level GRPO that optimi...

• 4.PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation

  arXiv:2607.09094v1 Announce Type: new Abstract: Legal precedent retrieval is a fundamental task in legal case preparation, planning, litigation strategy, and legal research. Current approaches for automatic precedent retrieval map legal documents to a low-dimensional semantic space and compute similarity based on the proximity of their representations. These approaches treat legal documents as monolithic texts, ignoring the rhetorical organization of the legal technicalities. Ergo, they overlook nuanced legal meanings and fail to distinguish the contextual significance of legal entities and concepts that vary based on their rhetorical roles within the document. To address this insufficiency, we propose the PRecG pipeline that computes the similarity between pairs of legal judgments by hierarchically learning their representations. The ...

• 5.Augmenting Fundamental Analysis with Large Language Models: A RAG-Based System for Generating Investor Briefs

  arXiv:2607.09121v1 Announce Type: new Abstract: In this study, we examine the opportunities brought by Large Language Models (LLMs) to various aspects of fundamental analysis of companies based on their reports as well as data and documents describing macroeconomic situation like GDP and inflation changes as well as documents filled to the U.S. Securities and Exchange Commission (SEC) which can be found in EDGAR. We were preprocessing those data and than sending via API to gpt-4o model in a Retrieval-Augmented Generation (RAG) like regime. We prepared as well a document describing an exemplar investor knowledge based on Kitchin cycles. We were scanning data important for analysis of 9 companies for 4 weeks. Using LLM we were producing automatic briefs about them. They were sent to nine participants who are individual investors to evaluat...

• 1.A Unified Approach to Interpreting Knowledge Distillation for Large Language Models via Interactions

  arXiv:2607.08776v1 Announce Type: new Abstract: Despite the success of knowledge distillation (KD) in Large Language Models (LLMs), the underlying mechanism behind its efficacy remains unclear. In this paper, we propose a unified approach to explore the common mechanism of various KD methods using interactions. Specifically, we decompose the output score of the LLM into the sum of numerous interactions. Each interaction represents a nonlinear relationship involving a set of input variables (e.g., words). Based on the decomposed interactions, we discover that the common mechanism underlying various KD methods is the sparsification of interactions, i.e., student models retain fewer interactions for inference while suppressing other interactions to zero effects. Furthermore, we discover that the performance variance across different KD metho...

• 2.iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis

  arXiv:2607.08778v1 Announce Type: new Abstract: Alzheimer's Disease (AD) is a complex neurodegenerative disorder that continues to impact millions of people worldwide. Predicting AD conversion during the prodromal stage remains critical for disease understanding and patient care. As such, survival models are widely used for AD risk prediction, yet they are typically static predictors with limited interpretability and no capacity for natural language reasoning. In this work, we propose iLENS, an interpretable large language model (LLM) guided framework based on mixture-of-experts (MoE) for survival prediction in AD conversion. Our approach uses LLM to synthesize structured neuroimaging measurements and unstructured information to guide expert routing. Our framework demonstrates competitive predictive performance and capability in patient s...

• 3.Signed Symmetric Quantization for Few-Bit Integers

  arXiv:2607.08779v1 Announce Type: new Abstract: The signed integer alphabet contains one more negative representable value than positive. Yet, by convention, the standard symmetric integer quantizer fixes its scale to be strictly positive, which assigns this extra representable value to the negative tail and can force clipping of positive outliers. In this work, we show that, at few-bit precision, such clipping is a non-trivial source of quantization error. Asymmetric quantization addresses this problem with a zero point, shifting the grid toward the observed data range; however, this flexibility is well-known to carry a runtime penalty. For example, in llama.cpp on an AMD EPYC(TM) "Turin" CPU, a 4-bit symmetric format uses up to 9% less memory with up to 2.45$\times$ higher throughput than its asymmetric counterpart. We highlight signed ...

• 4.Sticky Routing: Training MoE Models for Memory-Efficient Inference

  arXiv:2607.08780v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models activate only a sparse subset of experts per token, yet consecutive tokens frequently activate different experts -- causing constant weight swapping between slow storage and fast memory on edge devices. Existing remedies are either system-level (caching heuristics) or post-hoc (router fine-tuning), leaving the root cause unchanged during pretraining. We propose StickyMoE, a differentiable routing consistency loss that penalises abrupt expert switches between adjacent tokens, encouraging the router to maintain the same expert assignment across semantically coherent spans. StickyMoE requires no architectural changes, adds a single hyperparameter lambda, and unlike post-hoc methods, allows expert representations and routing decisions to co-adapt from the first tr...

• 5.Reward Transport: Property Control in Flow Matching via Noise-Space Alignment

  arXiv:2607.08781v1 Announce Type: new Abstract: The coupling in flow matching -- the rule pairing noise vectors with data points -- is typically treated as a computational choice. We show that this coupling can instead serve as an alignment interface: by matching noise and data according to a target molecular property, it embeds controllable structure directly into the learned flow field. Building on this view, we introduce Reward Transport, which uses optimal transport coupling at training time to align a scalar noise-space coordinate with molecular rewards; at inference, varying this coordinate steers the generated distribution without requiring an oracle, reward model, gradient guidance, or additional computation. In the coupling-preserving limit, thresholding this coordinate recovers the Cross-Entropy Method's truncated reward distrib...

• 1.AgenticFocus: Object-Preserving Mixed Reality Synthesis from Human FPV Video for Dexterous Humanoid Learning

  arXiv:2607.08857v1 Announce Type: new Abstract: Human egocentric video is a scalable supervision source for humanoid policy learning, but current pipelines struggle with hand-object occlusion, oversimplified motion, or specialized capture hardware. We introduce AgenticFocus, a Mixed Reality synthesis pipeline that converts ordinary first-person-view human videos into robot-trainable demonstrations by restoring occluded object geometry, reconstructing full-hand motion, and retargeting it to a humanoid embodiment through camera-relative alignment and layered compositing. The resulting dataset pairs focused visual observations with synchronized robot actions and states. AgenticFocus achieves lower trajectory error and smoother wrist motion than cross-embodiment baselines, with SPARC scores of -5.18 versus -5.56 and -6.05.

