Babak's Daily Briefing

Friday, March 27, 2026

Sources: 20 | Total Articles: 96

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

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

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

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

• 3.6G as a Platform for Value

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

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

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

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

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

• 1.PII Shield: A Browser-Level Overlay for User-Controlled Personal Identifiable Information (PII) Management in AI Interactions

  AI chatbots have quietly become the world's most popular therapists, coaches, and confidants. Users of cloud-based LLM services are increasingly shifting from simple queries like idea generation and poem writing, to deeply personal interactions. As Large Language Models increasingly assume the role of our confessors, we are witnessing a massive, unregulated transfer of sensitive personal identifiable information (PII) to powerful tech companies with opaque privacy practices. While the enterprise sector has made great strides in addressing data leakage concerns through sophisticated guardrails and PII redaction pipelines, these powerful tools have functionally remained inaccessible for the average user due to their technical complexity. This results in a dangerous trade off for individual users. In order to receive the therapeutic or produ...

• 2.GameplayQA: A Benchmarking Framework for Decision-Dense POV-Synced Multi-Video Understanding of 3D Virtual Agents

  Multimodal LLMs are increasingly deployed as perceptual backbones for autonomous agents in 3D environments, from robotics to virtual worlds. These applications require agents to perceive rapid state changes, attribute actions to the correct entities, and reason about concurrent multi-agent behaviors from a first-person perspective, capabilities that existing benchmarks do not adequately evaluate. We introduce GameplayQA, a framework for evaluating agentic-centric perception and reasoning through video understanding. Specifically, we densely annotate multiplayer 3D gameplay videos at 1.22 labels/second, with time-synced, concurrent captions of states, actions, and events structured around a triadic system of Self, Other Agents, and the World, a natural decomposition for multi-agent environments. From these annotations, we refined 2.4K diag...

• 3.Experiential Reflective Learning for Self-Improving LLM Agents

  Recent advances in large language models (LLMs) have enabled the development of autonomous agents capable of complex reasoning and multi-step problem solving. However, these agents struggle to adapt to specialized environments and do not leverage past interactions, approaching each new task from scratch regardless of their accumulated experience. We introduce Experiential Reflective Learning (ERL), a simple self-improvement framework that enables rapid environment adaptation through experiential learning. ERL reflects on task trajectories and outcomes to generate heuristics, capturing actionable lessons that transfer across tasks. At test time, relevant heuristics are retrieved based on the current task and injected into the agent's context to guide execution. On the Gaia2 benchmark, ERL improves success rate by 7.8% over a ReAct baseline...

• 4.Language-Grounded Multi-Agent Planning for Personalized and Fair Participatory Urban Sensing

  Participatory urban sensing leverages human mobility for large-scale urban data collection, yet existing methods typically rely on centralized optimization and assume homogeneous participants, resulting in rigid assignments that overlook personal preferences and heterogeneous urban contexts. We propose MAPUS, an LLM-based multi-agent framework for personalized and fair participatory urban sensing. In our framework, participants are modeled as autonomous agents with individual profiles and schedules, while a coordinator agent performs fairness-aware selection and refines sensing routes through language-based negotiation. Experiments on real-world datasets show that MAPUS achieves competitive sensing coverage while substantially improving participant satisfaction and fairness, promoting more human-centric and sustainable urban sensing syste...

• 5.Self-Evolving Multi-Agent Framework for Efficient Decision Making in Real-Time Strategy Scenarios

  Large language models (LLMs) have demonstrated exceptional potential in complex reasoning,pioneering a new paradigm for autonomous agent decision making in dynamic settings. However, in Real-Time Strategy (RTS) scenarios, LLMs suffer from a critical speed-quality trade-off. Specifically expansive state spaces and time limits render inference delays prohibitive, while stochastic planning errors undermine logical consistency. To address these challenges, we present SEMA (Self-Evolving Multi-Agent), a novel framework designed for high-performance, low-latency decision-making in RTS environments. This collaborative multi-agent framework facilitates self-evolution by adaptively calibrating model bias through in-episode assessment and cross-episode analysis. We further incorporate dynamic observation pruning based on structural entropy to model...

• 1.When Consistency Becomes Bias: Interviewer Effects in Semi-Structured Clinical Interviews

  arXiv:2603.24651v1 Announce Type: new Abstract: Automatic depression detection from doctor-patient conversations has gained momentum thanks to the availability of public corpora and advances in language modeling. However, interpretability remains limited: strong performance is often reported without revealing what drives predictions. We analyze three datasets: ANDROIDS, DAIC-WOZ, E-DAIC and identify a systematic bias from interviewer prompts in semi-structured interviews. Models trained on interviewer turns exploit fixed prompts and positions to distinguish depressed from control subjects, often achieving high classification scores without using participant language. Restricting models to participant utterances distributes decision evidence more broadly and reflects genuine linguistic cues. While semi-structured protocols ensure consiste...

