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Adapting a pretrained machine translation (MT) model to new terminology usually requires new parallel data and another round of fine-tuning, which is costly for low-resource languages. We ask whether controllability can ...
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Electroencephalographic (EEG) functional connectivity is quantified using diverse measures of statistical dependence, spectral coupling, phase synchronization, information transfer, directed interactions, cross-frequency...
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Independently developed AI models accumulate diverse and complementary capabilities, but a growing model collection does not automatically realize Collective AI. Addressing this gap requires understanding model capabilit...
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Security operations centers are beginning to delegate detection, triage, and containment to Autonomous Cyber Defense (ACD) agents at execution time. Erroneous high-impact playbooks can disrupt critical services and creat...
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Flow-matching models generate samples through iterative velocity predictions, but typically do not revisit these predictions using the intermediate representations that produced them. We introduce CorrFlow, a self-correc...
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Ensuring data security in the era of Artificial Intelligence (AI) and deepfake technology requires robust biometric systems. Conventional methods such as facial recognition, iris scans, fingerprints, and voice recognitio...
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Semiconductor quantum dots (QDs) are essential components for integrated photonic and quantum information systems, yet current epitaxial growth techniques face significant limitations in complexity of growth, material se...
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We demonstrate efficient electron doping of exfoliated MoS$_2$ through the deposition of N-heterocyclic carbene (NHC) molecules onto its surface. Raman spectroscopy reveals a pronounced softening of the optical A$_{1g}$ ...
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As multimodal resources are transformed into media outputs, agentic AI is expected to stay aligned with design intent over long horizons. Designers follow briefs specifying goals, constraints, and deliverables, with late...
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Existing convergence analyses of finite-width overparameterized deep neural networks under standard parameterization often rely on parameters remaining close to their initialization and typically neglect the role of nois...
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Several prominent voices in AI safety have recently opposed rights for AI on the grounds that such rights would make it harder for us to justify a mandatory off-switch for potentially dangerous systems. In this paper, we...
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Management of the token-based costs of using LLMs has emerged as a significant problem. Some frontier-model providers charge five times per response token than per prompt token, while others offer prefilling discounts fo...
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We study how text serialization affects table lookup in instruction-tuned language models. Our diagnostic asks the model to find the row containing a specified value under one named column, then return the value under an...
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Despite breakthroughs in AI-driven molecular generation, existing discovery pipelines operate largely in silico, with wet-lab feedback reserved for validation rather than iterative improvement. Whether large language mod...
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Small Language Models with quantisation and parameter-efficient adaptation are widely offered as the route to multilingual AI under compute constraints, and their efficiency is almost always reported as a single memory f...