This page shows the most relevant public items for machine-learning/month/202602, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Feb 13, 2026 • arXiv • Zijing Ou, Jacob Si, Junyi Zhu, Yingzhen Li
Diffusion alignment aims to optimize diffusion models for downstream objectives. While existing methods based on RL achieve success, they often suffer from reward over-optimization and mode collaps...
Paper • Feb 20, 2026 • bioRxiv • L. Zheng, J. Drucker, A. Chen, W. Wang
Designing therapeutic peptides requires balancing binding affinity with complex ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, a task that overwhelms traditional ...
Paper • Feb 10, 2026 • bioRxiv • Eduardo da Veiga Beltrame
This paper presents a systematic benchmark of architectural hyperparameters for variational autoencoder (VAE) methods in single-cell RNA-seq batch integration. The study compares scVI, MrVI, and LD...
Paper • Feb 16, 2026 • bioRxiv • Anonymous Team
The paper introduces an automated deep learning tool for the real-time assessment of pain in mice using the Mouse Grimace Scale (MGS). The model was trained on a large and diverse dataset with non-...
Paper • Feb 23, 2026 • bioRxiv • Zhang, X., Jeong, E., Yan, C., Feng, Y., Lyu, L., Guo, X., Chen, Y.
CellAwareGNN is a new graph neural network (GNN) foundation model that integrates single-cell enhanced knowledge graphs for drug indication prediction. By modeling the complex relationships between...
Paper • Feb 1, 2026 • arXiv • Fan Chen, Sinho Chewi, Constantinos Daskalakis, Alexander Rakhlin
We present algorithms for diffusion model sampling which obtain δ-error in polylog(1/δ) steps, given access to eO(δ)-accurate score estimates in L2. This is an exponential improvement over all prev...
Paper • Feb 17, 2026 • arXiv • Moritz Miller, Florent Draye, Bernhard Schölkopf
This paper addresses the fundamental challenge of 'feature disentanglement' in modern deep learning. We propose an Orthogonality Regularization technique to identify features that are both interpre...
Blog • Feb 14, 2026 • Medium • Shane Collins
In 2026, ML has moved from the cloud to our pockets, handling the 'normal messiness' of real life. This post explores the small, invisible ways AI has cleaned up our lives, from transcripts that ha...
Paper • Feb 24, 2026 • arxiv.org • Jingyu Xiao, Man Ho Lam, Ming Wang, Yuxuan Wan, Junliang Liu, Yintong Huo, Michael R. Lyu
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in automated front-end engineering, e.g., generating UI code from visual designs. However, existing front-end UI c...
Paper • Feb 13, 2026 • arXiv • Miaosen Zhang, Xu Yang, Qi Dai, Chong Luo
Supervised fine-tuning (SFT) is efficient but often yields inferior generalization compared to RL, a gap driven by RL's use of on-policy data. We propose a framework to bridge this chasm by enablin...
Paper • Feb 8, 2026 • arXiv • Fan Yang, Rui Meng, Trudi Di Qi, Yuxin Wen
We argue that while replacing data with model parameters characterizes the present of Federated Learning (FL), replacing parameters with preferences represents a more scalable and privacy-preservin...
Paper • Feb 12, 2026 • arXiv • Xiaoxiao Wang, Chunxiao Li, Junying Wang, Zicheng Zhang
The paper introduces STAR (STatistical and Agentic Reasoning), a novel framework designed to predict the performance of large models across diverse benchmarks from limited observations. Existing st...
Paper • Feb 12, 2026 • arXiv • Yurong Chen, Yu He, Michael I. Jordan, Fan Yao
Modern language agents must operate over long-horizon, multi-turn interactions, but most rely on full-context prompting which leads to unbounded memory growth. We introduce MEM1, an end-to-end rein...
Paper • Feb 13, 2026 • arXiv • Zhiwei Liu, Yuan He, Hao Peng, Zhiqiang Chen
Recently, diffusion LLMs have emerged as a promising alternative to autoregressive models. Diffusion LLMs address traditional limitations with two advances, including multi-token prediction (i.e., ...
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