Babak's Daily Briefing

Wednesday, April 22, 2026

Sources: 20 | Total Articles: 96

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

  Summary available at source link.

• 2.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.

• 3.SoftBank’s Physical AI push gives AI-RAN a sharper purpose

  SoftBank is starting to give AI-RAN a more concrete job description: not just running AI workloads near the network, but serving as the real-time infrastructure layer for robots and other physical systems. The company’s recent materials suggest it wants to move the AI-RAN conversation from telecom architecture to real-world machine action.

• 4.South Korea puts 6G inside its national AI push

  South Korea has unveiled a three-year national roadmap aimed at becoming one of the world’s top three AI powers by 2028, with 6G commercialization positioned as part of that broader push.

• 5.b-com’s Open XG Hub targets one of telecom’s biggest gaps: turning experimentation into deployment

  In an interview with Peter Pietrzyk, Managing Director of 6GWorld, Patrick Savell, Head of Connectivity at b-com, said platforms such as Open XG Hub are designed to help bridge one of the industry’s most persistent challenges: moving promising ideas from research environments into deployable network systems. The bigger point is that, as telecom becomes more software-driven and AI-native, the bottleneck is increasingly less about invention and more about validation, integration, and operational readiness.

• 1.ECLASS-Augmented Semantic Product Search for Electronic Components

  Efficient semantic access to industrial product data is a key enabler for factory automation and emerging LLM-based agent workflows, where both human engineers and autonomous agents must identify suitable components from highly structured catalogs. However, the vocabulary mismatch between natural-language queries and attribute-centric product descriptions limits the effectiveness of traditional retrieval approaches, e.g., BM25. In this work, we present a systematic evaluation of LLM-assisted dense retrieval for semantic product search on industrial electronic components, and investigate the integration of hierarchical semantics from the ECLASS standard into embedding-based retrieval. Our results show that dense retrieval combined with re-ranking substantially outperforms classical lexical methods and foundation model web-search baselines....

• 2.Mesh Memory Protocol: Semantic Infrastructure for Multi-Agent LLM Systems

  Teams of LLM agents increasingly collaborate on tasks spanning days or weeks: multi-day data-generation sprints where generator, reviewer, and auditor agents coordinate in real time on overlapping batches; specialists carrying findings forward across session restarts; product decisions compounding over many review rounds. This requires agents to share, evaluate, and combine each other's cognitive state in real time across sessions. We call this cross-session agent-to-agent cognitive collaboration, distinct from parallel agent execution. To enable it, three problems must be solved together. (P1) Each agent decides field by field what to accept from peers, not accept or reject whole messages. (P2) Every claim is traceable to source, so returning claims are recognised as echoes of the receiver's own prior thinking. (P3) Memory that survives ...

• 3.WebUncertainty: Dual-Level Uncertainty Driven Planning and Reasoning For Autonomous Web Agent

  Recent advancements in large language models (LLMs) have empowered autonomous web agents to execute natural language instructions directly on real-world webpages. However, existing agents often struggle with complex tasks involving dynamic interactions and long-horizon execution due to rigid planning strategies and hallucination-prone reasoning. To address these limitations, we propose WebUncertainty, a novel autonomous agent framework designed to tackle dual-level uncertainty in planning and reasoning. Specifically, we design a Task Uncertainty-Driven Adaptive Planning Mechanism that adaptively selects planning modes to navigate unknown environments. Furthermore, we introduce an Action Uncertainty-Driven Monte Carlo tree search (MCTS) Reasoning Mechanism. This mechanism incorporates the Confidence-induced Action Uncertainty (ConActU) str...

• 4.Prompt Optimization Enables Stable Algorithmic Collusion in LLM Agents

  LLM agents in markets present algorithmic collusion risks. While prior work shows LLM agents reach supracompetitive prices through tacit coordination, existing research focuses on hand-crafted prompts. The emerging paradigm of prompt optimization necessitates new methodologies for understanding autonomous agent behavior. We investigate whether prompt optimization leads to emergent collusive behaviors in market simulations. We propose a meta-learning loop where LLM agents participate in duopoly markets and an LLM meta-optimizer iteratively refines shared strategic guidance. Our experiments reveal that meta-prompt optimization enables agents to discover stable tacit collusion strategies with substantially improved coordination quality compared to baseline agents. These behaviors generalize to held-out test markets, indicating discovery of g...

• 5.Know When to Trust the Skill: Delayed Appraisal and Epistemic Vigilance for Single-Agent LLMs

  As large language models (LLMs) transition into autonomous agents integrated with extensive tool ecosystems, traditional routing heuristics increasingly succumb to context pollution and "overthinking". We argue that the bottleneck is not a deficit in algorithmic capability or skill diversity, but the absence of disciplined second-order metacognitive governance. In this paper, our scientific contribution focuses on the computational translation of human cognitive control - specifically, delayed appraisal, epistemic vigilance, and region-of-proximal offloading - into a single-agent architecture. We introduce MESA-S (Metacognitive Skills for Agents, Single-agent), a preliminary framework that shifts scalar confidence estimation into a vector separating self-confidence (parametric certainty) from source-confidence (trust in retrieved external...

• 1.Two-dimensional early exit optimisation of LLM inference

  arXiv:2604.18592v1 Announce Type: new Abstract: We introduce a two-dimensional (2D) early exit strategy that coordinates layer-wise and sentence-wise exiting for classification tasks in large language models. By processing input incrementally sentence-by-sentence while progressively activating deeper layers, our method achieves multiplicative computational savings that exceed those from optimizing either dimension independently. Experimental evaluation across four state-of-the-art LLMs (Llama 3.1, Llama 3.2, Gemma, Qwen; 3B-8B parameters) on three sentiment classification datasets demonstrates additional speed-ups of 1.4--2.3$\times$ over optimal layer-wise early exit for simpler tasks with vanilla models, with graceful degradation on complex multi-class problems. Fine-tuning reduces but does not eliminate this advantage. The approach is...

• 2.Probing for Reading Times

  arXiv:2604.18712v1 Announce Type: new Abstract: Probing has shown that language model representations encode rich linguistic information, but it remains unclear whether they also capture cognitive signals about human processing. In this work, we probe language model representations for human reading times. Using regularized linear regression on two eye-tracking corpora spanning five languages (English, Greek, Hebrew, Russian, and Turkish), we compare the representations from every model layer against scalar predictors -- surprisal, information value, and logit-lens surprisal. We find that the representations from early layers outperform surprisal in predicting early-pass measures such as first fixation and gaze duration. The concentration of predictive power in the early layers suggests that human-like processing signatures are captured ...

• 3.Characterizing AlphaEarth Embedding Geometry for Agentic Environmental Reasoning

  arXiv:2604.18715v1 Announce Type: new Abstract: Earth observation foundation models encode land surface information into dense embedding vectors, yet the geometric structure of these representations and its implications for downstream reasoning remain underexplored. We characterize the manifold geometry of Google AlphaEarth's 64-dimensional embeddings across 12.1 million Continental United States samples (2017--2023) and develop an agentic system that leverages this geometric understanding for environmental reasoning. The manifold is non-Euclidean: effective dimensionality is 13.3 (participation ratio) from 64 raw dimensions, with local intrinsic dimensionality of approximately 10. Tangent spaces rotate substantially, with 84\% of locations exceeding 60\textdegree{} and local-global alignment (mean$|\cos\theta| = 0.17$) approaching the r...

