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
+2已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-15 01:33 · 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 2026-11-14 02:07 — 2026-11-15 02:07（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：2026-11-15 01:33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 2026-11-15 01:32（北京时间）
版本更新 · v2arXiv:2611.12140

Visible Tails: Characterizing Adaptation to Unknown Moments in Heavy-Tailed Bandits

Tuan Ngoc Do, Hai Duc Nguyen

原文摘要

Heavy-tailed bandit learners must tell a poor arm from an arm whose favorable rewards have not yet appeared. We characterize this obstruction through a scale of one-sided visibility conditions on the optimal reward law: the mean hidden in a rare event is at most a constant multiple of a retained central moment. A quantile-trimmed UCB index that is given the visibility constants attains arm-specific moment rates at every exponent, with no lower bound on the exponent. Conversely, every strategy with a fixed exploration budget has polynomial regret on each law that violates the corresponding condition. For the variance benchmark we determine the order of both budget constants in the visibility level. The frontier depends on the largest exponent the benchmark promises, and the resulting classes are strictly nested. The same index has logarithmic expected regret when rewards are integrable, with a coefficient that can be exponential in the inverse gap.

cs.LG · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.12601

双状态槽注意力：解耦外观与身份的视频物体中心学习

Dual-State Slot Attention: Decoupling Appearance and Identity for Video Object-Centric Learning

Sieu Tran, Duc Nguyen, Hao Vo, Khoa Vo, Ngan Le

AI 速读 基于摘要

提出DSSA，将槽向量拆分为逐帧外观的局部状态与跨帧稳定的身份状态，并抑制弱匹配槽的虚假更新，以提升无监督视频物体中心学习的身份稳定性。

cs.CV

arXiv ↗PDF ↗
查看原文摘要

Unsupervised video object-centric learning aims to decompose dynamic scenes into persistent, object-level representations without supervision. However, existing slot-based methods struggle to maintain stable object identity in challenging settings such as rapid motion and partial occlusion. First, they typically encode both the per-frame appearance of an object and its identity across frames in a single slot vector, creating an objective conflict that leads to slot swapping: reconstruction requires sensitivity to transient visual changes, whereas temporal consistency requires invariance to them. Second, the token renormalization used in Slot Attention can amplify weakly attending slots, allowing them to absorb tokens from other objects and destabilize slot-to-object correspondence. We propose Dual-State Slot Attention (DSSA), a fully self-supervised framework that addresses these limitations by separating appearance from identity and by reducing spurious updates from weakly matching slots. DSSA decomposes each slot into a local state for per-frame appearance and an identity state for temporally stable object information, thereby aligning reconstruction and temporal consistency with separate representations. The identity state is updated through a learned recurrent transition that acts as a temporal filter on the local state, while competition-modulated aggregation (CMA) down-weights updates from weakly matching slots and prevents them from absorbing tokens from other objects. Experiments on MOVi-C, MOVi-D, and YouTube-VIS demonstrate that DSSA consistently improves segmentation quality and temporal consistency over prior methods, while also yielding stronger downstream object recognition and video dynamics prediction. Code is available at https://uark-aicv.github.io/dssa/.

版本更新 · v2arXiv:2610.14749

Solving Distributed Flexible Job Shop Scheduling Problem Using SAT

Phuong Dang Anh, Duc Nguyen Kim Trung, To Van Khanh

原文摘要

The Distributed Flexible Job Shop Scheduling Problem (DFJSP) requires the joint determination of factory assignment, machine selection, and operation sequencing to minimize the makespan. This paper presents an exact SAT-based approach built on an efficient SAT formulation that exploits problem structure through propagation, symmetry breaking, and incremental SAT solving to effectively address these coupled decisions. The proposed method is evaluated on 76 standard benchmark instances from the Fattahi, Brandimarte, and rdata datasets under two- and three-factory configurations. Together with previously established results, the new SAT optimality certificates bring the total number of proven-optimal instances to 72 out of 76, including three newly certified instances; for two of these instances, SAT also improves the previous best-known makespans. Across the benchmark configurations, SAT achieves the best results on four of the five configurations and remains competitive with strong exact approaches such as CP and LBBD1_CPIL, as well as existing metaheuristic methods. These results demonstrate that the proposed SAT framework is an effective exact approach for the DFJSP, combining strong solution quality with rigorous optimality certification and offering a complementary exact method for future DFJSP studies and evaluations on additional benchmark instances.

