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Xu-Jun Che

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#machine learning Preprint Jul 2026

QEncodeBench: Can Large Language Models Encode Classical Problems into Verified Quantum Oracles?

QEncodeBench tasks large language models with encoding classical constraint problems as phase oracles and scores the generated circuits with an adversarially self-validated verifier that decides full solution-set equivalence up to a global phase, with ancillas restored and resource budgets enforced.

Xu-Jun Che, Han-Han Wu, Yu-Chen Yuan et al. · 1 citation
Preprint Sep 2026

Privacy Amplification Without Independence: How Far Negative Dependence Carries the Guarantees of Poisson Subsampling

Poisson subsampling is the default sampler in differentially private optimization because its independence makes privacy amplification tractable. Practical systems, however, are moving toward structured participation: random allocation (balls-in-bins), per-epoch allocation, random check-ins, schemes widely believed to...

Xu-Jun Che, Depeng Xu · 0 citations
Preprint Aug 2026

MAVISEG: Manifold Propagation and Visual Prototypes for Zero-Shot Open-Vocabulary Segmentation in Diffusion Transformers

The results indicate that diffusion transformers carry more concept-level information than current attribution methods recover, and that much of it is lost on the way to the mask rather than absent from the model.

Rajatsubhra Chakraborty, Xu-Jun Che, Ritabrata Chakraborty et al. · 0 citations
Preprint Jul 2026

MARLIN: De Novo Molecular Structure Elucidation from Tandem Mass Spectra without a Ground-Truth Formula

On the NPLIB1 benchmark, MARLIN is the strongest method evaluated without a ground-truth formula across exact-match accuracy, structural distance, and fingerprint similarity, and it recovers the correct molecular formula as a byproduct about as often as a dedicated predictor without ever using one.

Xujun Che, Xiuxia Du, Depeng Xu · 0 citations

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