Babak's Daily Briefing

Monday, June 1, 2026

Sources: 20 | Total Articles: 96

• 1.RF Digital Twins: Why 5G-Advanced and 6G Need Predictive Simulation

  RF Digital Twins: Why 5G-Advanced and 6G Need Predictive Simulation As wireless systems become more tightly coupled across…

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

  Summary available at source link.

• 3.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.

• 4.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.

• 5.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.

• 1.Exploring Autonomous Agentic Data Engineering for Model Specialization

  Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data. Existing LLM-based data curation methods primarily rely on human-designed workflows, leaving it unexamined whether LLMs can autonomously execute an end-to-end data engineering pipeline for model specialization. We formalize \textbf{Autonomous Agentic Data Engineering}, a novel task designed to evaluate LLMs as autonomous data engineers that drive model specialization through end-to-end data curation. We frame data as an optimizable component and study agents that plan, generate, and iteratively optimize training data across multiple domains, guided by post-training performance improvement. Experiments show that autonomous LLM data engineers yield substantial g...

• 2.Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation

  Large Language Models (LLMs) have advanced autonomous agents from deep search, which retrieves concise factual answers, to deep research, which synthesizes scattered evidence into long-form reports. However, verifiable multimodal deep research remains challenging due to open-ended synthesis without deterministic ground truth and the need to interleave textual arguments with visual evidence. We propose \textsc{Ptah}, a multi-agent harness for interleaved report generation. \textsc{Ptah} orchestrates the lifecycle from user query to rendered web report through planning, research, and writing stages, where specialized agents construct visual-aware plans, collect claim-grounded evidence, maintain source-aligned images in a \textit{Visual Working Memory}, and compose reports through declarative multimodal tool use. A verifier agent serves as t...

• 3.Croissant Tasks: A Metadata Format for Reproducible Machine Learning Evaluations

  Reproducibility is fundamental to the scientific method, yet remains a critical challenge in machine learning. Contributing factors include underspecified execution details and brittle software environments. Human-centric remedies, such as checklists and manual verification, help but require intensive effort and fail to scale. To address this, we introduce Croissant Tasks: a declarative, machine-actionable metadata format that abstracts low-level implementation details into high-level specifications. This format enables conceptual reproducibility: verifying claims via independent, agent-generated implementations rather than brittle source code replication. We contribute: (1) the Croissant Tasks specification, formally decoupling task problem from solution; (2) an automated LLM pipeline that retrofits existing benchmarks into this format; ...

• 4.PTCG-Bench: Can LLM Agents Master Pokémon Trading Card Game?

  Given a strategically complex board game, human players can quickly learn to devise strategies after playing a few rounds. Autonomous agents require similar capabilities in realistic interactive environments, yet existing agent benchmarks often fail to fully capture such strategic and evolving decision-making scenarios. We present PTCG-Bench, a benchmark built on the Pok'{e}mon Trading Card Game (PTCG) that evaluates LLM agents at two complementary levels: (1) their decision-making performance within a single complex environment, and (2) their ability to self-evolving through accumulated experience. We further include a modular harness ablation to better interpret agent performance without conflating it with model capability. Our experiments show that, although LLM agents can achieve non-trivial gameplay performance, sustained and stable ...

• 5.Do Agents Need Semantic Metadata? A Comparative Study in Agentic Data Retrieval

  In the era of autonomous agents, machine-actionable data is critical for data-driven workflows. For more than a decade, semantic metadata like schema.org has anchored the FAIR principles (Findable, Accessible, Interoperable, and Reusable) for machine-actionable data and enabled discovery tools like Google Dataset Search. However, the rise of Large Language Models (LLMs) capable of navigating the unstructured web raises a fundamental question: Is semantic metadata still necessary for agentic data discovery, or can agents reliably retrieve actionable data directly from the web? We present a comparative analysis of agentic data retrieval across two distinct environments: a Baseline Agent searching billions of open-web documents, and a Semantic Agent leveraging a corpus of 90 million datasets using schema.org. We deploy an "LLM-as-a-judge" ev...

• 1.Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow

  arXiv:2605.30400v1 Announce Type: new Abstract: We present a protocol to evaluate ChatGPT's ability to generate disease-centric biomedical associations. It outlines how we generate the associations, validate the biological entities using biomedical ontologies, and verify associations using literature. The protocol includes a self-consistency strategy to assess generative reliability across ChatGPT models. To address ontology exact-match limitations, we provide a use case performing semantic verification through a workflow enabled by Retrieval-Augmented Generation (RAG) powered by open-source large language models (LLMs). This enables LLMs to establish truth over content generated by other LLMs and expose hallucination.

• 2.Exploring Autonomous Agentic Data Engineering for Model Specialization

  arXiv:2605.30407v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data. Existing LLM-based data curation methods primarily rely on human-designed workflows, leaving it unexamined whether LLMs can autonomously execute an end-to-end data engineering pipeline for model specialization. We formalize \textbf{Autonomous Agentic Data Engineering}, a novel task designed to evaluate LLMs as autonomous data engineers that drive model specialization through end-to-end data curation. We frame data as an optimizable component and study agents that plan, generate, and iteratively optimize training data across multiple domains, guided by post-training performance improvement. Experiments show that ...

• 3.Domain Adaptation and Reasoning Frameworks in Language Models: A Controlled Experiment with Historical Cosmology

  arXiv:2605.30415v1 Announce Type: new Abstract: We investigate how domain adaptation reshapes explanatory behavior in language models using historical cosmology as a controlled setting. In Phase 1, we train a small language model from scratch on a pre-Copernican corpus from which explicit heliocentric references were removed, and evaluate whether Earth-motion or heliocentric continuations nevertheless emerge. In Phase 2, we fine-tune a larger pretrained model using QLoRA on the same corpus in order to study how adaptation modifies explanatory framing and cosmological stance. Model outputs are evaluated using an LLM-as-judge framework that labels both cosmological stance (geocentric, heliocentric, or ambiguous) and explanatory frame (premodern versus modern). In the constrained setting of Phase 1, the smaller models occasionally generate ...

• 4.Cross-Lingual Steering for Figurative Language Generation

  arXiv:2605.30443v1 Announce Type: new Abstract: Multilingual large language models can generate figurative language, but whether the internal signals driving this behavior are language-specific or reusable across languages is unclear. Using activation steering as a probe, we estimate a direction for a figurative category from figurative--literal activation differences in one language and apply it during generation. Across five figurative categories, six languages, and four multilingual LLMs, these directions steer reliably within their own language, most robustly for metaphor and simile. More importantly, they transfer across languages: a direction learned in one increases the target behavior when applied to another, with German among the most receptive targets. Going further, directions assembled from other languages can match or even s...

• 5.Can LLM Teams Play What? Where? When?

  arXiv:2605.30459v1 Announce Type: new Abstract: Large language models (LLMs) remain limited on tasks requiring indirect reasoning, cultural knowledge, and coordinated hypothesis testing. We investigate whether team-based interaction improves LLM performance in What? Where? When? (ChGK), a quiz game designed to reward collective reasoning. We introduce three team strategies: Voting, Silent Team (the captain observes final answers), and Talkative Team (the captain observes both answers and rationales). To minimize data leakage, we evaluate these strategies on a dataset consisting of 572 ChGK questions released in 2025. Using six recent large-scale open models, we show that team-based strategies outperform single-model baselines, yielding gains of up to 20 percentage points in accuracy. The best team achieves 44.23% accuracy, and approaches...