cs.LO

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02521

On the Convergence of Alternating Regret Matching Algorithms

Anh Duc Nguyen, Ioannis Anagnostides, Brian Hu Zhang, Tuomas Sandholm

原文摘要

Alternating updates, that is, where each player is updated in turn, tend to typically perform better in regret matching algorithms than simultaneous updates. Alternation within the regret matching family has long been a central ingredient of practical solvers for zero-sum games. Yet our theoretical understanding is lagging behind. In this paper, we address this gap by developing a systematic way of analyzing such algorithms. Our framework centers on an alternating analog of the RVU property that we introduce, which we use to obtain the optimal $T^{-1}$ average-iterate convergence within this family. In particular, we establish this for two simple variants of predictive regret matching$^+$: i) increasing-regret predictive regret matching$^+$ and ii) half-prediction regret matching$^+$. Interestingly, we find that half prediction is the unique prediction multiplier that satisfies our alternating RVU property, giving it a strong theoretical justification. Going beyond average-iterate convergence, we refine our framework to obtain $T^{-1/2}$ best-iterate convergence and further establish convergence of their strategy profiles to a single Nash equilibrium, without assuming equilibrium uniqueness. We go on to establish separations between alternating and simultaneous updates, including a game in which half-prediction regret matching$^+$ converges under alternating updates but fails to converge in terms of the last iterate under simultaneous updates. This provides theoretical support for the empirical observation that alternation can stabilize learning dynamics. Finally, experiments on 15 game benchmarks demonstrate that the strong theoretical properties of the half-prediction mechanism translate to strong practical performance, being on par with state-of-the-art solvers.

cs.GT · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37215

Retrieve to Act: Motion Primitive Graph Retrieval for Robust VLA Execution

Pham Tri Quang, Bao-Ngoc Dao, Minh Duc Nguyen, Duc-Duy Nguyen, Hung T. Ho, Linh Dang Le, Trong-Bao Ho, Thien-Loc Ha 等 18 位作者

原文摘要

Vision-Language-Action (VLA) policies demonstrate remarkable semantic generalization, yet remain highly vulnerable to out-of-distribution (OOD) visual shifts during execution. While existing test-time methods attempt to suppress spurious visual cues, they fundamentally overlook the rich, uncorrupted kinematic priors embedded in the policy's own training experience. We introduce Retrieve to Act (R2A), a test-time scaling framework that formulates OOD robustness purely as a geometric motion primitive retrieval problem. R2A bypasses fragile visual features by structuring successful training demonstrations into an offline spatiotemporal motion primitive graph. During inference, a query-dependent Graph Neural Network dynamically localizes the robot's ongoing execution within this graph, retrieving structurally compatible future motion primitives. These retrieved geometric priors are then seamlessly harmonized with the frozen VLA's predicted actions via a learned, dimension-wise gating mechanism, adaptively rectifying erroneous spatial trajectories while preserving the policy's local reactivity. Extensive experiments across three state-of-the-art VLA architectures, GR00T N1.7, $π_{0.5}$, and StableVLA, demonstrate that R2A consistently and significantly enhances robustness against diverse contextual perturbations. R2A achieves absolute success rate improvements of up to +12% in simulation benchmarks (LIBERO-Plus, RoboTwin2.0-Plus) and +40% in real-world deployments, without requiring any finetuning of the base policies. Project page: https://retrieve2act.github.io/r2a/

cs.RO · cs.AI

查看论文
arXiv ↗PDF ↗