Babak's Daily Briefing

Monday, June 15, 2026

Sources: 20 | Total Articles: 96

• 1.The Hidden 6G Bottleneck: RF Hardware Design Is Becoming a Strategic Race

  As 5G-Advanced matures and 6G research moves closer to implementation, the wireless industry faces a deeper challenge than spectrum, standards or AI-native network architecture. Future wireless systems will depend on whether the industry can design, validate and manufacture increasingly complex RF modules fast enough.

• 2.6G in Dalian: What the Latest 3GPP Meetings Reveal About the Future Radio and Network

  The 6G physical layer is starting to converge. The protocol stack is being simplified in meaningful places. But the most consequential architecture decisions are now moving toward the June plenary in Singapore.

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

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

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

  Summary available at source link.

• 5.Amazon’s Globalstar deal gives Amazon Leo a faster path into D2D

  Amazon’s planned acquisition of Globalstar is about far more than satellites. It gives Amazon Leo a faster path into direct-to-device connectivity, combining spectrum, operational assets, and Apple-facing service continuity in a move that could reshape the hybrid terrestrial-NTN landscape.

• 1.SIMMER: Benchmarking Latent Failures in LLM Executable Planning with a World Model

  Large language models (LLMs) are increasingly deployed as planners for autonomous agents in household environments. While existing benchmarks evaluate whether LLM-generated plans execute successfully, they overlook a critical type of failure: latent failures. Unlike immediate failures that trigger instant feedback at execution time and enable timely correction, latent failures do not immediately halt plan execution but silently compromise goal achievement. In severe cases, they cause irreversible harm. To address this gap, we introduce SIMMER, a benchmark for evaluating latent failures in LLM planning through a human-curated symbolic world model grounded in the kitchen domain. SIMMER defines a world model comprising 77 actions, 262 unique objects, and approximately 46,800 possible interactions that are semantically realistic, derived from...

• 2.From Shield to Target: Denial-of-Service Attacks on LLM-Based Agent Guardrails

  LLM-based guardrails have emerged as a highly effective defense against prompt injection and jailbreak attacks in autonomous agents. However, we reveal that the very reasoning and task-following capabilities enabling this protection introduce a novel vulnerability: attackers can inject crafted data to trap the guardrail in extended reasoning loops, effectuating a systematic denial-of-service (DoS) attack. To systematically expose this threat, we design a beam-search optimization framework that crafts natural-language payloads to maximize guardrail reasoning length, utilizing an LLM proposer guided by a strategy bank. Based on the observation of guardrail's schema-following nature, we also provide another attack framework driven by mechanism-aware structural mutations with less computational load. The attack efficacy is systematically eval...

• 3.Large Language Model Based Agent for Automated Discovery in Computational Physics

  Scientific discovery in computational physics can often be framed as the optimization of quantitatively evaluable objectives subject to physical constraints. While researchers excel at formulating such problems, they frequently devote substantial effort to iterative refinement of methods and solution strategies. To accelerate this process, we introduce PhyNex, an autonomous agent that systematically explores the solution space of scorable scientific tasks by coupling large language model (LLM)-guided search with domain-specific computational tools that enforce physical consistency. PhyNex operates via progressive local search, accumulates reusable knowledge from both successful and failed attempts, and produces interpretable exploration trajectories that reveal which algorithmic components drive performance improvements. We validate PhyNe...

• 4.Minim: Privacy-Aware Minimal View for Agents via Trusted Local Sanitization

  Modern LLM-powered autonomous agents increasingly rely on rich user interface (UI) state observations to achieve reliable action grounding in complex digital environments. However, many deployments transmit the full UI state to remote inference servers even when most elements are irrelevant to the current task, which can leak sensitive but unnecessary context such as authentication codes, private notifications, and background application states. We propose MINIM, a trusted local broker that performs privacy-aware minimization on the client side before any observation leaves the device. Grounded in Contextual Integrity (CI), MINIM learns a dual-score representation for each UI element by predicting an inherent sensitivity score (s) and a task-conditioned necessity score (n). These scores drive a ternary disclosure policy that keeps essenti...

• 5.An LLM System for Autonomous Variational Quantum Circuit Design

  The design of high performing quantum circuits remains largely dependent on human expertise. We introduce an autonomous agentic framework that employs large language models (LLMs) to conduct iterative quantum circuit designs under explicit design constraints. Our system integrates seven components: Exploration, Generation, Discussion, Validation, Storage, Evaluation, and Review. These components form a closed-loop workflow that combines web-based knowledge acquisition, literature-grounded critique, executable code generation, and experimental feedback. We evaluate the framework on two tasks: quantum feature map construction for quantum machine learning and ansatz generation for variational quantum eigensolver applications in quantum chemistry. In image classification benchmarks, the best generated feature map outperforms representative qu...

• 1.The Coin Flip Judge? Reliability and Bias in LLM-as-a-Judge Evaluation

  arXiv:2606.13685v1 Announce Type: new Abstract: LLM-as-a-Judge is now widely used to rank model outputs, train reward models, and populate public leaderboards, but its run-to-run reliability remains under-characterized. We study repeated identical evaluations on 29 tasks spanning 10 categories using two OpenAI judge models (GPT-4o-mini and GPT-4.1-mini), with 50 pairwise trials and 50 pointwise trials per question, supplemented by temperature and prompt-sensitivity ablations. Across judges, pairwise preferences flip on average 13.6% of the time, with 28% of questions exceeding a 20% flip rate and one question reaching 56%. GPT-4o-mini also exhibits a significant first-position bias (72% A-majority, p = 0.024). At the same time, mean pointwise score gaps are small (0.19--0.36 on a 10-point scale) and not statistically significant in aggre...

• 2.Benchmarking Web Agent Safety under E-commerce Deceptive Interfaces

  arXiv:2606.13686v1 Announce Type: new Abstract: As autonomous web agents are increasingly deployed to perform real-world tasks, ensuring their safety has become a critical concern. In this work, we study web agent behavior under realistic deceptive interfaces in the e-commerce domain. We introduce WebDecept, a lightweight and configurable plugin framework that enables controlled injection of deceptive interface patterns into existing web environments. Using WebDecept, we instantiate seven deceptive patterns commonly observed on the open web, including targeted advertisements, domain redirection, and shopping manipulation. By injecting these patterns into the frontend during task execution, we perform controlled evaluation of multiple multimodal web agents. Our results show that current web agents are highly susceptible to multiple classe...

• 3.Which Models Perform Better in Inheritance Reasoning?

  arXiv:2606.13751v1 Announce Type: new Abstract: This paper presents the participation of team PSL in the QIAS 2026 Shared Task on Arabic Islamic inheritance reasoning. The task evaluates the ability of large language models to solve inheritance cases that require legal interpretation, multi-step reasoning, and precise numerical computation. We compare \textit{commercial} and \textit{open-source} models under a unified prompting strategy to assess their effectiveness in structured legal reasoning with minimal task-specific adaptation. \\ Our results show a clear gap in reliability between the two model families. Commercial models demonstrate stronger performance in identifying eligible heirs, applying exclusion rules, and maintaining consistency across reasoning steps. In contrast, open-source models exhibit greater instability, particula...