• 2.FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space

  arXiv:2607.08877v1 Announce Type: new Abstract: Pretrained generative robot policies based on flow matching and diffusion have achieved impressive results across a wide range of manipulation tasks. Yet real-world deployments routinely expose failure modes outside the pretraining distribution. Closing these gaps typically requires large-scale data collection or online reinforcement learning on physical hardware, which is impractical for rapid and safe adaptation. We present FlowDAgger, a sample- and compute-efficient method for adapting frozen generative robot policies from human interventions in latent space. Our key idea is action inversion: each human expert action is mapped to the noise that would have produced it under the frozen base policy, using reverse-time integration followed by local refinement. The resulting inverted noise pro...

• 3.SplatCtrl: Perception-Action Coupling via Gaussian Scene Representations and Reactive Robot Control

  arXiv:2607.08948v1 Announce Type: new Abstract: Robotic manipulators excel in structured environments but face substantial challenges in unstructured and dynamic settings. This paper presents SplatCtrl, a unified framework for real-time scene reconstruction and reactive robot motion generation to enable collision-free robotic arm control in previously unseen and continuously changing environments. Building on 3D Gaussian Splatting (3D-GS), we introduce a hybrid voxel-based filtering and dynamic Gaussian relocation strategy that supports efficient scene reconstruction from RGB-D streams while accommodating environmental changes. For safe and reactive control, we further propose a method for deriving continuous signed distance functions from isotropic Gaussians, providing stable and differentiable collision probability estimates that bridge...

• 4.CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding

  arXiv:2607.08974v1 Announce Type: new Abstract: Vision-language-action models (VLAs) inherit semantic capabilities from pretrained VLMs, yet large-scale post-training on robot data and architectural modifications can reshape the backbone so extensively that it becomes difficult to isolate what the VLM contributes to control. Directly converting pretrained VLMs into VLAs with minimal architectural change offers a more transparent path to understanding how VLM capabilities transfer across model scales. The core obstacle is output-distribution mismatch: predicting actions as bare numeric token sequences moves generation away from the VLM's pretrained language distribution, degrading the capabilities we seek to preserve. To address this, we propose CLAP (Causal Language-Action Prediction), which prepends each numeric action sequence with a na...

• 5.Dec-MARVEL: Decentralized Multi-Agent Exploration without Communication under Budget Constraints

  arXiv:2607.09060v1 Announce Type: new Abstract: Multi-UAV exploration is often constrained by unreliable communication, limited field-of-view sensing (e.g., lightweight onboard camera), and finite travel budgets that require each robot to reserve enough budget to return to its base. We present Dec-MARVEL, a decentralized budget-aware exploration framework for communication-free teams with directional sensing. Rather than exchanging maps, goals, or messages, each robot coordinates through its incidental observations: any teammate trajectory within its field of view serves as a coordination signal. A graph-attention actor fuses local frontier geometry, teammate motion, and budget features to select return-feasible waypoint-heading actions. The actor is trained with phase-conditioned critics, a training-only task-oriented privileged critic, ...

• 1.Deep Learning for Dynamic Programming with Recursive Utility Using First-order Conditions

  This paper proposes the certainty-equivalent first-order learning (CEFOL) algorithm, a deep learning algorithm for solving discrete-time dynamic programming problems with recursive utility. Dynamic programming with recursive utility is challenging because nonlinear certainty equivalent appears in the Bellman equation and the first-order optimality conditions but is difficult to evaluate. By introducing a separate neural network to represent the certainty equivalent, CEFOL enables the exploitation of the Bellman and model-specific first-order optimality conditions. In addition to certainty equivalent, CEFOL also uses neural networks to learn the value functions, policy functions, and Lagrange multipliers by using model-specific first-order conditions to construct residuals for minimization. By using first-order and KKT residuals to learn t...

• 2.When Does Order Flow Matter? State-Dependent L2 Liquidity-State Transitions in Crypto Futures

  Building event-conditioned market models requires separating macro-event labels from persistent microstructure state. We study this distinction in Binance BTCUSDT and ETHUSDT futures from 2023-2026, combining top-20 L2 order book data, trade-flow records, and macro-event windows. We define a supervised discrete L2 liquidity-state transition task, distinct from latent-regime detection and price-direction prediction, and evaluate models in rolling monthly out-of-sample folds with event-clustered validation and blocked permutation tests, admitting each feature layer only if it improves on the layer below it on the same panel. Within these event windows, the first-order predictive signal is the pre-event L2 liquidity state: a coarse pre-event state baseline strongly predicts post-event liquidity regimes, interpretable logit models over contin...

• 3.SHARC: SHAP-Based Interpretability in Machine Learning Risk Models for Regulatory Capital under ICAAP and CCAR

  The adoption of non-parametric machine learning models for regulatory capital estimation introduces a fundamental governance challenge: the inability to explain model outputs in a manner auditable by supervisory bodies. This 'black box' problem remains a major barrier to the adoption of Gaussian Process Regression (GPR) and related ML architectures in ICAAP and CCAR workflows despite their predictive advantages over traditional parametric approaches. This paper addresses this barrier through SHARC (SHAP for Regulatory Capital), an explainability framework for the Hybrid GPR-HS architecture and its stress-testing extension. SHapley Additive exPlanations (SHAP), derived from cooperative game theory and satisfying the properties of Local Accuracy, Missingness, Consistency, and Efficiency, are applied to Stressed Value-at-Risk (SVaR) output...

