arXiv:2602.15852v1 Announce Type: new Abstract: Clinical natural language processing (NLP) models have shown promise for supporting hospital discharge planning by leveraging narrative clinical documentation. However, note-based models are particularly vulnerable to temporal and lexical leakage, where documentation artifacts encode future clinical decisions and inflate apparent predictive performance. Such behavior poses substantial risks for real-world deployment, where overconfident or temporally invalid predictions can disrupt clinical workflows and compromise patient safety. This study focuses on system-level design choices required to […]

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arXiv:2602.04909v1 Announce Type: new Abstract: Direct Preference Optimization (DPO) and related methods align large language models from pairwise preferences by regularizing updates against a fixed reference policy. As the policy drifts, a static reference, however, can become increasingly miscalibrated, leading to distributional mismatch and amplifying spurious preference signals under noisy supervision. Conversely, reference-free variants avoid mismatch but often suffer from unconstrained reward drift. We propose Geometric Anchor Preference Optimization (GAPO), which replaces the fixed reference with a dynamic, […]

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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 C. Technical Details C.1. Notations The mathematical notations are described in Table […]

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arXiv:2601.00023v1 Announce Type: new Abstract: Efficient workload assignment to the workforce is critical in last-mile package delivery systems. In this context, traditional methods of assigning package deliveries to workers based on geographical proximity can be inefficient and surely guide to an unbalanced workload distribution among delivery workers. In this paper, we look at the problem of operational human resources workload balancing in last-mile urban package delivery systems. The idea is to consider the effort workload to optimize the […]

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arXiv:2601.14455v1 Announce Type: new Abstract: GitHub Actions is a widely used platform that allows developers to automate the build and deployment of their projects through configurable workflows. As the platform’s popularity continues to grow, it has become a target of choice for recent software supply chain attacks. These attacks exploit excessive permissions, ambiguous versions, or the absence of artifact integrity checks to compromise workflows. In response to these attacks, several security scanners have emerged to help developers harden […]

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LLM role-playing, i.e., using LLMs to simulate specific personas, has emerged as a key capability in various applications, such as companionship, content creation, and digital games. While current models effectively capture character tones and knowledge, simulating the inner thoughts behind their behaviors remains a challenge. Towards cognitive simulation in LLM role-play, previous efforts mainly suffer from two deficiencies: data with high-quality reasoning traces, and reliable reward signals aligned with human preferences. In this paper, we propose HER, a […]

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arXiv:2602.11185v1 Announce Type: new Abstract: Gradient signals in LLM training are highly anisotropic: recurrent linguistic structure concentrates energy into a small set of dominant spectral directions, while context specific information resides in a long tail. We show that this spike tail separation persists throughout training, with the spike occupying only about 1.5% of directions yet dominating optimizer statistics. This dominance suppresses tail learning by contracting tail updates through second moment normalization and tightening the globally stable learning rate […]

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arXiv:2601.07873v1 Announce Type: new Abstract: Knowledge editing aims to efficiently modify the internal knowledge of large language models (LLMs) without compromising their other capabilities. The prevailing editing paradigm, which appends an update matrix to the original parameter matrix, has been shown by some studies to damage key numerical stability indicators (such as condition number and norm), thereby reducing editing performance and general abilities, especially in sequential editing scenario. Although subsequent methods have made some improvements, they remain within […]

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