• 4.QIAS 2026: Overview of the Shared Task on Islamic Inheritance Reasoning

  arXiv:2606.13756v1 Announce Type: new Abstract: This paper presents a comprehensive overview of the QIAS 2026 shared task, organized as part of the OSACT7 Workshop and co-located with LREC 2026. The shared task was designed to evaluate the ability of large language models to perform complex reasoning in the religious and legal domain of Islamic inheritance. Unlike conventional question-answering benchmarks, QIAS 2026 focuses on end-to-end reasoning from natural language cases, requiring systems to perform the full inheritance calculation process, from identifying the eligible heirs to assigning the correct share to each beneficiary. To support this evaluation, the task was based on the MAWARITH benchmark, a dataset of $12{,}500$ Arabic inheritance cases annotated with intermediate reasoning steps and final answers. System submissions wer...

• 5.The Culture Funnel: You Can't Align What isn't in the Data

  arXiv:2606.13808v1 Announce Type: new Abstract: Current cultural alignment approaches focus on inference-time interventions, assuming models already contain sufficient cultural knowledge. We argue modern LLM pipelines suffer from a cultural data funnel. Using a multidimensional tagging framework across pretraining, fine-tuning, alignment, and reasoning datasets, we show explicit cultural signals decline sharply during post-training, while geographically concentrated, task-specialized data dominates. Multilinguality enhances geographic diversity of cultural knowledge but does not ensure balanced representation. Our tags improve downstream cultural benchmark performance, demonstrating that advances require shifting focus in training data pipelines. To facilitate future research, we release our culturally tagged dataset with 5.6M samples at...

• 1.Can Editing 1 Neuron Fix Repetition Loops in LLMs?

  arXiv:2606.13705v1 Announce Type: new Abstract: Yes. Can it cure doom loops? Probably not. The Gemma 4 instruction-tuned models share a reproducible failure: on long factual enumeration prompts, such as listing every episode of a TV series, the 88 IAU constellations, or the 151 original Pokemon, they collapse into repetition, either a tight verbatim loop or a list whose entries decay onto a single answer. These loops occur at rates as high as 95% and survive prompt rewording, inference-engine changes, and most sampling adjustments. In this paper we explore whether this behavior is localized enough to remove by weight edits. To localize the cause, we use per-layer ablation and per-neuron attribution, then confirm the strongest candidates with full-generation sweeps. The loops trace to a small set of MLP neurons (or, in the 26B-A4B Mixture-...

• 2.Efficient On-Device Diffusion LLM Inference with Mobile NPU

  arXiv:2606.13740v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) accelerate generation by denoising multiple tokens in parallel, making them attractive for latency-sensitive mobile inference. However, repeated denoising introduces substantial computation on smartphones. Mobile neural processing units (NPUs) offer high-throughput dense matrix computation, but efficiently exploiting them remains challenging: token commitment shrinks per-block effective workloads, token revision complicates KV cache reuse, and limited NPU-visible address space incurs costly remapping and data transfer overheads. In this paper, we propose llada.cpp, the first NPU-aware inference framework for accelerating dLLMs on smartphones. llada.cpp aligns block-wise dLLM inference with the execution characteristics of mobile NPUs through three tech...

• 3.High-Frequency Pricing at Scale for E-Commerce

  arXiv:2606.13741v1 Announce Type: new Abstract: This paper presents the design, development, and implementation of a specialized forecast-then-optimize algorithmic pricing tool for sales campaigns in fashion e-commerce. Sales events present unique challenges for pricing including volatile demand patterns, rapid pricing decisions, and the need to balance short-term revenue with long-term profitability. We describe our approach combining daily-resolution demand forecasting using gradient-boosted trees with a multi-objective optimization framework that maximizes both long-term profit and net merchandise value for more than 5 million articles. Our solution addresses key limitations of existing weekly-granularity systems by implementing a forecast-then-optimize architecture that reduces pricing decision time from hours to minutes. We validate ...

• 4.A fully GPU-based workflow for building physics emulators of hypersonic flows

  arXiv:2606.13742v1 Announce Type: new Abstract: The ability to resolve complex physical phenomena with high fidelity and at low computational cost is central to addressing key challenges in modern engineering. A prime example lies in hypersonic flows, where the precise prediction of the full flowfield topology, in particular with respect to shock wave location and intensity, is critical. Yet supersonic and hypersonic flows continue to be a stumbling block for traditional reduced-order models and neural emulators that struggle to capture steep gradients in flow states with physical consistency in applications of industrial relevance. To that end, we introduce a fully GPU based workflow that integrates accelerated data generation with the training of neural emulators augmented by uncertainty quantification and physics-aware refinement. Our ...

• 5.FedSPC: Shared Parameter Correction for Personalized Federated Learning

  arXiv:2606.13748v1 Announce Type: new Abstract: Personalized federated learning (PFL) is one of the important approaches in federated learning for addressing statistical heterogeneity while enabling client-specific adaptation. Many PFL methods split the model into shared and personalized parameters, which are jointly trained on each client. However, this creates an optimization issue: shared parameters are updated by clients optimizing different local objectives, which can lead to inconsistent shared updates and weaken the shared representation. To address this problem, we propose Federated Shared Parameter Correction (FedSPC), a modular correction method for PFL. FedSPC applies control-variate correction only to the shared parameters of a given PFL method, while leaving personalized parameters unchanged. It can be integrated into three c...

• 1.Occupancy-Grounded Room Segmentation for Hierarchical 3D Scene Graphs

  arXiv:2606.13727v1 Announce Type: new Abstract: Hierarchical 3D scene graphs (3DSGs) for indoor robots organize geometric and semantic information across spatial scales, with a room layer that connects object-level perception to room-scale reasoning. Existing systems construct this layer from different spatial substrates (\eg{} place clusters, wall planes, or segmentation outputs), and as a result, room nodes are not evaluated on a common geometric criterion. We present an occupancy-grounded 3DSG pipeline in which room nodes are anchored to tracked free-space regions derived from occupancy decomposition, giving each room an explicit polygonal footprint. We evaluate the pipeline on 12 Matterport3D scenes by matching predicted room polygons to annotated room instances and compare against Hydra, a representative state-of-the-art place-connec...

• 2.Scalable Dynamic Tactile Sensing Enabled by Passive and Flexible Acoustic Waveguides

  arXiv:2606.13746v1 Announce Type: new Abstract: Artificial dynamic tactile sensing requires sensitivity, robustness, and compliance, yet existing technologies face trade-offs when scaling to large-area arrays, compounded by wiring complexity and cost. Here, we report a passive distributed paradigm using deep sub-wavelength acoustic waveguides that decouples performance from structural flexibility. Elastic-membrane-capped Helmholtz resonators interconnected by spring-reinforced microtubes form an enclosed network with invariant acoustic transmission under macroscopic bending. By sparsely embedding microphones, the system achieves real-time localization (4 mm highest spatial resolution; >99% accuracy in a 4 microphones 64-node sensing array) and waveform reconstruction of low-frequency signals (<100 Hz). Fast Continuous Wavelet Transform an...