• 2.Demystifying When Pruning Works via Representation Hierarchies

  arXiv:2603.24652v1 Announce Type: new Abstract: Network pruning, which removes less important parameters or architectures, is often expected to improve efficiency while preserving performance. However, this expectation does not consistently hold across language tasks: pruned models can perform well on non-generative tasks but frequently fail in generative settings. To understand this discrepancy, we analyze network pruning from a representation-hierarchy perspective, decomposing the internal computation of language models into three sequential spaces: embedding (hidden representations), logit (pre-softmax outputs), and probability (post-softmax distributions). We find that representations in the embedding and logit spaces are largely robust to pruning-induced perturbations. However, the nonlinear transformation from logits to probabiliti...

• 3.Fine-Tuning A Large Language Model for Systematic Review Screening

  arXiv:2603.24767v1 Announce Type: new Abstract: Systematic reviews traditionally have taken considerable amounts of human time and energy to complete, in part due to the extensive number of titles and abstracts that must be reviewed for potential inclusion. Recently, researchers have begun to explore how to use large language models (LLMs) to make this process more efficient. However, research to date has shown inconsistent results. We posit this is because prompting alone may not provide sufficient context for the model(s) to perform well. In this study, we fine-tune a small 1.2 billion parameter open-weight LLM specifically for study screening in the context of a systematic review in which humans rated more than 8500 titles and abstracts for potential inclusion. Our results showed strong performance improvements from the fine-tuned mod...

• 4.Evaluating Fine-Tuned LLM Model For Medical Transcription With Small Low-Resource Languages Validated Dataset

  arXiv:2603.24772v1 Announce Type: new Abstract: Clinical documentation is a critical factor for patient safety, diagnosis, and continuity of care. The administrative burden of EHRs is a significant factor in physician burnout. This is a critical issue for low-resource languages, including Finnish. This study aims to investigate the effectiveness of a domain-aligned natural language processing (NLP); large language model for medical transcription in Finnish by fine-tuning LLaMA 3.1-8B on a small validated corpus of simulated clinical conversations by students at Metropolia University of Applied Sciences. The fine-tuning process for medical transcription used a controlled preprocessing and optimization approach. The fine-tuning effectiveness was evaluated by sevenfold cross-validation. The evaluation metrics for fine-tuned LLaMA 3.1-8B wer...

• 5.Enhancing Structured Meaning Representations with Aspect Classification

  arXiv:2603.24797v1 Announce Type: new Abstract: To fully capture the meaning of a sentence, semantic representations should encode aspect, which describes the internal temporal structure of events. In graph-based meaning representation frameworks such as Uniform Meaning Representations (UMR), aspect lets one know how events unfold over time, including distinctions such as states, activities, and completed events. Despite its importance, aspect remains sparsely annotated across semantic meaning representation frameworks. This has, in turn, hindered not only current manual annotation, but also the development of automatic systems capable of predicting aspectual information. In this paper, we introduce a new dataset of English sentences annotated with UMR aspect labels over Abstract Meaning Representation (AMR) graphs that lack the feature....

• 1.Beyond Accuracy: Introducing a Symbolic-Mechanistic Approach to Interpretable Evaluation

  arXiv:2603.23517v1 Announce Type: new Abstract: Accuracy-based evaluation cannot reliably distinguish genuine generalization from shortcuts like memorization, leakage, or brittle heuristics, especially in small-data regimes. In this position paper, we argue for mechanism-aware evaluation that combines task-relevant symbolic rules with mechanistic interpretability, yielding algorithmic pass/fail scores that show exactly where models generalize versus exploit patterns. We demonstrate this on NL-to-SQL by training two identical architectures under different conditions: one without schema information (forcing memorization), one with schema (enabling grounding). Standard evaluation shows the memorization model achieves 94% field-name accuracy on unseen data, falsely suggesting competence. Our symbolic-mechanistic evaluation reveals this model ...

• 2.Implicit Turn-Wise Policy Optimization for Proactive User-LLM Interaction

  arXiv:2603.23550v1 Announce Type: new Abstract: Multi-turn human-AI collaboration is fundamental to deploying interactive services such as adaptive tutoring, conversational recommendation, and professional consultation. However, optimizing these interactions via reinforcement learning is hindered by the sparsity of verifiable intermediate rewards and the high stochasticity of user responses. To address these challenges, we introduce Implicit Turn-wise Policy Optimization (ITPO). ITPO leverages an implicit process reward model to derive fine-grained, turn-wise process rewards from sparse outcome signals. Unlike volatile token-level rewards, these turn-level signals exhibit superior robustness and may utilize a normalization mechanism to further enhance training stability. We evaluate ITPO across three representative multi-turn collaborativ...