• 4.Scripts Through Time: A Survey of the Evolving Role of Transliteration in NLP

  arXiv:2604.18722v1 Announce Type: new Abstract: Cross-lingual transfer in NLP is often hindered by the ``script barrier'' where differences in writing systems inhibit transfer learning between languages. Transliteration, the process of converting the script, has emerged as a powerful technique to bridge this gap by increasing lexical overlap. This paper provides a comprehensive survey of the application of transliteration in cross-lingual NLP. We present a taxonomy of key motivations to utilize transliterations in language models, and provide an overview of different approaches of incorporating transliterations as input. We analyze the evolution and effectiveness of these methods, discussing the critical trade-offs involved, and contextualize their need in modern LLMs. The review explores various settings that show how transliteration is...

• 5.Investigating Counterfactual Unfairness in LLMs towards Identities through Humor

  arXiv:2604.18729v1 Announce Type: new Abstract: Humor holds up a mirror to social perception: what we find funny often reflects who we are and how we judge others. When language models engage with humor, their reactions expose the social assumptions they have internalized from training data. In this paper, we investigate counterfactual unfairness through humor by observing how the model's responses change when we swap who speaks and who is addressed while holding other factors constant. Our framework spans three tasks: humor generation refusal, speaker intention inference, and relational/societal impact prediction, covering both identity-agnostic humor and identity-specific disparagement humor. We introduce interpretable bias metrics that capture asymmetric patterns under identity swaps. Experiments across state-of-the-art models reveal ...

• 1.Compile to Compress: Boosting Formal Theorem Provers by Compiler Outputs

  arXiv:2604.18587v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated significant potential in formal theorem proving, yet state-of-the-art performance often necessitates prohibitive test-time compute via massive roll-outs or extended context windows. In this work, we address this scalability bottleneck by exploiting an informative structure in formal verification: the observation that compilers map a vast space of diverse proof attempts to a compact set of structured failure modes. We introduce a learning-to-refine framework that leverages this compression to perform efficient learning and proof exploration. We perform tree search that corrects errors locally conditioned on explicit verifier feedback, thereby circumventing the costs associated with accumulating a long history of proof attempts. Extensive evaluati...

• 2.Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning

  arXiv:2604.18639v1 Announce Type: new Abstract: Previous LLMs-based RL studies typically follow either supervised learning with high annotation costs, or unsupervised paradigms using voting or entropy-based rewards. However, their performance remains far from satisfactory due to the substantial annotation cost and issues such as model collapse or reward hacking. To address these issues, we introduce a new perspective inspired by cognitive learning theory and propose a novel approach called EasyRL. The core of EasyRL is to simulate the human cognitive acquisition curve by integrating reliable knowledge transfer from easy labeled data with a progressive divide-and-conquer strategy that tackles increasingly difficult unlabeled data. Specifically, we initialize a warm-up model using supervised RL with few-shot labeled data. This is followed b...

• 3.FASE : A Fairness-Aware Spatiotemporal Event Graph Framework for Predictive Policing

  arXiv:2604.18644v1 Announce Type: new Abstract: Predictive policing systems that allocate patrol resources based solely on predicted crime risk can unintentionally amplify racial disparities through feedback driven data bias. We present FASE, a Fairness Aware Spatiotemporal Event Graph framework, which integrates spatiotemporal crime prediction with fairness constrained patrol allocation and a closed loop deployment feedback simulator. We model Baltimore as a graph of 25 ZIP Code Tabulation Areas and use 139,982 Part 1 crime incidents from 2017 to 2019 at hourly resolution, producing a sparse feature tensor. The prediction module combines a spatiotemporal graph neural network with a multivariate Hawkes process to capture spatial dependencies and self exciting temporal dynamics. Outputs are modeled using a Zero Inflated Negative Binomial d...

• 4.Curiosity-Critic: Cumulative Prediction Error Improvement as a Tractable Intrinsic Reward for World Model Training

  arXiv:2604.18701v1 Announce Type: new Abstract: Local prediction-error-based curiosity rewards focus on the current transition without considering the world model's cumulative prediction error across all visited transitions. We introduce Curiosity-Critic, which grounds its intrinsic reward in the improvement of this cumulative objective, and show that it reduces to a tractable per-step form: the difference between the current prediction error and the asymptotic error baseline of the current state transition. We estimate this baseline online with a learned critic co-trained alongside the world model; regressing a single scalar, the critic converges well before the world model saturates, redirecting exploration toward learnable transitions without oracle knowledge of the noise floor. The reward is higher for learnable transitions and collap...

• 5.The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification

  arXiv:2604.18728v1 Announce Type: new Abstract: Many neural network (NN) verification systems represent the network's input-output relation as a constraint program. Sound and complete, representations involve integer constraints, for simulating the activations. Recent works convexly relax the integer constraints, improving performance, at the cost of soundness. Convex relaxations consider outputs that are unreachable by the original network. We study the worst case divergence between the original network and its convex relaxations; both qualitatively and quantitatively. The relaxations' space forms a lattice, where the top element corresponds to a full relaxation, with every neuron linearized. The bottom element corresponds to the original network. We provide analytical upper and lower bounds for the $\ell_\infty$-distance between the ful...

• 1.HALO: Hybrid Auto-encoded Locomotion with Learned Latent Dynamics, Poincar\'e Maps, and Regions of Attraction

  arXiv:2604.18887v1 Announce Type: new Abstract: Reduced-order models are powerful for analyzing and controlling high-dimensional dynamical systems. Yet constructing these models for complex hybrid systems such as legged robots remains challenging. Classical approaches rely on hand-designed template models (e.g., LIP, SLIP), which, though insightful, only approximate the underlying dynamics. In contrast, data-driven methods can extract more accurate low-dimensional representations, but it remains unclear when stability and safety properties observed in the latent space meaningfully transfer back to the full-order system. To bridge this gap, we introduce HALO (Hybrid Auto-encoded Locomotion), a framework for learning latent reduced-order models of periodic hybrid dynamics directly from trajectory data. HALO employs an autoencoder to identif...

• 2.Thrust Regulation Through Wing Linkage Modulation on the Aerobat Platform: Piezoelectric Slip-Stick Actuated Regulator Development

  arXiv:2604.18900v1 Announce Type: new Abstract: Aerobat is a bat-inspired flapping-wing robot with a wing gait generate by the computational structure, a planar linkage of carbon fiber links driven by a single motor. This design minimizes weight but couples both wings to a shared input motor, eliminating independent thrust control and preventing asymmetric maneuvers. This thesis investigates thrust regulation by modifying the effective length of the first radius link $R_1$ in the computational structure. Static experiments using FDM-printed $R_1$ links at three lengths (28.58, 29.33, and 30.08 mm) across 3,4, and 5 Hz flapping frequencies demonstrated that a 1.5 mm length increase produced a 37% increase in peak lift force and shifted peak force timing within the downstroke. An additional experiment using a string-actuated regulator mecha...

• 3.Task-Adaptive Admittance Control for Human-Quadrotor Cooperative Load Transportation with Dynamic Cable-Length Regulation

  arXiv:2604.18905v1 Announce Type: new Abstract: The collaboration between humans and robots is critical in many robotic applications, especially in those requiring physical human-robot interaction (pHRI). Previous research in pHRI has largely focused on robotic manipulators, employing impedance or admittance control to maintain operational safety. Conversely, research in human-quadrotor cooperative load transportation (CLT) is still in its infancy. This letter introduces a novel admittance controller designed for safe and effective human-quadrotor CLT using a quadrotor equipped with an actively-controlled winch. The proposed method accounts for the system's coupled dynamics, allowing the quadrotor and its cable to dynamically adapt to contact forces during CLT tasks, thereby enhancing responsiveness. We experimentally validated the task-a...