• 3.$\mu_0$: A Scalable 3D Interaction-Trace World Model

  arXiv:2606.13769v1 Announce Type: new Abstract: World models that capture how actions induce physical change enable scalable robot learning without reliance on embodiment-specific action labels. Pixel-space video models provide broad visual priors but expend model capacity on dense appearance reconstruction, while direct action models require embodiment-specific labels that hinder scalability. We present $\mu_0$, a scalable world model based on 3D traces. Rather than predicting dense pixels or directly modeling actions, $\mu_0$ forecasts smooth 3D trajectories for salient interaction points such as objects, tools, hands, and contact regions, yielding a compact, embodiment-agnostic motion interface. To enable training from diverse video sources, our TraceExtract system automatically extracts 3D supervision by selecting keypoints, construct...

• 4.FlowMo-WM: A World Model with Object Momentum and Hidden Ambient Drift

  arXiv:2606.13817v1 Announce Type: new Abstract: World models in robot learning predict future states from visual observations and actions, enabling agents to reason about the consequences of their controls. However, many action-conditioned models are evaluated in settings where motion is dominated by immediate control, whereas aquatic surface vehicles and other real-world objects continue moving under inertia and are displaced by hidden ambient drift, such as water currents or wind. We propose FlowMo-WM, an end-to-end trainable visual world model that infers object-centric motion state and a predictive long-history context associated with hidden drift from image-action histories without direct supervision of flow fields. FlowMo-WM factorizes image-action history into a short-history latent state, trained to summarize object-centric motion...

• 5.Multi-Agent Embodied Autonomous Driving: From V2X Information Exchange to Shared World Models

  arXiv:2606.13840v1 Announce Type: new Abstract: Autonomous driving is shifting from isolated vehicle intelligence toward multi-agent embodied systems that share perception, infer intent, and coordinate action under uncertainty. This survey examines this transition through the lens of Shared World Models (SWMs): predictive cross-agent representations maintained across vehicles, infrastructure, and other traffic participants. We review more than 380 publications spanning vehicle-to-everything (V2X) communication, collaborative perception, inter-agent cognition, cooperative planning, end-to-end cooperative driving, and simulation and data engines for closed-loop validation. The organizing question is how exchanged observations become aligned state, intent-aware interaction, and coordinated downstream action. Across the surveyed literature, e...

• 1.Discovery under Hypothesis Redundancy: A Geometric Theory of Discovery Bottlenecks

  Scientific discovery saturates when new hypotheses cease to provide independent information, even if the nominal hypothesis space remains large. We study hybrid discovery systems that combine structured local search with LLM-generated non-local proposals and pose the Search Compression Hypothesis: non-local exploration helps only when three geometric conditions co-occur: spectral compression, orthogonal escape from the explored span, and residual signal alignment with the target. We formalize these conditions, derive necessary conditions for hybrid advantage, and test the mechanism in controlled synthetic environments, large-scale A-share factor discovery, and symbolic-regression benchmarks; a public tabular operational sanity check tests the associated budget-allocation implication. Signal-planting and directed-versus-random experiments ...

• 2.A Longitudinal Attribute-Conditioned Neural Network for Modeling Health-State Transition Probabilities in Temporally Irregular Data: The LANTERN Framework

  Accurate estimation of long-term care transition probabilities is central to disability insurance pricing, reserving, and solvency assessment. Classical actuarial multi-state models commonly rely on Markov, semi-Markov, or proportional-hazard specifications, which provide a direct connection to cohort projection but may be restrictive for irregular longitudinal health data with nonlinear aging patterns and heterogeneous covariate histories. This paper develops a well-calibrated estimator of multi-state transition probabilities for irregular longitudinal health data. The model learns from individual health history, incorporates the time elapsed between observations, and conditions transition probabilities on demographic and socioeconomic attributes. It produces a valid probability distribution over the next observed health state, with four...

• 3.Neural Slack Variables for Shape Constraints

  Enforcing functional inequality constraints such as monotonicity and convexity in neural networks is a fundamental challenge in many industrial and scientific applications. Classical one-sided penalty methods, along with primal-dual methods gated by complementary slackness, provide constraint gradients only at violated locations, resulting in fragile satisfaction. Architectures that guarantee feasibility by construction, on the other hand, remain largely limited to elementary cases and impose additional inductive biases. We introduce neural slack variables, a deep learning native primal-side approach that converts constraint enforcement into a regression problem by coupling the primary network with a jointly learned auxiliary network. The auxiliary network serves as a valid target for the primary network's constraint quantities, inducing ...

• 4.Deterministic Policy Gradient for Learning Equilibrium in Time-Inconsistent Control Problems

  In this paper, we develop a continuous-time model-free reinforcement learning algorithm to learn deterministic equilibrium policies in general time-inconsistent control problems. Utilizing the extended Hamilton-Jacobi-Bellman system, we recast the original time-inconsistent problem into an equivalent two-stage problem. In the first stage, for given auxiliary functions, we employ the deterministic policy gradient approach to learn an optimal policy in an auxiliary time-consistent control problem. In the second stage, given the updated policy, we exploit the inner fixed point iterations and some martingale characterizations to learn the auxiliary functions. As a theoretical contribution, we provide some mild model assumptions and establish the convergence of inner fixed point iterations. By repeating this actor-critic style of iterations ac...

• 5.Weighted universal approximation of differentiable maps on infinite-dimensional manifolds

  We generalize the universal approximation theorem for functional input neural networks (FNN) to differentiable maps by including the approximation of the derivatives. A FNN maps the input from a possibly infinite-dimensional weighted manifold to the real-valued hidden layer, on which a non-linear scalar activation function is applied, and then returns the output into a Banach space via some linear readouts. By proving a weighted Nachbin theorem, we establish a universal approximation theorem (UAT) for differentiable maps, which goes beyond the usual formulation on compact sets and also includes the approximation of the derivatives. This leads us to approximation results for non-anticipative functionals including the horizontal and vertical derivatives. As a further application, we show that linear functions of the signature are able to ap...

• 1.Europe’s €1 trillion mobile industry at a crossroads ahead of Irish EU presidency

  Summary available at source link.

• 2.810 million women still not using mobile internet in low- and middle-income countries, compared to 595 million men

  Summary available at source link.

• 3.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.

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

  Summary available at source link.

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

  Summary available at source link.

• 1.ClinHallu: A Benchmark for Diagnosing Stage-Wise Hallucinations in Medical MLLM Reasoning

  Building trustworthy medical multimodal large language models (MLLMs) is critical for reliable clinical decision support. Existing medical hallucination benchmarks mainly focus on data collection, but often ignore where hallucinations originate within the reasoning process. We find that hallucination sources vary across samples: errors may arise from visual misrecognition, incorrect medical knowledge recall, or flawed reasoning integration. To enable source-level hallucination diagnosis, we introduce ClinHallu, a benchmark for stage-wise hallucination diagnosis in medical MLLM reasoning. ClinHallu contains 7,031 validated instances, where each instance is augmented with a structured reasoning trace decomposed into Visual Recognition, Knowledge Recall, and Reasoning Integration. We also use stage-replacement interventions to measure how co...

