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arXiv:2603.13274v1 Announce Type: new Abstract: Reasoning-oriented language models achieve strong performance by generating long chain-of-thought traces at inference time. However, this capability comes with substantial …
arXiv:2603.13275v1 Announce Type: new Abstract: Accurate prediction of surgical duration is pivotal for hospital resource management. Although recent supervised learning approaches-from machine learning (ML) to …
arXiv:2603.13276v1 Announce Type: new Abstract: Machine learning models deployed in real-world settings must operate under evolving data distributions and constrained computational resources. This challenge is …
arXiv:2603.13277v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) provide a powerful mechanism for decomposing the dense representations produced by Large Language Models (LLMs) into interpretable …
arXiv:2603.13279v1 Announce Type: new Abstract: This paper introduces and formalizes the Dynamic and Stochastic Vehicle Routing Problem with Emission Quota (DS-QVRP-RR), a novel routing problems …
arXiv:2603.13280v1 Announce Type: new Abstract: We introduce a Stability-Aware Frozen Euler autoencoder for Physics-Informed Tracking in Continuum Mechanics (SAFE-PIT-CM) that recovers material parameters and temporal …
arXiv:2603.13281v1 Announce Type: new Abstract: Multi model inference has recently emerged as a prominent paradigm, particularly in the development of agentic AI systems. However, in …
arXiv:2603.13282v1 Announce Type: new Abstract: Federated Learning (FL) with Low-Rank Adaptation (LoRA) has become a standard for privacy-preserving LLM fine-tuning. However, existing personalized methods predominantly …
arXiv:2603.13284v1 Announce Type: new Abstract: In this paper, we generate conceptual engineering designs of electric vertical take-off and landing (eVTOL) aircraft. We follow the paradigm …
arXiv:2603.13285v1 Announce Type: new Abstract: Existing evaluation methods largely rely on clean, static benchmarks, which can overestimate true model performance by failing to capture the …
arXiv:2603.13287v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for high-stakes decision-making, yet existing approaches struggle to reconcile scalability, interpretability, and reproducibility. …
arXiv:2603.13289v1 Announce Type: new Abstract: The increasing complexity of AI tasks has shifted the paradigm from monolithic models toward multi-agent large language model (LLM) systems. …