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arXiv:2603.03668v1 Announce Type: cross Abstract: Solving constraints involving inductive (aka recursive) definitions is challenging. State-of-the-art SMT/CHC solvers and first-order logic provers provide only limited support …
arXiv:2603.06565v1 Announce Type: new Abstract: Deep reinforcement learning agents are often misaligned, as they over-exploit early reward signals. Recently, several symbolic approaches have addressed these …
arXiv:2603.06064v1 Announce Type: new Abstract: Task planning, the problem of sequencing actions to reach a goal from an initial state, is a core capability requirement …
arXiv:2603.05528v1 Announce Type: cross Abstract: Recent multimodal systems often rely on separate expert modality encoders which cause linearly scaling complexity and computational overhead with added …
arXiv:2603.05514v1 Announce Type: cross Abstract: Systematic Literature Reviews (SLRs) are fundamental to scientific progress, yet the process is hindered by a fragmented tool ecosystem that …
arXiv:2603.05510v1 Announce Type: cross Abstract: Developing and deploying AI applications in organizations is challenging when human decision authority and oversight are underspecified across the system …
arXiv:2603.05525v1 Announce Type: cross Abstract: Deep learning, a subfield of machine learning, has gained importance in various application areas in recent years. Its growing popularity …
arXiv:2603.05517v1 Announce Type: cross Abstract: Autonomous LLM agents fail because long-horizon policy remains implicit in model weights and transcripts, while safety is retrofitted post hoc. …
arXiv:2603.05706v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is a promising tool for detecting misbehaviors and understanding the motivations of modern reasoning models. However, if …
arXiv:2603.05569v1 Announce Type: cross Abstract: Extracting insights from Electronic Health Record (EHR) databases often requires SQL expertise, creating a barrier for healthcare decision-making and research. …
arXiv:2603.05560v1 Announce Type: cross Abstract: We investigate the Continuous-Time Koopman Autoencoder (CT-KAE) as a lightweight surrogate model for long-horizon ocean state forecasting in a two-layer …
arXiv:2603.05565v1 Announce Type: cross Abstract: Generative AI compresses within-task skill differences while shifting economic value toward concentrated complementary assets, creating an apparent paradox: the technology …