• 3.Upper Entropy for 2-Monotone Lower Probabilities

  arXiv:2603.23558v1 Announce Type: new Abstract: Uncertainty quantification is a key aspect in many tasks such as model selection/regularization, or quantifying prediction uncertainties to perform active learning or OOD detection. Within credal approaches that consider modeling uncertainty as probability sets, upper entropy plays a central role as an uncertainty measure. This paper is devoted to the computational aspect of upper entropies, providing an exhaustive algorithmic and complexity analysis of the problem. In particular, we show that the problem has a strongly polynomial solution, and propose many significant improvements over past algorithms proposed for 2-monotone lower probabilities and their specific cases.

• 4.Synthetic Mixed Training: Scaling Parametric Knowledge Acquisition Beyond RAG

  arXiv:2603.23562v1 Announce Type: new Abstract: Synthetic data augmentation helps language models learn new knowledge in data-constrained domains. However, naively scaling existing synthetic data methods by training on more synthetic tokens or using stronger generators yields diminishing returns below the performance of RAG. To break the RAG ceiling, we introduce Synthetic Mixed Training, which combines synthetic QAs and synthetic documents. This leverages their complementary training signals, and enables log-linear improvements as both synthetic data volume and generator strength increase. This allows the model to outperform RAG by a 2.6\% relative gain on QuaLITY, a long-document reading comprehension benchmark. In addition, we introduce Focal Rewriting, a simple technique for synthetic document generation that explicitly conditions doc...

• 5.Safe Reinforcement Learning with Preference-based Constraint Inference

  arXiv:2603.23565v1 Announce Type: new Abstract: Safe reinforcement learning (RL) is a standard paradigm for safety-critical decision making. However, real-world safety constraints can be complex, subjective, and even hard to explicitly specify. Existing works on constraint inference rely on restrictive assumptions or extensive expert demonstrations, which is not realistic in many real-world applications. How to cheaply and reliably learn these constraints is the major challenge we focus on in this study. While inferring constraints from human preferences offers a data-efficient alternative, we identify the popular Bradley-Terry (BT) models fail to capture the asymmetric, heavy-tailed nature of safety costs, resulting in risk underestimation. It is still rare in the literature to understand the impacts of BT models on the downstream policy...

• 1.Saranga: MilliWatt Ultrasound for Navigation in Visually Degraded Environments on Palm-Sized Aerial Robots

  arXiv:2603.24699v1 Announce Type: new Abstract: Tiny palm-sized aerial robots possess exceptional agility and cost-effectiveness in navigating confined and cluttered environments. However, their limited payload capacity directly constrains the sensing suite on-board the robot, thereby limiting critical navigational tasks in Global Positioning System (GPS)-denied wild scenes. Common methods for obstacle avoidance use cameras and LIght Detection And Ranging (LIDAR), which become ineffective in visually degraded conditions such as low visibility, dust, fog or darkness. Other sensors, such as RAdio Detection And Ranging (RADAR), have high power consumption, making them unsuitable for tiny aerial robots. Inspired by bats, we propose Saranga, a low-power ultrasound-based perception stack that localizes obstacles using a dual sonar array. We pre...

• 2.FODMP: Fast One-Step Diffusion of Movement Primitives Generation for Time-Dependent Robot Actions

  arXiv:2603.24806v1 Announce Type: new Abstract: Diffusion models are increasingly used for robot learning, but current designs face a clear trade-off. Action-chunking diffusion policies like ManiCM are fast to run, yet they only predict short segments of motion. This makes them reactive, but unable to capture time-dependent motion primitives, such as following a spring-damper-like behavior with built-in dynamic profiles of acceleration and deceleration. Recently, Movement Primitive Diffusion (MPD) partially addresses this limitation by parameterizing full trajectories using Probabilistic Dynamic Movement Primitives (ProDMPs), thereby enabling the generation of temporally structured motions. Nevertheless, MPD integrates the motion decoder directly into a multi-step diffusion process, resulting in prohibitively high inference latency that l...

• 3.A Nonvolatile Switchable-polarity EPM Valve

  arXiv:2603.24811v1 Announce Type: new Abstract: Scalable control of pneumatic and fluidic networks remains fundamentally constrained by architectures that require continuous power input, dense external control hardware, and fixed routing topologies. Current valve arrays rely on such continuous actuation and mechanically fixed routing, imposing substantial thermal and architectural overhead. Here, we introduce the Switchable-polarity ElectroPermanent Magnet (S-EPM), a fundamentally new bistable magnetic architecture that deterministically reverses its external magnetic polarity through transient electrical excitation. By reconfiguring internal flux pathways within a composite magnet assembly, the S-EPM establishes two stable, opposing magnetic configurations without requiring sustained power. We integrate this architecture into a compact p...

• 4.Characterization of Constraints in Flexible Unknown Environments

  arXiv:2603.24813v1 Announce Type: new Abstract: This paper presents an online path planning algorithm for safe autonomous manipulation of a flexibly constrained object in an unknown environment. Methods for real time identification and characterization of perceived flexible constraints and global stiffness are presented. Used in tandem, these methods allow a robot to simultaneously explore, characterize, and manipulate an elastic system safely. Navigation without a-priori knowledge of the system is achieved using constraint exploration based on local force and position information. The perceived constraint stiffness is considered at multiple poses along an object's (system) trajectory. Using stiffness eigenvector information, global stiffness behavior is characterized and identified using an atlas of simple mechanical constraints, such as...

