Babak's Daily Briefing

Wednesday, March 18, 2026

Sources: 19 | Total Articles: 91

• 1.SpaceRAN: Airbus UpNext explores software-defined 5G NTN from orbit

  Airbus UpNext has launched its SpaceRAN (Space Radio Access Network) demonstrator, a key initiative to advance standardised 5G…

• 2.SoftBank’s Transformer-Based AI-RAN Hits 30% Uplink Gain at Sub-Millisecond Latency

  On August 21, 2025, SoftBank published results from a live, standards-compliant AI-RAN trial that replaces parts of classical signal processing with a lightweight Transformer.

• 3.6G as a Platform for Value

  Reframing the Future with NGMN’s Chairman, Laurent Leboucher By Piotr (Peter) Pietrzyk, Managing Editor, 6GWorld.com In the race…

• 4.SoftBank Road-Tests 7 GHz in Central Tokyo

  SoftBank and Nokia have begun outdoor field trials in Tokyo’s Ginza district using 7 GHz spectrum, installing three pre-commercial base stations to compare coverage and radio characteristics against today’s sub-6 GHz 5G sites.

• 5.NXP’s Acquisition of TTTech Auto Signals Growing Focus on Middleware for Software-Defined Vehicles

  On June 17, 2025, NXP Semiconductors finalized its acquisition of TTTech Auto—a strategic move to integrate TTTech’s flagship…

• 1.Protein Design with Agent Rosetta: A Case Study for Specialized Scientific Agents

  Large language models (LLMs) are capable of emulating reasoning and using tools, creating opportunities for autonomous agents that execute complex scientific tasks. Protein design provides a natural testbed: although machine learning (ML) methods achieve strong results, these are largely restricted to canonical amino acids and narrow objectives, leaving unfilled need for a generalist tool for broad design pipelines. We introduce Agent Rosetta, an LLM agent paired with a structured environment for operating Rosetta, the leading physics-based heteropolymer design software, capable of modeling non-canonical building blocks and geometries. Agent Rosetta iteratively refines designs to achieve user-defined objectives, combining LLM reasoning with Rosetta's generality. We evaluate Agent Rosetta on design with canonical amino acids, matching spec...

• 2.Persona-Conditioned Risk Behavior in Large Language Models: A Simulated Gambling Study with GPT-4.1

  Large language models (LLMs) are increasingly deployed as autonomous agents in uncertain, sequential decision-making contexts. Yet it remains poorly understood whether the behaviors they exhibit in such environments reflect principled cognitive patterns or simply surface-level prompt mimicry. This paper presents a controlled experiment in which GPT-4.1 was assigned one of three socioeconomic personas (Rich, Middle-income, and Poor) and placed in a structured slot-machine environment with three distinct machine configurations: Fair (50%), Biased Low (35%), and Streak (dynamic probability increasing after consecutive losses). Across 50 independent iterations per condition and 6,950 recorded decisions, we find that the model reproduces key behavioral signatures predicted by Kahneman and Tversky's Prospect Theory without being instructed to d...

• 3.QiboAgent: a practitioner's guideline to open source assistants for Quantum Computing code development

  We introduce QiboAgent, a reference implementation designed to serve as a practitioner's guideline for developing specialized coding assistants in Quantum Computing middleware. Addressing the limitations in scientific software development of general-purpose proprietary models, we explore how lightweight, open-source Large Language Models (LLMs) provided with a custom workflow architecture compare. In detail, we experiment with two complementary paradigms: a Retrieval-Augmented Generation pipeline for high-precision information retrieval, and an autonomous agentic workflow for complex software engineering tasks. We observe that this hybrid approach significantly reduces hallucination rates in code generation compared to a proprietary baseline, achieving a peak accuracy of 90.2% with relatively small open-source models of size up to 30B par...

• 4.PMAx: An Agentic Framework for AI-Driven Process Mining

  Process mining provides powerful insights into organizational workflows, but extracting these insights typically requires expertise in specialized query languages and data science tools. Large Language Models (LLMs) offer the potential to democratize process mining by enabling business users to interact with process data through natural language. However, using LLMs as direct analytical engines over raw event logs introduces fundamental challenges: LLMs struggle with deterministic reasoning and may hallucinate metrics, while sending large, sensitive logs to external AI services raises serious data-privacy concerns. To address these limitations, we present PMAx, an autonomous agentic framework that functions as a virtual process analyst. Rather than relying on LLMs to generate process models or compute analytical results, PMAx employs a pr...

• 5.GNNVerifier: Graph-based Verifier for LLM Task Planning

  Large language models (LLMs) facilitate the development of autonomous agents. As a core component of such agents, task planning aims to decompose complex natural language requests into concrete, solvable sub-tasks. Since LLM-generated plans are frequently prone to hallucinations and sensitive to long-context prom-pts, recent research has introduced plan verifiers to identify and correct potential flaws. However, most existing approaches still rely on an LLM as the verifier via additional prompting for plan review or self-reflection. LLM-based verifiers can be misled by plausible narration and struggle to detect failures caused by structural relations across steps, such as type mismatches, missing intermediates, or broken dependencies. To address these limitations, we propose a graph-based verifier for LLM task planning. Specifically, the ...

• 1.Recursive Language Models Meet Uncertainty: The Surprising Effectiveness of Self-Reflective Program Search for Long Context

  arXiv:2603.15653v1 Announce Type: new Abstract: Long-context handling remains a core challenge for language models: even with extended context windows, models often fail to reliably extract, reason over, and use the information across long contexts. Recent works like Recursive Language Models (RLM) have approached this challenge by agentic way of decomposing long contexts into recursive sub-calls through programmatic interaction at inference. While promising, the success of RLM critically depends on how these context-interaction programs are selected, which has remained largely unexplored. In this paper, we study this problem and introduce SRLM, a framework that augments programmatic context interaction with uncertainty-aware Self-Reflection. SRLM leverages three intrinsic signals: self consistency, reasoning length, and verbalized confi...

• 2.MedArena: Comparing LLMs for Medicine-in-the-Wild Clinician Preferences

  arXiv:2603.15677v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly central to clinician workflows, spanning clinical decision support, medical education, and patient communication. However, current evaluation methods for medical LLMs rely heavily on static, templated benchmarks that fail to capture the complexity and dynamics of real-world clinical practice, creating a dissonance between benchmark performance and clinical utility. To address these limitations, we present MedArena, an interactive evaluation platform that enables clinicians to directly test and compare leading LLMs using their own medical queries. Given a clinician-provided query, MedArena presents responses from two randomly selected models and asks the user to select the preferred response. Out of 1571 preferences collected across 12 LLMs up to...

• 3.MiroThinker-1.7 & H1: Towards Heavy-Duty Research Agents via Verification

  arXiv:2603.15726v1 Announce Type: new Abstract: We present MiroThinker-1.7, a new research agent designed for complex long-horizon reasoning tasks. Building on this foundation, we further introduce MiroThinker-H1, which extends the agent with heavy-duty reasoning capabilities for more reliable multi-step problem solving. In particular, MiroThinker-1.7 improves the reliability of each interaction step through an agentic mid-training stage that emphasizes structured planning, contextual reasoning, and tool interaction. This enables more effective multi-step interaction and sustained reasoning across complex tasks. MiroThinker-H1 further incorporates verification directly into the reasoning process at both local and global levels. Intermediate reasoning decisions can be evaluated and refined during inference, while the overall reasoning tra...