• 2.CORA: Analyzing and bridging thinking-answer gap in Multimodal RLVR via Consistency-Oriented Reasoning Alignment

  Reinforcement learning with verifiable rewards (RLVR) has successfully elicited the reasoning capabilities of large language models, motivating its extension to multimodal scenarios. Existing methods primarily focus on improving the visual coverage of reasoning traces and mitigating visual hallucinations, but underestimate the semantic inconsistency between the reasoning process and the final answer. In this paper, we delve into thinking-answer inconsistency in RLVR for large vision-language models (LVLMs), showing thorough analyses of rollouts collected throughout Group Relative Policy Optimization (GRPO) training process and post-RLVR evaluation outputs that this issue persists during training and remains present during inference. Motivated by the analysis, we propose Consistency-Oriented Reasoning Alignment (CORA), which introduces thi...

• 3.Towards Direct Latent-Space Synthesis for Parallel Branches in LLM-Agent Workflows

  Large language models increasingly serve as execution engines for agentic systems, yet they still consume context through a sequential text interface. This creates a mismatch with modern structured agent workflows, in which independent branches explore subtasks, retrieve evidence, or generate candidate solutions before a final synthesis step. Existing systems typically merge these branches by concatenating their textual outputs, which discards the parallel structure and incurs redundant prefill computation. In this work, we introduce Parallel-Synthesis, a plug-and-play framework that enables a synthesizer to directly consume the KV caches produced by parallel worker agents. Parallel-Synthesis combines a cache mapper that calibrates independently generated branch caches with a fine-tuned synthesizer adapter that enables generation from thi...

• 4.SIMMER: Benchmarking Latent Failures in LLM Executable Planning with a World Model

  Large language models (LLMs) are increasingly deployed as planners for autonomous agents in household environments. While existing benchmarks evaluate whether LLM-generated plans execute successfully, they overlook a critical type of failure: latent failures. Unlike immediate failures that trigger instant feedback at execution time and enable timely correction, latent failures do not immediately halt plan execution but silently compromise goal achievement. In severe cases, they cause irreversible harm. To address this gap, we introduce SIMMER, a benchmark for evaluating latent failures in LLM planning through a human-curated symbolic world model grounded in the kitchen domain. SIMMER defines a world model comprising 77 actions, 262 unique objects, and approximately 46,800 possible interactions that are semantically realistic, derived from...

• 5.From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI

  Large Language Models (LLMs) are undergoing a fundamental transformation from conversational generators into integrated AI systems capable of reasoning, action, memory, and self-improvement. We conceptualize this transition as a shift from Chatbot to Digital Colleague: from conversational answers to persistent work. We organize this transition along two tightly coupled dimensions. First, at the cognitive core level, LLMs are advancing from Chatbot-era "fast thinking" systems driven by next-token prediction toward Thinking LLMs that leverage inference-time computation, Chain-of-Thought reasoning, reflection, process supervision, and reinforcement learning to support more deliberate and reliable cognition. Second, at the tool-augmented task execution level, LLMs are progressing from tool-calling Agents that invoke external resources in an a...

• 1.EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments

  Large language model (LLM) agents have achieved strong performance on a wide range of benchmarks, yet most evaluations assume static environments. In contrast, real-world deployment is inherently dynamic, requiring agents to continually align their knowledge, skills, and behavior with changing environments and updated task conditions. To address this gap, we introduce EvoArena, a benchmark suite that models environment changes as sequences of progressive updates across terminal, software, and social domains. We further propose EvoMem, a patch-based memory paradigm that records memory evolution as structured update histories, enabling agents to reason about environmental evolution through changes in their memory. Experiments show that current agents struggle on EvoArena, achieving an average accuracy of 39.6% across evolving terminal, soft...

• 2.InterleaveThinker: Reinforcing Agentic Interleaved Generation

  Recent image generators have demonstrated impressive photorealism and instruction-following capabilities in single-image generation and editing. However, constrained by their architectures, they cannot achieve interleaved generation (text-image sequence), which has crucial applications in visual narratives, guidance, and embodied manipulation. Even the latest open-source Unified Multimodal Models (UMMs) exhibit limited performance in this regard. In this paper, we introduce InterleaveThinker, the first multi-agent pipeline designed to endow any existing image generator with interleaved generation capabilities. Specifically, we employ a planner agent to organize the image-text input sequence, instructing the image generator on the required execution at each step. Subsequently, we introduce a critic agent to evaluate the generator's outputs...

• 3.Mana: Dexterous Manipulation of Articulated Tools

  Articulated tool manipulation remains a major challenge in dexterous robotics due to the need to coordinate internal degrees of freedom and contact-rich interactions. While prior work has largely focused on rigid objects, articulated tool use remains underexplored because of its physical complexity and the difficulty of learning functional grasping and manipulation policies. We present Mana (Manipulation Animator), a general sim-to-real framework that reinterprets dexterous manipulation as an animation problem. Inspired by computer animation, Mana employs a coarse-to-fine pipeline that transforms procedurally-generated grasp keyframes into manipulation trajectories through motion planning and reinforcement learning. The data generation process is largely automatic, requiring only a few mouse clicks to specify functional affordances (<1 mi...

• 4.Modality Forcing for Scalable Spatial Generation

  Text-to-image (T2I) models contain rich spatial priors. Synthesizing photorealistic, cluttered scenes requires an understanding of geometry, including perspective and relative scale. Prior works adapt T2I models to leverage this prior for depth prediction, but they require dense depth data and involve complex recipes. We propose Modality Forcing, a simple, scalable post-training recipe for joint image-depth generation using a single DiT trained on sparse depth data. Modality Forcing enables conditional and joint generation of image and depth in any permutation by assigning separate noise levels per modality. Per-modality decoders let us train on sparse, real-world depth and achieve strong, generalizable depth prediction. We further show that Modality Forcing inherits the scalability of T2I pre-training: by training a set of T2I models fro...

• 5.Understanding Truncated Positional Encodings for Graph Neural Networks

  Positional encodings (PEs) enhance the power of graph neural networks (GNNs), both theoretically and empirically. Two of the most popular families of PEs - spectral (e.g., Laplacian eigenspaces, effective resistance) and walk-based (polynomials of the adjacency matrix) - are theoretically equivalent in expressive power, with expressivity between the 1-WL and 3-WL tests. However, this equivalence assumes the GNN uses the "complete" version of these PEs, which requires $O(n^3)$ time and space complexity. Instead, practitioners commonly use truncated variants of these encodings, such as the first $k$ eigenspaces or powers of the adjacency matrix. However, the theoretical properties of these truncated PEs are unknown. In this work, we initiate the study of these truncated PEs. Theoretically, we show that, under truncation, several families of...

• 1.Visual Language Models Train Robots to Read Human Emotions

  This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore. As robots advance in terms of dexterity and other physical capabilities , it becomes more likely that humans may find themselves working alongside them. If that happens, how will robots’ emotional capabilities need to advance for them to successfully work with people? In a recent study, researchers trained collaborative robots to read human emotions by not only accounting for facial expressions, but also contextual factors in the interactions as well. Through experiments with 40 volunteers, the researchers then evaluated how a robot’s ability to read human emotions and adjust its behaviour in turn impacted a human’s perception of the robot and its capabilities as the two collaborated on tasks. The results —which show that the emotional capabil...