• 1.QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits

  arXiv:2605.30358v1 Announce Type: new Abstract: Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, where the performance is highly constrained to noise. Addressing the limitation often requires hardware-facing capabilities beyond gate-sequence circuit specification, including mid-circuit measurement and classical feedback for quantum error correction (QEC), precise timing control for dynamical decoupling (DD), and pulse-level waveform access for calibration. OpenQASM-3 was introduced to expose exactly these capabilities, providing a hardware-level programming interface. However, despite the rapid progress of large language models in code generation, there is still no dataset specifically designed to train and evaluate LLMs on OpenQASM-3 programs that involve its advanced hardware-oriented features. To address th...

• 2.Gait2Hip-60: A Unified Deep Learning Benchmark for Predicting Hip Muscle Forces and Joint Moments from Multi-Cadence Gait Kinematics

  arXiv:2605.30374v1 Announce Type: new Abstract: Estimating hip muscle forces and joint moments during gait typically relies on musculoskeletal simulation, which is informative but time-consuming and difficult to apply in clinical settings. This study developed a deep learning framework to predict these hip dynamics parameters directly from lower-limb gait kinematics and compared three representative sequence models under a unified protocol. Gait data were collected from 60 healthy adults under three metronome-guided cadence conditions. Ten bilateral lower-limb joint angles were used as inputs, and OpenSim-derived hip muscle forces and hip joint moments were used as reference outputs. Three deep learning models of LSTM, Transformer, and Mamba were trained and evaluated using the same subject-level split, preprocessing pipeline, and metrics...

• 3.Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling

  arXiv:2605.30376v1 Announce Type: new Abstract: Modern time series architectures face a fundamental trade-off: channel-independent models scale well with increasing data volume but ignore critical inter-channel dependencies, while channel-dependent models are expressive but remain ``dimension-bounded'', struggling to generalize across heterogeneous datasets.To bridge this gap, we introduce Unicorn (Universal Correlation Network), a framework for scalable, multi-dataset pretraining on high-dimensional time series. At the core of Unicorn is a latent prototype codebook that decouples correlation modeling from specific channel identities. By projecting heterogeneous channels into a shared latent space, UniCorN learns identity-agnostic, reusable interaction patterns that transfer across domains with diverse dimensionalities and semantics. Exte...

• 4.When LLMs Learn to Be Consistently Wrong: A Multi-Model Study of Linear Representations of Synthetic Deception

  arXiv:2605.30381v1 Announce Type: new Abstract: Deceptive alignment, in which models maintain accurate internal representations while deliberately producing false outputs, remains a central challenge in AI safety. While strategic deception is the primary long-term concern, synthetic dishonesty - induced via direct optimization on incorrect answers - provides a controlled testbed for studying the representational basis of learned deception. We introduce a multi-model paradigm in which honest and deceptive variants of five transformer models (Pythia-1.4B, Gemma-2-2B/9B, Qwen2.5-7B, Llama-3.1-8B) are fine-tuned using LoRA on the same question distribution. Linear probes trained on mean-pooled hidden states detect synthetic dishonesty with near-perfect AUC (greater than or equal to 0.99) as early as layers 1-3 in four architectures, while Pyt...

• 5.LLMs Without Deep Neural Networks: New Architecture, Benefits and Case Study

  arXiv:2605.30385v1 Announce Type: new Abstract: The purpose of this article is to provide validation to my deep neural network alternative in the context of LLMs. Very recently, there has been a significant interest by Chinese researchers in a model called RBF network, as a substitute to standard DNNs, with increased explainability and higher accuracy. It turns out that my new model, discovered independently, is based on the exact same machinery. But with a major twist: it does not need DNN as it finds the global optimum of the loss function in closed form, in one iteration, thus eliminating the tedious training step. Here I provide a high-level overview of my technology, with case study and comparison to similar methods.

• 1.Structured interactions improve distributed coordination beyond model scaling in a real-world multi-robot system

  arXiv:2605.30383v1 Announce Type: new Abstract: Scaling individual robot capabilities is common but costly. Here we investigate a system-level design question in real-world multi-robot coordination: given matched hardware budgets, does restructuring communication among robots yield larger gains than increasing onboard model size? Using a representative transport-and-mapping task with 10 physical robots (5 runs per condition, 60 runs total), we find that switching from fully connected to modular hierarchical interactions improves normalised performance by 47 points (0--100), whereas doubling neural network hidden size yields at most 9 points. Nested mixed-effects model comparisons show a substantially larger improvement in model fit for topology than for scale. The pattern is confirmed in independent SMAC replications; heterogeneous benchm...

• 2.Learning-Based Navigation for Indoor Mobile Robots

  arXiv:2605.30468v1 Announce Type: new Abstract: This paper presents a learning-based navigation framework for indoor mobile robots. The proposed method combines a supervised neural global planner, trained from cost-aware A* expert trajectories, with the proposed Learning-Based DWA local planner, which is formulated as discrete candidate selection over the Dynamic Window Approach (DWA) action lattice. For local planning, the policy is first trained by behavior cloning and then refined by Proximal Policy Optimization (PPO) under feasibility-aware masking. The framework is implemented and evaluated in both simulated and real-world indoor environments. Experimental results show that the proposed method generates feasible global routes and reliable local motion commands for safe goal-directed navigation in the presence of obstacles. These resu...

• 3.ELAN4D: Embodiment-Centric 4D Supervision for Vision-Language-Action Models via Plug-and-Play Adaptation

  arXiv:2605.30484v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown promise for robotic manipulation, yet most existing policies operate reactively by directly regressing actions from current observations, without explicitly modeling future dynamics. This limits their ability to generalize under out-of-distribution perturbations. To address this issue, we propose ELAN4D, an embodiment-centric, 4D-aware training framework that enhances VLA policies with future robot keypoint tracks as predictive spatio-temporal supervision. Using only forward kinematics from proprioceptive states, we derive 3D displacement tracks of robot keypoints, such as joints and the end-effector, with negligible preprocess cost. These tracks provide metric and compact supervision without requiring external trackers or reconstruction. A plug...

• 4.CoMo3R-SLAM: Collaborative Monocular Dense SLAM with Learned 3D Reconstruction Priors for Outdoor Multi-Agent Systems

  arXiv:2605.30488v1 Announce Type: new Abstract: Collaborative dense SLAM is essential for multi-robot teams to achieve scalable and consistent 3D perception across large-scale outdoor environments. Existing systems typically depend on depth sensors, incurring significant payload, power, and calibration costs. Monocular RGB cameras are a lightweight alternative, but collaborative monocular dense SLAM remains difficult due to scale ambiguity, unreliable inter-agent data association, especially in outdoor scenes where low overlap and repetitive structures make traditional feature matching unreliable, motivating robust geometric information. We propose CoMo3R-SLAM, the first collaborative monocular dense RGB SLAM system that leverages robust learned feed-forward 3D reconstruction priors for outdoor multi-agent mapping. Each agent runs a prior...

