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This paper addresses the core challenge of sim-to-real transfer for tactile sensing in contact-rich dexterous manipulation
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This paper addresses the challenge of modeling discourse particles—short morphemes that convey pragmatic and interpersonal meaning—in large language models (LLMs), focusing specifically on Malay, a…
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This paper addresses the problem of improving reasoning capabilities of frozen language models via learning from verifiable rewards in a highly sample- and rollout-efficient manner
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This paper critiques the conventional Breit-Wigner approach to resonance scattering, which models resonances via a phase shift of the form tan δ = Γ1/(E1 - E) and identifies unstable particles with…
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This paper addresses the metrological challenge of accurately characterizing noise properties of nonlinear cryogenic microwave quantum devices—such as Josephson Traveling Wave Parametric Amplifiers…
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This paper investigates whether semantic metadata remains necessary for autonomous agents performing data retrieval, given the rise of large language models (LLMs) capable of navigating the unstruc…
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This paper addresses the critical challenge of efficiently scheduling entanglement distribution in scalable quantum networks with multiple user applications
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This paper addresses the challenge of interpreting energy-energy correlators (EEC) inside single inclusive jets in heavy-ion collisions, specifically in a quark-gluon plasma (QGP) environment, wher…
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This paper addresses the challenge of generating synthetic student errors for educational applications, where privacy and ethical constraints limit access to large labelled corpora of real student …
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This paper addresses the computational challenge of estimating accurate covariance matrices for large-scale structure (LSS) power spectrum measurements, which are critical inputs to cosmological pa…
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This paper addresses the gap between AI-generated game code and fully playable games by positioning graphical user interface (GUI) agents as critical playtesters
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This paper investigates whether large language models (LLMs) can learn to simulate annotator-specific label-explanation behavior, extending beyond modeling only label disagreement to capturing how …