• 4.Gated Memory Policy

  arXiv:2604.18933v1 Announce Type: new Abstract: Robotic manipulation tasks exhibit varying memory requirements, ranging from Markovian tasks that require no memory to non-Markovian tasks that depend on historical information spanning single or multiple interaction trials. Surprisingly, simply extending observation histories of a visuomotor policy often leads to a significant performance drop due to distribution shift and overfitting. To address these issues, we propose Gated Memory Policy (GMP), a visuomotor policy that learns both when to recall memory and what to recall. To learn when to recall memory, GMP employs a learned memory gate mechanism that selectively activates history context only when necessary, improving robustness and reactivity. To learn what to recall efficiently, GMP introduces a lightweight cross-attention module that...

• 5.AI-Enabled Image-Based Hybrid Vision/Force Control of Tendon-Driven Aerial Continuum Manipulators

  arXiv:2604.18961v1 Announce Type: new Abstract: This paper presents an AI-enabled cascaded hybrid vision/force control framework for tendon-driven aerial continuum manipulators based on constant-strain modeling in $SE(3)$ as a coupled system. The proposed controller is designed to enable autonomous, physical interaction with a static environment while stabilizing the image feature error. The developed strategy combines the cascaded fast fixed-time sliding mode control and a radial basis function neural network to cope with the uncertainties in the image acquired by the eye-in-hand monocular camera and the measurements from the force sensing apparatus. This ensures rapid, online learning of the vision- and force-related uncertainties without requiring offline training. Furthermore, the features are extracted via a state-of-the-art graph ne...

• 1.The Virtue of Sparsity in Complexity

  Sparsity or complexity? In modern high-dimensional asset pricing, these are often viewed as competing principles: richer feature spaces appear to favor complexity, while economic intuition has long favored parsimony. We show that this tension is misplaced. We distinguish capacity sparsity-the dimensionality of the candidate feature space-from factor sparsity-the parsimonious structure of priced risks-and argue that the two are complements: expanding capacity enables the discovery of factor sparsity. Revisiting the benchmark empirical design of Didisheim et al. (2025) and pushing it to higher complexity regimes, we show that nonlinear feature expansions combined with basis pursuit yield portfolios whose out-of-sample performance dominates ridgeless benchmarks beyond a critical complexity threshold. The evidence shows that the gains from co...

• 2.The CTLNet for Shanghai Composite Index Prediction

  Shanghai Composite Index prediction has become a hot issue for many investors and academic researchers. Deep learning models are widely applied in multivariate time series forecasting, including recurrent neural networks (RNN), convolutional neural networks (CNN), and transformers. Specifically, the Transformer encoder, with its unique attention mechanism and parallel processing capabilities, has become an important tool in time series prediction, and has an advantage in dealing with long sequence dependencies and multivariate data correlations. Drawing on the strengths of various models, we propose the CNN-Transformer-LSTM Networks (CTLNet). This paper explores the application of CTLNet for Shanghai Composite Index prediction and the comparative experiments show that the proposed model outperforms state-of-the-art baselines.

• 3.Spurious Predictability in Financial Machine Learning

  Adaptive specification search generates statistically significant backtests even under martingale-difference nulls. We introduce a falsification audit testing complete predictive workflows against synthetic reference classes, including zero-predictability environments and microstructure placebos. Workflows generating significant walk-forward evidence in these environments are falsified. For passing workflows, we quantify selection-induced performance inflation using an absolute magnitude gap linking optimized in-sample evidence to disjoint walk-forward realizations, adjusted for effective multiplicity. Simulations validate extreme-value scaling under correlated searches and demonstrate detection power under genuine structure. Empirical case studies confirm that many apparent findings represent methodological artifacts rather than genuine ...

• 4.The Acoustic Camouflage Phenomenon: Re-evaluating Speech Features for Financial Risk Prediction

  In computational paralinguistics, detecting cognitive load and deception from speech signals is a heavily researched domain. Recent efforts have attempted to apply these acoustic frameworks to corporate earnings calls to predict catastrophic stock market volatility. In this study, we empirically investigate the limits of acoustic feature extraction (pitch, jitter, and hesitation) when applied to highly trained speakers in in-the-wild teleconference environments. Utilizing a two-stream late-fusion architecture, we contrast an acoustic-based stream with a baseline Natural Language Processing (NLP) stream. The isolated NLP model achieved a recall of 66.25% for tail-risk downside events. Surprisingly, integrating acoustic features via late fusion significantly degraded performance, reducing recall to 47.08%. We identify this degradation as Ac...

• 5.PRAGMA: Revolut Foundation Model

  Modern financial systems generate vast quantities of transactional and event-level data that encode rich economic signals. This paper presents PRAGMA, a family of foundation models for multi-source banking event sequences. Our approach pre-trains a Transformer-based architecture with masked modelling on a large-scale, heterogeneous banking event corpus using a self-supervised objective tailored to the discrete, variable-length nature of financial records. The resulting model supports a wide range of downstream tasks such as credit scoring, fraud detection, and lifetime value prediction: strong performance can be achieved by training a simple linear model on top of the extracted embeddings and can be further improved with lightweight fine-tuning. Through extensive evaluation on downstream tasks, we demonstrate that PRAGMA achieves superior...

• 1.GSMA Report Urges Japan to Take Bold Action to Convert Technical Excellence into Global Digital Leadership

  Summary available at source link.

• 2.From Rich Text to Video: RCS Universal Profile 4.0 has arrived

  Summary available at source link.

• 3.Mobile Money accounted for $2 trillion in  transactions in 2025, doubling since 2021 as active accounts continue to grow

  Summary available at source link.

• 4.Strengthening the Global Fight Against Fraud and Scams – Takeaways from the Global Fraud Summit in Vienna

  Summary available at source link.

• 5.GSMA MWC26 Barcelona closes 20th anniversary edition

  Summary available at source link.

• 1.Discovering a Shared Logical Subspace: Steering LLM Logical Reasoning via Alignment of Natural-Language and Symbolic Views

  Large Language Models (LLMs) still struggle with multi-step logical reasoning. Existing approaches either purely refine the reasoning chain in natural language form or attach a symbolic solver as an external module. In this work, we instead ask whether LLMs contain a shared internal logical subspace that simultaneously aligns natural-language and symbolic-language views of the reasoning process. Our hypothesis is that this logical subspace captures logical reasoning capabilities in LLMs that are shared across views while remaining independent of surface forms. To verify this, we employ Canonical Correlation Analysis on the paired residual activations from natural-language and symbolic-language reasoning chains, learning a low-dimensional subspace with maximum cross-view correlation. Furthermore, we design a training-free approach that ste...

• 2.Epistemic orientation in parliamentary discourse is associated with deliberative democracy

  The pursuit of truth is central to democratic deliberation and governance, yet political discourse reflects varying epistemic orientations, ranging from evidence-based reasoning grounded in verifiable information to intuition-based reasoning rooted in beliefs and subjective interpretation. We introduce a scalable approach to measure epistemic orientation using the Evidence--Minus--Intuition (EMI) score, derived from large language model (LLM) ratings and embedding-based semantic similarity. Applying this approach to 15 million parliamentary speech segments spanning 1946 to 2025 across seven countries, we examine temporal patterns in discourse and its association with deliberative democracy and governance. We find that EMI is positively associated with deliberative democracy within countries over time, with consistent relationships in both...