• 4.Look-Ahead-Freedom as Temporal Non-Interference: A Verifiable Correctness Property for Backtesting and Agentic Trading Pipelines

  Look-ahead bias (using information from after a decision epoch to make the decision at that epoch) is the dominant way a backtest or a machine-learning evaluation flatters a system that will disappoint in deployment. The field manages it with construct-specific recipes and empirical detectors, which are sound only channel by channel and certify nothing by their silence. We show that look-ahead-freedom is a formal property in disguise: fixing an epoch, the demand that the future not influence the present is temporal non-interference over a time-indexed information lattice. From this identification we develop a pipeline calculus separating a datum's availability from its reference time, and settle the problem's boundary. Where availability may depend on data values, look-ahead-freedom is undecidable (indeed Pi-0-1-hard): leakage is recursiv...

• 5.Deep Learning for Dynamic Programming with Recursive Utility

  We propose the first deep learning algorithm, the Certainty Equivalent Learning (CEL) algorithm, for solving high-dimensional discrete-time dynamic programming problems with recursive utility. Dynamic programming with recursive utility is numerically challenging because the recursive utility does not have an explicit representation and the Bellman equation contains a certainty equivalent that is difficult to evaluate. The CEL algorithm learns this certainty-equivalent value directly with neural networks and jointly approximates value functions, policy functions, and certainty-equivalent functions. The CEL algorithm is mesh-free and simulation-based, allowing high-dimensional state and control spaces, and does not rely on Euler equations, first-order conditions, or differentiability of the state transition function. The CEL algorithm also ...

• 1.From fragmentation to control: why device manufacturers need an industry-led approach to homologation

  Summary available at source link.

• 2.Telco Common Corpus: The largest open, verified data commons for telecom AI

  Summary available at source link.

• 3.GSMA Launches Global Satellite Regulatory Playbook to Help Policymakers Build Future-Ready Connectivity Frameworks

  Summary available at source link.

• 4.GSMA welcomes China Tower to advance AI-ready mobile infrastructure

  Summary available at source link.

• 5.Scaling Telco AI from Ambition to Reality

  Summary available at source link.

• 1.Evolution of Accuracy and Visual-Cognitive Errors in a Decade of Vision-Language AI Models

  Vision language models (VLMs) have made remarkable progress in visual reasoning during the last decade. Most evaluations have used simple scenes (MS-COCO) that do not showcase complex human interactions or behaviors, only a handful of non-curated human descriptions as a benchmark, and have not focused on understanding the model's error types. Here, we introduce the Complex Social Behavior (CSB) dataset, containing 100 images depicting complex social interactions/behaviors. We analyze the progression of scene descriptions over a decade (2017-2025) of VLMs (four pre-Multimodal Large Language Models, MLLMs, and five MLLMs). We evaluate the accuracy of the models and 20 human descriptions relative to a gold standard on the CSB dataset and on a sample from MS-COCO. We analyzed five visual-cognitive error types: object detection, recognition, h...

• 2.VEXAIoT: Autonomous IoT Vulnerability EXploitation using AI Agents

  Internet of Things (IoT) systems are inherently vulnerable due to constrained hardware, outdated firmware, and insecure default configurations, creating a need for scalable and adaptive security testing approaches. While recent adoptions of Large Language Model (LLM) agents have demonstrated promise in penetration testing and Capture-the-Flag (CTF) environments, their application to IoT specific vulnerabilities remains unexplored. This paper presents an autonomous multi-agent framework, referred to as Vulnerability EXploitation using AI Agents (VEXAIoT), for vulnerability discovery and exploitation in IoT environments using LLM-based reasoning and offensive security tools. The framework combines a vulnerability detection agent and an attack execution agent to perform reconnaissance, plan attack sequences, and execute exploits against vuln...

• 3.Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation

  Enhancing the reasoning capabilities of large language model (LLM) agents requires effective orchestration of diverse expert models and tools. However, existing frameworks typically call APIs based on coarse-grained matching between tasks and the functions of expert models or tools, while overlooking critical factors such as performance variability and cost efficiency among functionally similar alternatives. To address this, we propose Agora, a framework that introduces an incentive-compatible auction mechanism for dynamically allocating tasks to expert models and tools. By treating reasoning steps as tradeable items, Agora enables agents to bid based on their rectified competence-ensuring that critical logic is routed to the most capable solver rather than the most overconfident one. Evaluations across five benchmarks show that Agora imp...

• 4.What VGGT Knows About Overlap: Probing Geometric Foundation Models for Co-Visibility

  A fundamental challenge in 3D reconstruction and robotic localization is co-visibility: determining which image pairs share overlapping visible surfaces, particularly in scenarios with minimal overlap. We demonstrate that VGGT implicitly encodes co-visibility as an emergent behavior: without any supervision for this task, its internal representations exhibit a clear hierarchical structure mirroring that of large language models, i.e. early layers build a 3D-aware scene representation, while late layers act as dedicated co-visibility reasoners. In particular, we identify layer L17 as a negative anchor that consistently routes non-co-visible pairs for this backbone, regardless of the evaluation setting, providing task-grounded evidence of layer specialization in a geometry-grounded foundation model. Building on this, we introduce Co-VGGT, w...

• 5.Practical Source Code Recovery from Binary Functions Using Anchor-Based Retrieval and LLM Reasoning

  We present a practical pipeline for recovering source code from stripped binary functions by combining reverse engineering, anchor-based source code retrieval, and large language model reasoning. Our binary-to-source-code retrieval method attempts to identify the source function from a source code database, rather than generating approximate decompiled pseudocode. It extracts anchors such as strings, constants, external calls, and available function names using Ghidra, retrieves candidate files via an inverted-index search database, narrows candidates to likely function snippets, and re-ranks them with a large language model (LLM) based on disassembly, decompiled code, and source metadata. Confident matches can also serve as anchors in later passes. In an evaluation backed by our high-fidelity source code database on a stripped, optimized...

