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 02:45 — 2026-11-16 02:45（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 5 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 · v8arXiv:2608.05543

omni-macos: On-Device Omni-Modal Search on Apple Silicon

Han Xiao

原文摘要

We present omni-macos, a search engine that embeds text, code, documents, images, audio and video into one representation space and runs on the Mac that already holds the files, so the files, the queries and the vectors stay on that machine. A background indexer and an interactive search box share the device. Each distinct chunk is embedded and stored once, and every file becomes searchable from a small first share of its content. Indexer work is split into smaller units while the user types, and one memory setting reaches every allocator found to draw on unified memory. Once the index is large, queries scan a one-bit replica of it, rescore a shortlist exactly and rank every modality in one list on a common score scale. The engine can also leave the GPU entirely, with the encoder on the Neural Engine and search on the CPU. We measure omni-macos on five Macs spanning an eightfold range of accelerator width and a thirty-twofold range of memory, and its ranking on a labeled multilingual and multimodal testbed. omni-macos is open source under Apache 2.0 at https://github.com/hanxiao/omni-macos.

cs.IR

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10144

Dataset Collections: Challenges of Large-Scale Data Aggregation in 3D Medical Image Datasets

Yannick Kirchhoff, Saikat Roy, Elisa Stegmeier, Hamideh Haghiri, Constantin Ulrich, Maximilian Rouven Rokuss, Tassilo Wald, Karol Gotkowski 等 33 位作者

原文摘要

The development of deep learning-based medical AI depends on high-quality annotated data. Yet, dataset creation remains constrained by the need for expert radiologist annotations, limiting curated datasets to relatively small cohorts. To overcome this limitation, recent efforts increasingly aggregate disparate public datasets into large-scale dataset collections. However, combining datasets at scale often obscures data provenance, introducing substantial risks of reuse propagation, unintended leakage, bias amplification and distorted data distributions. In this work, we study these problems on a massive collection of 565k 3D radiological images across 1034 public datasets and provide practical resources to address them. Firstly, we formalize dataset collections as a distinct paradigm in medical image analysis and characterize their structural challenges, including reuse propagation, duplication, and provenance fragmentation. Secondly, we perform a large-scale empirical study of duplication on this collection and demonstrate extensive exact and near-duplicate reuse across public datasets via perceptual hashing and manual tracing, identifying a lower bound of 118k duplicate pairs. We make the perceptual hashes publicly accessible for comparison through our website, providing researchers with a standardized reference for identifying potential overlaps and shared provenance. Thirdly, since hashing alone cannot detect all duplicates, we introduce MAP (Metadata for Aggregation and Provenance), a lightweight, portable metadata profile for documenting provenance, known overlaps, and deduplication decisions in dataset collections. Our contributions help dataset collections shift from coarse aggregations to reliable and provenance-aware data scaling mechanisms in medical image analysis. A web tool supporting our work is available at https://mic-dct.dkfz.de/.

eess.IV · cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03078

Cross-Layer Refinement and Frequency-aware Fusion Network for Multi-Source Remote Sensing Image Classification

Wenhan Xiao, Chuanzheng Gong, Feng Gao, Junyu Dong, Qian Du

原文摘要

Multi-source remote sensing image classification benefits from complementary spectral and structural information. However, identifying discriminative cross-modal features remains challenging. This letter proposes a Cross-Layer Refinement and Frequency-aware Fusion Network (CRFNet) within a lightweight dual-branch architecture. Specifically, a Cross-Layer Refinement Attention (CLRA) module propagates selected attention relationships across successive layers and employs top relation selection to progressively strengthen discriminative spatial dependencies while suppressing irrelevant responses. Furthermore, a Frequency-aware Complementary Fusion (FCF) module combines frequency-wise interactions with cross-modal channel attention in the Fourier domain. The resulting representations are then converted into spatial-domain gates to selectively modulate complementary features. Experiments on the Houston 2013 and Augsburg datasets demonstrate that the proposed CRFNet consistently outperforms several state-of-the-art methods. The codes are publicly available at https://github.com/oucailab/CRFNet

eess.IV · cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v4arXiv:2010.08936

Arboricity games: the core and the nucleolus

Han Xiao, Qizhi Fang

原文摘要

The arboricity of a graph is the minimum number of forests required to cover all its edges. In this paper, we examine arboricity from a game-theoretic perspective and investigate cost-sharing in the minimum forest cover problem. We introduce the arboricity game as a cooperative cost game defined on a graph. The players are edges, and the cost of each coalition is the arboricity of the subgraph induced by the coalition. We study properties of the core and propose an efficient algorithm for computing the nucleolus when the core is not empty. In order to compute the nucleolus in the core, we introduce the prime partition which is built on the lattice of maximum-density edge sets. The prime partition decomposes the edge set of a graph into a non-prime set and a partially ordered collection of prime sets defined from minimal densest minors and their invariant precedence relation. Moreover, edges from the same set of the partition always have the same value in a core allocation. Consequently, when the core is not empty, the prime partition significantly reduces the number of variables and constraints required in the linear programs of Maschler's scheme and allows us to compute the nucleolus in polynomial time. Besides, the prime partition provides a graph decomposition analogous to the celebrated core decomposition and the density-friendly decomposition, which may be of independent interest.

cs.GT · cs.DM

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34728

SKALOS: Learning to Distinguish Similar Skills with Capability Graphs

Wenhan Xiao, Ziwei Zhang, Xingcheng Fu, Chuanyue Yu, Haonan Yuan, Qingyun Sun, Runhua Xu, Jianxin Li

原文摘要

Skills provide LLM agents with reusable procedural knowledge, making appropriate skill selection essential for reliable agent behavior. As skill libraries expand, distinguishing semantically similar candidates becomes increasingly difficult. Existing methods encode or rewrite skill descriptions for relevance-based ranking, yet heterogeneous descriptions can obscure capability differences. To address this challenge, we propose SKALOS, a graph-native reranker that compares skills via explicit capability evidence. Its multi-hierarchy graph aligns equivalent expressions across skills while preserving skill-specific capability evidence. Since individually relevant capability elements may not jointly support a query, semiring-based capability witness learning scores each branch by its weakest evidence, checks joint support for the extracted requirements within one implementation, and selects the strongest alternative. To reduce the influence of extraction errors, we develop ranking-feedback graph calibration that adjusts unreliable evidence for candidate comparison. Experiments on SkillRet show that SKALOS improves NDCG@10 by 2.68 percentage points over a matched text-only reranker. Witness-guided skill selection also increases ALFWorld task success rate from 22% to 29%.

cs.AI

查看论文
arXiv ↗PDF ↗