• 2.How a Google DeepMind Spin-off Hunts Hidden Drug Targets

  For more than a decade, artificial intelligence has been touted as a way to dramatically accelerate drug discovery . Yet despite billions of dollars in investment, relatively few AI-designed medicines have made it to patients. That’s partially because the timelines for careful drug testing can’t be easily compressed—and partially because drug development is just really hard. Isomorphic Labs , the Google DeepMind spin-off that’s building on DeepMind’s Nobel Prize-winning work on protein structure prediction , may be making the most progress. The company has signed major drug-discovery partnerships with Novartis and Eli Lilly and recently raised US $2.1 billion in funding . In February, it published a technical report describing its new Isomorphic Drug Design Engine, a system created to discover the “pockets” on proteins where drugs can bin...

• 3.Timing Trick Cuts Energy Used in LLM Training by Up to 14 Percent

  OpenAI ’s fourth large language model (LLM), GPT-4 , took an estimated 50 gigawatt-hours to train, or the equivalent of 5,000 American homes ’ yearly power consumption. That was in 2023. Since then, the computational resources used to train frontier LLMs have only increased , though direct power usage numbers are hard to come by. Now, a research group at the University of Twente in the Netherlands has shown that you can save up to 14 percent of the energy used in LLM training without sacrificing speed by cleverly adjusting the clock frequency of the GPU during computation. Jeffrey Spaan , Ph.D. candidate at University of Twente and lead author on the article, presented the results at the Computing Frontiers conference in Catania, Sicily, last month. “My research is about finding computing waste,” Spaan says. “It’s similar to underutilizat...

• 4.AI Can Help Track the World’s Shrinking Glaciers

  Tracking how fast glaciers are shrinking is crucial for measuring the pace of climate change and projecting future sea level rises. This is normally a painstaking manual job, but a new approach that enables AI to analyze satellite images of glaciers anywhere in the world could help automate the monitoring process. Glaciers that flow directly into the ocean play a crucial role in the earth’s climate, but global warming is making them retreat ever faster. This can have severe knock-on effects as ice that breaks away from “calving fronts”—the ends of glaciers where icebergs shear off into the water—dumps massive amounts of freshwater into the sea, which can alter ocean currents and cause sea levels to rise. Bright white glaciers also reflect a lot of sunlight. When they shrink, they expose dark seawater that absorbs heat from the sun. All of...

• 5.Nvidia’s AI Hardware Comes to Windows in RTX Spark PCs

  At Computex 2026, an annual computer trade show held in Taipei, Taiwan, Nvidia made a long anticipated announcement—a version of the company’s Blackwell GB10 superchip for Windows PCs, called RTX Spark. Originally rumored to launch in 2025 , it was finally introduced at this year’s show. It came with full support from Microsoft, which announced two new devices powered by RTX Spark: the Surface Laptop Ultra and the Surface RTX Spark Dev Box . Asus, Dell, Lenovo, HP, and MSI also announced Windows PCs with RTX Spark. If this is triggering déjà vu, that’s for good reason. In June 2024, Qualcomm and Microsoft partnered to launch AI-focused Copilot+ PCs. Qualcomm’s Arm-based chips provided an alternative to x86-based chips from AMD and Intel used across dozens of budget and mid-range Windows laptops. It was met with mixed commercial success, h...

• 1.How to Grow Without Betting Big

  Matt Harrison Clough/Ikon Images Some of the most spectacular stories of corporate growth revolve around big bets — long-term investments, bold pivots, and major acquisitions. Think of ASML, which pursued next-generation semiconductor manufacturing technologies for more than 30 years; Adobe, which abandoned perpetual licenses in favor of cloud subscriptions; or Disney, which acquired Pixar, Marvel, […]

• 2.Agentic AI: What Leaders Wish They Knew Sooner

  As AI agents go beyond the hypothetical and enter actual workflows, many leaders see a gap between the promise and the reality. Are the agents ready? Moreover, are the humans? At the 2026 MIT Sloan CIO Symposium, we sought expert perspective and advice. We asked technology and business leaders, “What have you learned this year […]

• 3.The AI Atrophy Problem: How CIOs Fight It

  AI tools can help teams become faster and more efficient. But as organizations race to integrate artificial intelligence into more workflows, a problem is taking shape: the erosion of the critical thinking skills that leaders value. At the 2026 MIT Sloan CIO Symposium, we asked technology and business leaders to answer this question: What is […]

• 4.The Empathy Tax Female Leaders Pay

  Carolyn Geason-Beissel/MIT SMR | Getty Images The consulting manager took a call at 7:30 p.m., while volunteering at her son’s soccer practice, from an employee who felt “on the verge of quitting.” Later that same week, she responded to texts sent at 2 a.m. from team members who could not sleep amid corporate restructuring and […]

• 5.How Nespresso Builds Sustainability Into Its Business Model

  Photo courtesy of Nestlé Jean-Christophe Jaunin became CEO of Nespresso North America, the Nestlé unit that sells coffee brewing machines and capsules, on Jan. 1, 2026, after having served as global chief customer and technology officer. At the NYU Stern Center for Sustainable Business’s annual practice forum in March, MIT Sloan Management Review spoke with […]

• 1.The Income Channel to Labor-Market Polarization -- by Diego A. Comin, Ana Danieli, Martí Mestieri

  We study labor-market polarization by developing a framework with heterogeneous consumers, non-homothetic preferences, and endogenous labor supply a la Roy (1951). Because income-elastic sectors are intensive in high- and low-skilled occupations, shocks that raise either the average or the dispersion of income across households shift relative labor demand toward these occupations, generating an income channel to polarization. We quantify this channel by comparing the effect of shocks that affect the distribution of household income in our model and in a version with homothetic preferences in which the income distribution does not affect sectoral composition. The income channel is sizable: it explains 42% and 210% of the increases in the relative wages of high- and low-skill workers observed between 1980 and 2016, and 26% and 51% of the in...

• 2.Decomposing Shifts in the Beveridge Curve: Implications for Labor Market Dynamics and Inflation -- by Katharine G. Abraham, John C. Haltiwanger, Lea E. Rendell

  This paper explores the relationship between standard labor market tightness (vacancies divided by unemployment) and generalized labor market tightness (vacancies divided by a measure of effective searchers that accounts for potential hires from all sources including those out of the labor force or currently employed). We show that much of what the standard model attributes to variation in matching efficiency reflects changes in the ratio of effective searchers to unemployment rather than changes in “true” matching efficiency. Generalized tightness outperforms standard tightness in Phillips curve equations, both in models with tightness entering linearly and in better-fitting models with tightness entering nonlinearly. In models that distinguish between movements in generalized tightness due to movements along the generalized Beveridge cu...