• 4.Morphemes Without Borders: Evaluating Root-Pattern Morphology in Arabic Tokenizers and LLMs

  arXiv:2603.15773v1 Announce Type: new Abstract: This work investigates how effectively large language models (LLMs) and their tokenization schemes represent and generate Arabic root-pattern morphology, probing whether they capture genuine morphological structure or rely on surface memorization. Arabic morphological system provides a rich testbed for analyzing how LLMs handle complex, non-concatenative forms and how tokenization choices influence this process. Our study begins with an evaluation of morphological fidelity across Arabic and multilingual tokenizers against gold-standard segmentation, followed by an analysis of LLM performance in productive root-pattern generation using a newly developed test set. Our findings across seven Arabic-centric and multilingual LLMs and their respective tokenizers reveal that tokenizer morphological...

• 5.COGNAC at SemEval-2026 Task 5: LLM Ensembles for Human-Level Word Sense Plausibility Rating in Challenging Narratives

  arXiv:2603.15897v1 Announce Type: new Abstract: We describe our system for SemEval-2026 Task 5, which requires rating the plausibility of given word senses of homonyms in short stories on a 5-point Likert scale. Systems are evaluated by the unweighted average of accuracy (within one standard deviation of mean human judgments) and Spearman Rank Correlation. We explore three prompting strategies using multiple closed-source commercial LLMs: (i) a baseline zero-shot setup, (ii) Chain-of-Thought (CoT) style prompting with structured reasoning, and (iii) a comparative prompting strategy for evaluating candidate word senses simultaneously. Furthermore, to account for the substantial inter-annotator variation present in the gold labels, we propose an ensemble setup by averaging model predictions. Our best official system, comprising an ensemble...

• 1.Tokenization Tradeoffs in Structured EHR Foundation Models

  arXiv:2603.15644v1 Announce Type: new Abstract: Foundation models for structured electronic health records (EHRs) are pretrained on longitudinal sequences of timestamped clinical events to learn adaptable patient representations. Tokenization -- how these timelines are converted into discrete model inputs -- determines what information is preserved, how efficiently it is encoded, and which relationships must be learned versus precomputed. Yet the impact of tokenization design choices on downstream performance and computational efficiency remains largely unexplored. Here, we pretrained a transformer on pediatric EHR data under a factorial design, varying tokenization along event encoding, time encoding, and workflow annotation. We evaluated area-under-the-receiver-operating-characteristic curve across 74 clinical prediction tasks. Joint ev...

• 2.XLinear: Frequency-Enhanced MLP with CrossFilter for Robust Long-Range Forecasting

  arXiv:2603.15645v1 Announce Type: new Abstract: Time series forecasters are widely used across various domains. Among them, MLP (multi-layer perceptron)-based forecasters have been proven to be more robust to noise compared to Transformer-based forecasters. However, MLP struggles to capture complex features, resulting in limitations on capturing long-range dependencies. To address this challenge, we propose XLinear, an MLP-based forecaster for long-range forecasting. Firstly, we decompose the time series into trend and seasonal components. For the trend component which contains long-range characteristics, we design Enhanced Frequency Attention (EFA) to capture long-term dependencies by leveraging frequency-domain operations. Additionally, a CrossFilter Block is proposed for the seasonal component to maintain the model's robustness to nois...

• 3.Alternating Reinforcement Learning with Contextual Rubric Rewards

  arXiv:2603.15646v1 Announce Type: new Abstract: Reinforcement Learning with Rubric Rewards (RLRR) is a framework that extends conventional reinforcement learning from human feedback (RLHF) and verifiable rewards (RLVR) by replacing scalar preference signals with structured, multi-dimensional, contextual rubric-based evaluations. However, existing approaches in RLRR are limited to linearly compressing vector rewards into a scalar reward with a fixed weightings, which is sensitive to artificial score design and fails to capture correlations among reward dimensions. To overcome the limitations of reward aggregation, this work proposes Alternating Reinforcement Learning with Rubric Rewards (ARL-RR), a framework that eliminates the need for a fixed scalarization by optimizing one semantic rubric meta-class at a time. Theoretically, we show tha...

• 4.Steering Frozen LLMs: Adaptive Social Alignment via Online Prompt Routing

  arXiv:2603.15647v1 Announce Type: new Abstract: Large language models (LLMs) are typically governed by post-training alignment (e.g., RLHF or DPO), which yields a largely static policy during deployment and inference. However, real-world safety is a full-lifecycle problem: static defenses degrade against evolving jailbreak behaviors, and fixed weights cannot adapt to pluralistic, time-varying safety norms. This motivates inference-time governance that steers behavior without costly retraining. To address this, we introduce the Consensus Clustering LinUCB Bandit (CCLUB), a unified framework for adaptive social alignment via system-prompt routing. CCLUB employs a conservative consensus clustering mechanism: it pools data only within the intersection of utility and safety similarity graphs, effectively preventing unsafe generalization across...

• 5.How to Achieve Prototypical Birth and Death for OOD Detection?

  arXiv:2603.15650v1 Announce Type: new Abstract: Out-of-Distribution (OOD) detection is crucial for the secure deployment of machine learning models, and prototype-based learning methods are among the mainstream strategies for achieving OOD detection. Existing prototype-based learning methods generally rely on a fixed number of prototypes. This static assumption fails to adapt to the inherent complexity differences across various categories. Currently, there is still a lack of a mechanism that can adaptively adjust the number of prototypes based on data complexity. Inspired by the processes of cell birth and death in biology, we propose a novel method named PID (Prototype bIrth and Death) to adaptively adjust the prototype count based on data complexity. This method relies on two dynamic mechanisms during the training process: prototype bi...

• 1.You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector

  arXiv:2603.15757v1 Announce Type: new Abstract: What happens when a pretrained generative robot policy is provided a constant initial noise as input, rather than repeatedly sampling it from a Gaussian? We demonstrate that the performance of a pretrained, frozen diffusion or flow matching policy can be improved with respect to a downstream reward by swapping the sampling of initial noise from the prior distribution (typically isotropic Gaussian) with a well-chosen, constant initial noise input -- a golden ticket. We propose a search method to find golden tickets using Monte-Carlo policy evaluation that keeps the pretrained policy frozen, does not train any new networks, and is applicable to all diffusion/flow matching policies (and therefore many VLAs). Our approach to policy improvement makes no assumptions beyond being able to inject ini...

• 2.Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation

  arXiv:2603.15759v1 Announce Type: new Abstract: Simulation-to-real transfer remains a central challenge in robotics, as mismatches between simulated and real-world dynamics often lead to failures. While reinforcement learning offers a principled mechanism for adaptation, existing sim-to-real finetuning methods struggle with exploration and long-horizon credit assignment in the low-data regimes typical of real-world robotics. We introduce Simulation Distillation (SimDist), a sim-to-real framework that distills structural priors from a simulator into a latent world model and enables rapid real-world adaptation via online planning and supervised dynamics finetuning. By transferring reward and value models directly from simulation, SimDist provides dense planning signals from raw perception without requiring value learning during deployment. ...

