Category
arXiv:2602.17130v1 Announce Type: new Abstract: We propose a novel parallel algorithm for decomposing hard CircuitSAT instances. The technique employs specialized constraints to partition an original …
arXiv:2602.17145v1 Announce Type: new Abstract: As the need for more accurate and powerful Convolutional Neural Networks (CNNs) increases, so too does the size, execution time, …
arXiv:2602.17189v1 Announce Type: new Abstract: In this paper we present Texo, a minimalist yet highperformance formula recognition model that contains only 20 million parameters. By …
arXiv:2602.17217v1 Announce Type: new Abstract: Efficiently navigating complex environments requires agents to internalize the underlying logic of their world, yet standard world modelling methods often …
arXiv:2602.17221v1 Announce Type: new Abstract: Generative AI is reshaping knowledge work, yet existing research focuses predominantly on software engineering and the natural sciences, with limited …
arXiv:2602.17222v1 Announce Type: new Abstract: Predicting human decision-making in high-stakes environments remains a central challenge for artificial intelligence. While large language models (LLMs) demonstrate strong …
arXiv:2602.17229v1 Announce Type: new Abstract: The black-box nature of Large Language Models necessitates novel evaluation frameworks that transcend surface-level performance metrics. This study investigates the …
arXiv:2602.17234v1 Announce Type: new Abstract: To evaluate whether LLMs can accurately predict future events, we need the ability to \textit{backtest} them on events that have …
arXiv:2602.17245v1 Announce Type: new Abstract: The Web is evolving from a medium that humans browse to an environment where software agents act on behalf of …
arXiv:2602.16802v1 Announce Type: new Abstract: While Reinforcement Learning with Verifiable Rewards (RLVR) has shown strong effectiveness in reasoning tasks, it cannot be directly applied to …
arXiv:2602.16811v1 Announce Type: new Abstract: Recent advancements in Natural Language Processing and Deep Learning have enabled the development of Large Language Models (LLMs), which have …
arXiv:2602.16813v1 Announce Type: new Abstract: Language models based on discrete diffusion have attracted widespread interest for their potential to provide faster generation than autoregressive models. …