• 5.Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamics

  arXiv:2605.30503v1 Announce Type: new Abstract: Learning to reach arbitrary goals from sparse feedback requires agents to infer a rich notion of reachability across state--goal pairs. Goal-conditioned reinforcement learning (GCRL) tackles this challenge by learning policies that generalize across goals, but this generalization becomes increasingly difficult as the underlying dynamics become high-dimensional, hybrid, or contact-dependent. To address this issue, physics-informed GCRL (Pi-GCRL) introduces optimal-control-inspired inductive biases into goal-conditioned value learning. While Pi-GCRL methods have proven effective in navigation and object-free goal-reaching domains, their reliability in contact-rich tasks remains unclear, where contact interactions induce hybrid dynamics, mode-dependent controllability, and nonsmooth value lands...

• 1.Inspectable Neural Markov Models for Non-Stationary Time Series

  Modeling non-stationary stochastic systems requires balancing the representational capacity of deep learning with the structural transparency of classical probabilistic models. Markov transition matrices provide such a framework, but traditional frequency-based estimation collapses at high resolutions due to data sparsity. We propose a hybrid approach that parameterizes the manifold of stochastic matrices through a neural network, enabling estimation of time-inhomogeneous Markov chains in sparse-data regimes, and use financial markets as a testbed to investigate the Markov state variable as a critical inductive bias. We show that conditioning on realized volatility produces a more internally consistent Markovian structure than return-based states, achieving a $5.6\%$ reduction in Chapman-Kolmogorov discrepancy and superior held-out likeli...

• 2.Insurance Pricing Optimization via Off-Policy Evaluation

  Traditional insurance pricing relies on risk-based principles that ensure actuarial fairness and solvency but do not explicitly account for policyholders' price sensitivity. We formulate insurance pricing as a decision-making problem and study it using tools from off-policy evaluation and stochastic control. We propose a kernelized inverse propensity score estimator that exploits local structure in the action space and yields variance reduction compared to the classical inverse propensity score estimator. Building on these value estimates, we investigate policy optimization and present two practical approaches for computing optimal pricing rules: an interpretable data-shared Lasso formulation and a flexible policy parameterization based on neural networks. Using a controlled synthetic travel insurance environment, we empirically confirm t...

• 3.Historical Developments in Probability Measures for Asset Pricing: From State Prices to Modern Pricing Kernels

  This review summarizes the historical development of probability measures in asset pricing, from early mathematical finance and state price theory to risk-neutral valuation, martingale measures, forward measures, stochastic discount factors, incomplete-market measure selection, benchmark pricing, robust and nonlinear pricing, and modern data-driven probability transformations. The central theme is that asset pricing is not merely an exercise in estimating physical probabilities. Instead, pricing theory constructs, transforms, or selects probability measures so that market prices can be represented as expectations after discounting, numeraire normalization, marginal utility weighting, entropy penalization, calibration, or information conditioning. The paper emphasizes landmark contributions including Bachelier's probabilistic model of spec...

• 4.Nonlinear and Heavy-Tailed Predictability in Transition-Energy Financial Markets

  Transition-related financial markets are increasingly exposed to abrupt repricing episodes, elevated volatility, and heterogeneous macro-financial shocks. Under such conditions, conventional Gaussian-linear forecasting frameworks may provide an incomplete representation of the dependence structure linking fossil-energy, renewable-energy, technology, and utility-sector assets. This paper investigates whether transition-related financial returns exhibit residual non-linear predictability after controlling for heavy-tailed multivariate linear dynamics. To address this question, we develop a hybrid forecasting framework combining Student-t Vector Autoregressions with nonlinear recurrent residual learning architectures. The empirical analysis considers six major exchange-traded funds representing broad equity markets and key transition-sensiti...

• 5.Multiperiod Groundwater Markets

  Motivated by the emergence of local groundwater exchanges, we construct and analyze stochastic models of dynamic groundwater markets. Our primary focus is endogenizing the price formation and groundwater pumping strategies in a closed market with stochastic groundwater allocations and opportunities for intertemporal transfer through rights banking. In our model, several agents, interpreted as farmers or agricultural districts, make competitive decisions on water consumption to produce a basket of goods, as well as on trading allocations among themselves, or banking them for future periods. We define the respective discrete-time non-zero-sum non-cooperative game and construct its sub-game perfect Nash equilibria characterized by the groundwater price process $\{p^\circ(t)\}$. We furthermore construct an algorithm to determine equilibrium s...

• 1.GSMA Presents Vietnam with Government Leadership Award 2026, Recognising the Country as One of the World’s Most Dynamic Digital Leaders

  Summary available at source link.

• 2.Network Slicing to Help Drive Next Wave of 5G Innovation in India

  Summary available at source link.

• 3.Canadian Telecommunications Association and GSMA Convene Industry Leaders at “Inflection Point” for Canada’s Connectivity Future

  Summary available at source link.

• 4.iOS 26.5 brings E2EE  for RCS: A new milestone for secure cross‑platform messaging

  Summary available at source link.

• 5.GSMA Calls for Urgent Action to Protect Connectivity Resilience Across Africa

  Summary available at source link.

• 1.LongTraceRL: Learning Long-Context Reasoning from Search Agent Trajectories with Rubric Rewards

  Long-context reasoning remains a central challenge for large language models, which often fail to locate and integrate key information in extensive distracting content. Reinforcement learning with verifiable rewards (RLVR) has shown promise for this task, yet existing methods are limited by low-confusability distractors and sparse, outcome-only reward signals that cannot supervise intermediate reasoning steps. To address these issues, we introduce \textsc{LongTraceRL}. For data construction, we generate multi-hop questions via knowledge graph random walks and leverage search agent trajectories to build \emph{tiered distractors}: documents the agent read but did not cite (high confusability) and documents that appeared in search results but were never opened (low confusability), producing training contexts that are far more challenging tha...

• 2.Can Generative AI help people navigate Radical Moral Disagreements? The CONSIDER prototype

  Radical Moral Disagreements (RMDs) are highly polarising topics that are increasingly censored in everyday life, with growing evidence suggesting that this polarisation carries measurable costs to public mental health. To address these challenges, some researchers have proposed Large Language Models (LLMs) as a means to support more democratic deliberation and better moral reasoning. Yet existing tools are poorly calibrated to help people navigate RMDs, because of their intense and divisive characteristics. This paper introduces CONSIDER, a prototype for a one-to-one AI tool for RMD navigation. Drawing on Mill's account of the epistemic value of disagreement, CONSIDER aims at value clarification through structured disagreement with an opposing LLM-generated opinion. We describe CONSIDER's design logic and analyse potential risks posed by ...

• 3.What Am I Missing? Question-Answering as Hidden State Probing

  Test-time reasoning has become a significant field of study since the introduction of chain-of-thought reasoning in large language models (LLMs). However, the mechanisms of this reasoning process are still under-explored -- from the same input prompt, and even the same partial solution, LLMs can produce varied answers if sampled multiple times. We propose to leverage question-asking as an inference-time intervention that articulates information about the model's hidden state. To achieve that, we present a student-teacher setting where a student asks questions to a teacher. We train a probe on the student's hidden state before and after asking a question and find it is predictive of the trajectory's final correctness, even before generating the teacher's answer. This suggests there is a meaningful signal from the self-diagnosis that occurs...