• 5.Integrated Multi-Drone Task Allocation, Sequencing, and Optimal Trajectory Generation in Obstacle-Rich 3D Environments

  arXiv:2603.24908v1 Announce Type: new Abstract: Coordinating teams of aerial robots in cluttered three-dimensional (3D) environments requires a principled integration of discrete mission planning-deciding which robot serves which goals and in what order -- with continuous-time trajectory synthesis that enforces collision avoidance and dynamic feasibility. This paper introduces IMD-TAPP (Integrated Multi-Drone Task Allocation and Path Planning), an end-to-end framework that jointly addresses multi-goal allocation, tour sequencing, and safe trajectory generation for quadrotor teams operating in obstacle-rich spaces. IMD--TAPP first discretizes the workspace into a 3D navigation graph and computes obstacle-aware robot-to-goal and goal-to-goal travel costs via graph-search-based pathfinding. These costs are then embedded within an Injected Pa...

• 1.Adapting Altman's bankruptcy prediction model to the compositional data methodology

  Using standard financial ratios as variables in statistical analyses has been related to several serious problems, such as extreme outliers, asymmetry, non-normality, and non-linearity. The compositional-data methodology has been successfully applied to solve these problems and has always yielded substantially different results when compared to standard financial ratios. An under-researched area is the use of financial log-ratios computed with the compositional-data methodology to predict bankruptcy or the related terms of business default, insolvency or failure. Another under-researched area is the use of machine learning methods in combination with compositional log-ratios. The present article adapts the classical Altman bankruptcy prediction model and some of its extensions to the compositional methodology with pairwise log-ratios and ...

• 2.LineMVGNN: Anti-Money Laundering with Line-Graph-Assisted Multi-View Graph Neural Networks

  Anti-money laundering (AML) systems are important for protecting the global economy. However, conventional rule-based methods rely on domain knowledge, leading to suboptimal accuracy and a lack of scalability. Graph neural networks (GNNs) for digraphs (directed graphs) can be applied to transaction graphs and capture suspicious transactions or accounts. However, most spectral GNNs do not naturally support multi-dimensional edge features, lack interpretability due to edge modifications, and have limited scalability owing to their spectral nature. Conversely, most spatial methods may not capture the money flow well. Therefore, in this work, we propose LineMVGNN (Line-Graph-Assisted Multi-View Graph Neural Network), a novel spatial method that considers payment and receipt transactions. Specifically, the LineMVGNN model extends a lightweight...

• 3.High-Resolution Tensor-Network Fourier Methods for Exponentially Compressed Non-Gaussian Aggregate Distributions

  Characteristic functions of weighted sums of independent random variables exhibit low-rank structure in the quantized tensor train (QTT) representation, also known as matrix product states (MPS), enabling up to exponential compression of their fully non-Gaussian probability distributions. Under variable independence, the global characteristic function factorizes into local terms. Its low-rank QTT structure arises from intrinsic spectral smoothness in continuous models, or from spectral energy concentration as the number of components $D$ grows in discrete models. We demonstrate this on weighted sums of Bernoulli and lognormal random variables. In the former, despite an adversarial, incompressible small-$D$ regime, the characteristic function undergoes a sharp bond-dimension collapse for $D \gtrsim 300$ components, enabling polylogarithmic...

• 4.Conditionally Identifiable Latent Representation for Multivariate Time Series with Structural Dynamics

  We propose the Identifiable Variational Dynamic Factor Model (iVDFM), which learns latent factors from multivariate time series with identifiability guarantees. By applying iVAE-style conditioning to the innovation process driving the dynamics rather than to the latent states, we show that factors are identifiable up to permutation and component-wise affine (or monotone invertible) transformations. Linear diagonal dynamics preserve this identifiability and admit scalable computation via companion-matrix and Krylov methods. We demonstrate improved factor recovery on synthetic data, stable intervention accuracy on synthetic SCMs, and competitive probabilistic forecasting on real-world benchmarks.

• 5.FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading

  We present FinRL-X, a modular and deployment-consistent trading architecture that unifies data processing, strategy construction, backtesting, and broker execution under a weight-centric interface. While existing open-source platforms are often backtesting- or model-centric, they rarely provide system-level consistency between research evaluation and live deployment. FinRL-X addresses this gap through a composable strategy pipeline that integrates stock selection, portfolio allocation, timing, and portfolio-level risk overlays within a unified protocol. The framework supports both rule-based and AI-driven components, including reinforcement learning allocators and LLM-based sentiment signals, without altering downstream execution semantics. FinRL-X provides an extensible foundation for reproducible, end-to-end quantitative trading researc...

