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arXiv:2604.03058v1 Announce Type: new Abstract: LLMs can be socially sycophantic, affirming users when they ask questions like "am I in the wrong?" rather than providing …
arXiv:2604.03147v1 Announce Type: new Abstract: We present a method to identify a valence-arousal (VA) subspace within large language model representations. From 211k emotion-labeled texts, we …
arXiv:2604.02382v1 Announce Type: cross Abstract: The scale and complexity of modern cloud infrastructure have made Infrastructure-as-Code (IaC) essential for managing deployments. While large Language models …
arXiv:2604.02352v1 Announce Type: cross Abstract: Although LLMs are capable of generating functionally correct code, they also tend to produce less energy-efficient code in comparison to …
arXiv:2604.02709v1 Announce Type: new Abstract: The formal reasoning capabilities of LLMs are crucial for advancing automated software engineering. However, existing benchmarks for LLMs lack systematic …
arXiv:2604.02512v1 Announce Type: new Abstract: Large language models (LLMs) increasingly exhibit human-like patterns of pragmatic and social reasoning. This paper addresses two related questions: do …
arXiv:2604.02668v1 Announce Type: new Abstract: Large language models (LLMs) often exhibit sycophancy: agreement with user stance even when it conflicts with the model's opinion. While …
arXiv:2604.02335v1 Announce Type: new Abstract: Modeling groundwater flow in three-dimensional fractured crystalline media requires accounting for strong spatial heterogeneity induced by fractures. Fine-scale discrete fracture-matrix …
arXiv:2604.02350v1 Announce Type: cross Abstract: Neural networks excel at pattern recognition but struggle with constraint reasoning -- determining whether configurations satisfy logical or physical constraints. …
arXiv:2604.02733v1 Announce Type: new Abstract: Reasoning benchmarks typically evaluate whether a model derives the correct answer from a fixed premise set, but they under-measure a …
arXiv:2604.02615v1 Announce Type: new Abstract: Graph neural networks (GNNs) are a well-regarded tool for learned control of networked dynamical systems due to their ability to …
arXiv:2604.02474v1 Announce Type: new Abstract: Dynamical systems describe how a physical system evolves over time. Physical processes can evolve faster or slower in different environmental …