• 3.Air Pollution and Internal Migration in the United States -- by Michael Keller, Christopher R. Knittel, Benjamin Krebs, Simon Luechinger

  We estimate the effect of PM₂.₅ pollution on migration between commuting zones in the United States from 2005-2019. To account for the correlation between origin and destination commuting zones’ pollution levels and potential endogeneity, we estimate a dyadic migration model and isolate permanent changes in origin and destination pollution emanating from distant coal-fired power plants. Annual panel and long-difference estimates indicate that air pollution plays a key role in relocation decisions. For the typical commuting zone, an isolated average 2005-2019 PM₂.₅ concentration decrease of 3.85 μg/m³ would avert out-migration and increase in-migration, totaling 2 percent of the population annually.

• 4.Sent Away: Displacement, Neighborhoods, and Children’s Outcomes under Slum Clearance Policies -- by Fernanda Rojas-Ampuero, Felipe Carrera

  We examine the difference between two policies that target urban slums, relocation versus redevelopment on-site, on children’s future outcomes. We use evidence from a slum clearance program in Chile between 1979 and 1984, where two-thirds of slum-dwelling families were relocated to housing projects on the city’s periphery, and one-third received housing through on-site redevelopment at their original locations. We find that 40 years post-policy, displaced children receive 0.62 fewer years of schooling, earn 10.2% less, experience higher labor informality, and live in higher poverty areas compared to non-displaced children. Relocation to lower-opportunity areas and disruption of social networks explain the negative displacement effects.

• 5.Beliefs and Actions under Government Policy Uncertainty: Evidence from Student Loan Forgiveness -- by Dmitri K. Koustas, Michael Weber, Constantine Yannelis

  How does uncertainty about future government policy affect households’ beliefs and subsequent borrowing, spending and debt payment behavior? We study these questions through the lens of student loan forgiveness in the United States, which following electoral promises, was announced in 2022 but never implemented due to judicial rulings. We conduct a customized information provision experiment embedded in a survey eliciting real-time beliefs about future debt forgiveness and repayment, which we link to credit bureau data, employment verification data, and nondurables consumption. Eligible borrowers who are more optimistic about forgiveness reduce payments on student loans by \$40 per month and increase non-durable spending by $100 per month. We also find some evidence optimistic borrowers may postpone durable spending waiting for uncertain...

• 1.The Unintended Effects of Interest Rate Caps: Credit Reallocation to Safer Borrowers

  Several states have recently capped consumer loan rates with the stated purpose of protecting borrowers. In a recent Staff Report, we study how these interventions have played out in three states. In our first post about that study, we showed that rate caps lead riskier borrowers to face rationing in the credit market. One question that naturally arises is what lenders do with the credit they used to provide to high-risk borrowers before the caps were imposed. Lenders that lend exclusively to high-risk borrowers (at rates above the cap) may decide to stop lending to high-risk borrowers in that state. Others, however, may ...

• 2.The Unintended Effects of Interest Rate Caps: Credit Rationing for Risky Borrowers

  In imperial China, 3 percent was the maximum legal monthly loan rate; charging more was punishable by 40 to 100 blows with the “light cane.” (Rockoff 2003) Centuries later, many U.S. states are imposing the same cap (without corporal penalties) on alternative credit providers, such as payday, installment, and auto-title lenders, with the goal of lowering credit costs and delinquency for the high-risk borrowers that rely on these funding sources. A concern, however, is that lenders will simply refuse to lend to these borrowers at lower interest rates. Our recent Staff Report studies how interest rate caps have played out in several states that recently adopted them. Using hou...

• 3.Struggling Regional Small Businesses Deeply Pessimistic About 2026 Prospects

  We recently updated the suite of indicators describing the performance of small businesses in the Second District (defined, for the purpose of this study, as New York, New Jersey, and Connecticut) and nationally with data from the 2025 edition of the Small Business Credit Survey (SBCS). In this post, we find that regional small businesses reported severe declines in employment and revenue growth in 2025 and became more pessimistic about growth in 2026. In contrast, small firms in the rest of the nation enjoyed stable revenues and employment in 2025 and, while they also had lower expectations of futur...

• 4.Remote Work Leaves Younger Workers Sidelined

  Youth unemployment has risen dramatically since the pandemic—as has the prevalence of remote work. Our analysis suggests that these trends are related, with remote work making it more difficult for managers to train and mentor new employees. Accordingly, companies may be reluctant to hire less-experienced workers in distributed work arrangements. We estimate that remote work can explain 64 percent of the recent increase in unemployment among young college graduates. Further, the timing of this surge suggests that remote work—not generative AI—explains the bulk of the rise in youth unemployment.

• 5.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.

• 1.No Bridge Over Healing Waters for the G7

  The French G7 presidency’s focus on global imbalances is both welcome and problematic. While long-standing trade imbalances between the United States, Europe, and China are, if anything, becoming even more troublesome, the leaders’ summit in Évian-les-Bains will do nothing to address the problem.

• 2.Are Government Stakes the Key to AI Sovereignty?

  At first glance, reports that the US and Chinese governments are both considering taking stakes in national AI champions would seem to suggest a convergence between the world’s two leading AI powers. But a closer look reveals stark differences in their underlying strategies.

• 3.What Next for Global Trade?

  Summary available at source link.

• 4.From the American Revolution to Universal Suffrage

  Britain’s attempt to crush the American Revolution exposed a constitutional crisis at the heart of its empire. One English statesman saw in it proof that only broader suffrage could check royal power, tame corruption, and ensure that imperial rule remained compatible with liberty.

• 5.Jefferson’s Revolutionary Language

  Of the delegates who assembled in Philadelphia in 1776 for the Second Continental Congress, only Thomas Jefferson possessed the skill, knowledge, and linguistic gifts to make the Declaration of Independence an immortal text. He was the self-appointed conscience of America, and sensational imagery was his stock-in-trade.

• 1.Sovereign AI strategies are converging on bottleneck blueprints (Analyst Angle)

  As nations race to build sovereign AI capabilities, successful strategies are converging on a common formula: control critical bottlenecks. The real differentiator is no longer ambition or spending, but the ability to turn power, infrastructure, regulation and demand into lasting…

• 2.Anterix: ‘Terrific’ results from NTN testing with Lynk Global

  In sum, what to know: –Taking NTN beyond consumers: Anterix, which owns 900 MHz spectrum and targets the utility and enterprise private wireless space, is testing space-based direct-to-device connectivity with the aim of providing utilities with NTN connectivity. –Device line-up:…

• 3.Nokia and Indosat lock in nationwide AI-RAN rollout in Indonesia

  The Nvidia-powered AI-RAN deal moves the partners’ year-long collaboration to deployment In sum – what we know: Nokia and Indosat have signed a strategic network modernization and AI-RAN rollout agreement that puts their Nvidia-powered collaboration on a path to actual…

• 4.AI is making DCI a critical infrastructure priority, says AFL

  Speaking during a recent RCR webinar, AFL’s Noah Taylor said that as hyperscalers expand AI deployments, the networks linking data centers are becoming increasingly important alongside the facilities themselves In sum – what to know: DCI scales rapidly – Taylor…

• 5.Beyond the handshake – upgrading enterprise security with WPA3 (Reader Forum)

  Cisco says enterprises must move beyond WPA2 to WPA3 and strengthen wireless security through SAE, forward secrecy, and Wi-Fi 7 – to enable resilient zero-trust-ready enterprise mobility across modern networks security architecture evolution now. Wireless networks are no longer just…

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• 1.On the Feasibility of Passive Bistatic ISAC Based on Unmodified LoRa

  Integrated Sensing and Communication (ISAC) enables sensing capabilities by reusing communication signals, making it particularly attractive for large-scale deployments through signals of opportunity. While most existing ISAC research targets wideband systems, Low Power Wide Area Network (LPWAN) technologies such as LoRa remain largely unexplored from a radar-like sensing perspective. Existing LoRa-based approaches mainly focus on motion detection or require modifications of the communication waveform, limiting their applicability in deployed networks. This paper investigates the feasibility of radar-like sensing using unmodified LoRa communication signals as signals of opportunity in a purely passive bistatic ISAC configuration. The proposed approach focuses on Doppler-based sensing to enable target separation and super-resolved target e...

