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This work investigates the suitability of supported non-dominated points as representations of the Pareto front in multi-objective network optimization.
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This paper introduces requential coding, a novel technique for compressing neural network models by encoding training trajectories via self-generated.
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This paper addresses the challenge of achieving robust bipedal locomotion on flowable, granular slopes—a setting where traditional rigid-terrain contact.
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This paper addresses the paradox young job seekers face when using social media for career exploration: authentic peer-generated content offers valuable.
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This paper addresses key scalability bottlenecks in building computer use agents (CUAs) that automate complex digital workflows by interacting with GUIs.
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This paper investigates the historical evolution, legal definitions, and broader ethical context of the phrase "for statistical purposes only" as used by.
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This paper introduces VIA (Visual Interface Agent), a novel framework that repurposes off-the-shelf foundation model (FM)-powered agents to control robot.
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The paper addresses the challenge of remote identity document verification with a focus on detecting holograms—a key anti-counterfeiting security.
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Motif addresses a fundamental barrier in end-user programming — the "what-to-automate" problem, wherein users often fail to recognize which of their.
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This paper presents Q-Learning Lab, a browser-based, bilingual (Thai/English) educational tool designed to teach tabular Q-learning through detailed.
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This pilot study investigates student perspectives on teaching methods used in graduate-level physics coursework at a large, research-intensive (R1) U.S.
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This paper addresses a largely overlooked problem in memory-augmented AI agents: how they consume retrieved memory during multi-step tasks rather than.
