Babak's Daily Briefing

Wednesday, April 15, 2026

Sources: 20 | Total Articles: 96

• 1.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.

• 2.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.

• 3.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.

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

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

• 5.ODC’s $45M raise signals a bigger shift in AI-RAN, from network optimization to edge intelligence

  ORAN Development Company said it has closed a $45 million Series A backed by Booz Allen, Cisco Investments, Nokia, NVIDIA, AT&T, MTN and Telecom Italia to scale its U.S.-based Odyssey platform, which it positions as an AI-native RAN architecture combining communications, sensing and edge intelligence. The company said it plans to accelerate commercial deployment through 2026.

• 1.Transferable Expertise for Autonomous Agents via Real-World Case-Based Learning

  LLM-based autonomous agents perform well on general reasoning tasks but still struggle to reliably use task structure, key constraints, and prior experience in complex real-world settings. We propose a case-based learning framework that converts experience from past tasks into reusable knowledge assets, allowing agents to transfer prior case experience to new tasks and perform more structured analysis. Unlike methods based mainly on pretrained knowledge or static prompts, our framework emphasizes extracting and reusing task-relevant knowledge, analytical prompts, and operational skills from real cases. We evaluate the method on a unified benchmark of six complex task categories and compare it with Zero-Shot, Few-Shot, Checklist Prompt, and Rule Memory baselines. Results show that our method achieves consistently strong performance across ...

• 2.ORBIT: Guided Agentic Orchestration for Autonomous C-to-Rust Transpilation

  Large-scale migration of legacy C code to Rust offers a promising path toward improving memory safety, but LLM-based C-to-Rust translation remains challenging due to limited context windows and hallucinations. Prior approaches are evaluated primarily on small programs or datasets skewed toward small codebases, providing limited insight into scalability on real-world systems. They also rely on static context construction, which breaks down in the presence of complex cross-module dependencies and often requires manual intervention. Recent coding agents offer a promising alternative through dynamic codebase navigation and context curation. When used out of the box, however, they frequently produce incomplete translations that appear superficially correct. We present ORBIT, an autonomous agentic framework for project-level C-to-Rust translati...

• 3.Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory

  Structured memory representations such as knowledge graphs are central to autonomous agents and other long-lived systems. However, most existing approaches model time as discrete metadata, either sorting by recency (burying old-yet-permanent knowledge), simply overwriting outdated facts, or requiring an expensive LLM call at every ingestion step, leaving them unable to distinguish persistent facts from evolving ones. To address this, we introduce RoMem, a drop-in temporal knowledge graph module for structured memory systems, applicable to agentic memory and beyond. A pretrained Semantic Speed Gate maps each relation's text embedding to a volatility score, learning from data that evolving relations (e.g., "president of") should rotate fast while persistent ones (e.g., "born in") should remain stable. Combined with continuous phase rotation...

• 4.OOM-RL: Out-of-Money Reinforcement Learning Market-Driven Alignment for LLM-Based Multi-Agent Systems

  The alignment of Multi-Agent Systems (MAS) for autonomous software engineering is constrained by evaluator epistemic uncertainty. Current paradigms, such as Reinforcement Learning from Human Feedback (RLHF) and AI Feedback (RLAIF), frequently induce model sycophancy, while execution-based environments suffer from adversarial "Test Evasion" by unconstrained agents. In this paper, we introduce an objective alignment paradigm: \textbf{Out-of-Money Reinforcement Learning (OOM-RL)}. By deploying agents into the non-stationary, high-friction reality of live financial markets, we utilize critical capital depletion as an un-hackable negative gradient. Our longitudinal 20-month empirical study (July 2024 -- February 2026) chronicles the system's evolution from a high-turnover, sycophantic baseline to a robust, liquidity-aware architecture. We demo...

• 5.AgentWebBench: Benchmarking Multi-Agent Coordination in Agentic Web

  Agentic Web is an emerging paradigm where autonomous agents help users use online information. As the paradigm develops, content providers are also deploying agents to manage their data and serve it through controlled interfaces. This shift moves information access from centralized retrieval to decentralized coordination. To study this setting, we introduce AgentWebBench, a benchmark that evaluates how well a user agent synthesizes answers by interacting with website-specific content agents. We evaluate four tasks that cover common web information needs, spanning ranked retrieval (web search, web recommendation) and open-ended synthesis (question answering, deep research). Across seven advanced LLMs and three coordination strategies, multi-agent coordination generally lags behind centralized retrieval as expected, because user agent canno...

• 1.Filtered Reasoning Score: Evaluating Reasoning Quality on a Model's Most-Confident Traces

  arXiv:2604.11996v1 Announce Type: new Abstract: Should we trust Large Language Models (LLMs) with high accuracy? LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the reasoning used to produce it. This highlights a fundamental limitation of outcome-based evaluation: models may arrive at correct answers through flawed reasoning, and models with substantially different reasoning capabilities can nevertheless exhibit similar benchmark accuracy, for example due to memorization or over-optimization. In this paper, we ask: given existing benchmarks, can we move beyond outcome-based evaluation to assess the quality of reasoning itself? We seek metrics that (1) differentiate models with similar accuracy and (2) are robust to variations in input prompts and generation configurations. To this ...

• 2.Self-Distillation Zero: Self-Revision Turns Binary Rewards into Dense Supervision

  arXiv:2604.12002v1 Announce Type: new Abstract: Current post-training methods in verifiable settings fall into two categories. Reinforcement learning (RLVR) relies on binary rewards, which are broadly applicable and powerful, but provide only sparse supervision during training. Distillation provides dense token-level supervision, typically obtained from an external teacher or using high-quality demonstrations. Collecting such supervision can be costly or unavailable. We propose Self-Distillation Zero (SD-Zero), a method that is substantially more training sample-efficient than RL and does not require an external teacher or high-quality demonstrations. SD-Zero trains a single model to play two roles: a Generator, which produces an initial response, and a Reviser, which conditions on that response and its binary reward to produce an improv...

• 3.LLMs Struggle with Abstract Meaning Comprehension More Than Expected

  arXiv:2604.12018v1 Announce Type: new Abstract: Understanding abstract meanings is crucial for advanced language comprehension. Despite extensive research, abstract words remain challenging due to their non-concrete, high-level semantics. SemEval-2021 Task 4 (ReCAM) evaluates models' ability to interpret abstract concepts by presenting passages with questions and five abstract options in a cloze-style format. Key findings include: (1) Most large language models (LLMs), including GPT-4o, struggle with abstract meaning comprehension under zero-shot, one-shot, and few-shot settings, while fine-tuned models like BERT and RoBERTa perform better. (2) A proposed bidirectional attention classifier, inspired by human cognitive strategies, enhances fine-tuned models by dynamically attending to passages and options. This approach improves accuracy ...

