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GENERATIVE NEURAL NETWORKS WITH INVISIBLE TOKENS

Resumen de: WO2026128716A1

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for processing a network input using a generative neural network to generate an output sequence of output tokens. The system selects each output token from a vocabulary of tokens that includes a plurality of visible tokens and one or more pairs of invisible tokens. The system processes the output sequence of output tokens to generate a final output sequence by removing, from the output sequence, the beginning invisible token, the end invisible token, and each visible token that is between the beginning invisible token and the end invisible token. The system then provides the final output sequence in response to the network input.

DEEP LEARNING-ENHANCED CHEMILUMINESCENCE VERTICAL FLOW ASSAY FOR HIGH-SENSITIVITY ANALYTE DETECTION

Resumen de: WO2026128643A1

A method of measuring analyte(s) or biomarker(s) in a sample uses a cartridge-based vertical flow assay with a sensing membrane located within one or more cartridges and populated with a plurality of spots containing one or more capture agent(s). A mixture of the sample and detection reagents along with a chemiluminescence (CL) reagent solution are input into the one or more cartridges and the sensing membrane is imaged with a reader device configured to obtain one or more CL images and/or CL signals for the plurality of spots. The one or more images and/or CL signals for the plurality of spots are processed with an algorithm including machine learning or one or more trained neural networks configured to generate one or more outputs that include a classification of the sample and/or a quantification of the amount or concentration of the analyte(s) or biomarker(s) in the sample.

SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE AGENTS FOR FACTUAL CONSISTENT GENERATION

Resumen de: US20260169977A1

0000 Embodiments described herein provide a method for enhancing factual consistency of an artificial intelligence (AI) agent. The method includes obtaining a first dataset of documents and corresponding seed summaries; generating, by a first neural network based language model, inconsistent summaries by replacing texts in one or more seed summaries; forming a second dataset of documents and corresponding inconsistent summaries for evaluating a second neural network based language model; and generating, by the second neural network based language model, a detection of a factual inconsistency and/or an explanation of the factual inconsistency. The method further includes generating, by a third neural network based language model, an evaluation score indicating an accuracy level of the detected factual inconsistency based at least in part on the explanation; and building, at a server the AI agent employing the second neural network based language model when the evaluation score is greater than a threshold.

SYSTEMS, METHODS AND NODES IN DISTRIBUTED INFERENCE NETWORKS

Resumen de: WO2026123241A1

In some examples, a node in a distributed system can comprise multiple nodes configured for neural network inference, the node comprising at least one processor, and at least one memory including computer program code, wherein the multiple nodes are communicatively interconnected with one another for inter-node collective communication operations, the at least one memory and computer program code being configured to, with the at least one processor, cause the node to prefetch data to a second storage of the node during a collective communication operation of the multiple nodes.

Instance-Level Segmentation Applied to Dynamic Graphs for Enhanced Model Explainability

Resumen de: US20260170294A1

0000 This invention discloses a novel method for enhancing the explainability of graph neural networks (GNNs) applied to dynamic graphs. The method leverages instance-level segmentation to identify and segment important subgraphs or clusters of nodes that contribute significantly to the GNN's prediction. By generating explanations based on these segmented instances, the invention provides a more granular and dynamic understanding of the GNN's decision-making process. This approach offers enhanced granularity, dynamic explainability, and improved interpretability, enabling users to gain a deeper understanding of the GNN's behavior and build trust in its predictions.

ADVERSARIAL EXAMPLE PURIFICATION METHOD BASED ON CONDITIONAL DIFFUSION MODEL

Resumen de: WO2026123388A1

Disclosed in the present application is an adversarial example purification method based on a conditional diffusion model, comprising the following steps: acquiring a clean example dataset containing clean examples; using a white-box attack algorithm to attack a classification model to generate an adversarial example for each clean example; pairing the clean examples and the adversarial examples in one-to-one correspondence to form a training dataset; acquiring a pre-trained Stable Diffusion model; designing a fine-tuning process and a fine-tuning loss function; S6, presetting the number of iterations and a batch size, and using the training dataset to fine-tune network parameters of a cross-attention layer in the Stable Diffusion model to obtain a fine-tuned conditional diffusion model for adversarial example purification. In the present invention, an adversarial example is inputted as condition information into a UNet neural network to guide model learning, enabling the model to learn features of an adversarial perturbation, and improving computational efficiency, robustness and stability.

MULTI-AGENT REINFORCEMENT LEARNING WITH MATCHMAKING POLICIES

Resumen de: US20260170334A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a policy neural network having a plurality of policy parameters and used to select actions to be performed by an agent to control the agent to perform a particular task while interacting with one or more other agents in an environment. In one aspect, the method includes: maintaining data specifying a pool of candidate action selection policies; maintaining data specifying respective matchmaking policy; and training the policy neural network using a reinforcement learning technique to update the policy parameters. The policy parameters define policies to be used in controlling the agent to perform the particular task.

COMPUTER-READABLE RECORDING MEDIUM HAVING STORED THEREIN DATA SELECTION PROGRAM, INFORMATION PROCESSING APPARATUS, AND COMPUTER-IMPLEMENTED DATA SELECTION METHOD

Resumen de: US20260170336A1

A non-transitory computer-readable recording medium having stored therein a data selection program that causes a computer to execute a process including determining, for each of a plurality of pieces of graph structure data, a weight in data selection based on the number of adjacent nodes shared by two nodes for a set of the two nodes included in the graph structure data, and selecting training data to be used for training a neural network that predicts a presence or absence of a link between nodes included in the graph structure data input as input data from among the plurality of pieces of graph structure data based on the determined weight and information indicating a presence or absence of a link between nodes for each of one or more sets of two nodes.