• 3.Unveiling Fine-Grained Visual Traces: Evaluating Multimodal Interleaved Reasoning Chains in Multimodal STEM Tasks

  Multimodal large language models (MLLMs) have shown promising reasoning abilities, yet evaluating their performance in specialized domains remains challenging. STEM reasoning is a particularly valuable testbed because it provides highly verifiable feedback, but existing benchmarks often permit unimodal shortcuts due to modality redundancy and focus mainly on final-answer accuracy, overlooking the reasoning process itself. To address this challenge, we introduce StepSTEM: a graduate-level benchmark of 283 problems across mathematics, physics, chemistry, biology, and engineering for fine-grained evaluation of cross-modal reasoning in MLLMs. StepSTEM is constructed through a rigorous curation pipeline that enforces strict complementarity between textual and visual inputs. We further propose a general step-level evaluation framework for both ...

• 4.A-MAR: Agent-based Multimodal Art Retrieval for Fine-Grained Artwork Understanding

  Understanding artworks requires multi-step reasoning over visual content and cultural, historical, and stylistic context. While recent multimodal large language models show promise in artwork explanation, they rely on implicit reasoning and internalized knowl- edge, limiting interpretability and explicit evidence grounding. We propose A-MAR, an Agent-based Multimodal Art Retrieval framework that explicitly conditions retrieval on structured reasoning plans. Given an artwork and a user query, A-MAR first decomposes the task into a structured reasoning plan that specifies the goals and evidence requirements for each step. Retrieval is then conditionedon this plan, enabling targeted evidence selection and supporting step-wise, grounded explanations. To evaluate agent-based multi- modal reasoning within the art domain, we introduce ArtCoT-QA....

• 5.Pause or Fabricate? Training Language Models for Grounded Reasoning

  Large language models have achieved remarkable progress on complex reasoning tasks. However, they often implicitly fabricate information when inputs are incomplete, producing confident but unreliable conclusions -- a failure mode we term ungrounded reasoning. We argue that this issue arises not from insufficient reasoning capability, but from the lack of inferential boundary awareness -- the ability to recognize when the necessary premises for valid inference are missing. To address this issue, we propose Grounded Reasoning via Interactive Reinforcement Learning (GRIL), a multi-turn reinforcement learning framework for grounded reasoning under incomplete information. GRIL decomposes the reasoning process into two stages: clarify and pause, which identifies whether the available information is sufficient, and grounded reasoning, which perf...

• 1.Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items

  Recent advances in image generation and editing have opened new opportunities for virtual try-on. However, existing methods still struggle to meet complex real-world demands. We present Tstars-Tryon 1.0, a commercial-scale virtual try-on system that is robust, realistic, versatile, and highly efficient. First, our system maintains a high success rate across challenging cases like extreme poses, severe illumination variations, motion blur, and other in-the-wild conditions. Second, it delivers highly photorealistic results with fine-grained details, faithfully preserving garment texture, material properties, and structural characteristics, while largely avoiding common AI-generated artifacts. Third, beyond apparel try-on, our model supports flexible multi-image composition (up to 6 reference images) across 8 fashion categories, with coordin...

• 2.CityRAG: Stepping Into a City via Spatially-Grounded Video Generation

  We address the problem of generating a 3D-consistent, navigable environment that is spatially grounded: a simulation of a real location. Existing video generative models can produce a plausible sequence that is consistent with a text (T2V) or image (I2V) prompt. However, the capability to reconstruct the real world under arbitrary weather conditions and dynamic object configurations is essential for downstream applications including autonomous driving and robotics simulation. To this end, we present CityRAG, a video generative model that leverages large corpora of geo-registered data as context to ground generation to the physical scene, while maintaining learned priors for complex motion and appearance changes. CityRAG relies on temporally unaligned training data, which teaches the model to semantically disentangle the underlying scene f...

• 3.Phase Transitions in the Fluctuations of Functionals of Random Neural Networks

  We establish central and non-central limit theorems for sequences of functionals of the Gaussian output of an infinitely-wide random neural network on the d-dimensional sphere . We show that the asymptotic behaviour of these functionals as the depth of the network increases depends crucially on the fixed points of the covariance function, resulting in three distinct limiting regimes: convergence to the same functional of a limiting Gaussian field, convergence to a Gaussian distribution, convergence to a distribution in the Qth Wiener chaos. Our proofs exploit tools that are now classical (Hermite expansions, Diagram Formula, Stein-Malliavin techniques), but also ideas which have never been used in similar contexts: in particular, the asymptotic behaviour is determined by the fixed-point structure of the iterative operator associated with ...

• 4.FASTER: Value-Guided Sampling for Fast RL

  Some of the most performant reinforcement learning algorithms today can be prohibitively expensive as they use test-time scaling methods such as sampling multiple action candidates and selecting the best one. In this work, we propose FASTER, a method for getting the benefits of sampling-based test-time scaling of diffusion-based policies without the computational cost by tracing the performance gain of action samples back to earlier in the denoising process. Our key insight is that we can model the denoising of multiple action candidates and selecting the best one as a Markov Decision Process (MDP) where the goal is to progressively filter action candidates before denoising is complete. With this MDP, we can learn a policy and value function in the denoising space that predicts the downstream value of action candidates in the denoising pr...

• 5.Benign Overfitting in Adversarial Training for Vision Transformers

  Despite the remarkable success of Vision Transformers (ViTs) across a wide range of vision tasks, recent studies have revealed that they remain vulnerable to adversarial examples, much like Convolutional Neural Networks (CNNs). A common empirical defense strategy is adversarial training, yet the theoretical underpinnings of its robustness in ViTs remain largely unexplored. In this work, we present the first theoretical analysis of adversarial training under simplified ViT architectures. We show that, when trained under a signal-to-noise ratio that satisfies a certain condition and within a moderate perturbation budget, adversarial training enables ViTs to achieve nearly zero robust training loss and robust generalization error under certain regimes. Remarkably, this leads to strong generalization even in the presence of overfitting, a phe...

• 1.AI Agent Designs a RISC-V CPU Core From Scratch

  In 2020 researchers fine-tuned a GPT-2 model to design fragments of logic circuits ; in 2023 researchers used GPT-4 to help design an 8-bit processor with a novel instruction set; by 2024, a variety of LLMs could design and test chips with basic functionality, like dice rolls (though often these were flawed). Now Verkor.io, an AI chip design start-up, claims a bigger milestone: a RISC-V CPU core designed entirely by an agentic AI system. The CPU, dubbed VerCore, has a clock speed of 1.5 gigahertz and performance similar to a 2011-era laptop CPU. Suresh Krishna , co-founder at Verkor.io , says the team’s key claim is that this approach is more effective than using only specialized AI systems for specialized tasks within the overall design process. “ What we learned is that the better approach is to let the AI agent solve the whole problem,...

