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The paper addresses the challenge of mapping complex, informal, and adversarial political-elite networks at scale from multilingual news text
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This tutorial paper presents NeuraDock Agent, an open-source EEG analysis pipeline focused specifically on alpha-band dynamics for visual cognitive-load estimation
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This paper addresses the challenge of automating architecture evolution in large-scale industrial recommender systems, focusing on producing valid, high-quality model upgrades under strict producti…
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This paper introduces a novel hierarchy of formal language classes defined by the notion of an "observer," a function that determines which information about the input string is accessible to a com…
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PhyEditBench addresses a critical gap in instruction-based image editing evaluation by focusing on physics-based reasoning, a capability essential for realistic edits involving physical dynamics in…
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This paper addresses the problem of physical layer authentication (PLA) for mobile devices in indoor environments characterized by complex multipath propagation and obstructions
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This paper addresses a critical but underexplored problem in large language model (LLM) agent deployments — the hidden and cumulative cognitive cost borne by users, measured in user lifetime consume…
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This paper investigates the vulnerability of automated résumé screening systems powered by large language models (LLMs) to strategic prompt injection attacks
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This paper presents the first systematic, cross-platform security analysis of the widely deployed proximity file-transfer protocols Apple AirDrop and Android Quick Share
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This paper addresses the foundational theoretical problem of when and how governing ordinary differential equations (ODEs) can be uniquely and stably recovered from observed solution trajectories
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This paper addresses the limitation of existing Reinforcement Learning with Verifiable Rewards (RLVR) techniques for large language models (LLMs), which heavily rely on ground-truth answers or bina…
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This paper addresses the challenge of improving reasoning accuracy for small language models (SLMs) running locally on consumer hardware, where repeated sampling (self-consistency) or scaling up mo…