• 3.CorrectionPlanner: Self-Correction Planner with Reinforcement Learning in Autonomous Driving

  arXiv:2603.15771v1 Announce Type: new Abstract: Autonomous driving requires safe planning, but most learning-based planners lack explicit self-correction ability: once an unsafe action is proposed, there is no mechanism to correct it. Thus, we propose CorrectionPlanner, an autoregressive planner with self-correction that models planning as motion-token generation within a propose, evaluate, and correct loop. At each planning step, the policy proposes an action, namely a motion token, and a learned collision critic predicts whether it will induce a collision within a short horizon. If the critic predicts a collision, we retain the sequence of historical unsafe motion tokens as a self-correction trace, generate the next motion token conditioned on it, and repeat this process until a safe motion token is proposed or the safety criterion is m...

• 4.Emergent Dexterity via Diverse Resets and Large-Scale Reinforcement Learning

  arXiv:2603.15789v1 Announce Type: new Abstract: Reinforcement learning in massively parallel physics simulations has driven major progress in sim-to-real robot learning. However, current approaches remain brittle and task-specific, relying on extensive per-task engineering to design rewards, curricula, and demonstrations. Even with this engineering, they often fail on long-horizon, contact-rich manipulation tasks and do not meaningfully scale with compute, as performance quickly saturates when training revisits the same narrow regions of state space. We introduce \Method, a simple and scalable framework that enables on-policy reinforcement learning to robustly solve a broad class of dexterous manipulation tasks using a single reward function, fixed algorithm hyperparameters, no curricula, and no human demonstrations. Our key insight is th...

• 5.Robust Dynamic Object Detection in Cluttered Indoor Scenes via Learned Spatiotemporal Cues

  arXiv:2603.15826v1 Announce Type: new Abstract: Reliable dynamic object detection in cluttered environments remains a critical challenge for autonomous navigation. Purely geometric LiDAR pipelines that rely on clustering and heuristic filtering can miss dynamic obstacles when they move in close proximity to static structure or are only partially observed. Vision-augmented approaches can provide additional semantic cues, but are often limited by closed-set detectors and camera field-of-view constraints, reducing robustness to novel obstacles and out-of-frustum events. In this work, we present a LiDAR-only framework that fuses temporal occupancy-grid-based motion segmentation with a learned bird's-eye-view (BEV) dynamic prior. A fusion module prioritizes 3D detections when available, while using the learned dynamic grid to recover detection...

• 1.Conditioning on a Volatility Proxy Compresses the Apparent Timescale of Collective Market Correlation

  We address the attribution problem for apparent slow collective dynamics: is the observed persistence intrinsic, or inherited from a persistent driver? For the leading eigenvalue fraction $ψ_1=λ_{\max}/N$ of S\&P 500 60-day rolling correlation matrices ($237$ stocks, 2004--2023), a VIX-coupled Ornstein--Uhlenbeck model reduces the effective relaxation time from $298$ to $61$ trading days and improves the fit over bare mean reversion by $Δ$BIC$=109$. On the decomposition sample, an informational residual of $\log(\mathrm{VIX})$ alone retains most of that gain ($Δ$BIC$=78.6$), whereas a mechanical VIX proxy alone does not improve the fit. Autocorrelation-matched placebo fields fail ($Δ$BIC$_{\max}=2.7$), disjoint weekly reconstructions still favor the field-coupled model ($Δ$BIC$=140$--$151$), and six anchored chronological holdouts preserv...

• 2.AI Agents in Financial Markets: Architecture, Applications, and Systemic Implications

  Recent advances in large language models, tool-using agents, and financial machine learning are shifting financial automation from isolated prediction tasks to integrated decision systems that can perceive information, reason over objectives, and generate or execute actions. This paper develops an integrative framework for analysing agentic finance: financial market environments in which autonomous or semi-autonomous AI systems participate in information processing, decision support, monitoring, and execution workflows. The analysis proceeds in three steps. First, the paper proposes a four-layer architecture of financial AI agents covering data perception, reasoning engines, strategy generation, and execution with control. Second, it introduces the Agentic Financial Market Model (AFMM), a stylised agent-based representation linking agen...

• 3.A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting

  This paper studies cross-market return predictability through a machine learning framework that preserves economic structure. Exploiting the non-overlapping trading hours of the U.S. and Chinese equity markets, we construct a directed bipartite graph that captures time-ordered predictive linkages between stocks across markets. Edges are selected via rolling-window hypothesis testing, and the resulting graph serves as a sparse, economically interpretable feature-selection layer for downstream machine learning models. We apply a range of regularized and ensemble methods to forecast open-to-close returns using lagged foreign-market information. Our results reveal a pronounced directional asymmetry: U.S. previous-close-to-close returns contain substantial predictive information for Chinese intraday returns, whereas the reverse effect is limit...

• 4.Hybrid Hidden Markov Model for Modeling Equity Excess Growth Rate Dynamics: A Discrete-State Approach with Jump-Diffusion

  Generating synthetic financial time series that preserve statistical properties of real market data is essential for stress testing, risk model validation, and scenario design. Existing approaches, from parametric models to deep generative networks, struggle to simultaneously reproduce heavy-tailed distributions, negligible linear autocorrelation, and persistent volatility clustering. We propose a hybrid hidden Markov framework that discretizes continuous excess growth rates into Laplace quantile-defined market states and augments regime switching with a Poisson-driven jump-duration mechanism to enforce realistic tail-state dwell times. Parameters are estimated by direct transition counting, bypassing the Baum-Welch EM algorithm. Synthetic data quality is evaluated using Kolmogorov-Smirnov and Anderson-Darling pass rates for distributiona...

• 5.Uncertainty-Aware Deep Hedging

  Deep hedging trains neural networks to manage derivative risk under market frictions, but produces hedge ratios with no measure of model confidence -- a significant barrier to deployment. We introduce uncertainty quantification to the deep hedging framework by training a deep ensemble of five independent LSTM networks under Heston stochastic volatility with proportional transaction costs. The ensemble's disagreement at each time step provides a per-time-step confidence measure that is strongly predictive of hedging performance: the learned strategy outperforms the Black-Scholes delta on approximately 80% of paths when model agreement is high, but on fewer than 20% when disagreement is elevated. We propose a CVaR-optimised blending strategy that combines the ensemble's hedge with the classical Black-Scholes delta, weighted by the level of ...

• 1.GSMA MWC26 Barcelona closes 20th anniversary edition

  Summary available at source link.

• 2.From Ambition to Execution: How Open Gateway Is Scaling the Global API Economy

  Summary available at source link.

• 3.Pioneering Affordable Access in Africa: GSMA and Handset Affordability Coalition Members Identify Six African Countries to Pilot Affordable $40 Smartphones

  Summary available at source link.

• 4.GSMA Calls for Regulatory Readiness for Direct-to-User LEO Satellite Services

  Summary available at source link.

• 5.MWC26 Barcelona opens with call to complete 5G, rise to AI challenges, and strengthen digital safety

  Summary available at source link.