• 1.AnythingReality: Robust Online Gaussian Splatting SLAM for Open-Vocabulary VR Scene Exploration

  We present a novel integrated architecture for robust online 3D Gaussian splatting, real-time VR exploration, and speech-driven Vision-Language-Model interaction. Unlike methods assuming clean depth or external poses, our system combines ORB-SLAM3-based pose estimation with online Gaussian reconstruction for noisy real-world data. A VR pipeline enables immersive exploration of incremental reconstructions; a semantic module transcribes voice commands, generates scene descriptions, and records points of interest. Against state-of-the-art online Gaussian splatting methods, we improve image quality on our dataset (+14.5% PSNR, +8.6% SSIM, -14.3% LPIPS) and TUM-RGBD (+11.7% PSNR, +7.8% SSIM, -21.6% LPIPS), with comparable or superior frame rates via quality-speed configurations. We achieve an 88% VLM object-recognition rate.

• 2.Validity of LLMs as data annotators: AMALIA on authority

  A national language model offers a linguistic community its own instrument for measuring what its citizens say and value. Portugal's AMALIA, a publicly funded 9B-parameter model for European Portuguese, appears competitive on agreement alone: asked to code the moral foundation of authority, it agrees with trained human coders to within six F1 points of open models eight to thirteen times its size. Yet agreement is reliability, not validity. For theoretical constructs that must be inferred rather than read from surface features, the question is whether the model follows the construct's theory or reaches the right code by correlated shortcuts. We test this with the recovery gap: the loss in performance when a holistic prompt is decomposed into the codebook's atomic clauses and recombined by the theory's explicit rule. If calibration closes ...

• 3.Deep Learning Method for Stationary Distribution of Reflected Brownian Motion

  The stationary distribution of reflected Brownian motion (RBM) plays an important role in the analysis of high-dimensional stochastic systems, yet closed-form solutions are known only for a few special cases. Computing important performance metrics, such as tail probabilities, is even more intractable, despite their practical relevance. In this paper, we develop a deep learning approach that accurately and efficiently learns the Laplace transform of high-dimensional RBMs based on the basic adjoint relationship (BAR). Our framework combines a careful design of the loss function, training data sampling procedure, and neural network architecture. We evaluate the proposed method on RBM instances with known ground-truth tail probabilities and demonstrate near-perfect prediction in high-dimensional settings, highlighting its potential as a gene...

• 4.A Quantum Reservoir Architecture for Chaotic Forecasting and a Test of Whether Its High Dimension Helps

  Quantum reservoir computing uses a fixed quantum circuit as a feature generator and trains only a simple linear readout on top of it. This makes it cheap to train and free of the optimisation problems that affect many quantum machine-learning models. A natural worry is that the very large feature space the circuit produces might inflate apparent performance without adding anything real. This paper provides two things. First, it gives a complete, reproducible recipe for one such reservoir applied to forecasting chaotic systems, including how data is fed in, how the circuit is built, and how the readout is trained. Second, it gives a way to tell whether the reservoir's high dimension is actually doing useful work. We grow the size of the prediction problem and the size of the quantum reservoir together, so that extra capacity cannot be the ...

• 5.GIRAF: Towards Generalizable Human Interactions with Articulated Objects

  Synthesizing realistic full-body human interactions with articulated objects is a fundamental challenge for embodied AI and graphics, with applications in robotics training and virtual agents. Existing models remain limited: some focus on simple activities with static objects, while others restrict attention to hand-only manipulation. This leaves open the problem of generating coordinated full-body motion that approaches, manipulates, and moves articulated objects in a realistic and generalizable way. The key difficulty lies in reasoning jointly about locomotion, fine-grained contact, and object articulation. Models must capture subtle hand-object correspondences that transfer across object geometries, while also producing seamless transitions from navigation to manipulation. At the same time, the scarcity of large-scale paired motion-sce...

• 1.Building a Foundation Stack for General-Purpose Robots

  This article is brought to you by X Square Robot . Large language models gave artificial intelligence a working recipe. Pretrain a large model on broad data, and general capability follows. Robotics has no such recipe. Robotics systems have long been assembled from separate perception, planning, and control parts that rarely add up to intelligence a robot can carry from one task to another, or one machine to another. The central problem in embodied AI is to find the equivalent recipe, and the field does not yet agree on what it is. X Square Robot , a Chinese embodied-AI company, has made an unusually explicit bet. It argues that the recipe is an integrated stack, spanning the data a robot learns from, a world model for predicting changes in the physical world, and an action model that brings together perception, planning, reasoning, and d...

• 2.Large Tabular Models Excel Where LLMs Fail

  The large language models (LLMs) that form the basis of generative AI chatbots such as ChatGPT, Claude, and Gemini can generate uncannily human-like text and images. But these models still struggle with a skill that, ironically, looks at face value to be right in their wheelhouse: analyzing structured data. A new type of generative AI is set to change this situation. Although you can get your favorite chatbot to solve intractable math problems , review dense legal documents, compose a catchy pop song , or put together some slick PowerPoint slides, give it anything more than a small table and it doesn’t have a clue what to do. For most companies and organizations, the most important data sits in spreadsheets. Whether it’s a bank’s transaction logs, a marketing agency’s website metrics, clinical trial participants’ vital signs, or the vast ...

• 3.AI Models Overthink Problems—and It’s a Security Risk

  Large language models (LLMs) that can think through problems step-by-step have significantly increased the scope of tasks that AI can tackle. But new research suggests these reasoning capabilities also introduce a critical vulnerability that could allow attackers to slow these systems to a crawl. While earlier generations of LLMs would immediately produce a response to a user’s request, today’s most advanced models generate an internal monologue where they break down the problem into steps and reason about the best way to tackle it before providing an answer. This has allowed AI to tackle increasingly complex problems, particularly in areas like coding and math . However, previous research has shown that these models are susceptible to sometimes producing excessively long streams of reasoning that do little to boost performance, a phenome...

• 4.What Makes AI Art Worth Collecting?

