Later than expected, Google has begun rolling a pair of new Gemini Live features to Pixel 9 and Samsung Galaxy S25 devices. The company first previewed Gemini live video and screensharing during Mobile World Congress in March. As you might have guessed from their names, the two features allow you to take advantage of Gemini's multi-modal capabilities to ask Google's chatbot questions about what you see in front of your or on your phone's screen. It's here: ask Gemini about anything you see. If you don't own a Pixel 9 or Galaxy S25, Google says you can still access the new features through the Gemini app on Android.

OpenAI on Monday said it raised 40 billion in a new funding round that valued the ChatGPT maker at 300 billion, the biggest capital-raising session ever for a startup. The infusion of cash comes in a partnership with Japanese investment giant SoftBank Group and "enables us to push the frontiers of AI research even further," the San Francisco-based company said in a post on its website. "Their support will help us continue building AI systems that drive scientific discovery, enable personalized education, enhance human creativity, and pave the way toward AGI (artificial general intelligence) that benefits all of humanity," the company said.

Abstract: We demonstrate the first cross - domain cross - layer level - 4 autonomous optical network via a multi - AI - agent system. Field trials show ~ 9 8 % task completion rate across the distributed AI training lifecycle -- 3.2 higher than single agents using state - of - the - art LLMs. Since collaborative resource utilization across distributed facilities is essential for training workloads, t his evolution introduces significant complexity in network management, as controller s must operate across multiple domains, spanning from intra - and inter - datacenter s to long - haul wide area networks . Moreover, distributed training impose s stringent reliability requirements as it should restart from the checkpoint if a failure happens [ 2 ] . T herefore, in terms of distributed training communications, resilient operations and rapid fault recovery are essential .

With the recent advancements in wireless technologies, forecasting electromagnetic field (EMF) exposure has become critical to enable proactive network spectrum and power allocation, as well as network deployment planning. In this paper, we develop a deep learning (DL) time series forecasting framework referred to as \textit{EMForecaster}. The proposed DL architecture employs patching to process temporal patterns at multiple scales, complemented by reversible instance normalization and mixing operations along both temporal and patch dimensions for efficient feature extraction. We augment {EMForecaster} with a conformal prediction mechanism, which is independent of the data distribution, to enhance the trustworthiness of model predictions via uncertainty quantification of forecasts. This conformal prediction mechanism ensures that the ground truth lies within a prediction interval with target error rate $\alpha$, where $1-\alpha$ is referred to as coverage. However, a trade-off exists, as increasing coverage often results in wider prediction intervals. To address this challenge, we propose a new metric called the \textit{Trade-off Score}, that balances trustworthiness of the forecast (i.e., coverage) and the width of prediction interval. Our experiments demonstrate that EMForecaster achieves superior performance across diverse EMF datasets, spanning both short-term and long-term prediction horizons. In point forecasting tasks, EMForecaster substantially outperforms current state-of-the-art DL approaches, showing improvements of 53.97\% over the Transformer architecture and 38.44\% over the average of all baseline models. EMForecaster also exhibits an excellent balance between prediction interval width and coverage in conformal forecasting, measured by the tradeoff score, showing marked improvements of 24.73\% over the average baseline and 49.17\% over the Transformer architecture.

The Open Radio Access Network (O-RAN) architecture is reshaping telecommunications by promoting openness, flexibility, and intelligent closed-loop optimization. By decoupling hardware and software and enabling multi-vendor deployments, O-RAN reduces costs, enhances performance, and allows rapid adaptation to new technologies. A key innovation is intelligent network slicing, which partitions networks into isolated slices tailored for specific use cases or quality of service requirements. The RAN Intelligent Controller further optimizes resource allocation, ensuring efficient utilization and improved service quality for user equipment (UEs). However, the modular and dynamic nature of O-RAN expands the threat surface, necessitating advanced security measures to maintain network integrity, confidentiality, and availability. Intrusion detection systems have become essential for identifying and mitigating attacks. This research explores using large language models (LLMs) to generate security recommendations based on the temporal traffic patterns of connected UEs. The paper introduces an LLM-driven intrusion detection framework and demonstrates its efficacy through experimental deployments, comparing non fine-tuned and fine-tuned models for task-specific accuracy.

Large language models (LLMs) have made significant progress in general-purpose natural language processing tasks. However, LLMs are still facing challenges when applied to domain-specific areas like telecommunications, which demands specialized expertise and adaptability to evolving standards. This paper presents a novel framework that combines knowledge graph (KG) and retrieval-augmented generation (RAG) techniques to enhance LLM performance in the telecom domain. The framework leverages a KG to capture structured, domain-specific information about network protocols, standards, and other telecom-related entities, comprehensively representing their relationships. By integrating KG with RAG, LLMs can dynamically access and utilize the most relevant and up-to-date knowledge during response generation. This hybrid approach bridges the gap between structured knowledge representation and the generative capabilities of LLMs, significantly enhancing accuracy, adaptability, and domain-specific comprehension. Our results demonstrate the effectiveness of the KG-RAG framework in addressing complex technical queries with precision. The proposed KG-RAG model attained an accuracy of 88% for question answering tasks on a frequently used telecom-specific dataset, compared to 82% for the RAG-only and 48% for the LLM-only approaches.

