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This paper addresses the emerging vulnerability known as Cross-Site Prompting (XSP) in autonomous web agents, where natural language instructions embedded.
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This paper addresses the problem of test-time model selection among a pool of large language models (LLMs) under a fixed per-query inference cost budget.
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This work addresses practical limitations in quantum key distribution (QKD), specifically targeting noise, losses, and device imperfections, by proposing.
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This paper addresses the critical challenge of low-light image enhancement for visual perception in non-cooperative spacecraft on-orbit servicing.
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This paper addresses the critical security vulnerability of QR codes known as "blind trust," where users cannot visually verify the authenticity or origin.
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This paper investigates how short peptide tails conjugated to generation 2 poly(amidoamine) (PAMAM) dendrimers influence their interaction with DNA.
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This paper addresses the significant challenge faced by smart-home researchers in acquiring realistic, diverse, and privacy-preserving datasets of.
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This paper addresses the challenge of inducing low-rank structure in neural network weight matrices during training to improve compressibility without.
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This paper addresses the evaluation challenges of autonomous economic agents deployed in decentralized energy markets, where agent actions affect both.
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This work addresses the question of whether "Super Weights"—individual model parameters whose removal drastically harms language model performance—can be.
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This paper investigates the impact of post-training quantization (PTQ) on large language models (LLMs), focusing on the hidden assumption that standard.
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This paper addresses the challenge of threshold authorization in digital asset custody under the upcoming post-quantum transition, which complicates the.