• 4.Benchmarking Deflection and Hallucination in Large Vision-Language Models

  arXiv:2604.12033v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) increasingly rely on retrieval to answer knowledge-intensive multimodal questions. Existing benchmarks overlook conflicts between visual and textual evidence and the importance of generating deflections (e.g., Sorry, I cannot answer...) when retrieved knowledge is incomplete. These benchmarks also suffer from rapid obsolescence, as growing LVLM training sets allow models to answer many questions without retrieval. We address these gaps with three contributions. First, we propose a dynamic data curation pipeline that preserves benchmark difficulty over time by filtering for genuinely retrieval-dependent samples. Second, we introduce VLM-DeflectionBench, a benchmark of 2,775 samples spanning diverse multimodal retrieval settings, designed to probe model be...

• 5.Think Through Uncertainty: Improving Long-Form Generation Factuality via Reasoning Calibration

  arXiv:2604.12046v1 Announce Type: new Abstract: Large language models (LLMs) often hallucinate in long-form generation. Existing approaches mainly improve factuality through post-hoc revision or reinforcement learning (RL) with correctness-based rewards, but they do not teach the model to estimate which parts of its generation are reliable. As a result, models may still state incorrect claims confidently in their responses. Recent advances in reasoning have significantly improved LLM performance, and have been leveraged to estimate confidence by incorporating calibration into RL objectives. However, existing approaches remain limited to a single scalar confidence for the entire response, which is insufficient for long-form generation where uncertainty varies across individual claims. To mitigate this problem, we propose CURE, a framework...

• 1.Uncertainty Quantification in CNN Through the Bootstrap of Convex Neural Networks

  arXiv:2604.11833v1 Announce Type: new Abstract: Despite the popularity of Convolutional Neural Networks (CNN), the problem of uncertainty quantification (UQ) of CNN has been largely overlooked. Lack of efficient UQ tools severely limits the application of CNN in certain areas, such as medicine, where prediction uncertainty is critically important. Among the few existing UQ approaches that have been proposed for deep learning, none of them has theoretical consistency that can guarantee the uncertainty quality. To address this issue, we propose a novel bootstrap based framework for the estimation of prediction uncertainty. The inference procedure we use relies on convexified neural networks to establish the theoretical consistency of bootstrap. Our approach has a significantly less computational load than its competitors, as it relies on wa...

• 2.Schema-Adaptive Tabular Representation Learning with LLMs for Generalizable Multimodal Clinical Reasoning

  arXiv:2604.11835v1 Announce Type: new Abstract: Machine learning for tabular data remains constrained by poor schema generalization, a challenge rooted in the lack of semantic understanding of structured variables. This challenge is particularly acute in domains like clinical medicine, where electronic health record (EHR) schemas vary significantly. To solve this problem, we propose Schema-Adaptive Tabular Representation Learning, a novel method that leverages large language models (LLMs) to create transferable tabular embeddings. By transforming structured variables into semantic natural language statements and encoding them with a pretrained LLM, our approach enables zero-shot alignment across unseen schemas without manual feature engineering or retraining. We integrate our encoder into a multimodal framework for dementia diagnosis, com...

• 3.A Layer-wise Analysis of Supervised Fine-Tuning

  arXiv:2604.11838v1 Announce Type: new Abstract: While critical for alignment, Supervised Fine-Tuning (SFT) incurs the risk of catastrophic forgetting, yet the layer-wise emergence of instruction-following capabilities remains elusive. We investigate this mechanism via a comprehensive analysis utilizing information-theoretic, geometric, and optimization metrics across model scales (1B-32B). Our experiments reveal a distinct depth-dependent pattern: middle layers (20\%-80\%) are stable, whereas final layers exhibit high sensitivity. Leveraging this insight, we propose Mid-Block Efficient Tuning, which selectively updates these critical intermediate layers. Empirically, our method outperforms standard LoRA up to 10.2\% on GSM8K (OLMo2-7B) with reduced parameter overhead, demonstrating that effective alignment is architecturally localized rat...

• 4.When Reasoning Models Hurt Behavioral Simulation: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation

  arXiv:2604.11840v1 Announce Type: new Abstract: Large language models are increasingly used as agents in social, economic, and policy simulations. A common assumption is that stronger reasoning should improve simulation fidelity. We argue that this assumption can fail when the objective is not to solve a strategic problem, but to sample plausible boundedly rational behavior. In such settings, reasoning-enhanced models can become better solvers and worse simulators: they can over-optimize for strategically dominant actions, collapse compromise-oriented terminal behavior, and sometimes exhibit a diversity-without-fidelity pattern in which local variation survives without outcome-level fidelity. We study this solver-sampler mismatch in three multi-agent negotiation environments adapted from earlier simulation work: an ambiguous fragmented-au...

• 5.Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions

  arXiv:2604.11841v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) is a widely used strategy for efficient fine-tuning of large language models (LLMs), but its strictly linear structure fundamentally limits expressive capacity. The bilinear formulation of weight updates captures only first-order dependencies between low-rank factors, restricting the modeling of nonlinear and higher-order parameter interactions. In this paper, we propose Polynomial Expansion Rank Adaptation (PERA), a novel method that introduces structured polynomial expansion directly into the low-rank factor space. By expanding each low-rank factor to synthesize high-order interaction terms before composition, PERA transforms the adaptation space into a polynomial manifold capable of modeling richer nonlinear coupling without increasing rank or inference cost. We...

• 1.MVAdapt: Zero-Shot Multi-Vehicle Adaptation for End-to-End Autonomous Driving

  arXiv:2604.11854v1 Announce Type: new Abstract: End-to-End (E2E) autonomous driving models are usually trained and evaluated with a fixed ego-vehicle, even though their driving policy is implicitly tied to vehicle dynamics. When such a model is deployed on a vehicle with different size, mass, or drivetrain characteristics, its performance can degrade substantially; we refer to this problem as the vehicle-domain gap. To address it, we propose MVAdapt, a physics-conditioned adaptation framework for multi-vehicle E2E driving. MVAdapt combines a frozen TransFuser++ scene encoder with a lightweight physics encoder and a cross-attention module that conditions scene features on vehicle properties before waypoint decoding. In the CARLA Leaderboard 1.0 benchmark, MVAdapt improves over naive transfer and multi-embodiment adaptation baselines on bot...

• 2.BIND-USBL: Bounding IMU Navigation Drift using USBL in Heterogeneous ASV-AUV Teams

  arXiv:2604.11861v1 Announce Type: new Abstract: Accurate and continuous localization of Autonomous Underwater Vehicles (AUVs) in GPS-denied environments is a persistent challenge in marine robotics. In the absence of external position fixes, AUVs rely on inertial dead-reckoning, which accumulates unbounded drift due to sensor bias and noise. This paper presents BIND-USBL, a cooperative localization framework in which a fleet of Autonomous Surface Vessels (ASVs) equipped with Ultra-Short Baseline (USBL) acoustic positioning systems provides intermittent fixes to bound AUV dead-reckoning error. The key insight is that long-duration navigation failure is driven not by the accuracy of individual USBL measurements, but by the temporal sparsity and geometric availability of those fixes. BIND-USBL combines a multi-ASV formation model linking sur...