  In May, an anonymous artist who goes by SHL0MS on X posted that he had used AI to generate an image inspired by Claude Monet and asked people to weigh in on how it missed the mark. More than 600 responses called out issues, saying the colors were off, the depth was all wrong, and that AI didn’t understand how light worked. SHL0MS then revealed that the image was of a real Monet, one of around 250 variations of water lilies the artist had painted in his lifetime. He had simply downloaded a high-resolution image from Wikimedia and cropped out the signature. He minted the exchange as an NFT (a unique digital collectible recording ownership of the work), titled it “Inferior Image,” and sold it for just over US $40,000 after 28 bids. The stunt exposed how charged the conversation around AI art has become, and how quick people are to dismiss an...

• 5.Small AI Models Gain Traction Around the World

  One morning in 2019, Adebayo Alonge was in a Cape Town hotel room, preparing to demonstrate his startup’s AI answer to a serious problem in African health care: counterfeit medication, which kills thousands of people across the continent every year. The RxScanner is a handheld spectrometer that scans a pill with infrared light, then sends the item’s molecular profile to an AI model equipped with a pharmaceutical database. In seconds, the AI identifies the medication from its molecular profile—or reports that it’s phony. Pharmacies were using the system in more than a dozen countries, including Ghana, Kenya, Myanmar, and Alonge’s native Nigeria. But that morning in South Africa, it didn’t work. “I was shocked,” Alonge says. The spectrometer connected to the AI model—but the data center was 14,000 kilometers away and bandwidth was limited. ...

• 1.Monday assorted links

  1. Robots learn from puffins? 2. Claims about cats (NYT).  I guess they have no Coase theorem after all. 3. Iran and the Strait (WSJ). 4. “I used to be one of these people.”  And Timothy Lee.  As for this exchange, in the 2040 scenario strong AI is a power magnet in a way that […]

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• 2.“Markets are competitive if and only if P != NP”

  I prove that competitive market outcomes require computational intractability. If P = NP, firms can efficiently solve the collusion detection problem, identifying deviations from cooperative agreements in complex, noisy markets and thereby making collusion sustainable as an equilibrium. If P != NP, the collusion detection problem is computationally infeasible for markets satisfying a natural instance-hardness […]

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• 3.My talk at DeepMind

  Here is a transcript of my remarks, anything from the audience (Q&A with comments) has been cut out.  Excerpt: The problem will not be how does my life get meaning, but how do I deal with all the meaning my life will have? A kind of exhaustion. And this comes up in the labor supply […]

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• 4.Sunday assorted links

  1. NYT overview of Foucault. 2. Solar mirrors to turn night into day? (NYT) 3. How does the butterfly retain the memories of the caterpillar? 4. The Taliban declares war on smartphones. 5. Links from Sam Enright. 6. Why are U.S. ambulance rides so costly? 7. Richard Hanania on The Sopranos.

  The post Sunday assorted links appeared first on Marginal REVOLUTION.

• 5.The Trump Administration’s Threat to Scientific Research

  In The Nationalization of American Science I warned that the Trump administration’s rewriting of the seemingly mundane Regulation for Federal Financial Assistance was a tremendous threat to America’s historically successful decentralized system of science funding. Many others are now sounding the alarm. It’s not surprising that organizations like the AAAS oppose the rule, albeit with unusually […]

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

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

• 2.More Tariff Pass‑Through Is in the Pipeline

  The past year brought dramatic changes to U.S. trade policy, including sweeping new tariffs, as well as a Supreme Court decision that further reshaped the tariff landscape. Many businesses saw their costs increase significantly and faced complex decisions about whether to absorb the tariffs through lower profit margins, raise their prices to recover the higher costs, or some combination of the two. Last year, we found that most businesses had passed on at least some of these higher costs to their customers throug...

• 3.What Do Over 3,000 Bank Runs Teach Us About Banking Crises?

  Runs on financial institutions are one of the salient markers of financial crises. But the role of runs in crises is a topic of longstanding debate. Runs can be seen as the key turning point, whereby even small shocks can generate severe crises with widespread bank failures. Another view is that runs are mainly a symptom of deeper rot in the financial system, exacerbating crises rather than being their primary cause. Understanding this debate has first order implications for how to think about financial crises and the appropriate policy responses. In this post, we use a new database of more than 3,000 bank runs (introduced in our companion post) to show that poor fundamentals are central to explaining both when runs occur and...

• 4.Using AI to Let History Speak About Bank Runs

  Banking crises are commonly associated with bank runs and banking panics, yet our empirical understanding of bank runs is constrained by a lack of bank-level data. In a new paper, we use large language models (LLMs) to extract information on bank runs from millions of digitized historical newspaper pages, creating the most comprehensive database of bank runs in U.S. history. Every bank run episode that we identify is documented on a companion website where users can browse and examine individual episodes, and read the original newspaper articles. In this post, we describe how we built this dataset and discuss what its basic features reveal.

• 5.The Disappearing Overnight Drift

  In a 2021 Liberty Street Economics post, we documented the “overnight drift”—a large, persistent return to holding U.S. equity futures in the narrow window between 2:00 and 3:00 a.m. Eastern time, when European equity markets open. Five additional years of data later, that pattern appears to have faded: the 2:00–3:00 window that previously generated roughly 3.7 percent per annum has averaged close to zero since 2021. In this post, we revisit the overnight drift in light of the post-publication sample and use our inventory-risk framework to ask which of three observable channels—the dispersion of closing order imbalances, the level of return variance, or the risk-bearing capacity of liquidity providers—accounts for the cha...

• 1.The Mother of All Economic Shocks Is Chinese Mercantilism

  Because analysts tend to put the United States at the center of the global economic story, they have overlooked the single most important force of the past 50 years. But it will be obvious to later generations that Chinese mercantilism has been more globally consequential than any other economic shock or policy choice.

• 2.Letting Russia Burn

  From relentless Ukrainian drone strikes to soaring inflation and fuel shortages, Russia is paying an ever-higher price for Vladimir Putin's war of choice. But the war has become the organizing principle of Putin’s regime, leaving no path to lasting peace without a fundamental political transformation.

