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arXiv:2604.02472v1 Announce Type: new Abstract: B2B sales organizations must identify "persuadable" accounts within zero-inflated revenue distributions to optimize expensive human resource allocation. Standard uplift frameworks …
arXiv:2604.02923v1 Announce Type: new Abstract: Large Language Models (LLMs), particularly those employing Mixture-of-Experts (MoE) architectures, have achieved remarkable capabilities across diverse natural language processing tasks. …
arXiv:2604.02713v1 Announce Type: new Abstract: Conversational AI is increasingly deployed in emotionally charged and ethically sensitive interactions. Previous research has primarily concentrated on emotional benchmarks …
arXiv:2604.02621v1 Announce Type: new Abstract: Reinforcement Learning (RL) has been shown to substantially improve the reasoning capability of small and large language models (LLMs), but …
arXiv:2604.02881v1 Announce Type: new Abstract: Weight-space model merging combines independently fine-tuned models without accessing original training data, offering a practical alternative to joint training. While …
arXiv:2604.02699v1 Announce Type: new Abstract: A previous study reported that E-Prime (English without the verb "to be") selectively altered reasoning in language models, with cross-model …
arXiv:2604.02450v1 Announce Type: new Abstract: Advances in training, post-training, and inference-time methods have enabled frontier reasoning models to win gold medals in math competitions and …
arXiv:2604.02342v1 Announce Type: new Abstract: In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in tasks such as node classification, link prediction, and …
arXiv:2112.07874v2 Announce Type: cross Abstract: We examine the extent to which, in principle, linguistic graph representations can complement and improve neural language modeling. With an …
arXiv:2604.02485v1 Announce Type: new Abstract: Confirmation bias, the tendency to seek evidence that supports rather than challenges one's belief, hinders one's reasoning ability. We examine …
arXiv:2604.02556v1 Announce Type: new Abstract: Large language models (LLMs) have grown beyond the memory capacity of single GPU devices, necessitating quantization techniques for practical deployment. …
arXiv:2604.02482v1 Announce Type: new Abstract: This paper proposes a framework for Structural Extrapolated Data GEneration (SEDGE) based on suitable assumptions on the underlying data generating …