To address the limited controllability, unstable output consistency, and weakly constrained decision processes of large language models in text classification tasks, this work proposes a controllable prompt-driven text classification method that establishes an end-to-end unified modeling framework from instruction alignment to constrained decoding. Text classification is reformulated as an instruction-conditioned generative discriminative problem. Input texts and task instructions are jointly encoded to form a unified internal representation that integrates textual semantics with classification constraints. On this basis, a […]

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arXiv:2601.08060v1 Announce Type: new Abstract: Non-orthogonal multiple access (NOMA) is a promising technique for optical wireless communication (OWC), enabling multiple users to share the optical spectrum simultaneously through the power domain. However, the imperfection of channel state information (CSI) and residual errors in decoding process deteriorate the performance of NOMA, especially when multi-parameteric and realistic dense-user indoor scenarios are considered. In this work, we model a LiDAL-assisted RLNC-NOMA OWC system, where the light detection and localization (LiDAL) technique […]

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arXiv:2601.22652v1 Announce Type: new Abstract: Spectral gradient methods, such as the Muon optimizer, modify gradient updates by preserving directional information while discarding scale, and have shown strong empirical performance in deep learning. We investigate the mechanisms underlying these gains through a dynamical analysis of a nonlinear phase retrieval model with anisotropic Gaussian inputs, equivalent to training a two-layer neural network with the quadratic activation and fixed second-layer weights. Focusing on a spiked covariance setting where the dominant variance […]

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arXiv:2601.06296v2 Announce Type: replace-cross Abstract: A targeted learning (TL) framework is developed to estimate the difference in the restricted mean survival time (RMST) for a clinical trial with time-to-event outcomes. The approach starts by defining the target estimand as the RMST difference between investigational and control treatments. Next, an efficient estimation method is introduced: a targeted minimum loss estimator (TMLE) utilizing pseudo-observations. Moreover, a version of the copy reference (CR) approach is developed to perform a sensitivity analysis […]

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AI-driven generative audio is transforming film sound design, from Foley stages to diffusion models, enhancing creativity and cinematic storytelling.

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Note: I posted this on r/androiddev but thought the deployment side might interest this sub. One of the biggest pains in mobile ML deployment is that your trained model usually sits unencrypted in the APK. If you spent $50k fine-tuning a model, that’s a liability. I open-sourced a tool called TensorSeal that handles the encryption/decryption pipeline for Android. It ensures the model is only decrypted in memory (RAM) right before inference, keeping the disk footprint encrypted. It uses […]

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