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arXiv:2603.18046v1 Announce Type: new Abstract: When users query proprietary LLM APIs, they receive outputs with no cryptographic assurance that the claimed model was actually used. …
arXiv:2603.18056v1 Announce Type: new Abstract: Extreme neural network sparsification (90% activation reduction) presents a critical challenge for mechanistic interpretability: understanding whether interpretable features survive aggressive …
arXiv:2603.18078v1 Announce Type: new Abstract: We present the \textbf{Variational Phasor Circuit (VPC)}, a deterministic classical learning architecture operating on the continuous $S^1$ unit circle manifold. …
arXiv:2603.18079v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown strong results on multi-turn tool-use tasks, yet they operate in isolation during training, …
arXiv:2603.18083v1 Announce Type: new Abstract: Conventional federated learning (FL) frameworks often suffer from training degradation due to data uncertainty and heterogeneity across local clients. Probabilistic …
arXiv:2603.18088v1 Announce Type: new Abstract: Constraints are essential for stabilizing reinforcement learning fine-tuning (RFT) and preventing degenerate outputs, yet they inherently conflict with the optimization …
arXiv:2603.18107v1 Announce Type: new Abstract: Deep learning models in quantitative finance often operate as black boxes, lacking interpretability and failing to incorporate fundamental economic principles …
arXiv:2603.18111v1 Announce Type: new Abstract: Contrastive learning methods for time series anomaly detection (TSAD) heavily depend on the quality of negative sample construction. However, existing …
arXiv:2603.18112v1 Announce Type: new Abstract: Distributed training increases the number of batches processed per iteration either by scaling-out (adding more nodes) or scaling-up (increasing the …
arXiv:2603.18113v1 Announce Type: new Abstract: As large language models (LLMs) increasingly shape content generation, interaction, and decision-making across the Web, aligning them with human values …
arXiv:2603.18115v1 Announce Type: new Abstract: Phenotypic characterization is essential for understanding heterogeneity in chronic diseases and for guiding personalized interventions. Long COVID, a complex and …
arXiv:2603.18174v1 Announce Type: new Abstract: Conflict detection in policy languages is a solved problem -- as long as every rule condition is a crisp Boolean …