• 3.M2HRI: An LLM-Driven Multimodal Multi-Agent Framework for Personalized Human-Robot Interaction

  arXiv:2604.11975v1 Announce Type: new Abstract: Multi-robot systems hold significant promise for social environments such as homes and hospitals, yet existing multi-robot works treat robots as functionally identical, overlooking how robots individual identity shape user perception and how coordination shapes multi-robot behavior when such individuality is present. To address this, we introduce M2HRI, a multimodal multi-agent framework built on large language models that equips each robot with distinct personality and long-term memory, alongside a coordination mechanism conditioned on these differences. In a controlled user study (n = 105) in a multi-agent human-robot interaction (HRI) scenario, we find that LLM-driven personality traits are significantly distinguishable and enhance interaction quality, long-term memory improves personaliz...

• 4.Bipedal-Walking-Dynamics Model on Granular Terrains

  arXiv:2604.11981v1 Announce Type: new Abstract: Bipeds have demonstrated high agility and mobility in unstructured environments such as sand. The yielding of such granular media brings significant sinkage and slip of the bipedal feet, leading to uncertainty and instability of walking locomotion. We present a new dynamics-modeling approach to capture and predict bipedal-walking locomotion on granular media. A dynamic foot-terrain interaction model is integrated to compute the ground reaction force (GRF). The proposed granular dynamic model has three additional degree-of-freedom (DoF) to estimate foot sinkage and slip that are critical to capturing robot-walking kinematics and kinetics such as cost of transport (CoT). Using the new model, we analyze bipedal kinetics, CoT, and foot-terrain rolling and intrusion affects. Experiments are condu...

• 5.Complementarity by Construction: A Lie-Group Approach to Solving Quadratic Programs with Linear Complementarity Constraints

  arXiv:2604.11991v1 Announce Type: new Abstract: Many problems in robotics require reasoning over a mix of continuous dynamics and discrete events, such as making and breaking contact in manipulation and locomotion. These problems are locally well modeled by linear complementarity quadratic programs (LCQPs), an extension to QPs that introduce complementarity constraints. While very expressive, LCQPs are non-convex, and few solvers exist for computing good local solutions for use in planning pipelines. In this work, we observe that complementarity constraints form a Lie group under infinitesimal relaxation, and leverage this structure to perform on-manifold optimization. We introduce a retraction map that is numerically well behaved, and use it to parameterize the constraints so that they are satisfied by construction. The resulting solver ...

• 1.PRAGMA: Revolut Foundation Model

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

• 2.Quantum Computing for Financial Transformation: A Review of Optimisation, Pricing, Risk, Machine Learning, and Post-Quantum Security

  Quantum computing is becoming strategically relevant to finance because several core financial bottlenecks are already defined by combinatorial search, expectation estimation, rare-event analysis, representation learning, and long-horizon cryptographic resilience. This review examines that landscape across five connected domains: constrained portfolio optimisation, derivative pricing, tail-risk and scenario estimation, quantum machine learning, and post-quantum security. Rather than treating these topics as isolated demonstrations, the article studies them as linked layers of a financial-computation stack. Across all five domains, the review applies a common evaluative logic: identify the financial bottleneck, specify the relevant quantum primitive, compare it with an explicit classical benchmark, and assess the result under realistic imp...

• 3.SBBTS: A Unified Schrödinger-Bass Framework for Synthetic Financial Time Series

  We study the problem of generating synthetic time series that reproduce both marginal distributions and temporal dynamics, a central challenge in financial machine learning. Existing approaches typically fail to jointly model drift and stochastic volatility, as diffusion-based methods fix the volatility while martingale transport models ignore drift. We introduce the Schrödinger-Bass Bridge for Time Series (SBBTS), a unified framework that extends the Schrödinger-Bass formulation to multi-step time series. The method constructs a diffusion process that jointly calibrates drift and volatility and admits a tractable decomposition into conditional transport problems, enabling efficient learning. Numerical experiments on the Heston model demonstrate that SBBTS accurately recovers stochastic volatility and correlation parameters that prior Sch...

• 4.Sequential Audit Sampling with Statistical Guarantees

  Financial statement auditing is conducted under a risk-based evidence approach to obtain reasonable assurance. In practice, auditors often perform additional sampling or related procedures when an initial sample does not provide a sufficient basis for a conclusion. Across jurisdictions, current standards and practice manuals acknowledge such extensions, while the statistical design of sequential audit procedures has not been fully explored. This study formulates audit sampling with additional, sequentially collected items as a sequential testing problem for a finite population under sampling without replacement. We define null and alternative hypotheses in terms of a tolerable deviation rate, specify stopping and decision rules, and formulate exact sequential boundary conditions in terms of finite-population error probabilities. For pract...

• 5.Generative Path-Law Jump-Diffusion: Sequential MMD-Gradient Flows and Generalisation Bounds in Marcus-Signature RKHS

  This paper introduces a novel generative framework for synthesising forward-looking, càdlàg stochastic trajectories that are sequentially consistent with time-evolving path-law proxies, thereby incorporating anticipated structural breaks, regime shifts, and non-autonomous dynamics. By framing path synthesis as a sequential matching problem on restricted Skorokhod manifolds, we develop the \textit{Anticipatory Neural Jump-Diffusion} (ANJD) flow, a generative mechanism that effectively inverts the time-extended Marcus-sense signature. Central to this approach is the Anticipatory Variance-Normalised Signature Geometry (AVNSG), a time-evolving precision operator that performs dynamic spectral whitening on the signature manifold to ensure contractivity during volatile regime shifts and discrete aleatoric shocks. We provide a rigorous theoretic...

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

  Summary available at source link.

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

  Summary available at source link.

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

  Summary available at source link.

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

  Summary available at source link.

• 5.GSMA MWC26 Barcelona closes 20th anniversary edition

  Summary available at source link.

• 1.Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation

  On-policy distillation (OPD) has emerged as an efficient post-training paradigm for large language models. However, standard OPD requires a live teacher inference server throughout training, resulting in substantial infrastructure overhead. In this work, we investigate whether on-policy distillation can be performed offline. A natural approach is to precompute teacher log-probabilities once over SFT rollouts and reuse them during training. In practice, however, this offline variant fails to reliably match the performance of standard OPD. To understand this discrepancy, we identify a previously overlooked condition that is critical for any OPD pipeline, which we term teacher consistency. This condition requires that the same teacher model be used for both supervised fine-tuning and OPD. We show that violating teacher consistency introduces...

