Category: News

Goldman Sachs tests autonomous AI agents for process-heavy workAI News Goldman Sachs is pushing deeper into real use of artificial intelligence inside its operations, moving to systems that can carry out complex tasks on their own. The Wall Street bank is working with AI startup Anthropic to create autonomous AI agents powered by Anthropic’s Claude model that can handle work that used to require large
The post Goldman Sachs tests autonomous AI agents for process-heavy work appeared first on AI News.

 Goldman Sachs is pushing deeper into real use of artificial intelligence inside its operations, moving to systems that can carry out complex tasks on their own. The Wall Street bank is working with AI startup Anthropic to create autonomous AI agents powered by Anthropic’s Claude model that can handle work that used to require large
The post Goldman Sachs tests autonomous AI agents for process-heavy work appeared first on AI News. Read More  

What AI can (and can’t) tell us about XRP in ETF-driven marketsAI News For a long time, cryptocurrency prices moved quickly. A headline would hit, sentiment would spike, and charts would react almost immediately. That pattern no longer holds. Today’s market is slow, heavier than before, and shaped by forces that do not always announce themselves clearly. Capital allocation, ETF mechanics, and macro positioning now influence price behaviour
The post What AI can (and can’t) tell us about XRP in ETF-driven markets appeared first on AI News.

 For a long time, cryptocurrency prices moved quickly. A headline would hit, sentiment would spike, and charts would react almost immediately. That pattern no longer holds. Today’s market is slow, heavier than before, and shaped by forces that do not always announce themselves clearly. Capital allocation, ETF mechanics, and macro positioning now influence price behaviour
The post What AI can (and can’t) tell us about XRP in ETF-driven markets appeared first on AI News. Read More  

Exclusive: Why are Chinese AI models dominating open-source as Western labs step back?AI News Because Western AI labs won’t—or can’t—anymore. As OpenAI, Anthropic, and Google face mounting pressure to restrict their most powerful models, Chinese developers have filled the open-source void with AI explicitly built for what operators need: powerful models that run on commodity hardware. A new security study reveals just how thoroughly Chinese AI has captured this space. Research published by SentinelOne
The post Exclusive: Why are Chinese AI models dominating open-source as Western labs step back? appeared first on AI News.

 Because Western AI labs won’t—or can’t—anymore. As OpenAI, Anthropic, and Google face mounting pressure to restrict their most powerful models, Chinese developers have filled the open-source void with AI explicitly built for what operators need: powerful models that run on commodity hardware. A new security study reveals just how thoroughly Chinese AI has captured this space. Research published by SentinelOne
The post Exclusive: Why are Chinese AI models dominating open-source as Western labs step back? appeared first on AI News. Read More  

Cryptocurrency markets a testbed for AI forecasting modelsAI News Cryptocurrency markets have become a high-speed playground where developers optimise the next generation of predictive software. Using real-time data flows and decentralised platforms, scientists develop prediction models that can extend the scope of traditional finance. The digital asset landscape offers an unparalleled environment for machine learning. When you track cryptocurrency prices today, you are observing
The post Cryptocurrency markets a testbed for AI forecasting models appeared first on AI News.

 Cryptocurrency markets have become a high-speed playground where developers optimise the next generation of predictive software. Using real-time data flows and decentralised platforms, scientists develop prediction models that can extend the scope of traditional finance. The digital asset landscape offers an unparalleled environment for machine learning. When you track cryptocurrency prices today, you are observing
The post Cryptocurrency markets a testbed for AI forecasting models appeared first on AI News. Read More  

Meet OAT: The New Action Tokenizer Bringing LLM-Style Scaling and Flexible, Anytime Inference to the Robotics WorldMarkTechPost Robots are entering their GPT-3 era. For years, researchers have tried to train robots using the same autoregressive (AR) models that power large language models (LLMs). If a model can predict the next word in a sentence, it should be able to predict the next move for a robotic arm. However, a technical wall has
The post Meet OAT: The New Action Tokenizer Bringing LLM-Style Scaling and Flexible, Anytime Inference to the Robotics World appeared first on MarkTechPost.