• 2.Event-Level Sensing for Intelligent 6G ISAC

  The intelligent evolution of mission-critical networks, such as the Internet of vehicles (IoV) and the low-altitude economy (LAE), requires sixth-generation (6G) networks to move beyond discrete physical parameter estimation toward deeper environmental understanding. However, existing integrated sensing and communications (ISAC) studies mainly focus on target-level sensing, which provides fragmented snapshots of the physical world and lacks the behavioral semantic capability to interpret intent. This limitation hinders the intelligent evolution of such networks and prevents 6G from acquiring the essential sensing foundation to evolve into an "intelligent service engine". To bridge this gap, ISAC must advance toward event-level sensing, which models continuous-time states to enable persistent recognition and prediction of target intent and...

• 3.Vision-Based Efficient Joint Trajectory and Channel Tracking in Near-Field XL-MIMO Systems

  Accurate joint tracking of mobile users, surrounding scatterers, and dynamic channels is a critical task for sixth-generation (6G) wireless systems, essential for both ensuring high-quality communications and empowering advanced selsing applications such as autonomous driving and immersive extended reality. While extremely large-scale multiple-input multiple-output (XL-MIMO) inherently offers strong support for this task through its high spatial resolution and spectral efficiency, its massive scale of antenna arrays, coupled with near-field propagation characteristics, makes joint trajectory and channel tracking time-consuming and hardware-intensive. To address these challenges, we rethink the problem from a vision-based signal perspective. Specifically, we design a subarray-based partially connected hybrid beamforming (PC-HBF) architectu...

• 4.Temporally Consistent Graph Q-Networks for Intelligent Network Control

  Mobile networks continue to grow in complexity and next generation networks are expected to support both increasing traffic loads and more diverse services. As network complexity rises, optimizing antenna parameters under dynamic or changing objectives becomes increasingly challenging. We propose a novel multi-agent reinforcement learning (MARL) algorithm for high-level control and orchestration of mobile networks. The Temporally Consistent Graph Q-Network (TC-GQN) algorithm learns a self-predicting representation of the whole network that is task-independent and aggregates information from all base-stations. A graph neural network is trained using a global reward function to assign coordinated local actions based on the learned encoding of the global network state. We evaluate the algorithm in a simulated environment to orchestrate an en...

• 5.Spectrum Sharing Across Terrestrial and Non-Terrestrial Services in the FR3 Upper Midband

  The frequency bands between 7 and 24 GHz, also known as upper midband or Frequency Range (FR) 3, are being considered as an enabler of 6th Generation (6G) mobile networks. This portion of the spectrum exhibits different propagation characteristics compared to frequencies above 24 GHz, while also offering the potential to provide larger bandwidth allocations for mobile systems than those available in the sub-6 GHz range. 6G technology and spectrum policy, however, will need to guarantee coexistence with the incumbents that already use these frequency bands, which include a variety of services, from radiolocation to satellite-based communications, remote sensing, and radioastronomy. In this paper, we consider the challenge of coexistence between 6G terrestrial systems and satellite incumbents in different portions of the FR3 bands. Using a ...

• 1.Correlation emergence and the Epps effect in two coupled limit order books

  We give a unified analytic account of correlation emergence and the Epps effect in two coupled limit order books. The model starts from a discrete random-walk description of order flow with creation, cancellation and diffusion. A pair-trader coupling between the books is introduced at the level of order creation. We clarify how the discrete model reduces to coupled reaction--diffusion equations with a moving reaction boundary defining the transaction price. Using a regularised local-response representation of the coupling, we derive approximate closed-form expressions for realised correlations as a function of aggregation time. Here the Epps effect is shown to arise from three distinct mechanisms: asynchronous event clocks (subordination), finite coupling response times, and their combination.

• 2.CFOs Meet LLMs

  Business sentiment is a closely watched economic signal, but measuring it is slow and costly: surveys reach only a few hundred firms, arrive periodically, and take time to compile. We show that large language models hold the potential to address these shortcomings. We prompt an LLM to role-play as the CFO of a specific company at a specific date and focus on the economic-optimism question on the Duke-Federal Reserve CFO Survey over 2002-2025. We find that the LLM reproduces individual human responses: the predicted optimism score significantly forecasts the CFO's actual answer, surviving firm and year-quarter fixed effects and a control for the most recent prior response. Predictive accuracy increases with the amount of information supplied, as both respondent history and firm characteristics improve fit, and the relationship persists und...

• 3.Composite likelihood inference of fractional Gaussian processes with sequentially optimal subset selection

  The composite likelihood method reduces the computational cost of parameter estimation in time series by considering several subsets of observations instead of all observations at once. The asymptotic properties of this method are related to the Godambe information, an extension of the Fisher information that accounts for the dependence between subsets of observations. We aim to apply this method to linear Gaussian models, in particular fractional Brownian motion and fractional Gaussian noise. We derive theoretical expressions for their Fisher information and their Godambe information and deduce a subset selection design that sequentially maximizes the Godambe information. The size of the subsets then allows us to control the trade-off between estimation accuracy and computational cost. Through simulations, we compare this method with the...

• 4.Scenario Generation for Time Series and Curves: A Comparison of Nonparametric and Semiparametric Bootstrap

  Generating stochastic trajectories for asset classes is an increasingly relevant task in quantitative finance. Traditional approaches, such as the stationary bootstrap, preserve by construction the empirical distribution of asset-class returns, but do not ensure that each individual simulated path is economically realistic: scenarios may be valid in distribution while single trajectories fail to represent plausible states of the world. To address this limitation, we review semiparametric simulation methodologies that combine a parametric structure, which enforces realistic dynamics, with the resampling of model residuals, which preserves the stochastic component observed in historical data. The issue is particularly acute for interest rates, where direct resampling of rate changes may produce implausible yield-curve evolutions despite cor...

• 5.Reverse Stress Testing for Multivariate Scenarios: A Conditional Framework for Stressed Time Series

  This paper develops a methodological framework for reverse stress testing (RST) in which a multivariate stress scenario, coherent with the empirical dependence structure of a market, is reconstructed from a single exogenous shock prescribed on one asset class. The problem is formulated as the maximisation of the conditional density given the imposed shock, and is solved under three progressively weaker distributional assumptions. In the parametric setting, joint Gaussianity of the returns yields a closed-form modal scenario coinciding with the conditional mean of the non-shocked components. In the semiparametric setting, the modal scenario is estimated nonparametrically through the empirical likelihood methodology and the surrounding stressed trajectories are generated via a Gaussian or Student-t local sampling scheme. In the fully nonpar...