• 4.LinTree: Improving LLM Reasoning with Explicitly Structured Search Histories

  Large language models (LLMs) often solve reasoning problems by generating intermediate traces that explore and revise partial solutions. From a search perspective, these traces can be viewed as linearized search trees, where the model extends a partial solution, abandons it when it fails, and backtracks to try alternatives. Compared with traditional heuristic-guided search, such a policy has a potential advantage: it conditions on the whole search trace rather than only on the current local state. We first test whether LLMs utilize this advantage by comparing trace-conditioned reasoning policies against best-first search equipped with an LLM heuristic that only observes the current local state. Across three controlled reasoning environments, Blocks World, grid Navigation, and Sokoban, we find that raw access to search history alone is not...

• 5.BenHalluEval: A Multi-Task Hallucination Evaluation Framework for Large Language Models on Bengali

  Despite Bengali being the sixth most spoken language in the world, no prior work has systematically evaluated hallucination in large language models (LLMs) for Bengali. We introduce BenHalluEval, a fine-grained hallucination evaluation framework for Bengali covering four tasks: Generative Question Answering (GQA), Bangla-English Code-Mixed QA, Summarization, and Reasoning. We construct 12,000 hallucinated candidates using GPT-5.4 across twelve task-specific hallucination types, drawn from three existing Bengali datasets, and evaluate seven LLMs spanning reasoning-oriented, multilingual, and Bengali-centric categories under a dual-track protocol that independently measures false-positive rate on ground-truth instances (Track A) and hallucination detection rate on hallucinated candidates (Track B). To jointly penalise both failure modes and...

• 1.BenHalluEval: A Multi-Task Hallucination Evaluation Framework for Large Language Models on Bengali

  Despite Bengali being the sixth most spoken language in the world, no prior work has systematically evaluated hallucination in large language models (LLMs) for Bengali. We introduce BenHalluEval, a fine-grained hallucination evaluation framework for Bengali covering four tasks: Generative Question Answering (GQA), Bangla-English Code-Mixed QA, Summarization, and Reasoning. We construct 12,000 hallucinated candidates using GPT-5.4 across twelve task-specific hallucination types, drawn from three existing Bengali datasets, and evaluate seven LLMs spanning reasoning-oriented, multilingual, and Bengali-centric categories under a dual-track protocol that independently measures false-positive rate on ground-truth instances (Track A) and hallucination detection rate on hallucinated candidates (Track B). To jointly penalise both failure modes and...

• 2.A Unified and Reproducible Experimentation Framework for Speech Understanding

  Speech foundation models and Speech LLMs have advanced speech understanding, yet deployment-oriented model selection is hindered by non-comparable evaluations caused by mismatched post-processing, and by training results that are hard to reproduce across data scales and pipelines. We present SURE, a unified experimentation framework that standardizes prediction formats, normalization, and scoring. SURE evaluates strong systems across paradigms, from conventional pipelines to Speech LLMs, on representative tasks under realistic acoustic and linguistic stressors. Beyond evaluation, SURE introduces an agent-assisted training conversion flow that maps paper and code into versioned, runnable training pipelines under a unified protocol on matched open-data subsets. Overall, SURE improves comparability and reproducibility for deployment-oriented...

• 3.OrcaRouter: A Production-Oriented LLM Router with Hybrid Offline-Online Learning

  The rapid development of large language models, each with distinct capabilities and inference costs, raises a practical deployment question: given an incoming request, which model should handle it? We present OrcaRouter, a production-oriented LLM router that combines a LinUCB-based contextual bandit over lexical and sentence-embedding features with a hybrid offline-online learning protocol. Offline, OrcaRouter obtains full-information feedback by evaluating each candidate model on a curated set of routing prompts, yielding a reward matrix used to fit one ridge regressor per arm. At deployment time, it initializes from these parameters and can optionally continue learning from bandit feedback, updating only the selected model's arm after observing its reward. At the time of our RouterArena submission (May 20, 2026), OrcaRouter-Adaptive ran...

• 4.CSULoRA: Closest Safe Update Low-Rank Adaptation

  Low-rank adaptation has become a standard method for parameter-efficient fine-tuning of large language models, but even small amounts of unsafe or adversarial fine-tuning data can substantially weaken the safety behavior of aligned models. Existing safety-preserving LoRA methods often rely on hard interventions such as projection, pruning, thresholding, or additional training objectives. While these methods can suppress unsafe update directions, they may also remove task-relevant information or require extra tuning. We introduce CSULoRA, a post-hoc method for correcting trained LoRA adapters through closest safe update estimation. CSULoRA estimates a safety-aligned subspace from the weight displacement between a safety-aligned model and its corresponding base checkpoint. It then decomposes each LoRA update into fully aligned, partially al...

• 5.Archon: A Unified Multimodal Model for Holistic Digital Human Generation

  Digital humans are fundamental to immersive interaction, yet creating a unified model for holistic modalities, including text, audio, motion, and visual content, remains an open challenge. In this paper, we present Archon, a fully pretrained, human-centric unified multimodal model for holistic avatar generation. Archon unifies seven modalities with modality-specific tokenizers, and a native autoregressive unified multimodal model pretrained on synchronized modalities and 72 diverse tasks to model holistic joint distributions. To address the token explosion challenge in high-fidelity talking videos, we introduce a memory-efficient semantic video reparameterization, achieving 4x token reduction while preserving fine-grained dynamics, coupled with a semantic-driven video diffusion decoder. We further propose a "Thinking in Modality" that dec...

• 1.Finding Success in Industry as a Chip Designer

  I have been an application-specific IC (ASIC) designer for almost three decades. Over that time, I’ve moved through the full academic trajectory, from graduate student to full professor; later, I transitioned to industry after an unsuccessful stint at entrepreneurship. When I made the switch to the private sector in 2019, I began focusing on a critically important aspect of the electronic industry: silicon intellectual property. As much as 80 percent of the physical area in today’s most advanced chips is occupied by blocks that aren’t made for specific products or even designed by the consumer-facing companies that built them. Instead, chipmakers draw heavily on established silicon IP from companies like Arm , Cadence , Rambus , Synopsys , and the company I work for, Silicon Creations . Throughout my career, I’ve designed chips for very d...

• 2.South Africa Has AI Leverage. Its Draft Policy Leaves It Unused

  This article is adapted by the author with permission from Tech Policy Press . Read the original article . South Africa is not just another developing country struggling to govern artificial intelligence; it is the exception with leverage, and the window to act on it is closing. It holds approximately 88 percent of global platinum-group metal reserves , critical inputs to parts of the semiconductor and data-center supply chains that make AI infrastructure possible. It hosts the largest data-center market on the continent. Its existing hyperscaler relationships give it procurement leverage that most African states will never have . And a major geopolitical contest over AI infrastructure is being fought on its soil right now, between Chinese and American technology companies competing for control of the systems that will underpin an entire ...