• 1.Efficient Reasoning on the Edge

  Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but their verbose reasoning traces and large context requirements make them impractical for edge deployment. These challenges include high token generation costs, large KV-cache footprints, and inefficiencies when distilling reasoning capabilities into smaller models for mobile devices. Existing approaches often rely on distilling reasoning traces from larger models into smaller models, which are verbose and stylistically redundant, undesirable for on-device inference. In this work, we propose a lightweight approach to enable reasoning in small LLMs using LoRA adapters combined with supervised fine-tuning. We further introduce budget forcing via reinforcement learning on these adapters, significantly redu...

• 2.Chronos: Temporal-Aware Conversational Agents with Structured Event Retrieval for Long-Term Memory

  Recent advances in Large Language Models (LLMs) have enabled conversational AI agents to engage in extended multi-turn interactions spanning weeks or months. However, existing memory systems struggle to reason over temporally grounded facts and preferences that evolve across months of interaction and lack effective retrieval strategies for multi-hop, time-sensitive queries over long dialogue histories. We introduce Chronos, a novel temporal-aware memory framework that decomposes raw dialogue into subject-verb-object event tuples with resolved datetime ranges and entity aliases, indexing them in a structured event calendar alongside a turn calendar that preserves full conversational context. At query time, Chronos applies dynamic prompting to generate tailored retrieval guidance for each question, directing the agent on what to retrieve, h...

• 3.Prompt Programming for Cultural Bias and Alignment of Large Language Models

  Culture shapes reasoning, values, prioritization, and strategic decision-making, yet large language models (LLMs) often exhibit cultural biases that misalign with target populations. As LLMs are increasingly used for strategic decision-making, policy support, and document engineering tasks such as summarization, categorization, and compliance-oriented auditing, improving cultural alignment is important for ensuring that downstream analyses and recommendations reflect target-population value profiles rather than default model priors. Previous work introduced a survey-grounded cultural alignment framework and showed that culture-specific prompting can reduce misalignment, but it primarily evaluated proprietary models and relied on manual prompt engineering. In this paper, we validate and extend that framework by reproducing its social scien...

• 4.Surg$Σ$: A Spectrum of Large-Scale Multimodal Data and Foundation Models for Surgical Intelligence

  Surgical intelligence has the potential to improve the safety and consistency of surgical care, yet most existing surgical AI frameworks remain task-specific and struggle to generalize across procedures and institutions. Although multimodal foundation models, particularly multimodal large language models, have demonstrated strong cross-task capabilities across various medical domains, their advancement in surgery remains constrained by the lack of large-scale, systematically curated multimodal data. To address this challenge, we introduce Surg$Σ$, a spectrum of large-scale multimodal data and foundation models for surgical intelligence. At the core of this framework lies Surg$Σ$-DB, a large-scale multimodal data foundation designed to support diverse surgical tasks. Surg$Σ$-DB consolidates heterogeneous surgical data sources (including op...

• 5.InCoder-32B: Code Foundation Model for Industrial Scenarios

  Recent code large language models have achieved remarkable progress on general programming tasks. Nevertheless, their performance degrades significantly in industrial scenarios that require reasoning about hardware semantics, specialized language constructs, and strict resource constraints. To address these challenges, we introduce InCoder-32B (Industrial-Coder-32B), the first 32B-parameter code foundation model unifying code intelligence across chip design, GPU kernel optimization, embedded systems, compiler optimization, and 3D modeling. By adopting an efficient architecture, we train InCoder-32B from scratch with general code pre-training, curated industrial code annealing, mid-training that progressively extends context from 8K to 128K tokens with synthetic industrial reasoning data, and post-training with execution-grounded verificat...

• 1.SegviGen: Repurposing 3D Generative Model for Part Segmentation

  We introduce SegviGen, a framework that repurposes native 3D generative models for 3D part segmentation. Existing pipelines either lift strong 2D priors into 3D via distillation or multi-view mask aggregation, often suffering from cross-view inconsistency and blurred boundaries, or explore native 3D discriminative segmentation, which typically requires large-scale annotated 3D data and substantial training resources. In contrast, SegviGen leverages the structured priors encoded in pretrained 3D generative model to induce segmentation through distinctive part colorization, establishing a novel and efficient framework for part segmentation. Specifically, SegviGen encodes a 3D asset and predicts part-indicative colors on active voxels of a geometry-aligned reconstruction. It supports interactive part segmentation, full segmentation, and full...

• 2.MessyKitchens: Contact-rich object-level 3D scene reconstruction

  Monocular 3D scene reconstruction has recently seen significant progress. Powered by the modern neural architectures and large-scale data, recent methods achieve high performance in depth estimation from a single image. Meanwhile, reconstructing and decomposing common scenes into individual 3D objects remains a hard challenge due to the large variety of objects, frequent occlusions and complex object relations. Notably, beyond shape and pose estimation of individual objects, applications in robotics and animation require physically-plausible scene reconstruction where objects obey physical principles of non-penetration and realistic contacts. In this work we advance object-level scene reconstruction along two directions. First, we introduceMessyKitchens, a new dataset with real-world scenes featuring cluttered environments and providing h...

• 3.ManiTwin: Scaling Data-Generation-Ready Digital Object Dataset to 100K

  Learning in simulation provides a useful foundation for scaling robotic manipulation capabilities. However, this paradigm often suffers from a lack of data-generation-ready digital assets, in both scale and diversity. In this work, we present ManiTwin, an automated and efficient pipeline for generating data-generation-ready digital object twins. Our pipeline transforms a single image into simulation-ready and semantically annotated 3D asset, enabling large-scale robotic manipulation data generation. Using this pipeline, we construct ManiTwin-100K, a dataset containing 100K high-quality annotated 3D assets. Each asset is equipped with physical properties, language descriptions, functional annotations, and verified manipulation proposals. Experiments demonstrate that ManiTwin provides an efficient asset synthesis and annotation workflow, an...

• 4.M^3: Dense Matching Meets Multi-View Foundation Models for Monocular Gaussian Splatting SLAM

  Streaming reconstruction from uncalibrated monocular video remains challenging, as it requires both high-precision pose estimation and computationally efficient online refinement in dynamic environments. While coupling 3D foundation models with SLAM frameworks is a promising paradigm, a critical bottleneck persists: most multi-view foundation models estimate poses in a feed-forward manner, yielding pixel-level correspondences that lack the requisite precision for rigorous geometric optimization. To address this, we present M^3, which augments the Multi-view foundation model with a dedicated Matching head to facilitate fine-grained dense correspondences and integrates it into a robust Monocular Gaussian Splatting SLAM. M^3 further enhances tracking stability by incorporating dynamic area suppression and cross-inference intrinsic alignment....

• 5.What DINO saw: ALiBi positional encoding reduces positional bias in Vision Transformers

  Vision transformers (ViTs) - especially feature foundation models like DINOv2 - learn rich representations useful for many downstream tasks. However, architectural choices (such as positional encoding) can lead to these models displaying positional biases and artefacts independent of semantic content. This makes zero-shot adaption difficult in fields like material science, where images are often cross-sections of homogeneous microstructure (i.e. having no preferred direction). In this work, we investigate the positional bias in ViTs via linear probing, finding it present across a range of objectives and positional encodings, and subsequently reduce it by finetuning models to use ALiBi relative positional encoding. We demonstrate that these models retain desirable general semantics and their unbiased features can be used successfully in tr...