• 3.What AI Governance Demands

  AI governance must account for everything from the technology’s material requirements to its effects on communities, businesses, labor markets, the environment, and strategic competition. To this end, a framework of verification-based trust—broader than national regulation and more flexible than a classic treaty—is essential.

• 4.A Japanese Wake-Up Call for America

  Japan’s deepening currency and bond-market woes should be a wake-up call for other countries that appear to be on unsustainable fiscal paths, not least the United States, as well as France, Italy, and the United Kingdom. After all, a crisis in one country often draws investors' attention to others facing similar problems.

• 5.The Israel-Lebanon Recipe for Never-Ending War

  Lebanon’s political circumstances, together with Iran’s strengthened regional position, make Hezbollah’s complete disarmament highly unlikely. By conditioning Israel’s withdrawal from Lebanon on this outcome—but with no provisions about cutting Hezbollah’s Iranian lifeline—the new framework agreement is dead on arrival.

• 1.Korean telcos pivot to AI infra as government pursues AI G3 ambitions

  Over the past several weeks, SK Telecom, KT, and LG Uplus have each unveiled major initiatives centered on AI infrastructure, underscoring how telecom operators increasingly see AI platforms and digital…

• 2.Wireless Logic’s 21st acquisition exposes IoT consolidation paradox

  Wireless Logic’s acquisition of SIMETRY reinforces its position as IoT’s most aggressive consolidator. But while the managed connectivity market is widely tipped for merger-driven scale, inflated valuations and healthy growth…

• 3.Netgear’s quiet reinvention as a B2B and SaaS company (Analyst Angle)

  Netgear is quietly reinventing itself as an enterprise networking and software company, writes Maravedis Research. It is betting on cloud management, AI-driven automation, security services, and MSP-led growth to shed…

• 4.Friday (telco diary) | Telco reset, RAN reboot, P5G reality check

  From the newsletter (sign-up if you want it sooner): Vodafone finds itself centre stage again as telco consolidation gathers pace, while industry efforts to open up RAN software and scale…

• 5.Operators are seeking greater control of NTN: GSMA Intelligence

  In sum, what to know: -Mobile operators want more control over satellite connectivity and are taking steps to get it, according to a new report from GSMA Intelligence. -The NTN…

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• 1.Generative Communications: Overview, Technologies, and Trends

  The groundbreaking development of generative artificial intelligence (AI) is rapidly boosting the ability to generate content such as images and videos, reshaping communication paradigms. This article introduces generative communications (GenCom), a novel paradigm for 6G networks in which large AI models (LAMs) drive semantic understanding, reasoning, and content generation, embedding these into the communication process. Unlike traditional systems that strictly pursue accurate bit transmission, GenCom enables transmitters to convey only minimal yet sufficient information, while receivers leverage shared generative priors and knowledge bases to synthesize the intended output. Communication is thus redefined as controlled generation rather than data reproduction. We formalize the concept of GenCom, clarify its AI-native and generation-driv...

• 2.Can the Cloud Drive? Infrastructure Feasibility of Offloading Autonomous Driving Across 5G and 6G

  Frontier autonomous-driving models -- especially vision-language-action (VLA) models, whose forward pass approaches $\sim$60~TFLOPs -- are outgrowing economical onboard deployment, since peak hardware sits idle most of the day. Cloud inference can instead share GPUs across active vehicles, but the vehicle must upload through a capacity-limited uplink, reach a GPU without queueing, and return a decision within the closed-loop budget. This paper asks: can the cloud drive? We answer with an analytical framework coupling communication limits, a roofline GPU service model, stochastic latency, and utilization-aware cost across three model classes, three offloading strategies, and three communication generations, applied to New York City. Separating a reactive 100~ms budget from a 300~ms deliberative tier (presuming an onboard reactive fallback)...

• 3.Privacy-Preserving Intent Fulfilment and Assurance for 6G RAN

  Intent-based network management is the emerging paradigm for 6G service lifecycle automation, with the 3GPP intent management framework (TS~28.312) defining creation, translation, fulfilment, and assurance stages. Existing fulfilment and assurance approaches require deep packet inspection, per-flow state tracking, or access to vendor-internal node telemetry to verify that provisioned resources satisfy expressed intents. These requirements conflict with regulatory constraints (GDPR, ePrivacy Directive) in multi-tenant networks and with vendor opacity in multi-vendor O-RAN deployments. We present an architecture for privacy-preserving intent fulfilment and assurance in which a coordinator provisions resources from declared intent categories without traffic inspection, and verifies fulfilment using only aggregate standardised PM counters at ...

• 4.Parasitic MIMO Beamforming for Multi-Active Multi-Parasitic Antenna Arrays with Binary Control

  In 6G, MIMO dimensions continue to scale, yet the increased cost, power consumption, and hardware complexity associated with growing RF chains limit practical deployment. Parasitic antennas offer a promising alternative that can add spatial degrees of freedom and array gain without a proportional increase in RF chains. From a communication perspective, prior work on parasitic antennas has primarily focused on adjusting continuous reactance values using varactors, but such varactor-based tuning has increased cost and complexity in the analog control and practical RF circuit design. This paper proposes a multi-active multi-parasitic antenna (MAMP) architecture with binary controllers, where each parasitic element operates in one of two discrete reactance states. To validate the practicality of the system, we experimentally identify array ge...

• 5.RSMA-Assisted OFDM-OTFS Hybrid Framework for Mixed-Mobility Multiuser Systems

  In future 6G vehicular networks, users employing orthogonal frequency division multiplexing (OFDM) and orthogonal time frequency space (OTFS) waveforms may coexist under diverse mobility conditions, where both can experience high-mobility and low-mobility profiles. Since OFDM users can suffer severe inter-carrier interference (ICI) and OTFS users occupy larger spectrum resources, rate-splitting multiple access (RSMA) is a flexible framework that can efficiently handle these heterogeneous aspects. In this work, we propose a novel RSMA-assisted system to provide downlink communication to multiple OFDM and OTFS users. A common stream comprising the common messages of OFDM users spans the whole bandwidth to help OFDM users manage the ICI induced by potential high Doppler effects. OTFS users do not participate in the common stream. The private...

