arXiv:2511.01932v1 Announce Type: new Abstract: Image generation models are usually personalized in practical uses in order to better meet the individual users’ heterogeneous needs, but most personalized models lack explainability about how they are being personalized. Such explainability can be…
arXiv:2505.23495v4 Announce Type: replace-cross Abstract: Knowledge Graph Question Answering (KGQA) systems rely on high-quality benchmarks to evaluate complex multi-hop reasoning. However, despite their widespread use, popular datasets such as WebQSP and CWQ suffer from critical quality issues, including inaccurate or…
arXiv:2511.01934v1 Announce Type: new Abstract: Training tool-augmented LLMs has emerged as a promising approach to enhancing language models’ capabilities for complex tasks. The current supervised fine-tuning paradigm relies on constructing extensive domain-specific datasets to train models. However, this approach often…
arXiv:2511.02706v1 Announce Type: cross Abstract: Kernel discrepancies are a powerful tool for analyzing worst-case errors in quasi-Monte Carlo (QMC) methods. Building on recent advances in optimizing such discrepancy measures, we extend the subset selection problem to the setting of kernel…
arXiv:2511.01935v1 Announce Type: new Abstract: The determination of sample size in qualitative research has traditionally relied on the subjective and often ambiguous principle of data saturation, which can lead to inconsistencies and threaten methodological rigor. This study introduces a new,…
arXiv:2410.09964v2 Announce Type: replace Abstract: Single-cell RNA sequencing (scRNA-seq) enables the study of cellular diversity at single cell level. It provides a global view of cell-type specification during the onset of biological mechanisms such as developmental processes and human organogenesis.…
arXiv:2511.01937v1 Announce Type: new Abstract: Large language models (LLMs) trained for step-by-step reasoning often become excessively verbose, raising inference cost. Standard Reinforcement Learning with Verifiable Rewards (RLVR) pipelines filter out “easy” problems for training efficiency, leaving the model to train…
arXiv:2503.00307v3 Announce Type: replace Abstract: Part of the success of diffusion models stems from their ability to perform iterative refinement, i.e., repeatedly correcting outputs during generation. However, modern masked discrete diffusion lacks this capability: when a token is generated, it…
arXiv:2511.01938v1 Announce Type: new Abstract: Grokking is a puzzling phenomenon in neural networks where full generalization occurs only after a substantial delay following the complete memorization of the training data. Previous research has linked this delayed generalization to representation learning…
arXiv:2506.02935v4 Announce Type: replace Abstract: Multi-Task Learning (MTL) in Neural Combinatorial Optimization (NCO) is a promising approach to train a unified model capable of solving multiple Vehicle Routing Problem (VRP) variants. However, existing Reinforcement Learning (RL)-based multi-task methods can only…