• 2.PolicyLLM: Towards Excellent Comprehension of Public Policy for Large Language Models

  Large Language Models (LLMs) are increasingly integrated into real-world decision-making, including in the domain of public policy. Yet, their ability to comprehend and reason about policy-related content remains underexplored. To fill this gap, we present \textbf{\textit{PolicyBench}}, the first large-scale cross-system benchmark (US-China) evaluating policy comprehension, comprising 21K cases across a broad spectrum of policy areas, capturing the diversity and complexity of real-world governance. Following Bloom's taxonomy, the benchmark assesses three core capabilities: (1) \textbf{Memorization}: factual recall of policy knowledge, (2) \textbf{Understanding}: conceptual and contextual reasoning, and (3) \textbf{Application}: problem-solving in real-life policy scenarios. Building on this benchmark, we further propose \textbf{\textit{Po...

• 3.Boosting Visual Instruction Tuning with Self-Supervised Guidance

  Multimodal large language models (MLLMs) perform well on many vision-language tasks but often struggle with vision-centric problems that require fine-grained visual reasoning. Recent evidence suggests that this limitation arises not from weak visual representations, but from under-utilization of visual information during instruction tuning, where many tasks can be partially solved using language priors alone. We propose a simple and lightweight approach that augments visual instruction tuning with a small number of visually grounded self-supervised tasks expressed as natural language instructions. By reformulating classical self-supervised pretext tasks, such as rotation prediction, color matching, and cross-view correspondence, as image-instruction-response triplets, we introduce supervision that cannot be solved without relying on visua...

• 4.Modeling Co-Pilots for Text-to-Model Translation

  There is growing interest in leveraging large language models (LLMs) for text-to-model translation and optimization tasks. This paper aims to advance this line of research by introducing \textsc{Text2Model} and \textsc{Text2Zinc}. \textsc{Text2Model} is a suite of co-pilots based on several LLM strategies with varying complexity, along with an online leaderboard. \textsc{Text2Zinc} is a cross-domain dataset for capturing optimization and satisfaction problems specified in natural language, along with an interactive editor with built-in AI assistant. While there is an emerging literature on using LLMs for translating combinatorial problems into formal models, our work is the first attempt to integrate \textit{both} satisfaction and optimization problems within a \textit{unified architecture} and \textit{dataset}. Moreover, our approach is ...

• 5.Evaluating LLMs Code Reasoning Under Real-World Context

  Code reasoning tasks are increasingly crucial to evaluating large language models (LLMs). Yet most existing benchmarks rely on simplistic, LLM-generated snippets or human-written solutions to code challenges and often restrict inputs and outputs to primitive types, failing to reflect the structure and dependencies of real-world projects. These simplifications limit their ability to measure practical generalizability. We present R2Eval1, a benchmark of 135 code reasoning problems drawn from ten widely used Python projects. Unlike prior work, R2Eval serializes compound and custom types, preserving real-world data complexity and enabling a more realistic assessment of LLMs.

• 1.Lyra 2.0: Explorable Generative 3D Worlds

  Recent advances in video generation enable a new paradigm for 3D scene creation: generating camera-controlled videos that simulate scene walkthroughs, then lifting them to 3D via feed-forward reconstruction techniques. This generative reconstruction approach combines the visual fidelity and creative capacity of video models with 3D outputs ready for real-time rendering and simulation. Scaling to large, complex environments requires 3D-consistent video generation over long camera trajectories with large viewpoint changes and location revisits, a setting where current video models degrade quickly. Existing methods for long-horizon generation are fundamentally limited by two forms of degradation: spatial forgetting and temporal drifting. As exploration proceeds, previously observed regions fall outside the model's temporal context, forcing t...

• 2.SceneCritic: A Symbolic Evaluator for 3D Indoor Scene Synthesis

  Large Language Models (LLMs) and Vision-Language Models (VLMs) increasingly generate indoor scenes through intermediate structures such as layouts and scene graphs, yet evaluation still relies on LLM or VLM judges that score rendered views, making judgments sensitive to viewpoint, prompt phrasing, and hallucination. When the evaluator is unstable, it becomes difficult to determine whether a model has produced a spatially plausible scene or whether the output score reflects the choice of viewpoint, rendering, or prompt. We introduce SceneCritic, a symbolic evaluator for floor-plan-level layouts. SceneCritic's constraints are grounded in SceneOnto, a structured spatial ontology we construct by aggregating indoor scene priors from 3D-FRONT, ScanNet, and Visual Genome. SceneOnto traverses this ontology to jointly verify semantic, orientation,...

• 3.Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data

  Predicting counterfactual outcomes in longitudinal data, where sequential treatment decisions heavily depend on evolving patient states, is critical yet notoriously challenging due to complex time-dependent confounding and inadequate uncertainty quantification in existing methods. We introduce the Causal Diffusion Model (CDM), the first denoising diffusion probabilistic approach explicitly designed to generate full probabilistic distributions of counterfactual outcomes under sequential interventions. CDM employs a novel residual denoising architecture with relational self-attention, capturing intricate temporal dependencies and multimodal outcome trajectories without requiring explicit adjustments (e.g., inverse-probability weighting or adversarial balancing) for confounding. In rigorous evaluation on a pharmacokinetic-pharmacodynamic tum...

• 4.Accelerating Speculative Decoding with Block Diffusion Draft Trees

  Speculative decoding accelerates autoregressive language models by using a lightweight drafter to propose multiple future tokens, which the target model then verifies in parallel. DFlash shows that a block diffusion drafter can generate an entire draft block in a single forward pass and achieve state-of-the-art speculative decoding performance, outperforming strong autoregressive drafters such as EAGLE-3. Vanilla DFlash, however, still verifies only a single drafted trajectory per round, potentially limiting its acceptance length. We introduce DDTree (Diffusion Draft Tree), a method that constructs a draft tree directly from the per-position distributions of a block diffusion drafter. Under a fixed node budget, DDTree uses a simple best-first heap algorithm to select the continuations that are most likely to match the target model accordi...

• 5.Modeling Co-Pilots for Text-to-Model Translation

  There is growing interest in leveraging large language models (LLMs) for text-to-model translation and optimization tasks. This paper aims to advance this line of research by introducing \textsc{Text2Model} and \textsc{Text2Zinc}. \textsc{Text2Model} is a suite of co-pilots based on several LLM strategies with varying complexity, along with an online leaderboard. \textsc{Text2Zinc} is a cross-domain dataset for capturing optimization and satisfaction problems specified in natural language, along with an interactive editor with built-in AI assistant. While there is an emerging literature on using LLMs for translating combinatorial problems into formal models, our work is the first attempt to integrate \textit{both} satisfaction and optimization problems within a \textit{unified architecture} and \textit{dataset}. Moreover, our approach is ...

