arXiv:2602.18447v1 Announce Type: new Abstract: Chain-of-Thought reasoning significantly improves the performance of large language models on complex tasks, but incurs high inference latency due to long generation traces. Step-level speculative reasoning aims to mitigate this cost, yet existing approaches face a long-standing trade-off among accuracy, inference speed, and resource efficiency. We propose ConfSpec, a confidence-gated cascaded verification framework that resolves this trade-off. Our key insight is an asymmetry between generation and verification: while generating a correct reasoning step […]

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Open, well-documented datasets are essential for the reproducible development of vision systems for urban utility management. This Data Descriptor presents a curated RGB object-detection benchmark of four classes associated with electrical distribution and street-level utility assets: Inspection Chamber, Overhead-to-Underground Transition, General Protection Box, and Transformer Substation. The public release contains 997 valid image-label pairs partitioned into 698 training, 150 validation, and 149 test images. Images were acquired during 2019 in multiple localities across Spain, predominantly with a mobile […]

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Real-world heterogeneous graphs are inherently noisy and usually not in the optimal graph structures for downstream tasks, which often adversely affects the performance of GRL models in downstream tasks. Although Graph Structure Learning (GSL) methods have been proposed to learn graph structures and downstream tasks simultaneously, existing methods are predominantly designed for homogeneous graphs, while GSL for heterogeneous graphs remains largely unexplored. Two challenges arise in this context. Firstly, the quality of the input graph structure has a […]

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Here, I want to present a simple and conservative approach of implementing a weighted majority rule ensemble classifier in scikit-learn that yielded remarkably good results when I tried it in a kaggle competition. For me personally, kaggle competitions are just a nice way to try out and compare different approaches and ideas — basically an opportunity to learn in a controlled environment with nice datasets.

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arXiv:2602.05053v1 Announce Type: new Abstract: Inclement weather conditions can significantly impact driver visibility and tire-road surface friction, requiring adjusted safe driving speeds to reduce crash risk. This study proposes a hybrid predictive framework that recommends real-time safe speed intervals for freeway travel under diverse weather conditions. Leveraging high-resolution Connected Vehicle (CV) data and Road Weather Information System (RWIS) data collected in Buffalo, NY, from 2022 to 2023, we construct a spatiotemporally aligned dataset containing over 6.6 million records […]

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arXiv:2603.20278v1 Announce Type: new Abstract: Training deep research agents requires long-horizon trajectories that interleave search, evidence aggregation, and multi-step reasoning. However, existing data collection pipelines typically rely on proprietary web APIs, making large-scale trajectory synthesis costly, unstable, and difficult to reproduce. We present OpenResearcher, a reproducible pipeline that decouples one-time corpus bootstrapping from multi-turn trajectory synthesis and executes the search-and-browse loop entirely offline using three explicit browser primitives: search, open, and find, over a 15M-document corpus. Using GPT-OSS-120B […]

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There is a growing demand for efficient data removal to comply with regulations like the GDPR and to mitigate the influence of biased or corrupted data. This has motivated the field of machine unlearning, which aims to eliminate the influence of specific data subsets without the cost of full retraining. In this work, we propose a statistical framework for machine unlearning with generic loss functions and establish theoretical guarantees. For squared loss, especially, we develop Unlearning Least Squares […]

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