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arXiv:2603.20899v1 Announce Type: new Abstract: Large language models exhibit strong reasoning capabilities, yet often rely on shortcuts such as surface pattern matching and answer memorization …
arXiv:2603.20907v1 Announce Type: new Abstract: As users increasingly turn to LLMs for practical and personal advice, they become vulnerable to being subtly steered toward hidden …
arXiv:2603.20939v1 Announce Type: new Abstract: Large language models are increasingly used as personal assistants, yet most lack a persistent user model, forcing users to repeatedly …
arXiv:2603.20957v1 Announce Type: new Abstract: Frontier LLM companies have repeatedly assured courts and regulators that their models do not store copies of training data. They …
arXiv:2603.20975v1 Announce Type: new Abstract: Multi-agent LLM systems, where multiple prompted instances of a language model independently answer questions, are increasingly used for complex reasoning …
arXiv:2603.21016v1 Announce Type: new Abstract: Large language models (LLMs) used for multiple-choice and pairwise evaluation tasks often exhibit selection bias due to non-semantic factors like …
arXiv:2603.21036v1 Announce Type: new Abstract: We investigate how large language models perform on low-resource languages by benchmarking eight LLMs across five experimental conditions in English, …
arXiv:2603.21038v1 Announce Type: new Abstract: As text-based computer-mediated communication (CMC) increasingly structures everyday interaction, a central question re-emerges with new urgency: How do users reconstruct …
arXiv:2603.21078v1 Announce Type: new Abstract: This study proposes a segmental-level prosodic probing framework to evaluate neural TTS models' ability to reproduce consonant-induced f0 perturbation, a …
arXiv:2603.20266v1 Announce Type: new Abstract: Despite the rapid advancements in Artificial Intelligence (AI), Stochastic Differential Equations (SDEs) remain the gold-standard formalism for modeling systems under …
arXiv:2603.20295v1 Announce Type: new Abstract: Uncovering causal structures from observational data is crucial for understanding complex systems and making informed decisions. While reinforcement learning (RL) …
arXiv:2603.20296v1 Announce Type: new Abstract: Recent advances in collaborative knowledge distillation have demonstrated cutting-edge performance for resource-constrained distributed multimedia learning scenarios. However, achieving such competitiveness …