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

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

• 2.OpenAI Engineer Helps Companies Attract Buyers and Boost Sales

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

• 3.12 Graphs That Explain the State of AI in 2026

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

• 4.GoZTASP: A Zero-Trust Platform for Governing Autonomous Systems at Mission Scale

  ZTASP is a mission-scale assurance and governance platform designed for autonomous systems operating in real-world environments. It integrates heterogeneous systems—including drones, robots, sensors, and human operators—into a unified zero-trust architecture. Through Secure Runtime Assurance (SRTA) and Secure Spatio-Temporal Reasoning (SSTR), ZTASP continuously verifies system integrity, enforces safety constraints, and enables resilient operation even under degraded conditions. ZTASP has progressed beyond conceptual design, with operational validation at Technology Readiness Level (TRL) 7 in mission critical environments. Core components, including Saluki secure flight controllers, have reached TRL8 and are deployed in customer systems. While initially developed for high-consequence mission environments, the same assurance challenges are...

• 5.AI Models Map the Colorado River’s Hard Choices

  The Colorado River begins as snow. Every spring, the mountain snowpack of the Rockies melts into streams that feed into reservoirs that supply 40 million people across seven U.S. states. The system has worked, more or less, for a century. That century is over. By some measures, 2026 is shaping up to be the worst year the river has seen since records began. Flows are down 20 percent from 2000 levels . Lake Powell, the reservoir straddling Utah and Arizona, may drop below the threshold for generating hydropower before the year is out . The negotiations between the seven states over how to share what’s left have collapsed twice , and the U.S. federal government is threatening to impose its own plan. While the states argue and the river shrinks, a growing set of machine learning tools is being deployed across the basin. Federal water managers...

• 1.The Human Side of AI Adoption: Lessons From the Field

  Carolyn Geason-Beissel/MIT SMR Not a day goes by without another article being published about how AI could disrupt yet another aspect of our business or personal lives. In recent years, AI adoption has indeed taken off. However, if you pay close attention, you’ll notice a dichotomy. Many examples of successful early adoption of artificial intelligence […]

• 2.Managing Up: A Skill Set That Matters Now

  Carolyn Geason-Beissel/MIT SMR | Getty Images Are you skilled at managing up? If your talents are lacking when it comes to managing and dealing with the people above you in the organizational hierarchy, you can find yourself mired in some unpleasant and career-harming situations. Maybe you’re frustrated by a micromanaging supervisor or feeling marginalized by […]

• 3.The Trap That Skilled Negotiators Miss

  Brian Stauffer/theispot.com Say you walk into a car dealership determined to stay within budget. The salesperson shows you a car you like and quotes a price of $41,435. You know there’s room to negotiate, but when it’s time to counter, that first number quietly takes over. Your counteroffer, the concessions, and the final deal all […]

• 4.Rethink Responsibility in the Age of AI

  Mark Airs/Ikon Images Early one morning in 2018, a self-driving Uber vehicle fatally struck a pedestrian in Tempe, Arizona. The world had questions: Who was responsible? Was it the safety driver behind the wheel? The engineers who designed the algorithms? Uber’s leadership? Or the regulators who had allowed autonomous-vehicle testing? The inability to name a […]

• 5.Gain Consumer Insight With Generative AI

  Stuart Kinlough/Ikon Images Marketing leaders often face a dilemma: Deriving the insights they need in order to make confident decisions can cost tens of thousands of dollars and involve several months of data gathering and analysis, by which time market conditions may have shifted. Can generative AI fundamentally reshape this calculus? Drawing on recent research, […]

• 1.The Empathy Channel in Fertility -- by Sebastian Galiani, Raul A. Sosa

  Being around babies makes people want babies. We formalize this observation as the empathy channel: exposure to infants in the social environment activates neurobiological mechanisms that increase the desire for parenthood. As children become scarcer, this affective stimulus weakens, further eroding the motivation to have children. We embed the mechanism in a two-group overlapping-generations quantity-quality model. The empathy channel generates a positive externality, since each birth raises others’ desire for children, making the decentralized equilibrium inefficient. We characterize the optimal per-child subsidy and show that the first-order Pigouvian rate substantially overshoots the general-equilibrium optimum. The optimal targeting rule follows a Ramsey-like logic, directing the subsidy at the group with the most externality per fis...

• 2.Profit Regulation and Strategic Transfer Pricing by Vertically Integrated Firms: Evidence from Health Care -- by Pragya Kakani, Eric Yde, Genevieve P. Kanter, Richard G. Frank, Amelia M. Bond

  We provide evidence of strategic transfer pricing by vertically integrated health care firms in response to insurer profit regulations. Insurers increased prices at vertically integrated pharmacies by 9.5% following the introduction of caps on insurer profits in Medicare Part D. We detect larger price increases by insurers that were at greatest risk of exceeding the allowable profit level. More than one-fifth of these higher prices were borne by the federal government. Our analysis illustrates that vertically integrated firms can evade profit regulation by “tunneling” profits to unregulated subsidiaries, undermining regulatory intent and increasing health care spending.

• 3.Predicted Incrementality by Experimentation (PIE) for Ad Measurement -- by Brett R. Gordon, Robert Moakler, Florian Zettelmeyer

  Randomized controlled trials (RCTs) provide the most credible estimates of advertising incrementality but are difficult to scale. We propose Predicted Incrementality by Experimentation (PIE), which reframes ad measurement as a campaign-level prediction problem. PIE uses a sample of RCTs to learn a mapping from campaign features to causal effects, then applies it to campaigns not run as RCTs. Because the RCTs identify the causal effects, PIE can incorporate post-determined features—campaign-level aggregates such as test-group outcomes, exposure rates, and last-click conversions, computed after campaign completion. These metrics reflect the consumer behaviors that generate treatment effects, so they carry predictive information about incrementality even though they would be invalid controls in a causal model. Using 2,226 Meta ad experiments...

• 4.Bad News and Policy Views: Expectations, Disappointment, and Opposition to Affirmative Action -- by Louis-Pierre Lepage, Heather Sarsons, Michael Thaler

  There is widespread opposition to affirmative action policies. We study whether personal disappointments shape preferences for such policies. Specifically, we test whether individuals' college admissions outcomes, relative to their expectations, influence their attitudes toward affirmative action policies. Using a retrospective survey among recent White and Asian college applicants, we find that disappointed individuals—those who were admitted to fewer schools than anticipated—are relatively more likely to believe that affirmative action played an important role in their admissions outcomes, have the lowest support for affirmative action policies, and are more willing to donate to an anti-affirmative action organization. They also hold more negative views about the academic qualifications of under-represented minorities. To isolate the ca...

