Multi-task learning (MTL) aims to enable a single model to solve multiple tasks efficiently; however, current parameter-efficient fine-tuning (PEFT) methods remain largely limited to single-task adaptation. We introduce textbf{Free Sinewich}, a parameter-efficient multi-task learning framework that enables near-zero-cost weight modulation via frequency switching (textbf{Free}). Specifically, a textbf{Sine-AWB (Sinewich)} layer combines low-rank factors and convolutional priors into a single kernel, which is then modulated elementwise by a sinusoidal transformation to produce task-specialized weights. A lightweight Clock Net is introduced […]

Ver mais

arXiv:2603.16279v1 Announce Type: cross Abstract: This article presents a solution to intercept an agile drone by another agile drone carrying a catching net. We formulate the interception as a Competitive Reinforcement Learning problem, where the interceptor and the target drone are controlled by separate policies trained with Proximal Policy Optimization (PPO). We introduce a high-fidelity simulation environment that integrates a realistic quadrotor dynamics model and a low-level control architecture implemented in JAX, which allows for fast parallelized execution […]

Ver mais

Predicting the stress-strain behaviors of additively manufactured materials is crucial for part qualification in additive manufacturing (AM). Conventional physics-based constitutive models often oversimplify material properties, while data-driven machine learning (ML) models often lack physical consistency and interpretability. To address these issues, we propose a physics-informed machine learning (PIML) framework to improve the predictive performance and physical consistency for predicting the stress-strain curves of additively manufactured polymers and metals. A polynomial regression model is used to predict the yield […]

Ver mais

arXiv:2603.02649v1 Announce Type: cross Abstract: Adam and AdamW are a class of default optimizers for training deep learning models in machine learning. These adaptive algorithms converge faster but generalize worse compared to SGD. In fact, their proved generalization error $O(frac{1}{sqrt{N}})$ also is larger than $O(frac{1}{N})$ of SGD, where $N$ denotes training sample size. Recently, although some variants of Adam have been proposed to improve its generalization, their improved generalizations are still unexplored in theory. To fill this gap, […]

Ver mais

arXiv:2601.15473v1 Announce Type: new Abstract: Training modern deep learning models is increasingly constrained by GPU memory and compute limits. While Randomized Numerical Linear Algebra (RandNLA) offers proven techniques to compress these models, the lack of a unified, production-grade library prevents widely adopting these methods. We present Panther, a PyTorch-compatible library that consolidates established RandNLA algorithms into a single high-performance framework. Panther engineers efficient, drop-in replacements for standard components including sketched linear layers, 2D convolution, multi-head attention, and randomized […]

Ver mais

Hey everyone, I recently built my first reinforcement learning agent to play Super Mario Bros and Super Mario World. I documented the whole process in a video, and would love any feedback from people who know RL. I’m still learning and I’m sure there are better approaches I missed. Happy to answer any questions about the process too. https://youtu.be/6FQKz-yAt5Y submitted by /u/Marcell0123 [link] [comments]

Ver mais

arXiv:2601.08071v1 Announce Type: new Abstract: In this short paper, we advocate for the idea that continuation-based intermediate languages correspond to intermediate logics. The goal of intermediate languages is to serve as a basis for compiler intermediate representations, allowing to represent expressive program transformations for optimisation and compilation, while preserving the properties that make programs compilable efficiently in the first place, such as the “stackability” of continuations. Intermediate logics are logics between intuitionistic and classical logic in terms of […]

Ver mais

In the complex landscape of financial technology where millions of Americans depend on secure and resilient platforms for their retirement savings, the successful modernization of critical plan administration systems represents a pivotal achievement in digital transformation. Viswanath Mucheli Chenchu spearheaded the design and launch of a transformative platform at a leading financial services institution, establishing new standards for self-employed retirement plan administration and demonstrating how strategic technical leadership can directly enhance customer trust and financial security. The Challenge: […]

Ver mais

The evolution of cloud computing and artificial intelligence has fundamentally transformed how enterprises build and scale technology platforms. Modern cloud infrastructure must handle millions of concurrent users across dozens of geographic regions while maintaining security, reliability, and performance standards that meet the demands of government agencies and Fortune 500 companies alike. Engineers working at this scale navigate complex distributed systems, automate buildout pipelines, and build sophisticated monitoring frameworks that can detect potential outages before they impact customers. The […]

Ver mais

arXiv:2601.03271v1 Announce Type: new Abstract: In this work, we propose an enhancement to the Boyer-Moore-Horspool algorithm tailored for natural language text. The approach involves preprocessing the search pattern to identify its statistically least frequent character, referred to as the “anchor.” During the search, verification is first performed at this high-entropy position, allowing the algorithm to quickly discard non-matching windows. This fail-fast strategy reduces unnecessary comparisons, improving overall efficiency. Our implementation shows that incorporating basic linguistic statistics into classical […]

Ver mais