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arXiv:2604.06202v1 Announce Type: new Abstract: Large language models (LLMs) have transformed natural language processing, yet their capabilities remain uneven across languages. Most multilingual models are …
arXiv:2604.06727v1 Announce Type: new Abstract: Heterogeneity in time series data is more pronounced than in vision or language, as temporal dynamics vary substantially across domains …
arXiv:2604.06296v1 Announce Type: new Abstract: AI agents are increasingly deployed in real-world applications, including systems such as Manus, OpenClaw, and coding agents. Existing research has …
arXiv:2604.06349v1 Announce Type: new Abstract: Generalizing from a single labeled source domain to unseen target domains, without access to any target data during training, remains …
arXiv:2604.06265v1 Announce Type: new Abstract: Quantum-inspired tensor networks algorithms have shown to be effective and efficient models for machine learning tasks, including anomaly detection. Here, …
arXiv:2604.06208v1 Announce Type: new Abstract: A significant amount of data held in Oncology Electronic Medical Records (EMRs) is contained in unstructured provider notes -- including …
arXiv:2604.06336v1 Announce Type: new Abstract: Graph Transformers have recently attracted attention for molecular property prediction by combining the inductive biases of graph neural networks (GNNs) …
arXiv:2604.06199v1 Announce Type: new Abstract: As autonomous AI agents increasingly inhabit online environments and extensively interact, a key question is whether synthetic collectives exhibit self-regulated …
arXiv:2604.06210v1 Announce Type: new Abstract: As LLMs are globally deployed, aligning their cultural value orientations is critical for safety and user engagement. However, existing benchmarks …
arXiv:2604.06613v1 Announce Type: new Abstract: Modern reasoning models continue generating long after the answer is already determined. Across five model configurations, two families, and three …
arXiv:2604.06491v1 Announce Type: new Abstract: We introduce Discrete flow Matching policy Optimization (DoMinO), a unified framework for Reinforcement Learning (RL) fine-tuning Discrete Flow Matching (DFM) …
arXiv:2604.06492v1 Announce Type: new Abstract: We study stochastic convex optimization (SCO) with heavy-tailed gradients under pure epsilon-differential privacy (DP). Instead of assuming a bound on …