--Traffic engineering (TE) in large-scale computer networks has become a fundamental yet challenging problem, owing to the swift growth of global-scale cloud wide-area networks or backbone low-Earth-orbit satellite constellations. T o address the scalability issue of traditional TE algorithms, learning-based approaches have been proposed, showing potential of significant efficiency improvement over state-of-the-art methods. Nevertheless, the intrinsic limitations of existing learning-based methods hinder their practical application: they are not generalizable across diverse topologies and network conditions, incur excessive training overhead, and do not respect link capacities by default. This paper proposes TELGEN, a novel TE algorithm that learns to solve TE problems efficiently in large-scale networks, while achieving superior generalizability across diverse network conditions. TELGEN is based on the novel idea of transforming the problem of "predicting the optimal TE solution" into "predicting the optimal TE algorithm", which enables TELGEN to learn and efficiently approximate the end-to-end solving process of classical optimal TE algorithms. The learned algorithm is agnostic to the exact network topology or traffic patterns, and can efficiently solve TE problems given arbitrary inputs and generalize well to unseen topologies and demands. TELGEN achieved less than 3% optimality gap while ensuring feasibility in all cases, even when the test network had up to 20 more nodes than the largest in training. It also saved up to 84% solving time than classical optimal solver, and could reduce training time per epoch and solving time by 2 -4 orders of magnitude than latest learning algorithms on the largest networks. Traffic Engineering (TE) is becoming increasingly crucial amid the exponential growth in Internet traffic. Xue (xue@asu.edu) is with the School of Computing and Augmented Intelligence at the Arizona State University, Tempe, AZ, 85287, USA. The research of Zhou and Y u was supported in part by NSF grants 2045539 and 2433966. The research of Xue was sponsored in part by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-23-2-0225. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. Personal use of this material is permitted. Usually, TE is implemented by a central controller that has a global view of the network and can make informed decisions about routing and traffic splitting to optimize traffic [26]. With the emergence of large-scale and dynamic networks, classical TE faces fundamental challenges in terms of scalability, responsiveness and performance.

--This paper explores the design of beamforming codebooks for the base station (BS) and for the reconfigurable intelligent surfaces (RISs) in an active sensing scheme for uplink localization, in which the mobile user transmits a sequence of pilots to the BS through reflection at the RISs, and the BS and the RISs are adaptively configured by carefully choosing BS beamforming codeword and RIS codewords from their respective codebooks in a sequential manner to progressively focus onto the user . Most existing codebook designs for RIS are not tailored for active sensing, by which we mean the choice of the next codeword should depend on the measurements made so far, and the sequence of codewords should dynamically focus reflection toward the user . Moreover, most existing codeword selection methods rely on exhaustive search in beam training to identify the codeword with the highest signal-to-noise ratio (SNR), thus incurring substantial pilot overhead as the size of the codebook scales. This paper proposes a learning-based approach for codebook construction and for codeword selection for active sensing. The proposed learning approach aims to locate a target in the service area by recursively selecting a sequence of BS beamforming codewords and RIS codewords from the respective codebooks as more measurements become available without exhaustive beam training. The codebook design and the codeword selection fuse key ideas from the vector quantized variational autoencoder (VQ-V AE) and the long short-term memory (LSTM) network to learn respectively the discrete function space of the codebook and the temporal dependencies between measurements. The device is typically placed in the reflecting path between the transceivers, with its configuration wirelessly controlled by the transceivers via a control link. Manuscript submitted to IEEE Transactions on Wireless Communications on September 6, 2024, revised on January 12, 2025, accepted on March 5, 2025. Wei Y u is with The Edward S. Rogers Sr. This work is supported by the Natural Sciences and Engineering Research Council of Canada via the Canada Research Chairs program. The materials in this paper have been accepted in part at the IEEE Workshop on Signal Processing Advances in Wireless Communications (SP A WC), Lucca, Italy, September 2024 [1]. Codebook-based limited control link rate protocol can substantially reduce the control overhead [7], [8]. With the RIS codebook stored at the controller and at the RIS, the controller only needs to send the codeword index in order to configure the RIS.

Radio-based localization in dynamic environments, such as urban and vehicular settings, requires systems that can efficiently adapt to varying signal conditions and environmental changes. Factors such as multipath interference and obstructions introduce different levels of complexity that affect the accuracy of the localization. Although generalized models offer broad applicability, they often struggle to capture the nuances of specific environments, leading to suboptimal performance in real-world deployments. In contrast, specialized models can be tailored to particular conditions, enabling more precise localization by effectively handling domain-specific variations and noise patterns. However, deploying multiple specialized models requires an efficient mechanism to select the most appropriate one for a given scenario. In this work, we develop an adaptive localization framework that combines shallow attention-based models with a router/switching mechanism based on a single-layer perceptron (SLP). This enables seamless transitions between specialized localization models optimized for different conditions, balancing accuracy, computational efficiency, and robustness to environmental variations. We design three low-complex localization models tailored for distinct scenarios, optimized for reduced computational complexity, test time, and model size. The router dynamically selects the most suitable model based on real-time input characteristics. The proposed framework is validated using real-world vehicle localization data collected from a massive MIMO base station (BS), demonstrating its ability to seamlessly adapt to diverse deployment conditions while maintaining high localization accuracy.

OpenAI is close to finalizing a 40 billion ( 6 trillion) funding round led by SoftBank Group -- with investors including Magnetar Capital, Coatue Management, Founders Fund and Altimeter Capital Management in talks to participate, according to people familiar with the matter. Magnetar Capital -- an Evanston, Illinois-based hedge fund -- could contribute up to 1 billion, according to multiple people, all of whom asked not to be identified because the information is private. The artificial intelligence developer's funding round would be the largest of all time, according to data compiled by research firm PitchBook. The deal is set to value the company at 300 billion including dollars raised -- almost double the ChatGPT maker's previous valuation of 157 billion from when it raised money in October.