• 2.Optical Fiber Networks Can Keep Rail Networks Safe

  This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore. Rail networks are vast, which makes it difficult to conduct comprehensive, continuous safety monitoring. Researchers in China have suggested analyzing the vibrations of existing fiber cables buried underground alongside railway tracks to detect problems. In a study published 5 March in the Journal of Optical Communications and Networking , the research group demonstrated through experiments how the technique can successfully identify a number of issues associated with train safety, including faulty train wheels and broken sound barriers alongside the railway tracks. Sasha Dong is a junior chair professor in Southeast University’s School of Transportation, in Nanjing, China. She notes that traditional approaches for monitoring railways—such as ...

• 3.​Boston Dynamics and Google DeepMind Teach Spot to Reason​

  The amazing and frustrating thing about robots is that they can do almost anything you want them to do, as long as you know how to ask properly. In the not-so-distant past, asking properly meant writing code, and while we’ve thankfully moved beyond that brittle constraint, there’s still an irritatingly inverse correlation between ease of use and complexity of task. AI has promised to change that. The idea is that when AI is embodied within robots—giving AI software a physical presence in the world—those robots will be imbued with reasoning and understanding. This is cutting-edge stuff, though, and while we’ve seen plenty of examples of embodied AI in a research context, finding applications where reasoning robots can provide reliable commercial value has not been easy. Boston Dynamics is one of the few companies to commercially deploy leg...

• 4.Sarang Gupta Builds AI Systems With Real-World Impact

  Like many engineers, Sarang Gupta spent his childhood tinkering with everyday items around the house. From a young age he gravitated to projects that could make a difference in someone’s everyday life. When the family’s microwave plug broke, Gupta and his father figured out how to fix it. When a drawer handle started jiggling annoyingly, the youngster made sure it didn’t do so for long. Sarang Gupta Employer OpenAI in San Francisco Job Data science staff member Member grade Senior member Alma maters The Hong Kong University of Science and Technology; Columbia By age 11, his interest expanded from nuts and bolts to software. He learned programming languages such as Basic and Logo and designed simple programs including one that helped a local restaurant automate online ordering and billing. Gupta, an IEEE senior member, brings his mix of cu...

• 5.12 Graphs That Explain the State of AI in 2026

  The capabilities of leading AI models continue to accelerate, and the largest AI companies, including OpenAI and Anthropic , are hurtling toward IPOs later this year. Yet resentment toward AI continues to simmer, and in some cases has boiled over, especially in the United States, where local governments are beginning to embrace restrictions or outright bans on new data center development. It’s a lot to keep track of, but the 2026 edition of the AI Index from Stanford University’s Human-Centered Artificial Intelligence center pulls it off. The report, which comes in at over 400 pages, includes dozens of data points and graphs that approach the topic from multiple angles, from benchmark scores to investment and public perception. As in prior years (see our coverage from 2021 , 2022 , 2023 , 2024 , and 2025 ), we’ve read the report and ident...

• 1.Why Business Leaders Need to Champion Democracy

  Carolyn Geason-Beissel/MIT SMR | Getty Images Democracy is in decline across the world. More countries are experiencing erosion of political rights and civil liberties than gains, according to Freedom House. As of 2025, 92 countries, representing 74% of the world’s population, were classified as autocracies by the V-Dem Institute. Democratic backsliding is a primary concern […]

• 2.Industrial AI for the Physical World: Siemens’s Peter Koerte

  In this episode of the Me, Myself, and AI podcast, host Sam Ransbotham talks with Peter Koerte, a member of the managing board and chief strategy and technology officer of Siemens, about how industrial AI is quietly transforming the infrastructure that powers everyday life. While consumer AI grabs headlines, Peter explains how artificial intelligence is […]

• 3.Beyond the Model — Why Responsible AI Must Address Workforce Impact

  For the fifth year in a row, MIT Sloan Management Review and Boston Consulting Group (BCG) have assembled an international panel of AI experts that includes academics and practitioners to help us understand how responsible artificial intelligence (RAI) is being implemented across organizations worldwide. In prior years, we examined organizational RAI maturity; third-party, generative, and […]

• 4.How AI Helps the Best and Hurts the Rest

  Mark Shaver/theispot.com Can generative AI serve as an effective adviser for business owners and entrepreneurs? Intuitive chat-based natural language interfaces mean that anyone who can read and write can use GenAI tools for a wide range of tasks, even if they lack technical skills. This has obvious appeal for entrepreneurs and small business owners, many […]

• 5.Lessons From Innovation Pioneer Florence Nightingale

  Carolyn Geason-Beissel/MIT SMR | Wellcome Collection Florence Nightingale may be best remembered as the epitome of a kind, caring nurse, but she was also a force for disruptive innovation in health care. Three distinct elements of her work — communicating data compellingly, publicizing clear and simple instructions, and expanding professionalized training — carry timeless lessons […]

• 1.The Impact of Maternal Education on Early Childhood Development -- by Moriam Khanam, Mohammad Hajizadeh, Casey Warman

  This study leverages exogenous variation from a secondary school stipend program for female students in rural Bangladesh to estimate the causal effect of maternal education on early childhood development. Using data from the 2019 Bangladesh Multiple Indicator Cluster Survey, we find that the five years of stipend eligibility increase mothers' schooling by about one year. Instrumental variable estimates show that an additional year of maternal education improves early childhood development scores by 0.5 points on a scale of 0-10, with gains in overall developmental readiness (7.5 percentage points) and in the literacy–numeracy (7.7 percentage points) and physical (1.9 percentage points) domains. The results are robust across specifications. We also estimate the effects of maternal education on potential mechanisms, including children's nu...

• 2.Tariffs and the Term Structure of Inflation Expectations -- by Stéphane Auray, Michael B. Devereux, Anthony M. Diercks, Aurélien Eyquem, Joon Kim

  Inflation expectations derived from financial markets exhibited unprecedented dynamics in 2025: the correlation between one-year inflation swaps and one-year-ahead one-year forward rates turned significantly negative for the first time on record. We show that this decoupling occurred primarily on days when tariff news dominated market pricing, using a two-stage event classification validated by Bloomberg news trends. Standard small open-economy New Keynesian models in which tariffs generate a one-time price-level increase imply positive comovement across horizons and cannot explain this pattern. We explain these occurrences through the lens of an amended small open-economy New Keynesian model. Three ingredients prove critical for reproducing the observed negative conditional correlation between spot and forward inflation after tariff sho...

• 3.Bilateral Conflict Risk and Trade: Military Wars, Trade Wars, and Diplomatic Noise -- by Joshua Aizenman, Rodolphe Desbordes, Jamel Saadaoui

  How damaging is a “trade war” compared to a “military war” or a “war of words”? Aggregate conflict indicators cannot say, because they treat missile strikes, sanctions, and diplomatic protests as equivalent. We build a monthly bilateral indicator from GDELT event data, calibrated against human-curated ground truth, that decomposes hostility into four layers: kinetic fighting (“military war”), military posture, sanctions-context tensions (“trade war”), and routine diplomacy. The decomposed panel reveals a secular shift: over the past decade, governments have steadily substituted economic coercion for military confrontation, nearly doubling the trade-weighted share of hostility channelled through sanctions contexts. In a gravity trade model, the aggregate indicator is negative, large, and statistically significant, but the decomposition rev...

• 4.The “Peace Dividend” of International Trade: A New Empirical Approach -- by Ling Feng, Qiuyue Huang, Zhiyuan Li, Christopher M. Meissner

  This paper investigates the causal impact of international trade on interstate military conflicts using global bilateral data from 1962 to 2014. To address endogeneity concerns, we exploit exogenous spatial-temporal variation in international trade stemming from technological advances in air relative to maritime transport. Empirical results demonstrate a strong “peace dividend” of international trade: that is, increased trade significantly reduces the probability and intensity of conflicts between nations. This effect remains robust across specifications and withstands a wide range of potential confounders. Such findings highlight how economic interdependence shapes international conflict—a relationship that is especially relevant amid escalating geopolitical tensions and the global shift toward “decoupling”, “de-risking”, and greater tra...