• 5.Forecasting the Economic Effects of AI -- by Ezra Karger, Otto Kuusela, Jason Abaluck, Kevin A. Bryan, Basil Halperin, Todd R. Jones, Connacher Murphy, Philip Trammell, Matt Reynolds, Dan Mayland, Ria Viswanathan, Ananaya Mittal, Rebecca Ceppas de Castro, Josh Rosenberg, Philip Tetlock

  We elicit forecasts of how AI will affect the U.S. economy, comparing the beliefs of five groups: academic economists, employees at AI companies, policy researchers focused on AI, highly accurate forecasters, and the general public. The median respondent in each group expects substantial advances in AI capabilities by 2030, small declines in labor force participation consistent with demographic shifts, and an annual GDP growth rate of 2.5%, which exceeds both the typical medium-run (2.0%) and long-run (1.7%) baseline forecasts from government agencies and private-sector forecasters. Conditional on a “rapid” AI progress scenario, in which AI systems surpass human performance on many cognitive and physical tasks, experts forecast substantial, though not historically unprecedented, economic shifts: annualized GDP growth rising to around 4% a...

• 1.The R*–Labor Share Nexus

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

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

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

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

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

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

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

• 5.The Fed Has Two Tools to Influence Money Market Conditions 

  The Federal Reserve’s 2022-23 tightening cycle involved the use of two monetary policy tools: changes in administrative rates and changes in the size of its balance sheet. This post highlights the results of a recent Staff Report that explores how these tools affect money market conditions. Using confidential trade-level data, we find that both tools have significant effects on the pricing of funds sourced through repo. These results suggest that the Fed can manage how financing conditions are affected even as it influences economic conditions. For example, the Fed can lower its administrative rates to loosen economic conditions, while shrinking its balance sheet to maintain financing conditions in the money markets. 

• 1.Putting Africa’s Savings to Work

  Africa’s pension funds and financial institutions hold hundreds of billions of dollars in long-term savings, yet much of that capital flows into sovereign bonds because few scalable alternatives exist. The task now is to build the architecture that channels those savings into productive investments.

• 2.The Great Hungarian Reset

  Now that he has ousted Viktor Orbán, incoming Hungarian Prime Minister Péter Magyar inherits an economy burdened by a legacy of centralized mismanagement and systemic corruption. To address it, he will need a focused and pragmatic economic program.

• 3.The Hormuz Crisis and the Fate of the Global South

  If the shock from the closure of the Strait of Hormuz has revealed economic vulnerabilities, it has also illuminated stark differences in how countries absorb energy-price turbulence. Those that have invested in more resilient clean energy sources are faring better and offering lessons for everyone else.

• 4.Why Development Doesn’t Prevent War

  Violent conflicts have reached levels not seen since World War II, even as global poverty has fallen to historic lows, challenging long-held assumptions about the relationship between development and peace. This outcome calls for a reassessment of the theory of change that underpins development aid.

• 5.Russians Go Home

  At a time when illiberalism has presented itself as the wave of the future, Hungarians just voted overwhelmingly to reverse course. But unlike the country’s liberation in 1989, this week’s electoral result represents only the beginning of a longer process, the outcome of which will remain uncertain.

• 1.How Compu Dynamics Is Changing Data Center Delivery: A Conversation with Steve Altizer

  In this episode, Steve Altizer, Founder and CEO of Compu Dynamics, shares how he has built a uniquely integrated data center services company—spanning design, construction, electrical, mechanical, and long-term maintenance—while continuously evolving to meet the demands of a rapidly changing…

• 2.India broadband growth accelerates with fiber and FWA

  Fixed broadband revenue in India is expected to increase steadily, supported by upgrades to higher-ARPU FTTH plans and wider digital adoption In sum – what to know: Broadband growth – Fixed services revenue will rise to $20.1 billion by 2030,…

• 3.Report: Networks at the frontline – How 6G, ISAC and NTNs will redefine defense technology

  6G is no longer just the next generation of mobile. It’s the foundation for a new class of strategically critical infrastructure — one that integrates sensing, AI inference, and resilient connectivity across contested environments. Today’s shifting geopolitical climate has exposed…

• 4.Telcos on notice in AI era – Lumen’s call to arms

  Lumen chief executive Kate Johnson has penned an open letter to enterprise CEOs (and the whole telecoms industry): that there is a golden opportunity to connect the AI revolution, but legacy networks are not up to scratch, and the industry…

• 5.EU clears Orange’s full acquisition of MasOrange

  The deal will see Orange take sole control of MasOrange, the operator formed through the merger of Orange and MásMóvil in 2024 In sum – what to know: Regulatory clearance – The European Commission approved the deal under a simplified…

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• 1.Learning Low-Dimensional Representation for O-RAN Testing via Transformer-ESN

  Open Radio Access Network (O-RAN) architectures enhance flexibility for 6G and NextG networks. However, it also brings significant challenges in O-RAN testing with evaluating abundant, high-dimensional key performance indicators (KPIs). In this paper, we introduce a novel two-stage framework to learn temporally-aware low-dimensional representations of O-RAN testing KPIs. To be specific, stage one employs an information-theoretic H-score to train a hybrid self-attentive transformer and echo state network (ESN) reservoir, called Transformer-ESN, capturing temporal dynamics and producing task-aligned $8$-dimensional embeddings. Stage two evaluates these embeddings by training a lightweight multilayer perceptron (MLP) predictor exclusively on them for key target KPIs such as reference signal received quality (RSRQ) and spectral efficiency. Us...

• 2.Robust Semi-Supervised Temporal Intrusion Detection for Adversarial Cloud Networks

  Cloud networks increasingly rely on machine learning based Network Intrusion Detection Systems to defend against evolving cyber threats. However, real-world deployments are challenged by limited labeled data, non-stationary traffic, and adaptive adversaries. While semi-supervised learning can alleviate label scarcity, most existing approaches implicitly assume benign and stationary unlabeled traffic, leading to degraded performance in adversarial cloud environments. This paper proposes a robust semi-supervised temporal learning framework for cloud intrusion detection that explicitly addresses adversarial contamination and temporal drift in unlabeled network traffic. Operating on flow-level data, this framework combines supervised learning with consistency regularization, confidence-aware pseudo-labeling, and selective temporal invariance ...

• 3.Fluid Antennas Meet Rate-Splitting Multiple Access: A New Path Forward for 6G Networks

  Future sixth-generation (6G) networks require high spectral efficiency (SE), massive connectivity, and stringent reliability under imperfect channel state information at the transmitter. Rate-splitting multiple access (RSMA) addresses part of this challenge by flexibly managing interference through common and private message streams, while fluid antenna systems (FAS) offer low-cost spatial diversity by dynamically reconfiguring antenna positions within a compact aperture. In this paper, we first classify FAS-enabled multiple access systems from the perspectives of FAS deployment, objectives, and antenna configuration, along with some comparisons with benchmark schemes, thereby exhibiting the inherent efficiency of FAS-RSMA. Moreover, we reveal the mutually enhancing mechanism between FAS and RSMA: FAS strengthens the weakest effective lin...