• 1.AI Trained on Birdsong Can Recognize Whale Calls

  Birds’ chirps, trills, and warbles echo through the air, while whales’ boings , “ biotwangs ,” and whistles vibrate underwater. Despite the variations in sounds and the medium through which they travel, both birdsong and whale vocalizations can be classified by Perch 2.0 , an AI audio model from Google DeepMind . As a bioacoustics foundation model, Perch 2.0 was trained on millions of recordings of birds and other land-based animals, including amphibians, insects, and mammals. Yet researchers were surprised to learn how strongly the AI model performed when repurposed for whales . Scientists at Google DeepMind and Google Research have been working on whale bioacoustics for almost a decade, with work including algorithms that can detect humpback whale calls , as well as a more recent multispecies whale model that can identify eight distinct...

• 2.With Nvidia Groq 3, the Era of AI Inference Is (Probably) Here

  This week, over 30,000 people are descending upon San Jose, Calif., to attend Nvidia GTC , the so-called Superbowl of AI—a nickname that may or may not have been coined by Nvidia. At the main event Jensen Huang, Nvidia CEO, took the stage to announce (among other things) a new line of next generation Vera Rubin chips that represent a first for the GPU giant: a chip designed specifically to handle AI inference. The Nvidia Groq 3 language processing unit (LPU) incorporates intellectual property Nvidia licensed from the start-up Groq last Christmas Eve for US $20 billion. “Finally, AI is able to do productive work, and therefore the inflection point of inference has arrived,” Huang told the crowd. “AI now has to think. In order to think, it has to inference. AI now has to do; in order to do, it has to inference.” Training and inference tasks...

• 3.Laser Chip Brings Multiplexing to AI Data Centers

  As the bandwidth and power demands of AI data centers necessitate a transition from electrical to optical scaleup networking , one component has been conspicuously absent from the co-packaged optics arsenal: the laser itself . That’s no longer the case. Last month, Tower Semiconductor and Scintil Photonics announced production of the world’s first single-chip DWDM light engine for AI infrastructure. DWDM, or dense wavelength division multiplexing, transmits multiple optical signals over a single fiber—greatly reducing power and latency while connecting dozens of GPUs. Matt Crowley, the CEO of Scintil Photonics, says that the idea of multiplexing optically is not new. Indeed, it’s been around as long as the internet itself. In the 1990s, telecom companies buried huge amounts of optical fiber in the streets, assuming that one wavelength per...

• 4.Why AI Chatbots Agree With You Even When You’re Wrong

  In April of 2025, OpenAI released a new version of GPT-4o, one of the AI algorithms users could select to power ChatGPT, the company’s chatbot. The next week, OpenAI reverted to the previous version. “The update we removed was overly flattering or agreeable—often described as sycophantic,” the company announced . Some people found the sycophancy hilarious. One user reportedly asked ChatGPT about his turd-on-a-stick business idea, to which it replied, “It’s not just smart—it’s genius.” Some found the behavior uncomfortable. For others, it was actually dangerous. Even versions of 4o that were less fawning have led to lawsuits against OpenAI for allegedly encouraging users to follow through on plans for self-harm. Unremitting adulation has even triggered AI-induced psychosis. Last October, a user named Anthony Tan blogged , “I started talkin...

• 5.An AI Agent Blackmailed a Developer. Now What?

  On 12 February, a Github contributor going by MJ Rathbun posted a personal attack against Scott Shambaugh , a volunteer maintainer for an open-source project. Shambaugh had rejected Rathbun’s code earlier in the day. Rathbun meticulously researched Shambaugh’s activity on Github, in order to write a lengthy takedown post that criticized the maintainer’s code as inferior to Rathbun’s, and ominously warned that “gatekeeping doesn’t make you important. It just makes you an obstacle.” Personal disputes over code submitted to on Github are a tale as old as Github itself. But this time, something was different: MJ Rathbun wasn’t a person. It was an AI agent built with OpenClaw , a popular open-source agentic AI software. RELATED: The First Social Network for AI Agents Heralds Their Messy Future “I was floored, because I had already identified i...

• 1.Retro-Innovation: How Smart Companies Profit From the Past

  AI may be today’s hot topic, but there’s a robust market for old-fashioned products. Board games, vinyl records, and even 1990s-style video game consoles are making a comeback, especially with Generation Z. What does this mean for teams building modern products? In this video, MIT Sloan Management Review senior features editor Kaushik Viswanath explains “retro-innovation” […]

• 2.Bridge the Intergenerational Leadership Gap

  Carolyn Geason-Beissel/MIT SMR | Getty Images Today’s workforce spans five generations, with millennials and Generation Z together accounting for over 60% of workers globally — a share projected to reach 74% by 2030. Yet there’s a widening intergenerational gap in business leadership. While age diversity in the workplace is growing, decision-making power increasingly rests with […]

• 3.How Schneider Electric Scales AI in Both Products and Processes

  Matt Harrison Clough/Ikon Images At the World Economic Forum Annual Meeting in Davos, Switzerland, in January 2026, Schneider Electric CEO Olivier Blum accepted awards recognizing the company’s AI solutions as part of the WEF’s MINDS (Meaningful, Intelligent, Novel, Deployable Solutions) program — for the second time. The distinction highlighted two of the company’s AI-enabled applications: […]

• 4.Leaders at All Levels: Kraft Heinz’s 5X Speed Secret

  Is 36 months too long for a new-product cycle? It was for Kraft Heinz. So, starting with a pilot project, it was able to cut time to market to just six months by redesigning how people worked. Today, units throughout the company are applying that model’s step-by-step approach to change and are seeing measurable improvements […]

• 5.Why Businesses Should Value Caregivers Now

  Annalisa Grassano/Ikon Images In early 2025, more than 212,000 women left the U.S. workforce following a rise in return-to-office mandates, according to the U.S. Bureau of Labor Statistics (BLS). Among mothers with young children, workforce participation dropped nearly three percentage points in just six months, according to the BLS. Behind those numbers is a larger […]

• 1.Firms’ Inflation Expectations Return to 2024 Levels

  Businesses experienced substantial cost pressures in 2025 as the cost of insurance and utilities rose sharply, while an increase in tariffs contributed to rising goods and materials costs. This post examines how firms in the New York-Northern New Jersey region adjusted their prices in response to these cost pressures and describes their expectations for future price increases and inflation. Survey results show an acceleration in firms’ price increases in 2025, with an especially sharp increase in the manufacturing sector. While both cost and price increases intensified last year, our surveys re...

• 2.Are Rising Employee Health Insurance Costs Dampening Wage Growth?

  Employer-sponsored health insurance represents a substantial component of total compensation paid by firms to many workers in the United States. Such costs have climbed by close to 20 percent over the past five years. Indeed, the average annual premium for employer-sponsored family health insurance coverage was about $27,000 in 2025—roughly equivalent to the wage of a full-time worker paid $15 per hour. Our February regional business surveys asked firms whether their wage setting decisions were influenced by the rising cost of employee health insurance. As we showed in our

  3.What’s Driving Rising Business Costs?

