arXiv:2603.23560v1 Announce Type: new Abstract: We develop the first algorithms for computing the Skyscraper Invariant [FJNT24]. This is a filtration of the classical rank invariant for multiparameter persistence modules defined by the Harder-Narasimhan filtrations along every central charge supported at a single parameter value. Cheng’s algorithm [Cheng24] can be used to compute HN filtrations of arbitrary acyclic quiver representations in polynomial time in the total dimension, but in practice, the large dimension of persistence modules makes this direct […]

Ver mais

The Big Announcement On April 6, 2026, Ben Sigman, CEO of Bitcoin lending platform Libre Labs and self-described friend of Jovovich, posted on X announcing MemPalace, an open-source AI memory system built with Claude. The pitch was ambitious. From Sigman’s launch post: “My friend Milla Jovovich and I spent months creating an AI memory system with Claude. It just posted a perfect score on the standard benchmark — beating every product in the space, free or paid.” The […]

Ver mais

We address the intrinsic dimensionality (ID) of high-dimensional trajectories, comprising $n_s = 4,000,000$ data points, of the Fermi-Pasta-Ulam-Tsingou (FPUT) $β$ model with $N = 32$ oscillators. To this end, a deep autoencoder (DAE) model is employed to infer the ID in the weakly nonlinear regime ($βlesssim 1$). We find that the trajectories lie on a nonlinear manifold of dimension $m^{ast} = 2$ embedded in a $64$-dimensional phase space. The DAE further reveals that this dimensionality increases to $m^{ast} […]

Ver mais

In this tutorial, we build a complete Open WebUI setup in Colab, in a practical, hands-on way, using Python. We begin by installing the required dependencies, then securely provide our OpenAI API key through terminal-based secret input so that sensitive credentials are not exposed directly in the notebook. From there, we configure the environment variables needed for Open WebUI to communicate with the OpenAI API, define a default model, prepare a data directory for runtime storage, and launch […]

Ver mais

Agreement Technologies refer to open computer systems in which autonomous software agents interact with one another, typically on behalf of humans, in order to come to mutually acceptable agreements. With the advance of AI systems in recent years, it has become apparent that such agreements, in order to be acceptable to the involved parties, must remain aligned with ethical principles and moral values. However, this is notoriously difficult to ensure, especially as different human users (and their software […]

Ver mais

“Speed” in data engineering is a trade-off, not a single metric. To build effective systems, you must distinguish between two competing concepts: – Data Latency (Freshness): How long it takes for an event to reach your report. – Query Latency (Responsiveness): How long a user waits for a dashboard to load. The Conflict: Optimizing for real-time freshness often slows down query performance because the system can’t pre-calculate data. Conversely, pre-calculating data for “snappy” dashboards usually requires batching, which […]

Ver mais

arXiv:2601.17108v1 Announce Type: new Abstract: This paper proposes a Mamba-assisted neural network framework incorporating self-attention mechanism to achieve improved channel estimation with low complexity for orthogonal frequency-division multiplexing (OFDM) waveforms, particularly for configurations with a large number of subcarriers. With the integration of customized Mamba architecture, the proposed framework handles large-scale subcarrier channel estimation efficiently while capturing long-distance dependencies among these subcarriers effectively. Unlike conventional Mamba structure, this paper implements a bidirectional selective scan to improve channel estimation […]

Ver mais

arXiv:2601.09822v1 Announce Type: new Abstract: Despite recent advancements in Large Language Models (LLMs), complex Software Engineering (SE) tasks require more collaborative and specialized approaches. This concept paper systematically reviews the emerging paradigm of LLM-based multi-agent systems, examining their applications across the Software Development Life Cycle (SDLC), from requirements engineering and code generation to static code checking, testing, and debugging. We delve into a wide range of topics such as language model selection, SE evaluation benchmarks, state-of-the-art agentic frameworks […]

Ver mais

Graph Neural Networks (GNNs) have become essential in high-stakes domains such as drug discovery, yet their black-box nature remains a significant barrier to trustworthiness. While self-explainable GNNs attempt to bridge this gap, they often rely on standard message-passing backbones that inherit fundamental limitations, including the 1-Weisfeiler-Lehman (1-WL) expressivity barrier and a lack of fine-grained interpretability. To address these challenges, we propose SymGraph, a symbolic framework designed to transcend these constraints. By replacing continuous message passing with discrete structural […]

Ver mais