• 4.RIS-Aided Sensing: Experimental Validation of Radar 3D Imaging in the mmWave Band

  The transition toward 6G networks demands energy-efficient hardware capable of active interaction with the environment. Reconfigurable Intelligent Surfaces (RIS) have emerged as a key technology for Integrated Sensing and Communications (ISAC), enabling geometric environment recognition with minimal power consumption. However, achieving targeted 3D spatial mapping in a fully autonomous, closed-loop system remains a significant challenge. In this work, we validate experimentally an autonomous mmWave 3D imaging framework that integrates an Frequency-Modulated Continuous Wave (FMCW) radar with a 1-bit RIS and a Vector Network Analyzer (VNA) to perform targeted 3D reconstruction. The FMCW radar acts as a coarse localizer, providing real-time spatial priors to define dynamic Regions of Interest (ROI). These coordinates are translated into opti...

• 5.LightTune: Lightweight Forward-Only Online Fine-Tuning with Applications to Link Adaptation

  Deploying machine learning (ML) algorithms on mobile phones is bottlenecked by performance degradation under dynamic, real-world conditions that differ from the offline training conditions. While continual learning and adaptation are essential to mitigate this distributional shift, conventional online learning methods are often computationally prohibitive for resource-constrained devices. In this paper, we propose LightTune, a lightweight, backpropagation-free online fine-tuning framework with provable convergence guarantees. LightTune opportunistically refines ML models using live test-time data only when performance falls below a predefined threshold, ensuring minimal computational overhead and highly efficient responsiveness. As a practical demonstration, we integrate LightTune into a block error rate (BLER) prediction algorithm for 6G...

• 1.Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach

  We develop a Quantile Bayesian Vector Autoregression (QBVAR) to forecast real oil prices across different quantiles of the conditional distribution. The model allows predictor effects to vary across quantiles, capturing asymmetries that standard mean-focused approaches miss. Using monthly data from 1975 to 2025, we document three findings. First, the QBVAR improves median forecasts by 2-5\% relative to Bayesian VARs, demonstrating that quantile-specific dynamics matter even for point prediction. Second, uncertainty and financial condition variables strongly predict downside risk, with left-tail forecast improvements of 10-25\% that intensify during crisis episodes. Third, right-tail forecasting remains difficult; stochastic volatility models dominate for upside risk, though forecast combinations that include the QBVAR recover these losses...

• 2.A Herding-Based Model of Technological Transfer and Economic Convergence: Evidence from Central and Eastern Europe

  The long-run convergence of developing economies toward advanced countries exhibits robust empirical regularities, yet the mechanisms underlying technological diffusion remain insufficiently specified in standard growth models. In this paper, we extend the neoclassical framework by introducing a micro-founded mechanism of technological transfer as a driver of total factor productivity. Rather than treating technological progress as exogenous or purely innovation-driven, we model productivity growth as a process of adopting existing technologies from the global frontier. The diffusion process is described using a herding-type interaction mechanism, in which agents transition from non-adopters to adopters under the combined influence of individual incentives and peer effects. This approach yields a tractable aggregate representation of TFP ...

• 3.AI Patents in the United States and China: Measurement, Organization, and Knowledge Flows

  We develop a high-precision classifier to measure artificial intelligence (AI) patents by fine-tuning PatentSBERTa on manually labeled data from the USPTO's AI Patent Dataset. Our classifier substantially improves the existing USPTO approach, achieving 97.0% precision, 91.3% recall, and a 94.0% F1 score, and it generalizes well to Chinese patents based on citation and lexical validation. Applying it to granted U.S. patents (1976-2023) and Chinese patents (2010-2023), we document rapid growth in AI patenting in both countries and broad convergence in AI patenting intensity and subfield composition, even as China surpasses the United States in recent annual patent counts. The organization of AI innovation nevertheless differs sharply: U.S. AI patenting is concentrated among large private incumbents and established hubs, whereas Chinese AI p...

• 4.Regime-Aware Specialist Routing for Volatility Forecasting

  Volatility forecasting becomes challenging when market conditions change and model performance varies across regimes. Motivated by this instability, we develop a regime-aware specialist routing framework for ETF volatility forecasting. The framework uses online risk-sensitive evaluation and state-dependent gating to combine different forecasting specialists across calm and stressed market states. Using a daily panel of six ETFs under a rolling walk-forward design, we find that the strongest forecaster is regime-dependent rather than global. Relative to the rolling-best baseline, the proposed routing framework reduces high-volatility forecast loss by about 24\% and underprediction loss by about 22\%. These results suggest that specialist routing provides a practical adaptive forecasting architecture for changing market conditions.

• 5.Global Persistence, Local Residual Structure: Forecasting Heterogeneous Investment Panels

  On a 93-actor quarterly panel mixing macro indicators, institutional data, and firm-level investment ratios, global factor augmentation degrades prediction for actor subgroups whose dynamics are misrepresented by the shared basis. A two-stage architecture -- global pooled AR(1) for shared persistence, block-specific local models for residual dynamics -- improves full-panel out-of-sample $R^2$ from 0.630 to 0.677 ($Δ= +0.047$, CI $[+0.036, +0.058]$, 10/10 windows, placebo $p \leq 0.001$). A held-out decade test -- block partition frozen on 2005--2014 data, evaluated on unseen 2015--2024 windows -- confirms the gain ($Δ= +0.050$, 10/10). Dropping the tech/health block eliminates roughly 72\% of the gain, making it the primary driver; rank-matched decomposition confirms this reflects a genuine cross-sector co-movement factor, not a rank-capa...

• 1.Learning Low-Dimensional Representation for O-RAN Testing via Transformer-ESN

  Open Radio Access Network (O-RAN) architectures enhance flexibility for 6G and NextG networks. However, it also brings significant challenges in O-RAN testing with evaluating abundant, high-dimensional key performance indicators (KPIs). In this paper, we introduce a novel two-stage framework to learn temporally-aware low-dimensional representations of O-RAN testing KPIs. To be specific, stage one employs an information-theoretic H-score to train a hybrid self-attentive transformer and echo state network (ESN) reservoir, called Transformer-ESN, capturing temporal dynamics and producing task-aligned $8$-dimensional embeddings. Stage two evaluates these embeddings by training a lightweight multilayer perceptron (MLP) predictor exclusively on them for key target KPIs such as reference signal received quality (RSRQ) and spectral efficiency. Us...