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• 1.Does Regulation Bite at Gateways? Evidence from MiCA and Stablecoins

  Gateways are trading venues where regulation can change the assets investors can trade. We study this margin using MiCA-EU's Markets in Crypto-Assets Regulation-which led several exchanges to delist USDT pairs for European Economic Area users, while USDC obtained MiCA authorization. First, aggregate market shares and trading volumes barely move. Second, comparing Regulated-facing exchanges with globally oriented exchanges where MiCA is less likely to bind, we show that the cross-section shifts toward USDC-USDC share rises by 0.82 and relative trading volume by 0.54 pre-event standard deviations. Both reflect USDT trading contracting where it is delisted.

• 2.Stablecoins under Stress in a National Economy: Transaction-Level Evidence from Austrian Crypto-Asset Service Providers

  Cryptoassets are increasingly entangled with the traditional financial system, and how this activity integrates into national economies and behaves under stress bears on financial stability and the design of public digital money. However, blockchain pseudonymity and the lack of geographic identifiers force existing work to rely on indirect proxies to infer and locate market participants. Here we use a regulatory registry that directly identifies the on-chain addresses of all crypto-asset service providers (CASPs) registered in Austria, reconstructing their on-chain transaction activity across Bitcoin, Ether, USDC, and USDT through May 2025, and separating retail-like from institutionally mediated flows. We find that Austrian CASPs intermediate roughly USD 30 billion with external counterparties and are integrated globally rather than dome...

• 3.Estimating the Stochastic Discount Factor from Option Prices and Predicting the Equity Premium

  This paper proposes a stochastic discount factor (SDF) scaled by time-varying volatility. By utilizing prices and market data implied solely from S\&P 500 options, the proposed framework recovers a stable, non-monotonic SDF that captures the pure forward-looking expectations of market participants while mitigating observation noise. Our empirical analysis reveals that the SDF exhibits a distinctive hump on the shallow put side, which transitions into a more clearly defined W-shape as the time to maturity increases, identifying maturity as a key factor influencing the intensity of the central hump. We show that this structural feature can be theoretically rationalized by stochastic volatility dynamics under a constant market price of risk. The equity premium derived from the time-varying volatility scaled SDF demonstrates superior out-...

• 4.Grounded Event Extraction from SEC 8-K Filings with a Fine-Grained Taxonomy

  Form 8-K filings are the primary channel through which U.S. public companies disclose material events, but the SEC item codes attached to them are coarse: a single item spans routine administrative changes and chief executive departures, and many of the most market-moving disclosures fall into a catch-all item. Large language models make fine-grained labelling feasible at corpus scale, but only if the labels can be traced to the source text and shown to be reliable. We present a two-stage system that tags 8-K disclosures against a three-tier taxonomy of 119 event types. The first stage constrains output to valid taxonomy entries and anchors every tag to a verbatim quote via fuzzy n-gram validation; the second re-grades each cited quote against the category definition to produce a quality score. Applying the system to 292,984 filings from ...

• 5.Iterative detection of global factors near the BBP phase transition

  Detecting the number of global factors in high-dimensional correlation matrices is a central problem in multivariate statistics and random matrix theory, with important implications for asset pricing and econophysics. When the number of variables $p$ is comparable to the number of observations $n$, signal-to-noise separation becomes difficult, especially near the Baik--Ben Arous--Péché (BBP) transition, where weak factors may be confused with fluctuations at the Marčenko--Pastur spectral edge. In this work, we characterize the participation-ratio (PR) structure of the Brown--Harding (BH) factor model. Under strong common loadings, the leading coherent eigenvector $u_1$ satisfies $\mathrm{PR}(u_1)/p\to 1$, whereas weak-factor directions and typical idiosyncratic sample eigenvectors $u$ satisfy the delocalized benchmark $\mathrm{PR}(u)/p\to...

• 1.Multi-Agent Reinforcement Learning for SLA-Aware Network Slicing in UAV-Enabled MEC

  Unmanned Aerial Vehicle (UAV)-enabled Mobile Edge Computing (MEC) offers flexible capacity provisioning for heterogeneous network slices, including Hyper-Reliable and Low-Latency Communication (HRLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine-Type Communications (mMTC). However, guaranteeing slice-level Service-Level Agreements (SLAs) under dynamic user mobility, stochastic task arrivals, and constrained onboard energy and computing resources remains a fundamental challenge. This paper proposes a predictive multi-agent Reinforcement Learning (RL) framework that proactively maintains SLA stability in UAV-enabled MEC through coordinated trajectory control and computation resource allocation. A lightweight prediction module forecasts near-future user mobility, enabling UAVs to anticipate congestion and reposition before SLA viol...

• 2.Generative Communications: Overview, Technologies, and Trends

  The groundbreaking development of generative artificial intelligence (AI) is rapidly boosting the ability to generate content such as images and videos, reshaping communication paradigms. This article introduces generative communications (GenCom), a novel paradigm for 6G networks in which large AI models (LAMs) drive semantic understanding, reasoning, and content generation, embedding these into the communication process. Unlike traditional systems that strictly pursue accurate bit transmission, GenCom enables transmitters to convey only minimal yet sufficient information, while receivers leverage shared generative priors and knowledge bases to synthesize the intended output. Communication is thus redefined as controlled generation rather than data reproduction. We formalize the concept of GenCom, clarify its AI-native and generation-driv...

• 3.Can the Cloud Drive? Infrastructure Feasibility of Offloading Autonomous Driving Across 5G and 6G

  Frontier autonomous-driving models -- especially vision-language-action (VLA) models, whose forward pass approaches $\sim$60~TFLOPs -- are outgrowing economical onboard deployment, since peak hardware sits idle most of the day. Cloud inference can instead share GPUs across active vehicles, but the vehicle must upload through a capacity-limited uplink, reach a GPU without queueing, and return a decision within the closed-loop budget. This paper asks: can the cloud drive? We answer with an analytical framework coupling communication limits, a roofline GPU service model, stochastic latency, and utilization-aware cost across three model classes, three offloading strategies, and three communication generations, applied to New York City. Separating a reactive 100~ms budget from a 300~ms deliberative tier (presuming an onboard reactive fallback)...