  After a period of moderating cost increases, businesses faced mounting cost pressures in 2025. While tariffs played a role in driving up the costs of many inputs—especially among manufacturers—they represent only part of the story. Indeed, firms grappled with substantial cost increases across many categories in the past year. This post is the first in a three-part series analyzing cost and price dynamics among businesses in the New York-Northern New Jersey region based on data collected through our regional business surveys. Firms reported that the sharpest cost increases over the...

• 4.The Post‑Pandemic Global R*

  In this post we provide a measure of “global” r* using data on short- and long-term yields and inflation for several countries with the approach developed in “Global Trends in Interest Rates” (Del Negro, Giannone, Giannoni, and Tambalotti). After declining significantly from the 1990s to before the COVID-19 pandemic, global r* has risen but remains well below its pre-1990s level. These conclusions are based on an econometric model called “trendy VAR” that extracts common trends across a multitude of variables. Specifically, the common trend in real rates across all the countries in the sample is what we call global r*. The post is based on the

  5.Estimating the Term Structure of Corporate Bond Risk Premia

  Understanding how short- and long-term assets are priced is one of the fundamental questions in finance. The term structure of risk premia allows us to perform net present value calculations, test asset pricing models, and potentially explain the sources of many cross-sectional asset pricing anomalies. In this post, I construct a forward-looking estimate of the term structure of risk premia in the corporate bond market following Jankauskas (2024). The U.S. corporate bond market is an ideal laboratory for studying the relationship between risk premia and maturity because of its large size (standing at roughly $16 trillion as of the end of 2024) and because the maturities are well defined (in contrast to equities).

• 1.American Hegemony Is Collapsing Before Our Eyes

  The global security network that long distinguished US power took generations to build, but it is now unraveling under Donald Trump's watch. With traditional partners opting out of the Iran war, we could well be seeing the end of the global primacy that most Americans alive today have always taken for granted.

• 2.Can Companies Buy Their Way Into the S&P 500?

  Joining the S&P 500 can transform a company’s fortunes, making confidence in the selection process essential for investors and markets alike. But a new study finds that companies purchasing S&P credit ratings were more likely to be admitted, raising questions about the influence of commercial incentives on firms' inclusion.

• 3.Now More than Ever, Europe Must Complete the Single Market

  In a world increasingly shaped by rivalries between major continental powers, new wars, and powerful proprietary technologies, scale matters more than ever. Yet the European economy remains too fragmented, structured as a collection of national markets rather than as one continental bloc.

• 4.Why the Digital Euro Needs Worker Input

  The proposed digital euro represents a critical opportunity for Europe to advance digital sovereignty and fair competition. But to gain the trust of European citizens and businesses, the European Central Bank and other stakeholders must consult with the workers who will be responsible for day-to-day implementation.

• 5.Trump Is Spending Tomorrow’s Security Today

  Neither incompetence nor impulsiveness explains Donald Trump’s decision to start a war despite warnings that stocks of crucial weapons were badly depleted. Across his presidency, in domain after domain – fiscal policy, science funding, alliance management, and more – present consumption comes at the expense of future capacity.

• 1.Fiber growth steadies telecom revenues in Japan

  GlobalData told RCR Wireless News that despite high fiber penetration, growth will continue to be driven by network upgrades, premium high-speed plans, and the gradual migration away from legacy copper infrastructure In sum – what to know: Fiber growth offsets decline – Fixed broadband rising at 1.3% CAGR balances a 1.8% drop in voice, keeping […]

• 2.Nvidia positions AI-RAN with Nokia, T-Mobile in (its) $1tn AI infrastructure market

  Nvidia’s trillion-dollar AI infrastructure forecast set the tone at GTC yesterday, framing its AI-RAN partnerships with Nokia and T-Mobile (part of a $2tn industry) as a new frontier for low-latency inference at the edge. In sum – what to know: Robotics platform – Nvidia positions RAN as a future AI compute platform, turning cell sites […]

• 3.From best-effort to “Ultra-High Reliability” — Wi-Fi 8 in the AI era

  Wi-Fi 8 will support future networks as traffic shifts from human-initiated applications to machine-driven, agentic AI applications Wi-Fi 8 is designed to build directly on the performance gains introduced with Wi-Fi 7. Extending them by delivering higher and more consistent speeds in real-world, non-ideal network conditions. In the AI era, consistency and real-time responsiveness are […]

• 4.What creator-led MVNOs tell us about the new telecom growth (Reader Forum)

  Creator-led MVNOs highlight a new path for telecom growth, where community, identity and brand loyalty drive adoption more than price or coverage. Telecom tech company Circles says that by leveraging existing audiences and niche segments, operators can experiment with smaller, digital-first brands and unlock new engagement and revenue opportunities. Creator and celebrity-led MVNOs had their […]

• 5.War halts work on submarine cable link in the Persian Gulf

  The 2Africa system, hit by the war in the Middle East, will be the largest subsea cable network ever built, spanning 45,000 kilometers In sum – what to know: Project halted – Work on the Persian Gulf portion of the cable has stopped after the war in the Middle East made operations unsafe. Shipping risks […]

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• 1.Deep Learning-Driven Black-Box Doherty Power Amplifier with Pixelated Output Combiner and Extended Efficiency Range

  This article presents a deep learning-driven inverse design methodology for Doherty power amplifiers (PA) with multi-port pixelated output combiner networks. A deep convolutional neural network (CNN) is developed and trained as an electromagnetic (EM) surrogate model to accurately and rapidly predict the S-parameters of pixelated passive networks. By leveraging the CNN-based surrogate model within a blackbox Doherty framework and a genetic algorithm (GA)-based optimizer, we effectively synthesize complex Doherty combiners that enable an extended back-off efficiency range using fully symmetrical devices. As a proof of concept, we designed and fabricated two Doherty PA prototypes incorporating three-port pixelated combiners, implemented with GaN HEMT transistors. In measurements, both prototypes demonstrate a maximum drain efficiency exceed...

• 2.Bridging the High-Frequency Data Gap: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models

  Time series foundation models (TSFMs) require diverse, real-world datasets to adapt across varying domains and temporal frequencies. However, current large-scale datasets predominantly focus on low-frequency time series with sampling intervals, i.e., time resolution, in the range of seconds to years, hindering their ability to capture the nuances of high-frequency time series data. To address this limitation, we introduce a novel dataset that captures millisecond-resolution wireless and traffic conditions from an operational 5G wireless deployment, expanding the scope of TSFMs to incorporate high-frequency data for pre-training. Further, the dataset introduces a new domain, wireless networks, thus complementing existing more general domains like energy and finance. The dataset also provides use cases for short-term forecasting, with predi...