• 2.Robust Semi-Supervised Temporal Intrusion Detection for Adversarial Cloud Networks

  Cloud networks increasingly rely on machine learning based Network Intrusion Detection Systems to defend against evolving cyber threats. However, real-world deployments are challenged by limited labeled data, non-stationary traffic, and adaptive adversaries. While semi-supervised learning can alleviate label scarcity, most existing approaches implicitly assume benign and stationary unlabeled traffic, leading to degraded performance in adversarial cloud environments. This paper proposes a robust semi-supervised temporal learning framework for cloud intrusion detection that explicitly addresses adversarial contamination and temporal drift in unlabeled network traffic. Operating on flow-level data, this framework combines supervised learning with consistency regularization, confidence-aware pseudo-labeling, and selective temporal invariance ...

• 3.Fluid Antennas Meet Rate-Splitting Multiple Access: A New Path Forward for 6G Networks

  Future sixth-generation (6G) networks require high spectral efficiency (SE), massive connectivity, and stringent reliability under imperfect channel state information at the transmitter. Rate-splitting multiple access (RSMA) addresses part of this challenge by flexibly managing interference through common and private message streams, while fluid antenna systems (FAS) offer low-cost spatial diversity by dynamically reconfiguring antenna positions within a compact aperture. In this paper, we first classify FAS-enabled multiple access systems from the perspectives of FAS deployment, objectives, and antenna configuration, along with some comparisons with benchmark schemes, thereby exhibiting the inherent efficiency of FAS-RSMA. Moreover, we reveal the mutually enhancing mechanism between FAS and RSMA: FAS strengthens the weakest effective lin...

• 4.RIS-Aided Sensing: Experimental Validation of Radar 3D Imaging in the mmWave Band

  The transition toward 6G networks demands energy-efficient hardware capable of active interaction with the environment. Reconfigurable Intelligent Surfaces (RIS) have emerged as a key technology for Integrated Sensing and Communications (ISAC), enabling geometric environment recognition with minimal power consumption. However, achieving targeted 3D spatial mapping in a fully autonomous, closed-loop system remains a significant challenge. In this work, we validate experimentally an autonomous mmWave 3D imaging framework that integrates an Frequency-Modulated Continuous Wave (FMCW) radar with a 1-bit RIS and a Vector Network Analyzer (VNA) to perform targeted 3D reconstruction. The FMCW radar acts as a coarse localizer, providing real-time spatial priors to define dynamic Regions of Interest (ROI). These coordinates are translated into opti...

• 5.LightTune: Lightweight Forward-Only Online Fine-Tuning with Applications to Link Adaptation

  Deploying machine learning (ML) algorithms on mobile phones is bottlenecked by performance degradation under dynamic, real-world conditions that differ from the offline training conditions. While continual learning and adaptation are essential to mitigate this distributional shift, conventional online learning methods are often computationally prohibitive for resource-constrained devices. In this paper, we propose LightTune, a lightweight, backpropagation-free online fine-tuning framework with provable convergence guarantees. LightTune opportunistically refines ML models using live test-time data only when performance falls below a predefined threshold, ensuring minimal computational overhead and highly efficient responsiveness. As a practical demonstration, we integrate LightTune into a block error rate (BLER) prediction algorithm for ...

• 1.Advancing Network Digital Twin Framework for Generating Realistic Datasets

  The integration of accurate and reproducible wireless network simulations is a key enabler for research on open, virtualized, and intelligent communication systems. Network Digital Twins (NDTs) provide a scalable alternative to costly and time-consuming measurement campaigns, while enabling controlled experimentation and data generation for data-driven network design. In this paper, we present an open and user-friendly NDT framework that integrates controllable vehicular mobility with the site-specific ray tracer Sionna and the discrete-event ns-3 network simulator, enabling virtualized end-to-end modeling of wireless networks across the radio, network, and application layers. The proposed framework is particularly well-suited for dynamic vehicular networks and urban deployments, supporting realistic mobility, traffic dynamics, and the ex...

• 2.LightTune: Lightweight Forward-Only Online Fine-Tuning with Applications to Link Adaptation

  Deploying machine learning (ML) algorithms on mobile phones is bottlenecked by performance degradation under dynamic, real-world conditions that differ from the offline training conditions. While continual learning and adaptation are essential to mitigate this distributional shift, conventional online learning methods are often computationally prohibitive for resource-constrained devices. In this paper, we propose LightTune, a lightweight, backpropagation-free online fine-tuning framework with provable convergence guarantees. LightTune opportunistically refines ML models using live test-time data only when performance falls below a predefined threshold, ensuring minimal computational overhead and highly efficient responsiveness. As a practical demonstration, we integrate LightTune into a block error rate (BLER) prediction algorithm for ...

• 3.Network Slice Embedding over Space Division Multiplexed Elastic Optical Networks

  Network slicing over space division multiplexed elastic optical networks (SDM EONs) enables efficient multiservice provisioning on a shared optical substrate. However, embedding such slices requires coordinated spectrum and compute resource management under dynamic traffic, which most existing RMCSA studies treat independently. This paper focuses on the network slice embedding problem over space division multiplexed elastic optical networks (SDM EONs), aiming to develop efficient resource allocation strategies that ensure both high utilization and reliable service performance. While prior studies have investigated routing, modulation format, core, and spectrum allocation (RMCSA), they typically consider these dimensions separately from compute placement. To address this gap, this paper proposes a Waypoint Assisted Multi Segment Slice Mapp...

• 4.ISAC-Enabled Non-Terrestrial Networks for 6G: Design Principles, Standardization, Performance Tradeoffs, and Use Cases

  Non-Terrestrial Networks (NTN) have emerged as a key enabler to fully realize the vision of integrated, intelligent, and ubiquitous connectivity in 6G systems. However, several operational challenges, including severe Doppler effects, interference, and latency, hinder the seamless integration of NTN and Terrestrial Networks (TN). In this context, Integrated Sensing and Communication (ISAC), which unifies sensing and communication functionalities within a common framework, offers great potential to address these challenges while enabling new network capabilities. Due to its complementary functionalities, ISAC can play a pivotal role in enhancing NTN performance, although its practical adoption requires a fundamental rethinking of existing architectural and standardization frameworks. Motivated by this need, this article examines key aspect...

• 5.Security Implications of 5G Communication in Industrial Systems

  Traditionally, industrial control systems (ICS) were designed without security in mind, prioritizing availability and real-time communication. As these systems increasingly become targets of powerful adversaries, security can no longer be neglected. Driven by flexibility and automation needs, ICS are transitioning from wired to 5G communication, introducing new attack surfaces and a less reliable communication medium, thereby exacerbating existing security challenges. Given their critical role in society, a comprehensive evaluation of their security is imperative. To this end, we introduce SWICS, a fully virtual testbed simulating an ICS in a realistic 5G environment, and study how this transition affects security under varying channel conditions. Our results show three key findings: under optimal channel conditions, industrial 5G network...