跳到正文
研踪PaperTrail

论文库

检索计算机领域论文，按研究方向、作者或关键词筛选。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+0新增论文
+0其中 AI 相关
+0已有论文修订
+93新增 AI 导读

arXiv 最近完成扫描：2026-11-16 01:34 · 全站最近入库：2026-11-14 15:09各信源进度 →

统计口径与数据新鲜度

统计 2026-11-15 03:51 — 2026-11-16 03:51（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-16 01:34最近发现新增版本：2026-11-14 15:09库内论文最新更新时间：2026-11-13 02:59

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

23,348 已收录论文19,580 AI 相关6,729 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 4 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-16 01:33（北京时间）
版本更新 · v4arXiv:2602.17739

GeneZip: Region-Aware Compression for Long Context DNA Modeling

Jianan Zhao, Xixian Liu, Zhihao Zhan, Xinyu Yuan, Hongyu Guo, Jian Tang

原文摘要

Long-context DNA modeling is computationally expensive, motivating existing approaches to reduce sequence length through uniform downsampling. However, functional information and sequence redundancy are unevenly distributed across the genome, calling for finer resolution in functionally dense regions and stronger compression of redundant sequence. We introduce GeneZip, a region-aware DNA compression framework that learns where to allocate a limited token budget. Building on H-Net-style dynamic routing, GeneZip combines a Region-Aware Ratio objective, which uses regional labels derived from GENCODE gene annotations to specify compression targets during training, with bounded routing for stable token counts. At inference, it compresses raw DNA without annotations. GeneZip provides three main benefits. First, it preserves predictive utility under high compression: GeneZip-70M achieves 137.6-fold compression with a validation perplexity increase of 0.31 relative to uncompressed Mamba2-84M, and the best average rank across four DNALongBench tasks among compression models with leakage-controlled pretraining. Second, it learns region-aware allocation with emergent repeat-aware compression. Alternative regional priors yield task-dependent performance trade-offs. Without repeat-specific supervision, GeneZip compresses transposable-element-derived and tandem repeats more strongly than matched non-repeat sequence; its tandem-repeat compression preference transfers to a nine-species evaluation panel without further training. Third, it enables efficient long-context modeling: on 450-kb inputs, GeneZip-70M achieves 36.1-fold higher inference throughput than JanusDNA-7.7M on a single H100 GPU.

q-bio.GN · cs.AI · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10838

Learning Biological Hierarchies in Single-Cell Foundation Models

Xiangyu Guo, Ricardo Henao

原文摘要

Cell types are organized by taxonomic relationships that define lineages in the so-called cell ontology. However, most existing foundation models typically ignore cell-type lineages encoded in this cell ontology. We introduce a biologically informed foundation model called scOntoFM, which embeds hierarchical cell ontology into representation learning. Using Lowest Common Ancestor (LCA) distances from the ontology graph, our framework pairs efficient offline triplet sampling with hierarchical cell-ontology learning. This approach jointly preserves local neighborhoods and shapes the global embedding geometry to reflect the ontology structure. Across diverse cell- and gene-level benchmarks, the model consistently improves performance, with especially strong gains in zero-shot settings. Our model also balances batch integration with biological structure preservation, providing complementary perspectives to existing embedding evaluation frameworks and supporting scalable biological discovery.

cs.LG · cs.AI · q-bio.QM

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06166

FaCoDiff: Factor-Correlated Diffusion for Multivariate Probabilistic Forecasting

Jiaxuan Li, Yao Liu, Tingting Dai, Yuewei Xue, Shijie Dong, Zixiang Jin, Xiaoyu Guo, Qiao Liu

原文摘要

Multivariate probabilistic forecasting requires modeling cross-variable dependence alongside marginal uncertainty. The challenge is to learn this dependence while controlling marginal noise scales and avoiding dense covariance costs. We propose Factor-Correlated Diffusion (FaCoDiff), which couples a compact conditional statistical estimator with structured denoising. History-conditioned factor loadings use distinct coefficients for forecast-center correction, scale adjustment and noise dependence. Diagonal compensation preserves the estimated marginal noise variances, while low-rank operators enable residual Gaussian likelihood evaluation and noise transport without dense covariance storage or factorization. Factor-derived feature modulation explicitly conditions the denoiser, and a geometry-aware objective accounts for errors after noise transport. The same conditional geometry governs forward perturbations and reverse sampling. Across nine datasets, FaCoDiff achieves the lowest mean variogram score on eight datasets and ranks among the top two on 43 of 54 dataset-metric combinations in the reported comparisons. Matched ablations on Solar and Weather show that factor covariance improves mean Energy and Variogram scores under all four combinations of center and scale correction. Disrupting cross-variable sample pairings while preserving marginal trajectories worsens joint and aggregate scores on both datasets, demonstrating the predictive value of generated cross-variable dependence.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.19009

Guiding Distribution Matching Distillation with Gradient-Based Reinforcement Learning

Linwei Dong, Ruoyu Guo, Ge Bai, Quan Zheng, Yawei Luo, Changqing Zou

原文摘要

Diffusion distillation, exemplified by Distribution Matching Distillation (DMD), has shown great promise in few-step generation but often sacrifices quality for sampling speed. While integrating Reinforcement Learning (RL) into distillation offers potential, a naive fusion of these two objectives relies on suboptimal raw sample evaluation. This sample-based scoring creates inherent conflicts with the distillation trajectory and produces unreliable rewards due to the noisy nature of early-stage generation. To overcome these limitations, we propose GDMD, a novel framework that redefines the reward mechanism by prioritizing distillation gradients over raw pixel outputs as the primary signal for optimization. By reinterpreting the DMD gradients as implicit target tensors, our framework enables existing reward models to directly evaluate the quality of distillation updates. This gradient-level guidance functions as an adaptive weighting that synchronizes the RL policy with the distillation objective, effectively neutralizing optimization divergence. Empirical results show that GDMD sets a new SOTA for few-step generation. Specifically, our 4-step models outperform the quality of their multi-step teacher and substantially exceed previous DMDR results in GenEval and human-preference metrics, exhibiting strong scalability potential.

cs.LG · cs.CV

查看论文
arXiv ↗PDF ↗