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

  Summary available at source link.

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

  Summary available at source link.

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

  Summary available at source link.

• 4.GSMA MWC26 Barcelona closes 20th anniversary edition

  Summary available at source link.

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

  Summary available at source link.

• 1.SlotVTG: Object-Centric Adapter for Generalizable Video Temporal Grounding

  Multimodal Large Language Models (MLLMs) have shown strong performance on Video Temporal Grounding (VTG). However, their coarse recognition capabilities are insufficient for fine-grained temporal understanding, making task-specific fine-tuning indispensable. This fine-tuning causes models to memorize dataset-specific shortcuts rather than faithfully grounding in the actual visual content, leading to poor Out-of-Domain (OOD) generalization. Object-centric learning offers a promising remedy by decomposing scenes into entity-level representations, but existing approaches require re-running the entire multi-stage training pipeline from scratch. We propose SlotVTG, a framework that steers MLLMs toward object-centric, input-grounded visual reasoning at minimal cost. SlotVTG introduces a lightweight slot adapter that decomposes visual tokens int...

• 2.Is Mathematical Problem-Solving Expertise in Large Language Models Associated with Assessment Performance?

  Large Language Models (LLMs) are increasingly used in math education not only as problem solvers but also as assessors of learners' reasoning. However, it remains unclear whether stronger math problem-solving ability is associated with stronger step-level assessment performance. This study examines that relationship using the GSM8K and MATH subsets of PROCESSBENCH, a human-annotated benchmark for identifying the earliest erroneous step in mathematical reasoning. We evaluate two LLM-based math tutor agent settings, instantiated with GPT-4 and GPT-5, in two independent tasks on the same math problems: solving the original problem and assessing a benchmark-provided solution by predicting the earliest erroneous step. Results show a consistent within-model pattern: assessment accuracy is substantially higher on math problem items the same mode...

• 3.Demographic Fairness in Multimodal LLMs: A Benchmark of Gender and Ethnicity Bias in Face Verification

  Multimodal Large Language Models (MLLMs) have recently been explored as face verification systems that determine whether two face images are of the same person. Unlike dedicated face recognition systems, MLLMs approach this task through visual prompting and rely on general visual and reasoning abilities. However, the demographic fairness of these models remains largely unexplored. In this paper, we present a benchmarking study that evaluates nine open-source MLLMs from six model families, ranging from 2B to 8B parameters, on the IJB-C and RFW face verification protocols across four ethnicity groups and two gender groups. We measure verification accuracy with the Equal Error Rate and True Match Rate at multiple operating points per demographic group, and we quantify demographic disparity with four FMR-based fairness metrics. Our results sh...

• 4.Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes

  On-policy distillation (OPD) is appealing for large language model (LLM) post-training because it evaluates teacher feedback on student-generated rollouts rather than fixed teacher traces. In long-horizon settings, however, the common sampled-token variant is fragile: it reduces distribution matching to a one-token signal and becomes increasingly unreliable as rollouts drift away from prefixes the teacher commonly visits. We revisit OPD from the estimator and implementation sides. Theoretically, token-level OPD is biased relative to sequence-level reverse-KL, but it has a much tighter worst-case variance bound; our toy study shows the same tradeoff empirically, with stronger future-reward coupling producing higher gradient variance and less stable learning. Empirically, we identify three failure modes of sampled-token OPD: an imbalanced o...

• 5.EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agents

  As the Web transitions from static retrieval to generative interaction, the escalating environmental footprint of Large Language Models (LLMs) presents a critical sustainability challenge. Current paradigms indiscriminately apply computation-intensive strategies like Chain-of-Thought (CoT) to billions of daily queries, causing LLM overthinking, a redundancy that amplifies carbon emissions and operational barriers. This inefficiency directly undermines UN Sustainable Development Goals 13 (Climate Action) and 10 (Reduced Inequalities) by hindering equitable AI access in resource-constrained regions. To address this, we introduce EcoThink, an energy-aware adaptive inference framework designed to reconcile high-performance AI intelligence with environmental responsibility. EcoThink employs a lightweight, distillation-based router to dynamical...

• 1.Less Gaussians, Texture More: 4K Feed-Forward Textured Splatting

  Existing feed-forward 3D Gaussian Splatting methods predict pixel-aligned primitives, leading to a quadratic growth in primitive count as resolution increases. This fundamentally limits their scalability, making high-resolution synthesis such as 4K intractable. We introduce LGTM (Less Gaussians, Texture More), a feed-forward framework that overcomes this resolution scaling barrier. By predicting compact Gaussian primitives coupled with per-primitive textures, LGTM decouples geometric complexity from rendering resolution. This approach enables high-fidelity 4K novel view synthesis without per-scene optimization, a capability previously out of reach for feed-forward methods, all while using significantly fewer Gaussian primitives. Project page: https://yxlao.github.io/lgtm/

• 2.PSDesigner: Automated Graphic Design with a Human-Like Creative Workflow

  Graphic design is a creative and innovative process that plays a crucial role in applications such as e-commerce and advertising. However, developing an automated design system that can faithfully translate user intentions into editable design files remains an open challenge. Although recent studies have leveraged powerful text-to-image models and MLLMs to assist graphic design, they typically simplify professional workflows, resulting in limited flexibility and intuitiveness. To address these limitations, we propose PSDesigner, an automated graphic design system that emulates the creative workflow of human designers. Building upon multiple specialized components, PSDesigner collects theme-related assets based on user instructions, and autonomously infers and executes tool calls to manipulate design files, such as integrating new assets o...