 Robots are entering their GPT-3 era. For years, researchers have tried to train robots using the same autoregressive (AR) models that power large language models (LLMs). If a model can predict the next word in a sentence, it should be able to predict the next move for a robotic arm. However, a technical wall has
The post Meet OAT: The New Action Tokenizer Bringing LLM-Style Scaling and Flexible, Anytime Inference to the Robotics World appeared first on MarkTechPost. Read More  

Bridging 6G IoT and AI: LLM-Based Efficient Approach for Physical Layer’s Optimization Taskscs.AI updates on arXiv.org arXiv:2602.06819v1 Announce Type: cross
Abstract: This paper investigates the role of large language models (LLMs) in sixth-generation (6G) Internet of Things (IoT) networks and proposes a prompt-engineering-based real-time feedback and verification (PE-RTFV) framework that perform physical-layer’s optimization tasks through an iteratively process. By leveraging the naturally available closed-loop feedback inherent in wireless communication systems, PE-RTFV enables real-time physical-layer optimization without requiring model retraining. The proposed framework employs an optimization LLM (O-LLM) to generate task-specific structured prompts, which are provided to an agent LLM (A-LLM) to produce task-specific solutions. Utilizing real-time system feedback, the O-LLM iteratively refines the prompts to guide the A-LLM toward improved solutions in a gradient-descent-like optimization process. We test PE-RTFV approach on wireless-powered IoT testbed case study on user-goal-driven constellation design through semantically solving rate-energy (RE)-region optimization problem which demonstrates that PE-RTFV achieves near-genetic-algorithm performance within only a few iterations, validating its effectiveness for complex physical-layer optimization tasks in resource-constrained IoT networks.

 arXiv:2602.06819v1 Announce Type: cross
Abstract: This paper investigates the role of large language models (LLMs) in sixth-generation (6G) Internet of Things (IoT) networks and proposes a prompt-engineering-based real-time feedback and verification (PE-RTFV) framework that perform physical-layer’s optimization tasks through an iteratively process. By leveraging the naturally available closed-loop feedback inherent in wireless communication systems, PE-RTFV enables real-time physical-layer optimization without requiring model retraining. The proposed framework employs an optimization LLM (O-LLM) to generate task-specific structured prompts, which are provided to an agent LLM (A-LLM) to produce task-specific solutions. Utilizing real-time system feedback, the O-LLM iteratively refines the prompts to guide the A-LLM toward improved solutions in a gradient-descent-like optimization process. We test PE-RTFV approach on wireless-powered IoT testbed case study on user-goal-driven constellation design through semantically solving rate-energy (RE)-region optimization problem which demonstrates that PE-RTFV achieves near-genetic-algorithm performance within only a few iterations, validating its effectiveness for complex physical-layer optimization tasks in resource-constrained IoT networks. Read More  

Optimal Abstractions for Verifying Properties of Kolmogorov-Arnold Networks (KANs)cs.AI updates on arXiv.org arXiv:2602.06737v1 Announce Type: cross
Abstract: We present a novel approach for verifying properties of Kolmogorov-Arnold Networks (KANs), a class of neural networks characterized by nonlinear, univariate activation functions typically implemented as piecewise polynomial splines or Gaussian processes. Our method creates mathematical “abstractions” by replacing each KAN unit with a piecewise affine (PWA) function, providing both local and global error estimates between the original network and its approximation. These abstractions enable property verification by encoding the problem as a Mixed Integer Linear Program (MILP), determining whether outputs satisfy specified properties when inputs belong to a given set. A critical challenge lies in balancing the number of pieces in the PWA approximation: too many pieces add binary variables that make verification computationally intractable, while too few pieces create excessive error margins that yield uninformative bounds. Our key contribution is a systematic framework that exploits KAN structure to find optimal abstractions. By combining dynamic programming at the unit level with a knapsack optimization across the network, we minimize the total number of pieces while guaranteeing specified error bounds. This approach determines the optimal approximation strategy for each unit while maintaining overall accuracy requirements. Empirical evaluation across multiple KAN benchmarks demonstrates that the upfront analysis costs of our method are justified by superior verification results.

 arXiv:2602.06737v1 Announce Type: cross
Abstract: We present a novel approach for verifying properties of Kolmogorov-Arnold Networks (KANs), a class of neural networks characterized by nonlinear, univariate activation functions typically implemented as piecewise polynomial splines or Gaussian processes. Our method creates mathematical “abstractions” by replacing each KAN unit with a piecewise affine (PWA) function, providing both local and global error estimates between the original network and its approximation. These abstractions enable property verification by encoding the problem as a Mixed Integer Linear Program (MILP), determining whether outputs satisfy specified properties when inputs belong to a given set. A critical challenge lies in balancing the number of pieces in the PWA approximation: too many pieces add binary variables that make verification computationally intractable, while too few pieces create excessive error margins that yield uninformative bounds. Our key contribution is a systematic framework that exploits KAN structure to find optimal abstractions. By combining dynamic programming at the unit level with a knapsack optimization across the network, we minimize the total number of pieces while guaranteeing specified error bounds. This approach determines the optimal approximation strategy for each unit while maintaining overall accuracy requirements. Empirical evaluation across multiple KAN benchmarks demonstrates that the upfront analysis costs of our method are justified by superior verification results. Read More  