• 4.Privacy-Preserving Intent Fulfilment and Assurance for 6G RAN

  Intent-based network management is the emerging paradigm for 6G service lifecycle automation, with the 3GPP intent management framework (TS~28.312) defining creation, translation, fulfilment, and assurance stages. Existing fulfilment and assurance approaches require deep packet inspection, per-flow state tracking, or access to vendor-internal node telemetry to verify that provisioned resources satisfy expressed intents. These requirements conflict with regulatory constraints (GDPR, ePrivacy Directive) in multi-tenant networks and with vendor opacity in multi-vendor O-RAN deployments. We present an architecture for privacy-preserving intent fulfilment and assurance in which a coordinator provisions resources from declared intent categories without traffic inspection, and verifies fulfilment using only aggregate standardised PM counters a...

• 5.Parasitic MIMO Beamforming for Multi-Active Multi-Parasitic Antenna Arrays with Binary Control

  In 6G, MIMO dimensions continue to scale, yet the increased cost, power consumption, and hardware complexity associated with growing RF chains limit practical deployment. Parasitic antennas offer a promising alternative that can add spatial degrees of freedom and array gain without a proportional increase in RF chains. From a communication perspective, prior work on parasitic antennas has primarily focused on adjusting continuous reactance values using varactors, but such varactor-based tuning has increased cost and complexity in the analog control and practical RF circuit design. This paper proposes a multi-active multi-parasitic antenna (MAMP) architecture with binary controllers, where each parasitic element operates in one of two discrete reactance states. To validate the practicality of the system, we experimentally identify array ge...

• 1.Multi-Agent Reinforcement Learning for SLA-Aware Network Slicing in UAV-Enabled MEC

  Unmanned Aerial Vehicle (UAV)-enabled Mobile Edge Computing (MEC) offers flexible capacity provisioning for heterogeneous network slices, including Hyper-Reliable and Low-Latency Communication (HRLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine-Type Communications (mMTC). However, guaranteeing slice-level Service-Level Agreements (SLAs) under dynamic user mobility, stochastic task arrivals, and constrained onboard energy and computing resources remains a fundamental challenge. This paper proposes a predictive multi-agent Reinforcement Learning (RL) framework that proactively maintains SLA stability in UAV-enabled MEC through coordinated trajectory control and computation resource allocation. A lightweight prediction module forecasts near-future user mobility, enabling UAVs to anticipate congestion and reposition before SLA viol...

• 2.Privacy-Preserving Intent Fulfilment and Assurance for 6G RAN

  Intent-based network management is the emerging paradigm for 6G service lifecycle automation, with the 3GPP intent management framework (TS~28.312) defining creation, translation, fulfilment, and assurance stages. Existing fulfilment and assurance approaches require deep packet inspection, per-flow state tracking, or access to vendor-internal node telemetry to verify that provisioned resources satisfy expressed intents. These requirements conflict with regulatory constraints (GDPR, ePrivacy Directive) in multi-tenant networks and with vendor opacity in multi-vendor O-RAN deployments. We present an architecture for privacy-preserving intent fulfilment and assurance in which a coordinator provisions resources from declared intent categories without traffic inspection, and verifies fulfilment using only aggregate standardised PM counters a...

• 3.From Agentic to Autogenic Network Management for AI-Native 6G and Beyond: A Standards Perspective

  Standards bodies, including TM Forum, 3GPP, and ETSI, are converging on Agentic AI as the foundation for next-generation network management, where Large AI Model (LAM)-based agents autonomously interpret intent, coordinate resources, and adapt operational behaviors at runtime. However, achieving this vision at the scale and complexity of 6G networks requires management systems that can generate and evolve their own automation software during operation. We introduce Autogenic network management, a reference architecture that extends agentic capabilities with self-programming, self reflection, self-orienting, and self-architecting capabilities. The architecture supports practical staged deployment beginning with human-supervised LAM-based agents and progressing toward autonomous operation as confidence builds. We demonstrate the approach th...

• 4.Quality-Aware Personalized AI Service Provisioning in UAV-Assisted 6G Networks

  In sixth-generation (6G) artificial intelligence (AI) services, two quality dimensions should be jointly addressed: conventional quality (e.g., latency) and Quality of AI Services (QoAIS; output fidelity, continuity, personalization). Existing methods emphasize conventional quality, while neglecting QoAIS, particularly for personalized outputs in dynamic aerial-terrestrial settings. This paper introduces HyPE, a Hybrid Predictive-in-context-lEarning framework for holistically quality-aware personalized AI service provisioning in Unmanned Aerial Vehicle (UAV)-assisted 6G networks. HyPE integrates: (i) mobility-aware prediction to forecast spatio-temporal request distributions, (ii) learning-augmented decision leveraging Large Language Model (LLM)-based reasoning to optimize UAV trajectories and inference assignments, and (iii) pre-/post-pr...

• 5.6G Sensing Security: Distributed Game-Theoretic RL for Urban Beamforming and Attacker Detection

  In next-generation networks, communication systems will no longer be limited to data transmission and will be expected to acquire awareness of the surrounding environment. This leads to the concept of integrated sensing and communication (ISAC), where the same wireless infrastructure is used for both communication and environmental sensing. Thus, ISAC enables the system to transmit information efficiently and observe and interpret channel variations and user behavior. Motivated by this capability, this work focuses on detecting an active attacker in an urban environment scenario, where the attacker intentionally manipulates beamforming directions to increase interference and mislead the transmitter into allocating the main lobe of beam toward itself instead of legitimate users. We apply game-theoretic approaches to model the interaction b...