• 3.Unleashing Guidance Without Classifiers for Human-Object Interaction Animation

  Generating realistic human-object interaction (HOI) animations remains challenging because it requires jointly modeling dynamic human actions and diverse object geometries. Prior diffusion-based approaches often rely on hand-crafted contact priors or human-imposed kinematic constraints to improve contact quality. We propose LIGHT, a data-driven alternative in which guidance emerges from the denoising pace itself, reducing dependence on manually designed priors. Building on diffusion forcing, we factor the representation into modality-specific components and assign individualized noise levels with asynchronous denoising schedules. In this paradigm, cleaner components guide noisier ones through cross-attention, yielding guidance without auxiliary classifiers. We find that this data-driven guidance is inherently contact-aware, and can be enh...

• 4.PixelSmile: Toward Fine-Grained Facial Expression Editing

  Fine-grained facial expression editing has long been limited by intrinsic semantic overlap. To address this, we construct the Flex Facial Expression (FFE) dataset with continuous affective annotations and establish FFE-Bench to evaluate structural confusion, editing accuracy, linear controllability, and the trade-off between expression editing and identity preservation. We propose PixelSmile, a diffusion framework that disentangles expression semantics via fully symmetric joint training. PixelSmile combines intensity supervision with contrastive learning to produce stronger and more distinguishable expressions, achieving precise and stable linear expression control through textual latent interpolation. Extensive experiments demonstrate that PixelSmile achieves superior disentanglement and robust identity preservation, confirming its effec...

• 5.Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models

  Video world models have shown immense potential in simulating the physical world, yet existing memory mechanisms primarily treat environments as static canvases. When dynamic subjects hide out of sight and later re-emerge, current methods often struggle, leading to frozen, distorted, or vanishing subjects. To address this, we introduce Hybrid Memory, a novel paradigm requiring models to simultaneously act as precise archivists for static backgrounds and vigilant trackers for dynamic subjects, ensuring motion continuity during out-of-view intervals. To facilitate research in this direction, we construct HM-World, the first large-scale video dataset dedicated to hybrid memory. It features 59K high-fidelity clips with decoupled camera and subject trajectories, encompassing 17 diverse scenes, 49 distinct subjects, and meticulously designed ex...

• 1.How NYU’s Quantum Institute Bridges Science and Application

  This sponsored article is brought to you by NYU Tandon School of Engineering . Within a 6 mile radius of New York University’s (NYU) campus, there are more than 500 tech industry giants, banks, and hospitals. This isn’t just a fact about real estate, it’s the foundation for advancing quantum discovery and application. While the world races to harness quantum technology, NYU is betting that the ultimate advantage lies not solely in a lab, but in the dense, demanding, and hyper-connected urban ecosystem that surrounds it. With the launch of its NYU Quantum Institute (NYUQI), NYU is positioning itself as the central node in this network; a “full stack” powerhouse built on the conviction that it has found the right place, and the right time, to turn quantum science into tangible reality. Proximity advantage is essential because quantum scienc...

• 2.Training Driving AI at 50,000× Real Time

  This is a sponsored article brought to you by General Motors. Visit their new Engineering Blog for more insights. Autonomous driving is one of the most demanding problems in physical AI. An automated system must interpret a chaotic, ever-changing world in real time—navigating uncertainty, predicting human behavior, and operating safely across an immense range of environments and edge cases. At General Motors, we approach this problem from a simple premise: while most moments on the road are predictable, the rare, ambiguous, and unexpected events — the long tail — are what ultimately defines whether an autonomous system is safe, reliable, and ready for deployment at scale. (Note: While here we discuss research and emerging technologies to solve the long tail required for full general autonomy, we also discuss our current approach or solvin...

• 3.What Happens When You Host an AI Café

  “Can I get an interview?” “Can I get a job when I graduate?” Those questions came from students during a candid discussion about artificial intelligence, capturing the anxiety many young people feel today. As companies adopt AI-driven interview screeners, restructure their workforces, and redirect billions of dollars toward AI infrastructure , students are increasingly unsure of what the future of work will look like. We had gathered people together at a coffee shop in Auburn, Alabama, for what we called an AI Café. The event was designed to confront concerns about AI directly, demystifying the technology while pushing back against the growing narrative of technological doom. AI is reshaping society at breathtaking speed. Yet the trajectory of this transformation is being charted primarily by for-profit tech companies, whose priorities re...

