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arXiv:2603.06619v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a key driver of progress in large language models, but scaling RL to long chain-of-thought …
arXiv:2603.06621v1 Announce Type: new Abstract: Process Reward Models (PRMs) are rapidly becoming the backbone of LLM reasoning pipelines, yet we demonstrate that state-of-the-art PRMs are …
arXiv:2603.06622v1 Announce Type: new Abstract: Accurate short-term power load forecasting is important to effectively manage, optimize, and ensure the robustness of modern power systems. This …
arXiv:2603.06623v1 Announce Type: new Abstract: Large-scale flow matching models have achieved strong performance across generative tasks such as text-to-image, video, 3D, and speech synthesis. However, …
arXiv:2603.06625v1 Announce Type: new Abstract: Pavement condition data is important in providing information regarding the current state of the road network and in determining the …
arXiv:2603.06626v1 Announce Type: new Abstract: Traditional Mixture-of-Experts (MoE) training typically proceeds without any structural priors, effectively requiring the model to simultaneously train expert weights while …
arXiv:2603.06632v1 Announce Type: new Abstract: Illicit transaction detection is often driven by transaction level attributes however, fraudulent behavior may also manifest through network structure such …
arXiv:2603.06634v1 Announce Type: new Abstract: Many scientific problems in the context of machine learning can be reduced to the search of polynomial answers in appropriate …
arXiv:2603.06635v1 Announce Type: new Abstract: Recent progress in language modeling has expanded the range of tasks that can be approached through natural language interfaces, including …
arXiv:2603.06636v1 Announce Type: new Abstract: Due to the strong context-awareness capabilities demonstrated by large language models (LLMs), recent research has begun exploring their integration into …
arXiv:2603.06638v1 Announce Type: new Abstract: The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning. Yet, existing …
arXiv:2603.06642v1 Announce Type: new Abstract: Test-Time Training (TTT) language models achieve theoretically infinite context windows with an O(1) memory footprint by replacing the standard exact-attention …