arXiv:2511.03939v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is the standard for aligning Large Language Models (LLMs), yet recent progress has moved beyond canonical text-based methods. This survey synthesizes the new frontier of alignment research by addressing…
arXiv:2411.06635v4 Announce Type: replace Abstract: Single-cell RNA sequencing enables high-resolution analysis of cellular heterogeneity, yet disentangling biological signal from batch effects remains a major challenge. Existing batch-correction algorithms suppress or discard batch-related variation rather than modeling it. We propose scMEDAL,…
arXiv:2511.03953v1 Announce Type: new Abstract: We address the problem of quickest change detection in Markov processes with unknown transition kernels. The key idea is to learn the conditional score $nabla_{mathbf{y}} log p(mathbf{y}|mathbf{x})$ directly from sample pairs $( mathbf{x},mathbf{y})$, where both…
arXiv:2505.07683v3 Announce Type: replace Abstract: The Cancer Genome Atlas (TCGA) has enabled novel discoveries and served as a large-scale reference dataset in cancer through its harmonized genomics, clinical, and imaging data. Numerous prior studies have developed bespoke deep learning models…
arXiv:2511.03966v1 Announce Type: new Abstract: The need to remove specific student data from cognitive diagnosis (CD) models has become a pressing requirement, driven by users’ growing assertion of their “right to be forgotten”. However, existing CD models are largely designed…
arXiv:2505.23653v2 Announce Type: replace Abstract: Recent work suggests that large language models (LLMs) can perform multi-hop reasoning implicitly — producing correct answers without explicitly verbalizing intermediate steps — but the underlying mechanisms remain poorly understood. In this paper, we study…
arXiv:2511.03972v1 Announce Type: new Abstract: An important question in deep learning is how higher-order optimization methods affect generalization. In this work, we analyze a stochastic Gauss-Newton (SGN) method with Levenberg-Marquardt damping and mini-batch sampling for training overparameterized deep neural networks…
arXiv:2508.15706v2 Announce Type: replace Abstract: Communication-efficient distributed training algorithms have received considerable interest recently due to their benefits for training Large Language Models (LLMs) in bandwidth-constrained settings, such as across datacenters and over the internet. Despite reducing communication frequency, these…
arXiv:2511.03976v1 Announce Type: new Abstract: Since its emergence, SARS-CoV-2 has demonstrated a rapid and unpredictable evolutionary trajectory, characterized by the continual emergence of immune-evasive variants. This poses persistent challenges to public health and vaccine development. While large-scale generative pre-trained transformers…
arXiv:2509.18629v2 Announce Type: replace Abstract: Foundation models excel across diverse tasks, but adapting them to specialized applications often requires fine-tuning, an approach that is memory and compute-intensive. Parameter-efficient fine-tuning (PEFT) methods mitigate this by updating only a small subset of…