RAPID: Reconfigurable, Adaptive Platform for Iterative Designcs.AI updates on arXiv.org arXiv:2602.06653v1 Announce Type: cross
Abstract: Developing robotic manipulation policies is iterative and hypothesis-driven: researchers test tactile sensing, gripper geometries, and sensor placements through real-world data collection and training. Yet even minor end-effector changes often require mechanical refitting and system re-integration, slowing iteration. We present RAPID, a full-stack reconfigurable platform designed to reduce this friction. RAPID is built around a tool-free, modular hardware architecture that unifies handheld data collection and robot deployment, and a matching software stack that maintains real-time awareness of the underlying hardware configuration through a driver-level Physical Mask derived from USB events. This modular hardware architecture reduces reconfiguration to seconds and makes systematic multi-modal ablation studies practical, allowing researchers to sweep diverse gripper and sensing configurations without repeated system bring-up. The Physical Mask exposes modality presence as an explicit runtime signal, enabling auto-configuration and graceful degradation under sensor hot-plug events, so policies can continue executing when sensors are physically added or removed. System-centric experiments show that RAPID reduces the setup time for multi-modal configurations by two orders of magnitude compared to traditional workflows and preserves policy execution under runtime sensor hot-unplug events. The hardware designs, drivers, and software stack are open-sourced at https://rapid-kit.github.io/ .

 arXiv:2602.06653v1 Announce Type: cross
Abstract: Developing robotic manipulation policies is iterative and hypothesis-driven: researchers test tactile sensing, gripper geometries, and sensor placements through real-world data collection and training. Yet even minor end-effector changes often require mechanical refitting and system re-integration, slowing iteration. We present RAPID, a full-stack reconfigurable platform designed to reduce this friction. RAPID is built around a tool-free, modular hardware architecture that unifies handheld data collection and robot deployment, and a matching software stack that maintains real-time awareness of the underlying hardware configuration through a driver-level Physical Mask derived from USB events. This modular hardware architecture reduces reconfiguration to seconds and makes systematic multi-modal ablation studies practical, allowing researchers to sweep diverse gripper and sensing configurations without repeated system bring-up. The Physical Mask exposes modality presence as an explicit runtime signal, enabling auto-configuration and graceful degradation under sensor hot-plug events, so policies can continue executing when sensors are physically added or removed. System-centric experiments show that RAPID reduces the setup time for multi-modal configurations by two orders of magnitude compared to traditional workflows and preserves policy execution under runtime sensor hot-unplug events. The hardware designs, drivers, and software stack are open-sourced at https://rapid-kit.github.io/ . Read More  

Which Graph Shift Operator? A Spectral Answer to an Empirical Questioncs.AI updates on arXiv.org arXiv:2602.06557v1 Announce Type: cross
Abstract: Graph Neural Networks (GNNs) have established themselves as the leading models for learning on graph-structured data, generally categorized into spatial and spectral approaches. Central to these architectures is the Graph Shift Operator (GSO), a matrix representation of the graph structure used to filter node signals. However, selecting the optimal GSO, whether fixed or learnable, remains largely empirical. In this paper, we introduce a novel alignment gain metric that quantifies the geometric distortion between the input signal and label subspaces. Crucially, our theoretical analysis connects this alignment directly to generalization bounds via a spectral proxy for the Lipschitz constant. This yields a principled, computation-efficient criterion to rank and select the optimal GSO for any prediction task prior to training, eliminating the need for extensive search.

 arXiv:2602.06557v1 Announce Type: cross
Abstract: Graph Neural Networks (GNNs) have established themselves as the leading models for learning on graph-structured data, generally categorized into spatial and spectral approaches. Central to these architectures is the Graph Shift Operator (GSO), a matrix representation of the graph structure used to filter node signals. However, selecting the optimal GSO, whether fixed or learnable, remains largely empirical. In this paper, we introduce a novel alignment gain metric that quantifies the geometric distortion between the input signal and label subspaces. Crucially, our theoretical analysis connects this alignment directly to generalization bounds via a spectral proxy for the Lipschitz constant. This yields a principled, computation-efficient criterion to rank and select the optimal GSO for any prediction task prior to training, eliminating the need for extensive search. Read More