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This paper addresses the challenge of evaluating generative UI (GenUI) systems, where large language models (LLMs) synthesize complete user interfaces.
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This paper tackles a fundamental limitation in financial news analysis for stock prediction: the reduction of complex, multi-dimensional news articles to.
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This paper addresses the challenge of building effective user foundation models (UFMs) for open-web real-time bidding (RTB) where user identity is.
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This paper addresses a crucial limitation in existing co-decoding approaches for secure code generation with large language models (LLMs).
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This paper addresses the problem of mode collapse in silicon sampling, where large language models (LLMs) generate synthetic survey responses with.
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This paper addresses the acute problem of alert fatigue in Security Operations Centers (SOCs) caused by an overwhelming volume of security detections that.
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This paper performs the first comprehensive empirical study characterizing Solana bots from both their off-chain software implementations and their.
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This paper addresses the limitations of existing Multimodal Retrieval-Augmented Generation (MM-RAG) systems in handling complex multi-hop reasoning tasks.
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Echoverse tackles the challenge of training computer-use agents that must interact with complex, stateful, and login-gated applications.
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The paper addresses the challenge that life-science AI agents face in retrieving targeted, citable evidence from the rapidly expanding biomedical literature.
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This paper addresses the challenge of improving memory locality and computational efficiency in lossless compression of sparse binary matrices using k2-trees.
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This paper examines the reliability of benchmarks used to evaluate Computer-Use Agents (CUA) that automate tasks on the web and desktop environments.