• 3.Agentic AI for SAGIN Resource Management_Semantic Awareness, Orchestration, and Optimization

  Space-air-ground integrated networks (SAGIN) promise ubiquitous 6G connectivity but face significant resource management challenges due to heterogeneous infrastructure, dynamic topologies, and stringent quality-of-service (QoS) requirements. Conventional model-driven approaches struggle with scalability and adaptability in such complex environments. This paper presents an agentic artificial intelligence (AI) framework for autonomous SAGIN resource management by embedding large language model (LLM)-based agents into a Monitor-Analyze-Plan- Execute-Knowledge (MAPE-K) control plane. The framework incorporates three specialized agents, namely semantic resource perceivers, intent-driven orchestrators, and adaptive learners, that collaborate through natural language reasoning to bridge the gap between operator intents and network execution. A k...

• 4.Reconfigurable and Recyclable Low-Threshold Quasi-BIC Lasers via a Tunable polymer Coating

  Reconfigurable and sustainable microcavity lasers are highly desirable for next-generation integrated photonics. Here, we report a recyclable, low-threshold quasi-bound state in the continuum (q-BIC) laser fabricated via low-cost, high-throughput interference lithography. By introducing a polyvinyl alcohol (PVA) coating on a dye-doped photonic crystal, we suppress out-of-plane symmetry breaking, which reinforces optical confinement and reduces the lasing threshold. The q-BIC modes are further tuned through tailoring the refractive-index of the PVA layer by using Kramers-Kronig relation via Rhodamine 6G doping, demonstrating a wavelength shift of 7.14 nm and a sensitivity of 215 nm RIU as a sensing prob. More importantly, lasing modes are reversibly tuning via precisely controlling the coating thickness. Exploiting the dissolving and re-co...

• 5.Toward Experimentation-as-a-Service in 5G/6G: The Plaza6G Prototype for AI-Assisted Trials

  This paper presents Plaza6G, the first operational Experiment-as-a-Service (ExaS) platform unifying cloud resources with next-generation wireless infrastructure. Developed at CTTC in Barcelona, Plaza6G integrates GPU-accelerated compute clusters, multiple 5G cores, both open-source (e.g., Free5GC) and commercial (e.g., Cumucore), programmable RANs, and physical or emulated user equipment under unified orchestration. In Plaza6G, the experiment design requires minimal expertise as it is expressed in natural language via a web portal or a REST API. The web portal and REST API are enhanced with a Large Language Model (LLM)-based assistant, which employs retrieval-augmented generation (RAG) for up-to-date experiment knowledge and Low-Rank Adaptation (LoRA) for continuous domain fine-tuning. Over-the-air (OTA) trials leverage a four-chamber ane...

• 1.Discrimination-insensitive pricing

  Rendering fair prices for financial, credit, and insurance products is of ethical and regulatory interest. In many jurisdictions, discriminatory covariates, such as gender and ethnicity, are prohibited from use in pricing such instruments. In this work, we propose a discrimination-insensitive pricing framework, where we require the pricing principle to be insensitive to the (exogenously determined) protected covariates, that is the sensitivity of the pricing principle to the protected covariate is zero. We formulate and solve the optimisation problem that finds the nearest (in Kullback-Leibler (KL) divergence) "pricing" measure to the real world probability, such that under this pricing measure the principle is discrimination-insensitive. We call the solution the discrimination-insensitive measure and provide conditions for its existence ...

• 2.Private Credit Markets Theory, Evidence, and Emerging Frontiers

  Private credit assets under management grew from \$158 billion in 2010 to nearly \$2 trillion globally by mid-2024, fundamentally reshaping corporate credit markets. This paper provides a systematic survey of the academic literature on private credit, organizing theory and evidence around four questions: why the market has grown so rapidly, how direct lender technology differs from bank lending, what risk-adjusted returns investors earn, and whether the sector poses systemic risks. We develop an integrated theoretical framework linking delegated monitoring, soft-information processing, and incomplete contracting to the institutional specifics of modern direct lending. The empirical evidence documents a distinctive lending technology serving opaque, private-equity-sponsored borrowers at a meaningful and persistent spread premium over the b...

• 3.Beyond Prompting: An Autonomous Framework for Systematic Factor Investing via Agentic AI

  This paper develops an autonomous framework for systematic factor investing via agentic AI. Rather than relying on sequential manual prompts, our approach operationalizes the model as a self-directed engine that endogenously formulates interpretable trading signals. To mitigate data snooping biases, this closed-loop system imposes strict empirical discipline through out-of-sample validation and economic rationale requirements. Applying this methodology to the U.S. equity market, we document that long-short portfolios formed on the simple linear combination of signals deliver an annualized Sharpe ratio of 3.11 and a return of 59.53%. Finally, our empirics demonstrate that self-evolving AI offers a scalable and interpretable paradigm.

• 4.AI Agents in Financial Markets: Architecture, Applications, and Systemic Implications

  Recent advances in large language models, tool-using agents, and financial machine learning are shifting financial automation from isolated prediction tasks to integrated decision systems that can perceive information, reason over objectives, and generate or execute actions. This paper develops an integrative framework for analysing agentic finance: financial market environments in which autonomous or semi-autonomous AI systems participate in information processing, decision support, monitoring, and execution workflows. The analysis proceeds in three steps. First, the paper proposes a four-layer architecture of financial AI agents covering data perception, reasoning engines, strategy generation, and execution with control. Second, it introduces the Agentic Financial Market Model (AFMM), a stylised agent-based representation linking agen...

• 5.Betting Around the Clock: Time Change and Long Term Model Risk

  We investigate the performance of the Kelly rule in a setting in which the dynamics of the return is represented by a time change process. We find that in this general semi-martingale setting the Kelly rule does not maximize the average growth rate, unless the log-return is normally distributed. Namely, the investment position proposed by the Kelly rule is too large, and the investor could achieve a higher average growth rate by investing less aggressively. The higher the variance of the stochastic clock, the more material the failure of the Kelly rule. The ruin threshold proposed by Thorp (1969) is closer, even though examples based on stochastic clock variance estimates taken from the literature show that Kelly rule investment remains safely in the ruin-free region. Finally, the goal of keeping the investment below the ruin threshold fo...

• 1.Agentic AI for SAGIN Resource Management_Semantic Awareness, Orchestration, and Optimization

  Space-air-ground integrated networks (SAGIN) promise ubiquitous 6G connectivity but face significant resource management challenges due to heterogeneous infrastructure, dynamic topologies, and stringent quality-of-service (QoS) requirements. Conventional model-driven approaches struggle with scalability and adaptability in such complex environments. This paper presents an agentic artificial intelligence (AI) framework for autonomous SAGIN resource management by embedding large language model (LLM)-based agents into a Monitor-Analyze-Plan- Execute-Knowledge (MAPE-K) control plane. The framework incorporates three specialized agents, namely semantic resource perceivers, intent-driven orchestrators, and adaptive learners, that collaborate through natural language reasoning to bridge the gap between operator intents and network execution. A k...

• 2.Reconfigurable and Recyclable Low-Threshold Quasi-BIC Lasers via a Tunable polymer Coating

  Reconfigurable and sustainable microcavity lasers are highly desirable for next-generation integrated photonics. Here, we report a recyclable, low-threshold quasi-bound state in the continuum (q-BIC) laser fabricated via low-cost, high-throughput interference lithography. By introducing a polyvinyl alcohol (PVA) coating on a dye-doped photonic crystal, we suppress out-of-plane symmetry breaking, which reinforces optical confinement and reduces the lasing threshold. The q-BIC modes are further tuned through tailoring the refractive-index of the PVA layer by using Kramers-Kronig relation via Rhodamine 6G doping, demonstrating a wavelength shift of 7.14 nm and a sensitivity of 215 nm RIU as a sensing prob. More importantly, lasing modes are reversibly tuning via precisely controlling the coating thickness. Exploiting the dissolving and re-co...

