arXiv:2601.20882v1 Announce Type: new Abstract: Even though demonstrating extraordinary capabilities in code generation and software issue resolving, AI agents’ capabilities in the full software DevOps cycle are still unknown. Different from pure code generation, handling the DevOps cycle in real-world software, including developing, deploying, and managing, requires analyzing large-scale projects, understanding dynamic program behaviors, leveraging domain-specific tools, and making sequential decisions. However, existing benchmarks focus on isolated problems and lack environments and tool interfaces for DevOps. We introduce […]

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arXiv:2601.20084v1 Announce Type: new Abstract: Interpretability in black-box dense retrievers remains a central challenge in Retrieval-Augmented Generation (RAG). Understanding how queries and documents semantically interact is critical for diagnosing retrieval behavior and improving model design. However, existing dense retrievers rely on static embeddings for both queries and documents, which obscures this bidirectional relationship. Post-hoc approaches such as re-rankers are computationally expensive, add inference latency, and still fail to reveal the underlying semantic alignment. To address these limitations, we […]

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2025 has anything but quiet for the AI and semiconductor industry. While AI giants are making progress in their respective expertise, major semiconductor companies NVIDIA and AMD have been in the spotlight for backing such companies with their best AI chips. Alibaba reportedly looking to buy AMD’s MI308 AI chip Now, AMD has entered the scene after securing export license for the MI308, and agreeing to pay a 15% fee to the U.S. government on approved sales to […]

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arXiv:2602.18585v1 Announce Type: new Abstract: Precise localization and recognition of flowers are crucial for advancing automated agriculture, particularly in plant phenotyping, crop estimation, and yield monitoring. This paper benchmarks several YOLO architectures such as YOLOv5s, YOLOv8n/s/m, and YOLOv12n for flower object detection under two annotation regimes: single-image single-bounding box (SISBB) and single-image multiple-bounding box (SIMBB). The FloralSix dataset, comprising 2,816 high-resolution photos of six different flower species, is also introduced. It is annotated for both dense (clustered) and […]

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Microsoft and OpenAI continue to work closely across research, engineering, and product development, building on years of deep collaboration and shared success.

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arXiv:2602.07062v1 Announce Type: new Abstract: Scrap quality directly affects energy use, emissions, and safety in steelmaking. Today, the share of non-metallic inclusions (contamination) is judged visually by inspectors – an approach that is subjective and hazardous due to dust and moving machinery. We present an assistive computer vision pipeline that estimates contamination (per percent) from images captured during railcar unloading and also classifies scrap type. The method formulates contamination assessment as a regression task at the railcar level […]

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arXiv:2510.00309v2 Announce Type: replace-cross Abstract: The Lipschitz bandit problem extends stochastic bandits to a continuous action set defined over a metric space, where the expected reward function satisfies a Lipschitz condition. In this work, we introduce a new problem of Lipschitz bandit in the presence of stochastic delayed feedback, where the rewards are not observed immediately but after a random delay. We consider both bounded and unbounded stochastic delays, and design algorithms that attain sublinear regret guarantees in […]

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arXiv:2601.05219v2 Announce Type: replace Abstract: One-shot prediction enables rapid adaptation of pretrained foundation models to new tasks using only one labeled example, but lacks principled uncertainty quantification. While conformal prediction provides finite-sample coverage guarantees, standard split conformal methods are inefficient in the one-shot setting due to data splitting and reliance on a single predictor. We propose Conformal Aggregation of One-Shot Predictors (CAOS), a conformal framework that adaptively aggregates multiple one-shot predictors and uses a leave-one-out calibration scheme to […]

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Victoria, Seychelles, January 23, 2026 – MEXC, the world’s fastest-growing digital asset exchange and a pioneer of true zero-fee trading, successfully concluded its “Zero-Fee Gala,” attracting over 120,000 participants and generating more than $8 billion in futures trading volume. The enthusiastic participation demonstrates strong user interest in the event and deepening trust in MEXC’s commitment to creating meaningful value for its global trading community. The promotion ran from December 22, 2025, to January 21, 2026 (UTC), combining multiple […]

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