CHIP SYSTEM, IMAGE PROCESSING METHOD AND ELECTRONIC DEVICE

Resumen de: EP4760630A1

Embodiments of this application provide a chip system, an image processing method, and an electronic device, and are applied to the field of chip image processing technologies. The chip system includes a graphics processing unit, a neural-network processing unit, and a scheduling circuit. The graphics processing unit is configured to output a first rendered image based on an image subtask. The scheduling circuit is configured to indicate, based on a scheduling subtask, the neural-network processing unit to obtain the first rendered image. The neural-network processing unit is configured to: based on a computing subtask, perform neural network algorithm processing on the first rendered image, and output a computing result. The scheduling circuit is further configured to indicate, based on the scheduling subtask, the graphics processing unit to obtain the computing result. The graphics processing unit is further configured to perform image processing on the computing result based on the image subtask to obtain a second rendered image. In embodiments of this application, image processing is performed based on multi-IP coordination to improve imaging quality. This manner reduces power consumption and processing complexity of the chip system, and is applicable to different chip architectures.

INFRINGEMENT DETECTION METHOD, DEVICE AND SYSTEM

Resumen de: EP4760634A2

A method for detecting an infringement by vehicle operator is described. The method comprises detecting a vehicle; receiving one or more image of at least a part of the vehicle operator; automatically analysing with a neural network the one or more captured received image to detect an infringing act; and providing the one or more captured received images comprising the detected infringing act to thereby detect the infringement. Also described are a system, a device, a computer system and a computer program product all for detecting an infringement by a vehicle operator. The device may comprise one or more flash for illuminating the vehicle or a part thereof with light at a narrow band and one or more camera comprising a narrow band filter that lets through only the wavelengths of light produced by the one or more flash.

TRAINING NEURAL NETWORKS FOR SENSOR DATA REPRESENTATION LEARNING THROUGH FUTURE FRAME PREDICTION

Resumen de: EP4760597A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining respective sensor data captured by one or more sensors of an autonomous vehicle at each of a sequence of time steps, the sequence of time steps comprising one or more context time steps followed by one or more prediction time steps; generating respective ground truth birds-eye-view (BEV) representations of the respective sensor data for each of the prediction time steps; for each prediction time step, processing the respective sensor data at one or more preceding time steps in the sequence using a future prediction neural network to generate a predicted BEV representation for the prediction time step; and training the future prediction neural network based on, for each prediction time step, an error between the ground truth BEV representation for the prediction time step and the predicted BEV representation for the prediction time step.

TRAINING APPARATUS, ESTIMATION APPARATUS, TRAINING METHOD, ESTIMATION METHOD, AND PROGRAM

Resumen de: EP4760728A1

The present disclosure is designed to improve the accuracy of total energy estimation by neural network potential (NNP). An aspect of the present disclosure provides a learning apparatus including a control unit to carry out learning of an estimation model which is a mathematical model for energy estimation of an analysis target atom based on an evaluation target feature quantity indicating a first sum to a u-th sum, wherein the u-th sum is a sum of a three-dimensional (3D) wave function representing a u-th atomic orbital of the evaluation target atom located in a system including one or more atoms in descending order of occupied energy potential, and a 3D wave function of a u-th atomic orbital of each of other atoms in the system within a predetermined range of distances from the evaluation target in descending order of occupied energy potential, wherein in the learning, the estimation model is updated to reduce a difference between the sum of results of executing the estimation model for each atom in the system and the energy of the system estimated by density functional theory.

Configurable task prompts for neural network document transcription

Resumen de: GB2702481A

A processor comprises circuits causing a neural network 320 to generate a document transcription 354 of a document image 321 according to a configurable combination of annotation types 325 provided within input 302 to the neural network, wherein the document transcription comprises respective annotations of the annotation types for corresponding portions of content included in the document transcription. The document transcription may comprise a sequence of tokens, wherein the respective annotations correspond to description tokens in the sequence and the portions of content correspond to content tokens. The configurable combination of annotation types may comprise respective indicators that enable or disable individual annotation types of the plurality of annotation types. Annotation corresponding to disabled annotation types are not included in the document transcription. Annotation types may be specified in a multi-dimensional tuple and comprise: bounding boxes, semantic class labels, structured text, or plain text. The neural network may comprise a Vision Transformer (ViT) encoder 322, a compressor 324, and a decoder 326, wherein the document image 321 is input to the ViT encoder and the configurable combination of annotation types 325 is input to the decoder, and wherein the decoder outputs the document transcription. figure 3

Radio frequency digital signal classification

Nº publicación: GB2702430A 17/06/2026

Solicitante:

SECR DEFENCE [GB]
The Secretary of State for Defence

Resumen de: GB2702430A

The invention relates to the field of Radio Frequency (RF) signal classification and recognition, specifically a method of training a Convolutional Neural Network (CNN) and applying the trained CNN for the classification or identification of digitally modulated RF signals. The training comprises the steps of providing time-based training signals, generating IQ representations of those signals, applying k-means clustering to the IQ representations and generating a vector diagram by preserving timing indices of the IQ representations. A combined k-means cluster and vector image is generated (see Figure 3b). Once trained, the model is suitable for classifying digital signals, particularly digitally modulated communication signals. Figs. 2b & 3b