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In-situ high-performance computing (HPC) simulations produce massive datasets that remain difficult to stream into game engines for interactive visualization. We present an in-situ, disk-free pipeline that couples simula...
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Reinforcement learning with verifiable rewards (RLVR) trains a language model by sampling responses, scoring them with an automatic verifier, and updating the policy from the resulting rewards. The policy therefore recei...
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The rapid evolution of digital circuit complexity demands innovative approaches to hardware design automation. While Large Language Models (LLMs) have influenced software development, their application to Hardware Descri...
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Gene perturbation models aim to predict transcriptional responses to unseen genetic interventions, but comparisons rely on heterogeneous evaluation metrics and differentially-expressed (DE) gene subsets. We investigate t...
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Relation extraction from aviation accident narratives is challenging because genuine safety relations are sparse among possible concept pairs. This study evaluates a decomposed framework separating relation classificatio...
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Nowadays, it is easy to set up any element of a VR experience so users can interact with it physically, except for interactive VR characters. Can we extend this to animated VR characters? In this work, we studied whether...
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Second-order cone programs (SOCPs) are a fundamental class of convex optimization problems widely applied across engineering domains. Classical interior-point methods are robust and accurate, but their iterative nature c...
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Training-time explanation regularization is often evaluated by whether saliency maps become more concentrated within annotated regions. We argue that this criterion alone is insufficient: transformation consistency, lesi...
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Deep generative design can produce complex geometries, but unconstrained representations may create undercuts that prevent release from a two-part mold. We present CAST, which reformulates three-dimensional shape generat...
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Language-model agents increasingly solve complex tasks through parallel reasoning and asynchronous information gathering. Controlled replay across two model families and three benchmarks shows that delivery timing redire...
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SNNs are attracting interest due to their potential for energy efficiency and event-driven processing. However, neuromorphic hardware is not yet mature enough to enable embedded deployment of complex spiking network arch...
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Trading discipline often fails people. Our study tested whether four AI trading agents followed their strategies and risk rules. Offering traders the evidence and an agent evaluation toolkit to separate luck from followi...
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Typed decision models (TDMs) return a probability over caller-defined options, and Jev is described by its vendor as calibrated. But in deployment class frequencies change, so calibration measured on a fixed test set nee...
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Transparency systems -- such as Key Transparency (KT), Certificate Transparency, and binary transparency -- require that all honest participants see the same view of an untrusted server's directory, or, failing that, tha...
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We present a controlled, large-scale evaluation of how corpus scale, document representation, and reranking affect the effectiveness and efficiency of biomedical Retrieval-Augmented Generation (RAG) systems. Retrieval co...