• 3.Thermal Cameras and AI Help Ships Steer Clear of Gray Whales

  On a sunny Tuesday afternoon in May, San Francisco Bay is busy. Container ships the size of skyscrapers deliver their wares to the Port of Oakland, tankers bear fuel, and ferries carry tourists to their hikes and commuters to their jobs at AI startups. Looking down at this marine traffic from Angel Island, located near the entrance to the bay, a group of excited scientists point to some sparkles on the surface of the water: Three gray whales are coming up for breath. A collaboration of government agencies and scientists hopes to keep interspecies traffic running safely, thanks to an AI-based whale detection system that launched on 19 May. Developed by WhaleSpotter , based in Somerville, Mass., the system uses an AI model to detect whales in footage from thermal cameras looking down at the bay from Point Blunt on Angel Island. Detections a...

• 4.Reclaiming Social Engineering for Good

  “Social engineering” sounds like something out of a conspiracy thriller, charged with totalitarian control and fringe paranoia. More mundanely, it’s come to be associated with phishing and other scams, in which fraudsters manipulate people into disclosing personal information. Yet the concept is older and more benign: it is the deliberate shaping of human behavior, often at scale. It predates silicon—and became pervasive, and ungoverned, especially once its practitioners learned to hide it. Authoritarian regimes and more recently scammers and big companies have profited from it. To defend ourselves from bad actors, and to benefit from social engineering’s good side, we need to reclaim the name, and govern it prudently . The roots of engineering In 1894, Dutch entrepreneur Jacques van Marken urged companies to hire “social engineers” to ma...

• 5.AI with Model-Based Design: Virtual Sensor Modeling

  This webinar presents a workflow offering end-to-end solutions for designing, training, validating and verifying, compressing, and deploying AI-based virtual sensor models to embedded processors within a single environment. Highlights Integrate AI models into Simulink for system-level simulation, verification, and simulation-based testing Apply formal verification techniques to assert neural network behavior Compress the AI model for memory footprint reduction and execution speedup Generate library-free C code from AI models and performing PIL tests Profile code performance and evaluate design and model selection tradeoffs Design and train AI-based virtual sensors using MATLAB Register now for this free webinar!

• 1.Three Things to Know About Assessing Customer Reviews

  master1305/Getty Images How should companies effectively use or respond to an unwieldy array of customer opinions? While consumer feedback can be invaluable, three recent research articles suggest that it may also be influenced by gender, niche preferences, or sky-high expectations, complicating whether and how companies should respond. 1. Not all users post critical reviews. A […]

• 2.A Three-Minute Protocol to Reduce AI Manipulation Risk

  izusek/Getty Images Of the potential weaknesses of any security system, the human layer has always posed a key risk. The arrival of AI tools has made human cognition even more of a vulnerability. Companies face three overlapping security threats from AI’s effects on human cognition. First, weaponized persuasion lets attackers manipulate employees’ judgment through personalized, […]

• 3.Does Cultural Training Help Expats Succeed?

  funky-data/Getty Images Every year, multinational corporations invest billions in global mobility programs. The standard playbook includes training in the customs, values, and communication styles of the host country. However, our meta-analysis of research on migrants, including relocated workers, suggests that cultural knowledge plays a minimal role in expats’ successful adjustment. In a study recently published […]

• 4.AI for Interoperability in Health Care: Philips’s Carla Goulart Peron

  In this episode of the Me, Myself, and AI podcast, Philips’s chief medical officer Carla Goulart Peron shares how artificial intelligence is reshaping health care — not by replacing clinicians but by expanding access, improving diagnostics, and freeing doctors to focus more time on patients. Drawing on her experience practicing medicine in Brazil’s strained public […]

• 5.When Employees Are Drowning in Change

  Patrick George/Ikon Images In 2021-2022, CareRx was handling an ambitious expansion. In a span of 20 months, the Canadian pharmacy services company tripled its business through a series of acquisitions. Each acquired company brought its own processes, systems, and cultural norms. Employees barely had time to adjust before the next change arrived. “We were growing […]

• 1.Defining Innovatisation: The Case of NewSpace and the Changing Space Sector -- by Benoit Cornet, Marc-André Chavy-Macdonald, Dominique Foray

  The space sector has become far more dynamic and innovative, with new actors (e.g., start-ups, venture capital) entering and the ever-growing importance of private firms. In this paper we introduce a novel concept, innovatisation, to understand this phenomenon. Innovatisation describes the transformation of a sector between two modes. In a mode of technological achievements (TA), only technological (not economic) performance matters, primarily for prestige purposes; in innovation, customer preferences, commercial opportunities, and costs become essential. Studying the economics of Apollo and the commercialization attempts of the 1980s, we show how the space sector has long featured a logic of TA. Then, analyzing recent trends, we provide quantitative empirical evidence (e.g., costs) that innovation now shapes the sector, thanks to various...

• 2.Deep-Tech Innovation: A Multi-Method Study toward a Conceptual Framework and Research Agenda -- by Johann Kortsch, Stefan Raff-Heinen, David Bendig, Martin Murmann, Colin Schulz, Fiona Murray

  The term “deep-tech innovation” has attracted growing attention in research, policy, and practice, but it is applied inconsistently and lacks an agreed-upon definition. This limits cumulative knowledge building and blurs how deep-tech innovation relates to adjacent concepts. We address this gap by developing a framework that treats deep-tech innovation as a distinct object of inquiry. Using a multi-method design that combines a systematic, integrative, concept-centric literature review and semi-structured interviews with deep-tech founders, we identify twelve defining attributes structured across three levels: invention, venture, and ecosystem. At the invention level (the conceptual core), we specify six attributes: three foundational attributes that capture the scientific and technological basis of the invention, and three attributes tha...

• 3.Housing Capital and Intergenerational Mobility in the United States -- by Ariel J. Binder, Max Risch, John L. Voorheis

  We document the intergenerational mobility of housing capital in the United States using a new dataset linking Decennial Census data to administrative property and income-tax records for over 3.4 million families. We find that housing capital is substantially more persistent across generations than earnings. Moreover, the gap in housing capital between White and Black children widens sharply across the parental distribution, more so than the earnings gap. Using a capital accumulation and transmission framework, we study how assets and earnings jointly shape intergenerational housing capital persistence. Less than half of this persistence operates through children's earnings, leaving substantial scope for direct transmissions of capital assets and knowledge. Differences in earnings explain most of the White-Black housing gap at the bottom ...

• 4.The Federal Government's Discretionary Spending -- by Karen Dynan, Douglas Elmendorf, Theresa Gullo

  This paper examines federal discretionary spending, including its place in the overall budget, its composition, and the economic and political forces shaping its size. Both defense and nondefense discretionary spending show no trend relative to national output over the past three decades, reflecting underlying factors rather than explicit targets. This stability implies a less favorable fiscal outlook than appears in official projections. Because discretionary spending is generally set annually, it will face continuing pressure from concerns about rising federal debt, a challenge compounded by the erosion of the structured budget process envisioned in the Budget Act fifty years ago.

• 5.Towards a Methodology for Measuring Rental Property Ownership in the United States -- by Stephanie Kestelman, Rebecca Diamond, John Eric Humphries, Kate Pennington, Winnie van Dijk, John L. Voorheis

  Roughly one-third of U.S. households rent their homes, yet measuring who owns rental property is difficult: ownership is frequently obscured by LLCs, partnerships, and other intermediary entities that separate legal from economic control. We develop a method that traces ownership through administrative records — combining deeds and property assessments with the Census Bureau's Business Register, IRS Schedule K-1 filings, and SEC filings on REITs — to identify ultimate owners and construct property portfolios across the full landlord size distribution. Applying the method to 11 large CBSAs, we find that individual landlords own a large majority of rental units, though their share varies meaningfully across markets. We also show that the widely used mailing-address aggregation approach both under- and over-states portfolio size in systemati...

