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arXiv:2603.22525v1 Announce Type: new Abstract: Operator learning models are rapidly emerging as the predictive core of digital twins for nuclear and energy systems, promising real-time …
arXiv:2603.22530v1 Announce Type: new Abstract: Unstructured Electronic Health Record (EHR) data, such as clinical notes, contain clinical contextual observations that are not directly reflected in …
arXiv:2603.22586v1 Announce Type: new Abstract: In-context learning (ICL) allows a model to adapt at inference time by conditioning on examples rather than updating parameters. Existing …
Abstract The governance of emerging technologies with increased autonomy in the military has become a topical issue in recent years, especially considering the rapid advances …
arXiv:2603.20578v1 Announce Type: new Abstract: The prevailing approach to improving large language model (LLM) reasoning has centered on expanding context windows, implicitly assuming that more …
arXiv:2603.21250v1 Announce Type: new Abstract: Logical reasoning encompasses deduction, induction, and abduction. However, while Large Language Models (LLMs) have effectively mastered the former two, abductive …
arXiv:2603.21272v1 Announce Type: new Abstract: Externalized reasoning is already exploited by transformer-based agents through chain-of-thought, but structured retrieval -- indexing over one's own reasoning state …
arXiv:2603.20222v1 Announce Type: new Abstract: Emotion detection is a central problem in NLP, with recent progress driven by transformer-based models trained on established datasets. However, …
arXiv:2603.20270v1 Announce Type: new Abstract: Generating executable simulations from natural language specifications remains a challenging problem due to the limited reasoning capacity of large language …
arXiv:2603.21029v1 Announce Type: new Abstract: Autonomous driving requires reliable reasoning over fine-grained 3D scene facts. Fine-grained question answering over multi-modal driving observations provides a natural …
arXiv:2603.20869v1 Announce Type: new Abstract: Financial time-series forecasting in real-world high-frequency markets is often hindered by delayed or partially stale observations caused by asynchronous data …
arXiv:2603.20911v1 Announce Type: new Abstract: Large language models make agent-based simulation more behaviorally expressive, but they also sharpen a basic methodological tension: fluent, human-like output …