• 4.These AI Workstations Look Like PCs but Pack a Stronger Punch

  The rise of generative AI has spurred demand for AI workstations that can run or train models on local hardware. Yet modern PCs have proven inadequate for this task . A typical laptop has only enough memory to load a large language model (LLM) with 8 billion to 13 billion parameters—much smaller, and much less intelligent, than frontier models that are presumed to have over a trillion parameters. Even the most capable workstation PCs struggle to serve LLMs with more than 70 billion parameters. Tenstorrent’s QuietBox 2 is an attempt to fill that gap. Though it looks like a PC workstation, the QuietBox 2 contains four of the company’s custom Blackhole AI accelerators, 128 gigabytes of GDDR6 memory—specialized memory used in GPUs—and 256 GB of DDR5 system memory (for a total of 384 GB). This configuration provides enough memory to load OpenA...

• 5.The Coming Drone-War Inflection in Ukraine

  WHEN KYIV-BORN ENGINEER Yaroslav Azhnyuk thinks about the future, his mind conjures up dystopian images. He talks about “swarms of autonomous drones carrying other autonomous drones to protect them against autonomous drones, which are trying to intercept them, controlled by AI agents overseen by a human general somewhere.” He also imagines flotillas of autonomous submarines, each carrying hundreds of drones, suddenly emerging off the coast of California or Great Britain and discharging their cargoes en masse to the sky. “How do you protect from that?” he asks as we speak in late December 2025; me at my quiet home office in London, he in Kyiv, which is bracing for another wave of missile attacks . Azhnyuk is not an alarmist. He cofounded and was formerly CEO of Petcube , a California-based company that uses smart cameras and an app to let ...

• 1.How Morningstar’s CEO Drives Relentless Execution

  Aleksandar Savic Many investors rely on Morningstar for independent financial analysis and insights, but few people are familiar with the company behind the ratings. From Morningstar’s origins rating mutual funds, the company has expanded its product line, customer base, and global footprint and realized a tenfold increase in revenues and profits between 2005 and 2025. […]

• 2.An AI Reckoning for HR: Transform or Fade Away

  Carolyn Geason-Beissel/MIT SMR | Getty Images For decades, human resource leaders have talked about the need to shift their focus from having responsibility for compliance to acting as architects of talent strategy. And for decades, the pattern of HR being stuck in age-old roles has persisted. But there is new pressure to redefine the role. […]

• 3.Shifting AI From Fear to Optimism: U.S. Department of Labor’s Taylor Stockton

  In this episode of the Me, Myself, and AI podcast, host Sam Ransbotham speaks with Taylor Stockton, chief innovation officer at the U.S. Department of Labor, about how artificial intelligence is reshaping the workforce. Taylor emphasizes that AI is having an economywide impact, transforming tasks within nearly every job rather than affecting only certain industries […]

• 4.Why Leaders Lose the Room in High-Stakes Meetings

  Carolyn Geason-Beissel/MIT SMR | Getty Images Most advice about leadership communication focuses on presentation skills: Be concise, be clear, tell better stories. But the most consequential leadership communication happens in meetings where tough issues are being discussed and real decisions are being made. Even some of the most skilled leaders find themselves in moments where […]

• 5.How Goldman Sachs Stays Agile: HR Leader Jacqueline Arthur

  Aleksandar Savic After World War II, Goldman Sachs ranked 10th among the top 30 U.S. investment banks. Twenty-seven of those once-mighty Wall Street rivals, including Salomon, Lehman, and First Boston, have been relegated to the annals of business history. Goldman, in contrast, is a global powerhouse, employing more than 46,000 people, operating in more than […]

• 1.Medicaid Coverage for Obesity Medications: Utilization and Net-of-Rebate Spending -- by Coady Wing, Wei-Lun Lo, Maddie Potter, Tarik Yuce, Alberto Ortega, John Cawley, Thuy D. Nguyen, Kosali I. Simon

  We document state variation in Medicaid coverage for obesity-indicated GLP-1 medications over time, and use a stacked difference-in-differences design to estimate the effects of coverage on utilization and net-of-rebate spending. Nine quarters out, coverage increases prescriptions for obesity-indicated GLP-1 medications by 0.82 per 100 enrollee-months (SE = 0.10). Coverage had no effect on GLP-1 prescribing for diabetes or cardiovascular indications, suggesting that off-label prescribing of diabetes formulations for obesity is not very common in the Medicaid program. The expansions do not appear to affect consumer spending at major online GLP-1 compounding firms, which suggests that the utilization response in our main analysis reflects new utilization rather than crowd-out. We find that coverage increases net-of-rebate Medicaid spending ...