• 1.The Regional Side of the Story: K‑Shaped Pattern in Region, Wider Gap in Gas Spending

  In this post, we use the inaugural release of our regional consumer spending indicators to ask whether these patterns hold for a significant portion of the Second District, and how regional spending patterns by income have been similar to or different from the national patterns we documented earlier. We find similar K‑shaped patterns in both retail and gas spending in our region as we do in the nation, with the K‑shaped pattern in gasoline in response to the recent gas price shock being more pronounced in the region.

• 2.Food Insecurity and Consumer Pessimism

  Current discussions regarding a bifurcated U.S. economy highlight the increasing economic divide between lower- and higher-income Americans in spending and earnings growth and wealth accumulation. While many households are doing fine and economic activity overall has been expanding at a solid pace, large segments of the population are facing high levels of economic insecurity and financial strain, and consumer sentiment on the whole has dropped to low levels. In this post, we use newly collected data from the Survey of Consumer Expectations (SCE) to update our 2020 analysis of disproportionate financial hardship experienced during the early pandemic and to investigate recent changes in food insecurity a...

• 3.Assessing the Current State of Wage Inflation

  Economists often look at nominal wage growth to gauge labor market imbalances, price pressures, and households’ spending ability. But to use wage growth for these purposes, it is important to look through short-run fluctuations and retrieve underlying wage inflation. In this post, we use our own measure of wage growth persistence—called Trend Wage Inflation (TWIn in short)—to summarize what we learned from wage growth behavior in the past years and draw conclusions for what may lie ahead. Since peaking in late 2021, TWIn has been on a steady decline, reaching levels near those of the 2017-19 period. In the past few months, however, this decline seems to have lost momentum. Our analysis shows that most of the decline in TWIn between 2022 and 2025 was common across industries. Recently, however, a few sectors have shown a decoupling of wage...

• 4.AI’s Macroeconomic Challenges and Promises

  In the third quarter of 2025, America's largest tech firms for the first time spent more on capital investment than they earned from operations. The implication is that AI, a technology with the potential to make the economy more productive, is, for now, absorbing resources faster than it is generating returns. This post discusses how the tension between AI's long-run promise and its short-run costs affects the outlooks for inflation, real activity, and financial stability.

• 5.The Global Credit Cycle in Corporate Bond Returns

  The global corporate nonfinancial bond market is both a large investment asset class and a vital source of funding for nonfinancial firms. With $19 trillion outstanding at the end of 2024, a broad portfolio of corporate bonds would be expected to be well diversified. Yet, in 37 percent of months between 1998 and 2024, more than 80 percent of bonds in the ICE Global Bond Indices—a portfolio with over 10,000 constituents spanning diverse industries, credit ratings, and regions—moved in the same direction, suggesting a large degree of synchronization. In this post, we introduce the global credit factor, which proxies for the global price of risk in international corporate bond markets. The global credit factor creates a global credit cycle in bond risk premia and generates predictable comovement in bond prices.

• 1.Why Isn’t Europe Poorer Than the US?

  Nobel laureate Paul Krugman has a point when he says that European living standards have largely kept pace with those in the US, despite flagging growth and innovation. But this will not remain true indefinitely: restoring Europe's ability to compete at the technological frontier remains essential to its future well-being.

• 2.The Real Choice Confronting Developing Countries

  Lost in the debate about whether developing countries should pursue manufacturing- or services-led growth is the potential for new AI tools to make even informal workers far more productive. If governments can broaden access to such technologies, they no longer need to choose between strategies.

• 3.India’s Great Political Realignment

  In recent years, the ruling Bharatiya Janata Party’s focus on religious identity, together with its “purification” of voter rolls, has created a more binary—and potentially volatile—political landscape, where elections increasingly reflect ethno-religious identity. This month’s elections in five states largely confirmed this trend.

• 4.Central Banking in an Age of Global Supply Shocks

  The core assumptions underlying the decades-old inflation-targeting orthodoxy have proven to be unfit for a geopolitically fragmented world characterized by frequent supply-side disruptions. Under such conditions, central banks' traditional monetary policies are only as effective as markets believe them to be.

• 5.The Tech-MAGA Breakup Is Coming

  Donald Trump has managed to fuse a backward-looking populist movement with a Silicon Valley vanguard whose guiding assumption is that AI will render a large share of ordinary people economically redundant. But with the tech billionaires now rewiring America's electricity grid and driving up costs, a split seems inevitable.

• 1.Beyond 5G Advanced – what next for smart energy and smart grid networks? (Analyst Angle)

  As utilities prepare for 6G, AI-native networks, digital twins and integrated sensing promise to transform the power grid into an autonomous, software-defined system where connectivity, intelligence and energy converge to deliver resilient, self-optimising operations at scale. The electric power grid…

• 2.Ambient IoT’s next challenge isn’t the tag – it’s the network (Reader Forum)

  Ambient IoT has proven battery-free sensing is possible, but enterprise adoption now depends on something bigger: reliable network infrastructure. As deployments scale, trusted, continuous visibility—not the tag itself—will determine which solutions succeed in real-world operations. Ambient IoT has reached an…

• 3.Defense network resilience is moving from redundancy to cognitive design

  Panelists say defense networks must assume day-one attack and lean on civilian infrastructure to stay up The definition of resilience in defense communications is shifting, and so is the way to deliver it. At the Defense Communications Forum, a panel…

• 4.Softbank targets sovereign AI demand in Japan

  The company’s AI Data Center GPU Cloud is intended for customers facing restrictions on transferring data overseas and those seeking infrastructure optimized specifically for the Japanese market In sum – what to know: Sovereign AI focus – Softbank said the…

• 5.Resilient connectivity is critical for global enterprises scaling in India (Reader Forum)

  As India accelerates its digital transformation, resilient, intelligent connectivity is becoming essential for global enterprises seeking to scale operations, ensure business continuity, and unlock opportunities across the country’s fast-growing AI, cloud, fintech, and digital services sectors. India is fast emerging…

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• 1.Distributionally Robust Physical-Layer Security for Satellite Communication via Aerial Reconfigurable Intelligent Surface

  Satellite communications are envisioned as a key enabler for ubiquitous coverage in future 6G networks, yet the broadcast nature renders them vulnerable to eavesdropping, especially given the long-distance transmissions and associated high uncertainties. In this paper, we propose the physical layer security enhancement for multi-beam satellite communications with the assistance of an aerial reconfigurable intelligent surface (ARIS). Considering the high dynamics and uncertainties of channels, we characterize the channel distribution with moment-based ambiguity sets. Accordingly, a distributionally robust secrecy rate optimization is formulated through joint design of transmit and reflection beamforming. We then introduce a conditional value-at-risk-based reformulation to convert the probabilistic constraints into deterministic forms. An a...