• 5.How Have Universities Survived for Nearly a Millenium -- by David M. Cutler, Edward L. Glaeser

  How have universities managed to survive and evolve over almost 1,000 years to become wildly heterogeneous, unusually fractious, multi-product, non-profit entities? Universities began as teachers’ guilds, and they still give faculty a remarkable degree of autonomy. That structure attracts and empowers intellectuals, who are selected in part on their taste for knowledge, and those entrepreneurs and philanthropists have enabled universities to morph in ways that firms rarely do. Intellectual autonomy can also explain why universities are so often at odds with legal authorities and why faculty fight so often with each other and with their bosses. This essay presents a model of university organization and sketches the evolution of the university’s products and conflicts over the last 900 years. We also discuss the social value of university e...

• 1.Bank Failures: The Roles of Solvency and Liquidity

  Do banks fail because of runs or because they become insolvent? Answering this question is central to understanding financial crises and designing effective financial stability policies. Long-run historical evidence reveals that the root cause of bank failures is usually insolvency. The importance of bank runs is somewhat overstated. Runs matter, but in most cases they trigger or accelerate failure at already weak banks, rather than cause otherwise sound banks to fail.

• 2.The R*–Labor Share Nexus

  Over the past quarter century, the U.S. economy has experienced significant declines in both the labor share of income and the natural rate of interest, referred to as R*. Existing research has largely analyzed these two developments in isolation. In this post, we provide a simple model that captures the joint evolution of the labor share and R*, which we call the R*–labor share nexus. Our key finding is that structural changes affecting R* also influence the evolution of the labor share, and thereby wages and prices. This highlights a potentially important channel, absent from many macroeconomic models, through which the factors that determine R* also affect the labor share and, in turn, broader macroeconomic developments, with implications for monetary policy.

• 3.Use of Gen AI in the Workplace and the Value of Access to Training

  The rapid spread of generative AI (AI) tools is reshaping the workplace at a remarkable rate. Yet relatively little is known about whether workers have access to these tools, how the tools affect workers’ daily productivity, and how much workers value the training needed to use the tools effectively. In this post, we shed light on these issues by drawing on supplemental questions in the November 2025 Survey of Consumer Expectations (SCE), fielded to a representative sample of the U.S. population. We find that adoption of AI tools at work is heterogeneous, that a sizable share of workers see AI training as important, and that a significant share of employers are nonetheless not yet providing access to AI tools or training on how to use them.

• 4.What Millions of Homeowner’s Insurance Contracts Reveal About Risk Sharing

  Housing is the largest component of assets held by households in the United States, totaling $48 trillion in 2025. When natural disasters strike, the resulting damage to homes can be large relative to households’ liquid savings. Homeowner’s insurance is the primary financial tool households use to protect themselves against property risk. Despite the economic importance of homeowner’s insurance, we know surprisingly little about how insurance contracts are actually designed with respect to property risk. In this post, which is based on our new paper, “Economics of Property Insurance,” we examine how homeowner’s insurance contracts are structured in practice. Using a new granular dataset covering millions of homeowner’s insurance policies, we document ...

• 5.A Closer Look at Emerging Market Resilience During Recent Shocks

  A succession of shocks to the global economy in recent years has focused attention on the improved economic and financial resilience of emerging market economies. For some of these economies, this assessment is well-founded and highlights the fruits of deep, structural economic reforms since the 1990s. However, for a much larger universe of countries, the ability to weather shocks is still mixed and many remain vulnerable. In this post, we explore the divide between the two sets of countries and focus on the effects of recent economic shocks, including the ongoing conflict in the Middle East.

• 1.American Jews’ Netanyahu Problem

  Political polarization, the collapse of institutional gatekeepers, and social media’s amplification of fringe voices were always going to put pressure on American Jews. But Benjamin Netanyahu's alliances and policies have compounded these forces, putting US Jews in the crossfire of partisan politics and Middle East adventurism.

• 2.To Strengthen Climate Resilience, Focus on Social Protection

  The international community is increasingly trying to distinguish between climate, development, and humanitarian finance—as if they can be neatly compartmentalized. But this siloed approach overlooks how social-protection programs providing cash transfers to vulnerable households can strengthen resilience to climate shocks.

• 3.The Deafening Silence on Offshore Wealth

  The kind of hidden wealth that the Panama Papers exposed a decade ago fuels corruption, distorts markets, drives inequality, finances authoritarian regimes, and weakens democracies. Yet it endures—not because it is inevitable, but because powerful interests benefit from it.

• 4.The Risky Geography of the Cloud

  Iran's recent drone strikes on Gulf states mark the first time commercial data centers have been targeted in an organized fashion. Europe, especially, should reflect on the assumptions underlying digital-infrastructure investments and deployments, because attacks on data centers in the Middle East can jeopardize its own economic security.

• 5.Trump’s Self-Defeating Attacks on the Fed

  Instead of quietly winding down the meritless investigation of US Federal Reserve Chair Jerome Powell, the Trump administration has ramped it up, with prosecutors showing up unannounced at the Fed’s construction site. But the president's political charade will only delay the confirmation of Kevin Warsh as Powell’s successor.

• 1.The satellite arms race between Amazon Leo and Starlink isn’t what it seems

  A closer look shows that Amazon and Starlink are chasing different markets: mass-market connectivity vs high-margin enterprise services This week, Blue Origin hit a milestone — or thereabouts. The Jeff Bezos-founded rocket company had been noodling with the concept of…

• 2.For 6G spectrum allocation, the clock is ticking

  With AI traffic expected to surge, Qualcomm sees spectrum decisions made today shaping 6G leadership in the future While the standards and technology needed to support commercial 6G launches in 2029 are moving with strong momentum, policy decisions regarding access…

• 3.SFR bid tests French telecom market structure

  The potential acquisition of French telco SFR by Orange, Bouygues Telecom, and Iliad could reshape pricing dynamics and investment incentives in the local telecoms market In sum – what to know: Market consolidation – Fewer operators could ease price competition,…

• 4.Rising GPS jamming attacks threaten critical sectors

  An uptick in GPS jamming is disrupting navigation across maritime and aviation routes in active conflict zones — as well as outside GPS jamming incidents have grown extensively in recent years. According to multiple sources, thousands of jamming attacks have…

• 5.Verizon details World Cup 2026 network strategy

  Verizon explains how the company is scaling network capacity and deploying infrastructure across host venues In sum – what to know: Capacity boost – Verizon will increase bandwidth by 3–5x across U.S. stadiums using 5G, C-band, and mmWave spectrum to…

• 1.Source Error

  Check Feed

• 1.Networked Tracking of Multiple Moving Targets in 6G Network

  This paper considers a networked tracking architecture in 6G integrated sensing and communication (ISAC) systems, where multiple base stations (BSs) cooperatively transmit radio signals and process received echo signals to track multiple moving targets. Compared to the single-BS counterpart, networked tracking allows the moving targets to be associated with different BSs over time such that the wireless resources can be dynamically allocated among BSs based on target locations. However, networked tracking imposes new challenges for algorithm design and resource allocation. In this paper, we first design the networked Kalman Filter (NKF) that is suitable for multi-BS based tracking, then characterize the posterior Cramer-Rao bound (PCRB) under this NKF, and last design the beamforming vectors of all the BSs to minimize the tracking PCRB. N...