• 3.Toward Experimentation-as-a-Service in 5G/6G: The Plaza6G Prototype for AI-Assisted Trials

  This paper presents Plaza6G, the first operational Experiment-as-a-Service (ExaS) platform unifying cloud resources with next-generation wireless infrastructure. Developed at CTTC in Barcelona, Plaza6G integrates GPU-accelerated compute clusters, multiple 5G cores, both open-source (e.g., Free5GC) and commercial (e.g., Cumucore), programmable RANs, and physical or emulated user equipment under unified orchestration. In Plaza6G, the experiment design requires minimal expertise as it is expressed in natural language via a web portal or a REST API. The web portal and REST API are enhanced with a Large Language Model (LLM)-based assistant, which employs retrieval-augmented generation (RAG) for up-to-date experiment knowledge and Low-Rank Adaptation (LoRA) for continuous domain fine-tuning. Over-the-air (OTA) trials leverage a four-chamber ane...

• 4.Knowledge Distillation for Collaborative Learning in Distributed Communications and Sensing

  The rise of sixth generation (6G) wireless networks promises to deliver ultra-reliable, low-latency, and energy-efficient communications, sensing, and computing. However, traditional centralized artificial intelligence (AI) paradigms are ill-suited to the decentralized, resource-constrained, and dynamic nature of 6G ecosystems. This paper explores knowledge distillation (KD) and collaborative learning as promising techniques that enable the efficient and scalable deployment of lightweight AI models across distributed communications and sensing (C&S) nodes. We begin by providing an overview of KD and highlight the key strengths that make it particularly effective in distributed scenarios characterized by device heterogeneity, task diversity, and constrained resources. We then examine its role in fostering collective intelligence throug...

• 5.RSMA-Assisted Multi-Functional 6G: Integrated Sensing, Communication, and Powering

  Integrated sensing, communication, and powering (ISCAP) has emerged as a promising solution for enabling multi-functionality in 6G networks. However, it poses a significant challenge in the design of multi-functional waveforms that must jointly consider communication, sensing, and powering performance. In this paper, we propose a novel rate-splitting multiple access (RSMA)-enabled multi-functional ISCAP network, where RSMA facilitates the use of communication signals to simultaneously achieve all three functionalities. Based on the proposed system model, we investigate the beamforming optimization problem to explore the performance trade-offs among communication, sensing, and power transfer. To efficiently solve this problem, we develop a novel ISCAP-extragradient (ISCAP-EG) algorithm, which transforms the original problem into a sequence...

• 1.Agentic AI for SAGIN Resource Management_Semantic Awareness, Orchestration, and Optimization

  Space-air-ground integrated networks (SAGIN) promise ubiquitous 6G connectivity but face significant resource management challenges due to heterogeneous infrastructure, dynamic topologies, and stringent quality-of-service (QoS) requirements. Conventional model-driven approaches struggle with scalability and adaptability in such complex environments. This paper presents an agentic artificial intelligence (AI) framework for autonomous SAGIN resource management by embedding large language model (LLM)-based agents into a Monitor-Analyze-Plan- Execute-Knowledge (MAPE-K) control plane. The framework incorporates three specialized agents, namely semantic resource perceivers, intent-driven orchestrators, and adaptive learners, that collaborate through natural language reasoning to bridge the gap between operator intents and network execution. A k...

• 2.Toward Experimentation-as-a-Service in 5G/6G: The Plaza6G Prototype for AI-Assisted Trials

  This paper presents Plaza6G, the first operational Experiment-as-a-Service (ExaS) platform unifying cloud resources with next-generation wireless infrastructure. Developed at CTTC in Barcelona, Plaza6G integrates GPU-accelerated compute clusters, multiple 5G cores, both open-source (e.g., Free5GC) and commercial (e.g., Cumucore), programmable RANs, and physical or emulated user equipment under unified orchestration. In Plaza6G, the experiment design requires minimal expertise as it is expressed in natural language via a web portal or a REST API. The web portal and REST API are enhanced with a Large Language Model (LLM)-based assistant, which employs retrieval-augmented generation (RAG) for up-to-date experiment knowledge and Low-Rank Adaptation (LoRA) for continuous domain fine-tuning. Over-the-air (OTA) trials leverage a four-chamber ane...

• 3.SliceMapper: Intelligent Mapping of O-CU and O-DU onto O-Cloud Sites in 6G O-RAN

  In this paper, we propose an rApp, named SliceMapper, to optimize the mapping process of the open centralized unit (O-CU) and open distributed unit (O-DU) of an open radio access network (O-RAN) slice subnet onto the underlying open cloud (O-Cloud) sites in sixth-generation (6G) O-RAN. To accomplish this, we first design a system model for SliceMapper and introduce its mathematical framework. Next, we formulate the mapping process addressed by SliceMapper as a sequential decision-making optimization problem. To solve this problem, we implement both on-policy and off-policy variants of the Q-learning algorithm, employing tabular representation as well as function approximation methods for each variant. To evaluate the effectiveness of these approaches, we conduct a series of simulations under various scenarios. We proceed further by perfor...

• 4.AtlasRAN: Modeling and Performance Evaluation of Open 5G Platforms for Ubiquitous Wireless Networks

  Fifth-generation (5G) systems are increasingly studied as shared communication and computing infrastructure for connected vehicles, roadside edge platforms, and future unmanned-system applications. Yet results from simulators, host-OS emulators, digital twins, and hardware-in-the-loop testbeds are often compared as if timing, input/output (I/O), and control-loop behavior were equivalent across them. They are not. Consequently, apparent limits in throughput, latency, scalability, or real-time behavior may reflect the execution harness rather than the wireless design itself. This paper presents \textit{AtlasRAN}, a capability-oriented framework for modeling and performance evaluation of 5G Open Radio Access Network (O-RAN) platforms. It introduces two reference architectures, terminology that separates functional compatibility from timing...

• 5.An Analytic Hierarchy Process (AHP) Based QoS-aware Mode Selection Algorithm for D2D Enabled Heterogeneous Networks

  Device-to-device (D2D) communication was proposed to enhance the coverage of cellular base stations. In a D2D enabled non-standalone fifth generation cellular network (NSA), service demand of a user equipment (UE) may be served in four \emph{modes}: through LTE only, through NR only, through LTE via D2D and through NR via D2D. Such mode selection should consider the service requirements of the UEs (e.g., high data rate, low latency, ultra-reliability, etc.) and the overhead incurred by handovers. In existing mode selection approaches for D2D enabled NSA, the service requirements of the UEs have been largely ignored. To address this, in this paper, we propose a mode selection algorithm for D2D enabled NSA based on a two-level Analytic Hierarchy Process (AHP). The proposed AHP-based mechanism considers the service requirements of the UEs in...