• 2.An efficient Progressive Swapping to the Middle distribution protocol adapted to imperfect quantum memories in quantum networks

  The distribution of entangled pairs of photons on the links composing a quantum network, combined with Bell state measurements and teleportation, is the basic apparatus to transfer quantum bits (qubits) over long distances. Entanglement distribution establishes an end-to-end entangled pair while consuming intermediate pairs on links and holding them for a certain time period. The technical literature identifies two main kinds of protocols, parallel and sequential ones, the latter having an advantage in resource consumption over the former. In this paper, we introduce an efficient swapping protocol called Progressive Swapping to the Middle (PSM) as it combines the existing Progressive Swapping (PS) protocol from both extremities of a path that meet in the middle where the received pairs are swapped. We compare PSM with two parallel protoco...

• 3.DRIFT: Joint Channel Estimation and Prediction Towards Pilotless 6G Non-Terrestrial Networks

  Non-terrestrial networks (NTNs) are expected to play a pivotal role in sixth-generation (6G) systems by enabling ubiquitous connectivity and massive communication. In this context, channel prediction emerges as a key technique to improve the spectrum utilization efficiency by limiting the pilot overhead. However, many proposed predictors based on artificial intelligence (AI) are characterized by high inference complexity, posing challenges to onboard implementation. In this paper, we address the challenge of designing accurate yet computationally efficient channel prediction techniques tailored to low Earth orbit (LEO) NTNs, where strict power constraints limit model complexity, to enable spectral efficiency gains. We propose an iterative joint channel estimation and prediction framework in the context of 6G NTNs that significantly reduce...

• 4.Intent-Based Orchestration in Open RAN: An ns-3 Simulation Framework

  This paper presents an extensible ns-3-based simulation framework for evaluating intent-based, semantics-aware control in Open RAN architectures. The framework integrates external Radio Access Network (RAN) Intelligent Controller (RIC) components and supports fine-grained control via internal distributed applications (dApps), enabling intent-based RAN orchestration across different timescales while maintaining standardized network behavior. As an illustrative use case, we implement an intent-based dApp for radio resource management (RRM) under realistic observability constraints. The scheduling problem is formulated using realistic key performance measurements (KPMs) available to dApps, together with a newly introduced Intent Satisfaction Score (ISS), which quantifies the delivery of intent-relevant information by combining distortion- an...

• 5.ARIADNE: AI-RAN Informed Link Adaptation in Digital Twin Network Environments

  Artificial Intelligence (AI)-powered Radio Access Network (RAN) networks have attracted significant attention from both industry and academia. Meanwhile, Digital Twins offer a safe playground for experimenting with AI/Machine Learning (ML)-based solutions for advanced AI-RAN research. By enabling the testing of online algorithms before deployment on the RAN, they reduce costs and safety risks associated with physical field testing. In this article, we propose ARIADNE, an online Reinforcement Learning (RL)-based module that seamlessly integrates with SIONNA and is tasked with performing link adaptation. We explore different design choices and demonstrate how ARIADNE can surpass industry-standard and state-of-the-art methods by achieving up to 11% and 20% improvements in Spectral Efficiency, respectively. Finally, we show that RL learns a M...

• 1.Residual Supply and the Price of Risk Absorption

  When redeeming open-end funds sell and natural buyers do not step in at once, some limited-capital investor must take the other side and carry the inventory until prices recover. This paper asks what return that investor requires. A continuous-time market-clearing model delivers an expected-return restriction in which the price of residual supply depends on inventory risk, trading costs, funding frictions, and the scarcity of balance sheet available to absorb it. Mapping U.S. mutual fund flows through predetermined holdings over 2003--2024, we measure one observable component of this residual supply. Forced-sale pressure predicts actual fund selling, contemporaneous price declines, and positive returns over the following one to six months. The premium roughly doubles when market-wide absorption capacity is tight, and it concentrates in st...

• 2.Change-point estimation for Weibull time series with copula-based Markov models

  We study offline change-point estimation for time series data exhibiting nonlinear serial dependence. To address this problem, we propose a copula-based Markov chain model with Weibull marginal distributions, which is suitable for modeling nonnegative data such as event times and volatility measures. Nonlinear dependence is incorporated through the Clayton and Joe copulas, allowing the model to capture asymmetric lower-tail and upper-tail dependence structures, respectively. We derive the corresponding likelihood function and estimate the change point and model parameters using maximum likelihood estimation implemented through the Newton--Raphson algorithm. Confidence intervals are constructed via a parametric bootstrap Monte Carlo procedure. Extensive numerical studies are conducted to evaluate the finite-sample performance and robustnes...

• 3.From Classical Optimization to Bayesian Integration: A Comprehensive Analysis of Systematic Portfolio Management

  This paper compares a series of contemporary portfolio construction approaches by employing ten U.S. stocks (TSLA, WMT, BAC, GS, LLY, MRK, GOOG, META, AAPL and XOM) in a time frame from September 2023 to December 2025. The paper explores both basic mean-variance optimization, constrained optimization, Fama French five factor regression modeling, Monte Carlo simulation, and the Black-Litterman model to determine how constraints to a solution, risk factors to a strategy, simulated approximations, and specific market views may all impact the outcome of portfolio allocation, performance and stability. Overall, the results show that standard optimization may result in highly concentrated portfolios, while constrained optimization leads to changes in portfolio allocations by altering the efficient frontier, five factor regression models suggest...

• 4.Three-Currency HJM for Brazilian Credit Markets

  This paper develops a three-currency Heath-Jarrow-Morton framework in which corporate credit is treated as a separate economy, connected to the nominal and real economies through synthetic inflation and credit exchange rates. The framework produces a testable identity. Under joint no-arbitrage, the credit spread of an issuer expressed over the inflation-rateindexed risk-free curve equals the same issuer's credit spread expressed over the nominalrate-indexed risk-free curve plus the model-implied breakeven inflation forward at the same maturity. The identity holds within any single calibration of the framework. It is empirically falsifiable across two parallel corporate-bond segments of the same market, in a segmented market the two segments may price different corporate credit economies, and the gap between their implied corporate forward...

• 5.Deep Learning Forecasting of the U.S. Aggregate Bond Index

  This study looks at the statistical properties and predictability using deep learning methods of the U.S. aggregate bond index in daily observations spanning 2018 to February 2026. We first establish that index levels are extremely persistent and consistent with unitroot behavior (Dickey and Fuller), while log returns are covariance-stationary with weak linear dependence and pronounced volatility clustering characteristic of ARCH-type processes (Engle; Bollerslev). Motivated by the trade-off between stationarity and information retention, we construct a "stationary but maximally persistent" representation via fractional differencing (Granger and Joyeux; Hosking) following the procedure of López de Prado, and evaluate shorthorizon forecast using two neural paradigms: (i) Multilayer Perceptrons (MLPs) trained on lagged vectors with joint la...

• 1.Distributionally Robust Physical-Layer Security for Satellite Communication via Aerial Reconfigurable Intelligent Surface

  Satellite communications are envisioned as a key enabler for ubiquitous coverage in future 6G networks, yet the broadcast nature renders them vulnerable to eavesdropping, especially given the long-distance transmissions and associated high uncertainties. In this paper, we propose the physical layer security enhancement for multi-beam satellite communications with the assistance of an aerial reconfigurable intelligent surface (ARIS). Considering the high dynamics and uncertainties of channels, we characterize the channel distribution with moment-based ambiguity sets. Accordingly, a distributionally robust secrecy rate optimization is formulated through joint design of transmit and reflection beamforming. We then introduce a conditional value-at-risk-based reformulation to convert the probabilistic constraints into deterministic forms. An a...

