Systems don’t fail because they are poorly written; they fail because they cannot keep up with the complex and ever-changing needs of a business. When speaking with Ignacio Brasca, who is a staff software engineer with several years of experience building complex data systems, he emphasized that software behaves much like a biological system—and that the entropy involved in making it grow cannot be underestimated. As organizations and systems evolve together, developers must plan for long-term change from […]

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arXiv:2604.18645v1 Announce Type: new Abstract: This paper addresses the Variable Gapped Longest Common Subsequence (VGLCS) problem, a generalization of the classical LCS problem involving flexible gap constraints between consecutive solutions’ characters. The problem arises in molecular sequence comparison, where structural distance constraints between residues must be respected, and in time-series analysis where events are required to occur within specified temporal delays. We propose a search framework based on the root-based state graph representation, in which the state space […]

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As we move into 2026, the demand for fast, reliable, and real-time financial data continues to grow. Developers, analysts, and financial institutions increasingly rely on accurate market insights to build smarter tools and make informed decisions. Financial APIs are now transforming how market data is accessed, analyzed, and applied across global markets. Whether you are building a next-generation personal finance application or powering a high-frequency trading system, Stock Market Data APIs and Forex Data APIs play a critical […]

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arXiv:2601.00222v1 Announce Type: new Abstract: Vector quantization (VQ) is a prevalent and fundamental technique that discretizes continuous feature vectors by approximating them using a codebook. As the diversity and complexity of data and models continue to increase, there is an urgent need for high-capacity, yet more compact VQ methods. This paper aims to reconcile this conflict by presenting a new approach called LooC, which utilizes an effective Low-dimensional codebook for Compositional vector quantization. Firstly, LooC introduces a parameter-efficient […]

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arXiv:2602.14642v1 Announce Type: new Abstract: Inverse problems and inverse design in multiphase media, i.e., recovering or engineering microstructures to achieve target macroscopic responses, require operating on discrete-valued material fields, rendering the problem non-differentiable and incompatible with gradient-based methods. Existing approaches either relax to continuous approximations, compromising physical fidelity, or employ separate heavyweight models for forward and inverse tasks. We propose GenPANIS, a unified generative framework that preserves exact discrete microstructures while enabling gradient-based inference through continuous latent embeddings. […]

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Artificial intelligence is changing how we build software. Just a few years ago, writing code that could talk, decide, or use external data felt hard. Today, thanks to new tools, developers can build smart agents that read messages, reason about them, and call functions on their own. One such platform that makes this easy is LangChain. With LangChain, you can link language models, tools, and apps together. You can also wrap your agent inside a FastAPI server, then […]

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arXiv:2604.06267v1 Announce Type: new Abstract: Multimodal variational autoencoders (VAEs) have emerged as a powerful framework for survival risk modeling in multiple myeloma by integrating heterogeneous omics and clinical data. However, when trained under survival supervision, standard latent regularization strategies often fail to preserve prognostically relevant variation, leading to unstable or overly constrained representations. Despite numerous proposed variants, it remains unclear which aspects of latent design fundamentally govern performance in this setting. In this work, we conduct a controlled […]

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