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UltraX addresses the challenge of refining large-scale pre-training corpora for Large Language Models (LLMs) as the availability of new training data.
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This paper investigates the validity of large language models (LLMs) as instruments for annotating theoretical constructs in text, focusing on AMALIA-9B.
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This work addresses the complexity and high expertise required to process and analyze ALMA interferometric data acquired in ALMA Phasing System (APS) mode.
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WaspMOT addresses a key gap in multi-object tracking (MOT) benchmarks by focusing on long-term identity preservation over extended durations.
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This paper addresses the challenging problem of estimating 3D geometry in underwater environments, where traditional RGB-based reconstruction struggles.
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This paper addresses the limitations of existing LLM-based web search agents, especially in handling complex queries requiring both deep multi-hop.
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This paper addresses a key limitation in sparse autoencoders (SAEs) when applied to vision-language models (VLMs): vanilla SAEs learn concept features.
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This paper addresses the representational challenges in managing LLM-mediated workflows, which today are often fragmented across source code, runtime.
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The paper introduces SciReasoner, a novel multimodal scientific foundation model that performs native structural reasoning across diverse scientific.
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This study investigates how AI-driven search interfaces, exemplified by ChatGPT Search, alter the traditional economic model of web traffic allocation.
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This paper addresses the Distribution Network Reconfiguration (DNR) problem, a central challenge in the operation of electrical distribution networks.
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This paper addresses the challenge of accurately segmenting echocardiographic videos, which is complicated by speckle noise, low signal-to-noise ratio.