• 2.An efficient Progressive Swapping to the Middle distribution protocol adapted to imperfect quantum memories in quantum networks

  The distribution of entangled pairs of photons on the links composing a quantum network, combined with Bell state measurements and teleportation, is the basic apparatus to transfer quantum bits (qubits) over long distances. Entanglement distribution establishes an end-to-end entangled pair while consuming intermediate pairs on links and holding them for a certain time period. The technical literature identifies two main kinds of protocols, parallel and sequential ones, the latter having an advantage in resource consumption over the former. In this paper, we introduce an efficient swapping protocol called Progressive Swapping to the Middle (PSM) as it combines the existing Progressive Swapping (PS) protocol from both extremities of a path that meet in the middle where the received pairs are swapped. We compare PSM with two parallel protoco...

• 3.DRIFT: Joint Channel Estimation and Prediction Towards Pilotless 6G Non-Terrestrial Networks

  Non-terrestrial networks (NTNs) are expected to play a pivotal role in sixth-generation (6G) systems by enabling ubiquitous connectivity and massive communication. In this context, channel prediction emerges as a key technique to improve the spectrum utilization efficiency by limiting the pilot overhead. However, many proposed predictors based on artificial intelligence (AI) are characterized by high inference complexity, posing challenges to onboard implementation. In this paper, we address the challenge of designing accurate yet computationally efficient channel prediction techniques tailored to low Earth orbit (LEO) NTNs, where strict power constraints limit model complexity, to enable spectral efficiency gains. We propose an iterative joint channel estimation and prediction framework in the context of 6G NTNs that significantly reduce...

• 4.Jamming-Resilient PRB Reservation for Latency-Critical O-RAN Network Slicing

  Open radio access network (O-RAN) architectures enable near real-time, software-driven control of network slicing through programmable xApps deployed on the near-real-time RAN Intelligent Controller (near-RT RIC). In industrial 5G downlink systems, adversarial jamming can abruptly reduce the effective physical resource block (PRB) capacity, triggering queue buildup and persistent latency violations, particularly in the presence of low spectral efficiency cell edge user equipments. This paper proposes a reserve-based resilience framework for PRB allocation in sliced O-RAN deployments. A finite pool of reserved PRBs is controlled by a near-RT RIC xApp that provides hybrid mitigation by proactively clearing backlog to build latency margin and reactively allocating reserve capacity during jammer active intervals. We formulate reserve activati...

• 5.Intent-Based Orchestration in Open RAN: An ns-3 Simulation Framework

  This paper presents an extensible ns-3-based simulation framework for evaluating intent-based, semantics-aware control in Open RAN architectures. The framework integrates external Radio Access Network (RAN) Intelligent Controller (RIC) components and supports fine-grained control via internal distributed applications (dApps), enabling intent-based RAN orchestration across different timescales while maintaining standardized network behavior. As an illustrative use case, we implement an intent-based dApp for radio resource management (RRM) under realistic observability constraints. The scheduling problem is formulated using realistic key performance measurements (KPMs) available to dApps, together with a newly introduced Intent Satisfaction Score (ISS), which quantifies the delivery of intent-relevant information by combining distortion- an...

• 1.An efficient Progressive Swapping to the Middle distribution protocol adapted to imperfect quantum memories in quantum networks

  The distribution of entangled pairs of photons on the links composing a quantum network, combined with Bell state measurements and teleportation, is the basic apparatus to transfer quantum bits (qubits) over long distances. Entanglement distribution establishes an end-to-end entangled pair while consuming intermediate pairs on links and holding them for a certain time period. The technical literature identifies two main kinds of protocols, parallel and sequential ones, the latter having an advantage in resource consumption over the former. In this paper, we introduce an efficient swapping protocol called Progressive Swapping to the Middle (PSM) as it combines the existing Progressive Swapping (PS) protocol from both extremities of a path that meet in the middle where the received pairs are swapped. We compare PSM with two parallel protoco...

• 2.Kairos: Lightweight Testing Framework for Timing-Induced Interaction Failures in LTE and 5G Core Networks

  As cellular core networks evolve toward distributed and cloud-native architectures, control-plane interactions become more intricate and bring new challenges. Among these challenges, we find that introducing specific timing between two control-plane interactions can cause network function crash, which we define as timing-induced interaction failures. Prior research primarily addresses identifying malformed inputs and specification violations, while timing-induced interaction failures remain largely unexplored. In this paper, we conduct a systematic study of timing-induced interaction failures in LTE and 5G core networks. First, we establish a taxonomy of control-plane interaction patterns and analyze the failure modes of each pattern. Then, we design and implement Kairos, a lightweight testing framework to expose timing-induced interactio...

• 3.Temporally Encoded Double DQN for Proactive PRB Allocation in O-RAN Enabled Industrial Networks

  Fifth-generation (5G) wireless systems are increasingly adopted in smart manufacturing to support heterogeneous industrial workloads through services such as enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communication (URLLC). However, industrial traffic is inherently process-driven and temporally correlated. So, static or reactive schedulers in the Open Radio Access Network (O-RAN) are inadequate for such non-stationary conditions, leading to sub-optimal utilization and violation of latency-reliability guarantees. This paper proposes a temporal-aware deep reinforcement learning (DRL) xApp for proactive Physical Resource Block (PRB) allocation in O-RAN-enabled industrial networks. The proposed framework integrates a long short-term memory (LSTM) encoder within a Double Deep Q-Network (DQN) to model sequential dependencie...

• 4.Jamming-Resilient PRB Reservation for Latency-Critical O-RAN Network Slicing

  Open radio access network (O-RAN) architectures enable near real-time, software-driven control of network slicing through programmable xApps deployed on the near-real-time RAN Intelligent Controller (near-RT RIC). In industrial 5G downlink systems, adversarial jamming can abruptly reduce the effective physical resource block (PRB) capacity, triggering queue buildup and persistent latency violations, particularly in the presence of low spectral efficiency cell edge user equipments. This paper proposes a reserve-based resilience framework for PRB allocation in sliced O-RAN deployments. A finite pool of reserved PRBs is controlled by a near-RT RIC xApp that provides hybrid mitigation by proactively clearing backlog to build latency margin and reactively allocating reserve capacity during jammer active intervals. We formulate reserve activati...

• 5.Intent-Based Orchestration in Open RAN: An ns-3 Simulation Framework

  This paper presents an extensible ns-3-based simulation framework for evaluating intent-based, semantics-aware control in Open RAN architectures. The framework integrates external Radio Access Network (RAN) Intelligent Controller (RIC) components and supports fine-grained control via internal distributed applications (dApps), enabling intent-based RAN orchestration across different timescales while maintaining standardized network behavior. As an illustrative use case, we implement an intent-based dApp for radio resource management (RRM) under realistic observability constraints. The scheduling problem is formulated using realistic key performance measurements (KPMs) available to dApps, together with a newly introduced Intent Satisfaction Score (ISS), which quantifies the delivery of intent-relevant information by combining distortion- an...