• 2.Reserve Demand Estimation with Minimal Theory -- by Ricardo Lagos, Gastón Navarro

  We propose a new reserve-demand estimation strategy---a middle ground between atheoretical reduced-form econometric approaches and fully structural quantitative-theoretic approaches. The strategy consists of an econometric specification that satisfies core restrictions implied by theory and controls for changes in administered-rate spreads that induce rotations and shifts in reserve demand. The resulting approach is as user-friendly as existing reduced-form econometric methods but improves upon them by incorporating a minimal set of theoretical restrictions that any reserve demand must satisfy. We apply this approach to U.S. data and obtain reserve-demand estimates that are broadly consistent with the structural estimates.

• 3.Identifying Uncertainty, Learning about Productivity, and Human Capital Acquisition: A Reassessment of Labor Market Sorting and Firm Monopsony Power -- by Cristina Gualdani, Elena Pastorino, Áureo de Paula, Sergio Salgado

  We examine the empirical content of a large class of dynamic matching models of the labor market with ex-ante heterogeneous firms and workers, symmetric uncertainty and learning about workers’ productivity, and firms’ monopsony power. We allow workers’ human capital, acquired before and after entry into the labor market, to be general across firms to varying degrees. Such a framework nests and extends known models of worker turnover across firms, occupational choice, wage growth, wage differentials across occupations, firms, and industries, and wage dispersion across workers and over the life cycle. We establish intuitive conditions under which the model primitives are semiparametrically identified solely from data on workers’ wages and jobs, despite the dynamics of these models giving rise to complex patterns of selection based on endoge...

• 4.Financial Conditions Targeting in a Multi-Asset Open Economy -- by Ricardo J. Caballero, Alp Simsek

  We analyze monetary policy responses to noisy financial conditions in an open economy where exchange rates and domestic asset prices affect aggregate demand. Noise traders operate in both markets, and specialized arbitrageurs have limited risk-bearing capacity. Monetary policy creates cross-market spillovers: by adjusting the interest rate to stabilize one market, the central bank influences volatility in the other. We show that targeting a financial conditions index (FCI)—a weighted average of exchange rates and domestic asset prices—delivers substantial macroeconomic benefits. FCI targeting commits the central bank to respond to unexpected movements in financial conditions beyond what discretionary monetary policy implies. These stronger responses improve diversification across markets: each market becomes more exposed to external shock...

• 5.Standardized Test Scores and Academic Performance at a Public University System -- by Theodore J. Joyce, Mina Afrouzi Khosroshahi, Sarah Truelsch, Kerstin Gentsch, Kyle Du

  Recent studies of Ivy-Plus institutions suggest that standardized test scores (SAT/ACT) are far better predictors of college success than high school grade point average (HS-GPA), prompting a return to the requirement that test scores be submitted for admission at elite colleges. We ask whether re-establishing the SAT requirement for admission at a large urban public university system would improve the predictability of academic outcomes. Using administrative data for the 2010-2019 first-year cohorts, we update earlier work of students from public universities as to the relative predictive power of HSGPA and SAT scores on first-year outcomes and graduation rates. Contrary to findings at elite private institutions, we find that HSGPA is the dominant predictor of academic success in this public system. A one-standard-deviation increase in H...

• 1.Sports Betting Is Everywhere, Especially on Credit Reports

  Since 2018, more than thirty states have legalized mobile sports betting, leading to more than a half trillion dollars in wagers. In our recent Staff Report, we examine how legalized sports betting affects household financial health by comparing betting activity and consumer credit outcomes between states that legalized to those that have not. We find that legalization increases spending at online sportsbooks roughly tenfold, but betting does not stop at state boundaries. Nearby areas where betting is not legal still experience roughly 15 percent the increase of counties where it is legal. At the same time, consumer financial health suffers. Our analysis finds rising delinquencies in participating states,...

• 2.China’s Electric Trade

  China has spent considerable government resources to develop advanced electric technology industries, such as those that produce electric vehicles, lithium batteries, and solar panels. These efforts have spilled over to international trade as improvements in price and quality have increased the global demand for these goods. One consequence is that passenger cars and batteries have been disproportionately large contributors to the rise in the country’s trade surplus in recent years. This has not been the case, though, for solar panels, as falling prices due to a supply glut pulled down export revenues despite higher volumes.

• 3.The New York Fed DSGE Model Forecast—March 2026

  This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model. We describe very briefly our forecast and its change since December 2025. To summarize, growth in 2026 is expected to be more robust, and inflation more persistent, than predicted in December. Stronger investment is the main driver for higher growth, while cost-push shocks, possibly capturing the effects of tariffs, are the key factors behind higher inflation. Projections for the short-run real natural rate of interest (r*) are the same as in December.

• 4.Firms’ Inflation Expectations Return to 2024 Levels

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

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

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

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RCR Wireless

Semantic Scholar – Machine Learning

Telecom & 6G AI

arXiv Quantitative Finance

arXiv – 6G & Networking

arXiv – Network Architecture (6G/Slicing)