• 1.On the Feasibility of Passive Bistatic ISAC Based on Unmodified LoRa

  Integrated Sensing and Communication (ISAC) enables sensing capabilities by reusing communication signals, making it particularly attractive for large-scale deployments through signals of opportunity. While most existing ISAC research targets wideband systems, Low Power Wide Area Network (LPWAN) technologies such as LoRa remain largely unexplored from a radar-like sensing perspective. Existing LoRa-based approaches mainly focus on motion detection or require modifications of the communication waveform, limiting their applicability in deployed networks. This paper investigates the feasibility of radar-like sensing using unmodified LoRa communication signals as signals of opportunity in a purely passive bistatic ISAC configuration. The proposed approach focuses on Doppler-based sensing to enable target separation and super-resolved target e...

• 2.Event-Level Sensing for Intelligent 6G ISAC

  The intelligent evolution of mission-critical networks, such as the Internet of vehicles (IoV) and the low-altitude economy (LAE), requires sixth-generation (6G) networks to move beyond discrete physical parameter estimation toward deeper environmental understanding. However, existing integrated sensing and communications (ISAC) studies mainly focus on target-level sensing, which provides fragmented snapshots of the physical world and lacks the behavioral semantic capability to interpret intent. This limitation hinders the intelligent evolution of such networks and prevents 6G from acquiring the essential sensing foundation to evolve into an "intelligent service engine". To bridge this gap, ISAC must advance toward event-level sensing, which models continuous-time states to enable persistent recognition and prediction of target intent and...

• 3.Vision-Based Efficient Joint Trajectory and Channel Tracking in Near-Field XL-MIMO Systems

  Accurate joint tracking of mobile users, surrounding scatterers, and dynamic channels is a critical task for sixth-generation (6G) wireless systems, essential for both ensuring high-quality communications and empowering advanced selsing applications such as autonomous driving and immersive extended reality. While extremely large-scale multiple-input multiple-output (XL-MIMO) inherently offers strong support for this task through its high spatial resolution and spectral efficiency, its massive scale of antenna arrays, coupled with near-field propagation characteristics, makes joint trajectory and channel tracking time-consuming and hardware-intensive. To address these challenges, we rethink the problem from a vision-based signal perspective. Specifically, we design a subarray-based partially connected hybrid beamforming (PC-HBF) architectu...

• 4.Temporally Consistent Graph Q-Networks for Intelligent Network Control

  Mobile networks continue to grow in complexity and next generation networks are expected to support both increasing traffic loads and more diverse services. As network complexity rises, optimizing antenna parameters under dynamic or changing objectives becomes increasingly challenging. We propose a novel multi-agent reinforcement learning (MARL) algorithm for high-level control and orchestration of mobile networks. The Temporally Consistent Graph Q-Network (TC-GQN) algorithm learns a self-predicting representation of the whole network that is task-independent and aggregates information from all base-stations. A graph neural network is trained using a global reward function to assign coordinated local actions based on the learned encoding of the global network state. We evaluate the algorithm in a simulated environment to orchestrate an ...

• 5.Spectrum Sharing Across Terrestrial and Non-Terrestrial Services in the FR3 Upper Midband

  The frequency bands between 7 and 24 GHz, also known as upper midband or Frequency Range (FR) 3, are being considered as an enabler of 6th Generation (6G) mobile networks. This portion of the spectrum exhibits different propagation characteristics compared to frequencies above 24 GHz, while also offering the potential to provide larger bandwidth allocations for mobile systems than those available in the sub-6 GHz range. 6G technology and spectrum policy, however, will need to guarantee coexistence with the incumbents that already use these frequency bands, which include a variety of services, from radiolocation to satellite-based communications, remote sensing, and radioastronomy. In this paper, we consider the challenge of coexistence between 6G terrestrial systems and satellite incumbents in different portions of the FR3 bands. Using a ...

• 1.Temporally Consistent Graph Q-Networks for Intelligent Network Control

  Mobile networks continue to grow in complexity and next generation networks are expected to support both increasing traffic loads and more diverse services. As network complexity rises, optimizing antenna parameters under dynamic or changing objectives becomes increasingly challenging. We propose a novel multi-agent reinforcement learning (MARL) algorithm for high-level control and orchestration of mobile networks. The Temporally Consistent Graph Q-Network (TC-GQN) algorithm learns a self-predicting representation of the whole network that is task-independent and aggregates information from all base-stations. A graph neural network is trained using a global reward function to assign coordinated local actions based on the learned encoding of the global network state. We evaluate the algorithm in a simulated environment to orchestrate an ...

• 2.Beyond Virtual Delay: Improving Packet Delay Bound in Network Calculus

  In network calculus, a fundamental result is the classical delay bound given by the horizontal deviation between the arrival and service curves. While widely used, the classical bound is derived from the notion of virtual delay. In this work, we first show that the maximum packet delay is always upper-bounded by the maximum virtual delay, revealing inherent conservatism when applying the virtual-delay-based bound to packet delay. Motivated by this insight, we revisit packet delay analysis and derive a new packet delay bound that requires no assumptions beyond the arrival and service curves. Specializing the new bound to a system with leaky-bucket arrival curve and rate-latency service curve shows strict improvement over the classical bound, which is further demonstrated through a case study in time-sensitive networking (TSN).

• 3.Spectrum Sharing Across Terrestrial and Non-Terrestrial Services in the FR3 Upper Midband

  The frequency bands between 7 and 24 GHz, also known as upper midband or Frequency Range (FR) 3, are being considered as an enabler of 6th Generation (6G) mobile networks. This portion of the spectrum exhibits different propagation characteristics compared to frequencies above 24 GHz, while also offering the potential to provide larger bandwidth allocations for mobile systems than those available in the sub-6 GHz range. 6G technology and spectrum policy, however, will need to guarantee coexistence with the incumbents that already use these frequency bands, which include a variety of services, from radiolocation to satellite-based communications, remote sensing, and radioastronomy. In this paper, we consider the challenge of coexistence between 6G terrestrial systems and satellite incumbents in different portions of the FR3 bands. Using a ...

• 4.Modular Multi-Domain Digital Twin Architecture: Sustainable Intent-Driven 6G Management

  Future 6G networks will operate across distributed and heterogeneous domain infrastructures, making conventional single-domain management insufficient for proactive, trustworthy automation. Network Digital Twins (NDTs) enable what-if analysis, AI-assisted optimization, and risk-free validation of control actions before deployment, yet monolithic end-to-end twins remain impractical due to scalability, fidelity, and cross-domain coordination challenges. Accordingly, this paper proposes a Digital Twin-enabled 6G architecture that exposes NDT capabilities as a specialized service domain within a multi-domain orchestration framework built on a state-of-the-art service-based 6G architecture. A DT Orchestrator interprets \textit{predictive} and \textit{prescriptive} what-if queries and composes domain-specific DT modules and simulators on demand...

• 5.LLM-Enabled NWDAF: A Step Toward AI-Native 6G Network Intelligence

  Summary available at source link.