• 2.Towards Energy Impact on AI-Powered 6G IoT Networks: Centralized vs. Decentralized

  The emergence of sixth-generation (6G) technologies has introduced new challenges and opportunities for machine learning (ML) applications in Internet of Things (IoT) networks, particularly concerning energy efficiency. As model training and data transmission contribute significantly to energy consumption, optimizing these processes has become critical for sustainable system design. This study first conduct analysis on the energy consumption model for both centralized and decentralized architecture and then presents a testbed deployed within the German railway infrastructure, leveraging sensor data for ML-based predictive maintenance. A comparative analysis of distributed versus Centralized Learning (CL) architectures reveals that distributed models maintain competitive predictive accuracy (~90%) while reducing overall electricity consump...

• 3.Transformer Architecture with Minimal Inference Latency for Multi-Modal Wireless Networks

  Next-generation wireless networks are expected to leverage multi-modal data sources to execute various wireless communication tasks such as beamforming and blockage prediction with situational-awareness. To do so, multi-modal transformers emerged as an effective tool, however, existing transformer-based approaches suffer from high inference latency and large memory footprints when processing multi-modal data. Hence, such existing solutions cannot handle wireless communication tasks that require fast inference to track a dynamically changing environment with moving vehicles and blockages. One major bottleneck is the reliance on attention mechanisms whose complexity grows quadratically with respect to the number of tokens. Hence, in this paper, a novel, fast multi-modal transformer inference framework is designed to practically support wire...

• 4.Far-Field Absolute Gain Antenna Measurements at Sub-THz Frequencies: A New Interpretation

  The evolution of large aperture antennas and arrays at the sub-THz band (100-300 GHz) results in traditional far-field (FF) gain measurements to require large distances due to the high frequency nature making them impractical in many laboratory environments. In the presented work, absolute antenna gain measurements are performed in localized distance clusters for commercial horn antennas in the sub-THz range of 145-170 GHz using the three-antenna method, leveraging a theoretically derived modified FF equation along with the Friis transmission equation to enable a compact measurement setup. By applying the proposed modified FF formulation, the approach aims to redefine the FF distance by considering the combined effects of both the transmitting and receiving antennas, accounting for their aperture sizes and radiation characteristics. This ...

• 5.Passive RIS Is Not Silent: Revisiting Performance Limits Under Thermal Noise

  Reconfigurable intelligent surfaces (RISs) have emerged as a promising solution for enabling energy-efficient and flexible spectrum usage in wireless communication, particularly in the context of sixth-generation (6G) networks. While passive RIS architectures are widely regarded as virtually noiseless due to the lack of active components, this idealized assumption can lead to misleading performance evaluations. In this paper, we revisit this assumption and demonstrate that the thermal noise generated by passive RIS elements, though often neglected, can significantly affect system performance. We propose a tractable approximated analytical framework that incorporates RIS-induced thermal noise into the system and derive closed-form expressions for key performance metrics, such as outage probability and throughput. Simulation results validat...

• 1.Tuning in to Frequencies: How Global Assets Align U.S. Put-Call Parity Residuals

  Put-call parity holds under risk-neutral pricing, yet enforcement exposes arbitrageurs to path-dependent capital costs. The carry gap-the annualized wedge between option-implied and OIS discount factors-is a Q-measure object, but P-measure investment opportunities may shape its enforcement burden. We document this alignment in SPX and RUT options: low-frequency global asset returns raise in-sample R^2 by 0.093 and 0.082 and lift pooled out-of-sample R^2 from 0.221 to 0.364 (SPX) and 0.171 to 0.309 (RUT). Effective horizons differ by asset-IEFA (70 days), IGOV (400 days), IAU (336 days)-and asset terms largely absorb the OIS baseline, providing systematic evidence of a P-Q channel.

• 2.The Cost of a Free Lunch: Evidence from U.S. Derivatives Markets

  Put-call parity is a terminal-payoff identity; quoted residuals against traded futures are near zero. Yet enforcing parity is path-dependent, exposing arbitrageurs to daily settlement, margin, and finite capital. Using minute-level NBBO data on S&P 500 and Russell 2000 options, I extract option-implied discount factors, compare them with the OIS curve, and construct an annualized carry gap. A reduced-form specification centered on a volatility times sqrt(tau) path-risk term links the carry gap to implementation risk, trading frictions, and financial conditions, with coefficient signs stable across leave-one-year-out validation. The carry gap is an implementation wedge invisible in price space but systematic in carry space.

• 3.Probabilistic Forecasting for Day-ahead Electricity Prices, Battery Trading Strategies and the Economic Evaluation of Predictive Accuracy

  Electricity price forecasting supports decision-making in energy markets and asset operation. Probabilistic forecasts are increasingly adopted to explicitly quantify uncertainty, typically issued as quantile predictions or ensembles of the full predictive distribution. However, how improvements in statistical forecast quality translate into economic value remains unclear. Battery storage arbitrage in day-ahead markets is a popular application-based benchmark for this purpose. We analyze quantile-based trading strategies (QBTS) and identify two critical flaws: they do not incentivize honest probabilistic forecasting and they ignore the intertemporal dependence structure of electricity prices. We therefore frame battery optimization as a stochastic program based on fully probabilistic forecasts and examine decision quality measurement for r...

• 4.Cross-Stock Predictability via LLM-Augmented Semantic Networks

  Text-based financial networks are increasingly used to study cross-stock return predictability. A common approach constructs links from similarities in firms' disclosure embeddings, but such networks often contain spurious edges because textual proximity does not necessarily imply economic connection. We propose a two-stage framework that first builds a sparse candidate graph from 10-K embeddings and then uses a large language model to classify and filter candidate edges according to their economic relations. The refined graph is used to aggregate pair-level mean-reversion signals into stock-level trading signals with relation-aware and distance-based weights. In a backtest on S&P 500 constituents from 2011 to 2019, LLM-based edge filtering improves the long-short Sharpe ratio from 0.742 to 0.820 and reduces maximum drawdown from $-$1...

• 5.Structural Dynamics of G5 Stock Markets During Exogenous Shocks: A Random Matrix Theory-Based Complexity Gap Approach

  We identify a robust structural signature of stock markets during exogenous shock events by analyzing collective return dynamics across G5 countries. Using Random Matrix Theory, we introduce the complexity gap, defined as the difference between the normalized largest eigenvalue and the average pairwise correlation, to quantify changes in market structure. This measure reveals a consistent three-phase pattern across multiple shocks, including the 2025 U.S. tariff event, the COVID-19 crisis, and country-specific shocks in Japan and China during 2024. Before a shock, markets show a positive complexity gap, reflecting a rich structure with multiple interacting factors. During shocks, the gap collapses to near zero, signaling strong synchronization under a single dominant mode. Post-shock recovery follows a nonmonotonic path: an initial wideni...

