Quantum error correction must detect and correct errors without directly reading the quantum information. Classical low-density parity check (LDPC) codes have a well-established design theory: by choosing how many checks connect to each bit, retaining appropriate randomness, and avoiding short loops, designers can pursue both a large minimum distance and a threshold phenomenon in which the decoding failure rate drops sharply below a predicted noise level.Quantum error correction must detect and correct errors without directly reading the quantum information. Classical low-density parity check (LDPC) codes have a well-established design theory: by choosing how many checks connect to each bit, retaining appropriate randomness, and avoiding short loops, designers can pursue both a large minimum distance and a threshold phenomenon in which the decoding failure rate drops sharply below a predicted noise level.[#item_full_content]

Reconstructing a three-dimensional object or environment from multiple photographs is a central problem in computer vision. The resulting models support applications such as robotics, augmented and virtual reality, digital twins, and cultural heritage preservation. An effective reconstruction system must represent both the visual appearance of a scene and the geometry of its surfaces. Achieving these goals simultaneously, however, remains challenging.Reconstructing a three-dimensional object or environment from multiple photographs is a central problem in computer vision. The resulting models support applications such as robotics, augmented and virtual reality, digital twins, and cultural heritage preservation. An effective reconstruction system must represent both the visual appearance of a scene and the geometry of its surfaces. Achieving these goals simultaneously, however, remains challenging.[#item_full_content]

A research team led by Sung Wook Baik, a professor in the Department of Software at Sejong University, has published a comprehensive survey examining annotation errors in widely used object detection datasets and methods for identifying and validating them.A research team led by Sung Wook Baik, a professor in the Department of Software at Sejong University, has published a comprehensive survey examining annotation errors in widely used object detection datasets and methods for identifying and validating them.[#item_full_content]

The results are in: Which AI model is the most fallible? Persuadable? Correctible? University of Arizona researchers assessed seven different generative AI large language models, or LLMs, for these three qualities during lengthy conversations. Their work, published in Nature’s Scientific Reports, reveals intrinsic limitations that might go undetected during one-off interactions.The results are in: Which AI model is the most fallible? Persuadable? Correctible? University of Arizona researchers assessed seven different generative AI large language models, or LLMs, for these three qualities during lengthy conversations. Their work, published in Nature’s Scientific Reports, reveals intrinsic limitations that might go undetected during one-off interactions.[#item_full_content]

While many fear artificial intelligence will replace humans, using AI to take over some human roles has benefits. Companies can use the technology to conduct surveys and polls, while behavioral scientists can run experiments on digital twins to gather faster insights without risking harm or distress to real participants.While many fear artificial intelligence will replace humans, using AI to take over some human roles has benefits. Companies can use the technology to conduct surveys and polls, while behavioral scientists can run experiments on digital twins to gather faster insights without risking harm or distress to real participants.[#item_full_content]

“Find the best-selling product from last year.” When an AI system attempts to answer a question like this by querying a company database, even a single reference to a nonexistent item can cause the query to fail. Until now, correcting such an error often required regenerating the entire SQL query from scratch.”Find the best-selling product from last year.” When an AI system attempts to answer a question like this by querying a company database, even a single reference to a nonexistent item can cause the query to fail. Until now, correcting such an error often required regenerating the entire SQL query from scratch.[#item_full_content]

Reliable navigation, place recognition and object interaction require autonomous robots to maintain precise environmental maps. Semantic simultaneous localization and mapping (SLAM) adds meaning to a robot’s map by representing landmarks as recognizable objects rather than only geometric points.Reliable navigation, place recognition and object interaction require autonomous robots to maintain precise environmental maps. Semantic simultaneous localization and mapping (SLAM) adds meaning to a robot’s map by representing landmarks as recognizable objects rather than only geometric points.[#item_full_content]

Large language models (LLMs), the artificial intelligence systems underpinning the functioning of ChatGPT, Gemini and other similar conversational agents, are now widely used worldwide. In addition to processing, interpreting and generating texts, some of these models can solve basic logical problems and answer some user questions with striking accuracy.Large language models (LLMs), the artificial intelligence systems underpinning the functioning of ChatGPT, Gemini and other similar conversational agents, are now widely used worldwide. In addition to processing, interpreting and generating texts, some of these models can solve basic logical problems and answer some user questions with striking accuracy.[#item_full_content]

Researchers from Skoltech and Sberbank’s Center for Practical Artificial Intelligence have proposed a new method, TOHA, for detecting hallucinations in large language models operating in retrieval-augmented generation (RAG) systems. The approach analyzes the topological structure of a model’s attention maps and makes it possible to identify responses that are not supported by the provided context. The method does not require training additional models and uses only a small amount of annotated data for configuration.Researchers from Skoltech and Sberbank’s Center for Practical Artificial Intelligence have proposed a new method, TOHA, for detecting hallucinations in large language models operating in retrieval-augmented generation (RAG) systems. The approach analyzes the topological structure of a model’s attention maps and makes it possible to identify responses that are not supported by the provided context. The method does not require training additional models and uses only a small amount of annotated data for configuration.[#item_full_content]

Computer scientists worldwide have been developing a wide range of artificial intelligence (AI) systems. Some of these systems rely on an individual AI agent, while others consist of multiple interacting agents that exchange information, cooperate and revise each other’s responses or predictions.Computer scientists worldwide have been developing a wide range of artificial intelligence (AI) systems. Some of these systems rely on an individual AI agent, while others consist of multiple interacting agents that exchange information, cooperate and revise each other’s responses or predictions.[#item_full_content]