arXiv:2601.00240v1 Announce Type: new Abstract: LLM-empowered agents can exhibit not only demographic bias (e.g., gender, religion) but also intergroup bias triggered by minimal “us” versus “them” cues. When this intergroup boundary aligns with an agent-human divide, the risk shifts from disparities among human demographic groups to a more fundamental group-level asymmetry, i.e., humans as a whole may be treated as the outgroup by agents. To examine this possibility, we construct a controlled multi-agent social simulation based on allocation […]

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Logging is the heartbeat of a production application. In the early days of a project, a simple dev.log tail is sufficient. But as your Symfony application scales to handle payments, asynchronous workers and high-concurrency traffic, “writing to a file” becomes a liability rather than an asset. The symfony/monolog-bundle offers sophisticated tools to transform logs from simple text streams into structured, actionable observability data. This guide explores 10 advanced logging patterns that go beyond the defaults. We will use strict typing, PHP Attributes, and modern YAML configuration. […]

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arXiv:2603.26744v1 Announce Type: new Abstract: One of the main challenges in data mining is choosing the optimal number of clusters without prior information. Notably, existing methods are usually in the philosophy of cluster validation and hence have underlying assumptions on data distribution, which prevents their application to complex data such as large-scale images and high-dimensional data from the real world. In this regard, we propose an approach named CNMBI. Leveraging the distribution information inherent in the data space, […]

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arXiv:2603.03406v1 Announce Type: new Abstract: How should two language models interact to produce better code than either can alone? The conventional approach — a reasoning model plans, a code specialist implements — seems natural but fails: on HumanEval+, plan-then-code degrades performance by 2.4 percentage points versus the code specialist alone. We show that reversing the interaction changes everything. When the code specialist generates freely and the reasoning model reviews instead of plans, the same two models on the […]

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arXiv:2402.00152v4 Announce Type: replace-cross Abstract: Constructing the architecture of a neural network is a challenging pursuit for the machine learning community, and the dilemma of whether to go deeper or wider remains a persistent question. This paper explores a comparison between deeper neural networks (DeNNs) with a flexible number of layers and wider neural networks (WeNNs) with limited hidden layers, focusing on their optimal generalization error in Sobolev losses. Analytical investigations reveal that the architecture of a neural […]

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arXiv:2602.16872v1 Announce Type: new Abstract: Optical Character Recognition (OCR) is a fundamental task for digitizing information, serving as a critical bridge between visual data and textual understanding. While modern Vision-Language Models (VLM) have achieved high accuracy in this domain, they predominantly rely on autoregressive decoding, which becomes computationally expensive and slow for long documents as it requires a sequential forward pass for every generated token. We identify a key opportunity to overcome this bottleneck: unlike open-ended generation, OCR […]

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Young Teilhard spots a crisis: earnest souls are abandoning Christianity not from weakness, but from and the search for something grander. His remedy? Reveal the “Soul of the World”: that cosmic fabric where God touches matter, where Christ meets the universe’s longing. Faith mustn’t float in clouds; it must sanctify earth itself.

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Background: This study aims to develop and validate federated learning models for predicting major postoperative complications and mortality using a large multicenter dataset from the OneFlorida Data Trust. We hypothesize that federated learning models will offer robust generalizability while preserving data privacy and security. Methods: This retrospective, longitudinal, multicenter cohort study included 358,644 adult patients admitted to five healthcare institutions, who underwent 494,163 inpatient major surgical procedures from 2012-2023. We developed and internally and externally validated federated learning […]

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Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results 3. Preliminaries and Notations Herein, different from the typical setting of existing […]

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arXiv:2603.25926v1 Announce Type: new Abstract: Soft context compression reduces the computational workload of processing long contexts in LLMs by encoding long context into a smaller number of latent tokens. However, existing frameworks apply uniform compression ratios, failing to account for the extreme variance in natural language information density. While adopting a density-aware dynamic compression ratio seems intuitive, empirical investigations reveal that models struggle intrinsically with operations parameterized by input dependent, continuous structural hyperparameters. To resolve this pitfall, we […]

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