• 1.Networked Tracking of Multiple Moving Targets in 6G Network

  This paper considers a networked tracking architecture in 6G integrated sensing and communication (ISAC) systems, where multiple base stations (BSs) cooperatively transmit radio signals and process received echo signals to track multiple moving targets. Compared to the single-BS counterpart, networked tracking allows the moving targets to be associated with different BSs over time such that the wireless resources can be dynamically allocated among BSs based on target locations. However, networked tracking imposes new challenges for algorithm design and resource allocation. In this paper, we first design the networked Kalman Filter (NKF) that is suitable for multi-BS based tracking, then characterize the posterior Cramer-Rao bound (PCRB) under this NKF, and last design the beamforming vectors of all the BSs to minimize the tracking PCRB. N...

• 2.Towards Energy Impact on AI-Powered 6G IoT Networks: Centralized vs. Decentralized

  The emergence of sixth-generation (6G) technologies has introduced new challenges and opportunities for machine learning (ML) applications in Internet of Things (IoT) networks, particularly concerning energy efficiency. As model training and data transmission contribute significantly to energy consumption, optimizing these processes has become critical for sustainable system design. This study first conduct analysis on the energy consumption model for both centralized and decentralized architecture and then presents a testbed deployed within the German railway infrastructure, leveraging sensor data for ML-based predictive maintenance. A comparative analysis of distributed versus Centralized Learning (CL) architectures reveals that distributed models maintain competitive predictive accuracy (~90%) while reducing overall electricity consump...

• 3.Transformer Architecture with Minimal Inference Latency for Multi-Modal Wireless Networks

  Next-generation wireless networks are expected to leverage multi-modal data sources to execute various wireless communication tasks such as beamforming and blockage prediction with situational-awareness. To do so, multi-modal transformers emerged as an effective tool, however, existing transformer-based approaches suffer from high inference latency and large memory footprints when processing multi-modal data. Hence, such existing solutions cannot handle wireless communication tasks that require fast inference to track a dynamically changing environment with moving vehicles and blockages. One major bottleneck is the reliance on attention mechanisms whose complexity grows quadratically with respect to the number of tokens. Hence, in this paper, a novel, fast multi-modal transformer inference framework is designed to practically support wire...

• 4.Far-Field Absolute Gain Antenna Measurements at Sub-THz Frequencies: A New Interpretation

  The evolution of large aperture antennas and arrays at the sub-THz band (100-300 GHz) results in traditional far-field (FF) gain measurements to require large distances due to the high frequency nature making them impractical in many laboratory environments. In the presented work, absolute antenna gain measurements are performed in localized distance clusters for commercial horn antennas in the sub-THz range of 145-170 GHz using the three-antenna method, leveraging a theoretically derived modified FF equation along with the Friis transmission equation to enable a compact measurement setup. By applying the proposed modified FF formulation, the approach aims to redefine the FF distance by considering the combined effects of both the transmitting and receiving antennas, accounting for their aperture sizes and radiation characteristics. This ...

• 5.Passive RIS Is Not Silent: Revisiting Performance Limits Under Thermal Noise

  Reconfigurable intelligent surfaces (RISs) have emerged as a promising solution for enabling energy-efficient and flexible spectrum usage in wireless communication, particularly in the context of sixth-generation (6G) networks. While passive RIS architectures are widely regarded as virtually noiseless due to the lack of active components, this idealized assumption can lead to misleading performance evaluations. In this paper, we revisit this assumption and demonstrate that the thermal noise generated by passive RIS elements, though often neglected, can significantly affect system performance. We propose a tractable approximated analytical framework that incorporates RIS-induced thermal noise into the system and derive closed-form expressions for key performance metrics, such as outage probability and throughput. Simulation results validat...

• 1.Scheduling in Multi-Hop Wireless Networks With Deadlines

  We analyze the problem of scheduling in wireless networks to meet end-to-end service guarantees, defined by instantaneous throughput and hard packet deadlines. Using a network slicing model to decouple the queueing dynamics between flows, we show that the network's ability to meet hard deadline guarantees under interference is largely influenced by the link scheduling policy. We characterize throughput- and deadline-optimal policies for a solitary flow operating in isolation, which provide bounds on feasibility in the general case with multiple flows. We prove that packet delays can grow arbitrarily large in the multi-flow setting under a worst-case stabilizing policy, showing that queue stability is not sufficient to guarantee tight deadlines. We derive conditions on end-to-end packet delays in terms of link inter-scheduling times, and s...

• 2.Safety-Aware AoI Scheduling for LEO Satellite-Assisted Autonomous Driving

  Autonomous platoons traversing infrastructure gaps increasingly depend on LEO satellite backhaul for safety-critical updates, yet no existing framework jointly addresses compound Doppler from simultaneous satellite and vehicle motion, sub-slot handover outages that exceed collision-alert deadlines, and heterogeneous freshness requirements across three vehicular priority classes. The core challenge is a \emph{timescale mismatch}: coarse control slots hide sub-slot outages, which makes both AoI spike analysis and safety verification ill-posed. Ping-pong handover oscillations further compound AoI cost in a way that purely reactive schedulers cannot mitigate. We address these challenges through a unified framework that couples a two-timescale AoI model with tiered time-average safety constraints enforced by virtual queues. A closed-form ping-...

• 3.Toward EU Sovereignty in Space: A Comparative Simulation Study of IRIS 2 and Starlink

  The evolution of 6th generation (6G) networks increasingly relies on satellite-based Non-Terrestrial Networks (NTNs) to extend broadband connectivity to remote and unserved regions, and to support public safety. In this paper we compare two representative and conceptually different satellite constellation architectures, namely Starlink and IRIS 2. Starlink is a commercial private Internet constellation by SpaceX, based on dense Low Earth Orbit (LEO) satellites. It is primarily designed to deliver high-capacity broadband services for civil applications, with performance targets comparable to those of terrestrial networks. In contrast, IRIS 2 is a planned public initiative to be deployed by the European Union, based on a multi-layer combination of LEO, Medium Earth Orbit (MEO), and Geo-stationary Earth Orbit (GEO) satellites. It is primaril...

• 4.Towards Trustworthy 6G Network Digital Twins: A Framework for Validating Counterfactual What-If Analysis in Edge Computing Resources

  Network Digital Twins (NDTs) enable safe what-if analysis for 6G cloud-edge infrastructures, but adoption is often limited by fragmented workflows from telemetry to validation. We present a data-driven NDT framework that extends 6G-TWIN with a scalable pipeline for cloud-edge telemetry aggregation and semantic alignment into unified data models. Our contributions include: (i) scalable cloud-edge telemetry collection, (ii) regime-aware feature engineering capturing the network's scaling behavior, and (iii) a validation methodology based on Sign Agreement and Directional Sensitivity. Evaluated on a Kubernetes-managed cluster, the framework extrapolates performance to unseen high-load regimes. Results show both Deep Neural Network (DNN) and XGBoost achieve high regression accuracy (R2 > 0.99), while the XGBoost model delivers superior dir...

• 5.Cross-Domain Query Translation for Network Troubleshooting: A Multi-Agent LLM Framework with Privacy Preservation and Self-Reflection

  This paper presents a hierarchical multi-agent LLM architecture to bridge communication gaps between non-technical end users and telecommunications domain experts in private network environments. We propose a cross-domain query translation framework that leverages specialized language models coordinated through multi-agent reflection-based reasoning. The resulting system addresses three critical challenges: (1) accurately classify user queries related to telecommunications network issues using a dual-stage hierarchical approach, (2) preserve user privacy through the anonymization of semantically relevant personally identifiable information (PII) while maintaining diagnostic utility, and (3) translate technical expert responses into user-comprehensible language. Our approach employs ReAct-style agents enhanced with self-reflection mechan...