arXiv:2512.12325v3 Announce Type: replace Abstract: We prove that a classic sub-Gaussian mixture proposed by Robbins in a stochastic setting actually satisfies a path-wise (deterministic) regret bound. For every path in a natural “Ville event” $mathcal E_alpha$, this regret till time…
arXiv:2601.09871v2 Announce Type: replace-cross Abstract: Human-AI complementarity is the claim that a human supported by an AI system can outperform either alone in a decision-making process. Since its introduction in the humanAI interaction literature, it has gained traction by generalizing…
arXiv:2604.19903v1 Announce Type: new Abstract: Cement production is among the largest contributors to industrial air pollution, emitting ~3 Mt NOx The industry-standard mitigation approach, selective non-catalytic reduction (SNCR), exhibits low NH3 utilization efficiency, resulting in operational inefficiencies and increased reagent…
arXiv:2604.20468v1 Announce Type: cross Abstract: Industrial robot applications require increasingly flexible systems that non-expert users can easily adapt for varying tasks and environments. However, different adaptations benefit from different interaction modalities. We present an interactive framework that enables robot skill…
arXiv:2604.19930v1 Announce Type: new Abstract: Neural surrogates for stiff differential-algebraic equations (DAEs) face two key challenges: soft-constraint methods leave algebraic residuals that stiffness amplifies into large errors, while hard-constraint methods require trajectory data from computationally expensive stiff integrators. We introduce…
arXiv:2604.11098v2 Announce Type: replace-cross Abstract: Large-scale three-dimensional (3D) scene reconstruction in low-altitude intelligent networks (LAIN) demands highly efficient wireless image transmission. However, existing schemes struggle to balance severe pilot overhead with the transmission accuracy required to maintain reconstruction fidelity. To…
Ultra-efficient chip design enables extremely strong cryptography algorithms to run on energy-constrained edge devices.
arXiv:2508.18948v2 Announce Type: replace-cross Abstract: We develop a gauge-covariant stochastic effective field theory for stability and finite-width effects in deep neural systems. The model uses classical commuting fields: a complex matter field, a real Abelian connection field, and a fictitious…
arXiv:2603.17478v2 Announce Type: replace Abstract: This study explores the combination of automated machine learning (AutoML) with model-based deep unfolding (DU) for optimizing wireless beamforming and waveforms. We convert the iterative proximal gradient descent (PGD) algorithm into a deep neural network,…
arXiv:2604.20468v1 Announce Type: cross Abstract: Industrial robot applications require increasingly flexible systems that non-expert users can easily adapt for varying tasks and environments. However, different adaptations benefit from different interaction modalities. We present an interactive framework that enables robot skill…
