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新论文 · v1arXiv:2611.04973

Software Design Patterns for Latency Measurements with eBPF in SDN Environments

Pedro Lucas M. S. Rosa, Bruno L. Dalmazo, Leticia S. Machado, Jéferson Campos Nobre

原文摘要

This work proposes, implements, and evaluates a modular architecture based on software design patterns applied to latency measurement in Software-Defined Networks (SDN) using the Extended Berkeley Packet Filter (eBPF) and the Express Data Path (XDP). Unlike approaches that focus solely on measurement precision, this study examines how design patterns can structure observability systems in an extensible and maintainable way while preserving data-plane efficiency. The proposed architecture applies the Observer, Strategy, and Factory Method patterns to modularize the metric collection and analysis pipeline, allowing dynamic integration of policies and data sinks. The experimental environment was implemented with Mininet, Python agents, and eBPF/XDP programs, and was evaluated in terms of processing time, overhead, and evolution effort. The results show that the introduction of design patterns shows no clear evidence of measurable performance degradation, maintaining continuous measurements with a minimum of 5% overhead. The findings suggest that software engineering practices may be applied to the construction of eBPF-based monitoring and observability systems.

cs.PF · cs.NI · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04972

Caging Vision-Language Navigation for Legged Robots via Barrier-Certified Safety Architecture

Abhijeet M. Kulkarni, Saimouli Katragadda, Yuze Du, Zijun Sha, Guoquan Huang

原文摘要

By harnessing the inherent commonsense capabilities of pretrained Large Language Models (LLMs) and Vision-Language Models (VLMs), current Vision-Language Action(VLA) models for legged robots excel at interpreting language-based navigation commands but lack the physical grounding and explicit safety assurance required for real-world operation. Therefore, this paper aims to cage Legged-Vision-Language Navigation (VLN) through a safety architecture that provides physical grounding and ensures navigation safety. The key idea is not to abandon the powerful capabilities of end-to-end VLA planning, but rather to augment and cage them. In particular, we integrate a high-level VLA planner with a low-level physical-grounding layer that leverages efficient state estimation, in-situ 3D spatial perception, and an Signed Distance Field (SDF) representation to generate executable trajectories. We provide a navigation planner with provable collision-avoidance guarantees under the stated assumptions by combining a low-dimensional Bezier-parameterized MPPI planner with Discrete Exponential Barrier Function (dEBF) constraints. The proposed Legged-VLN system is modular, making it compatible with various VLM/VLA models and agnostic to the robot platform. The proposed system is validated with extensive real-world experiments on humanoid and quadrupedal robots, demonstrating versatile, efficient, and safe vision-language driven navigation capabilities.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04968

Who is the Pronoun Pro? Comparing LLMs and Humans at Number-Disambiguation of 'they'

Carlos Hartmann

原文摘要

This study evaluates whether large language models (LLMs) can reliably replace or complement human research assistants in linguistically complex annotation. In a controlled annotation task, 73 human annotators (local students and crowdworkers with and without a background in linguistics) and more than 40 LLM configurations number-disambiguated 174 authentic uses of 'they' from Reddit, including innovative and context-sensitive cases. The best LLM, Kimi K3, reached 94.5% accuracy, above the human majority vote (86.2%) and the mean of the top half of human annotators (78.5%); only the single best human annotator scored higher (95.7%). LLMs also offer substantial practical advantages in cost and speed. However, qualitative error analysis reveals a crucial complementarity: human reasoning in low-agreement cases exposes inconsistencies in the annotation typology and thereby improves the methodological framework itself. The study contributes (i) a benchmark-style human-LLM comparison for a semantically demanding task, (ii) transparent cost and time evidence, and (iii) a methodological argument against a purely replacement-oriented view of automation. LLMs are viable for large-scale annotation support in linguistics, but their responsible use requires human critical oversight. The dataset with its gold-standard labels, all human and LLM annotations, and the fully reproducible code are openly available as a Quarto-based, public repository.

cs.CL · cs.AI · cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02030

Query-efficient scan-and-search watermark localization

Liyuan Wang, Xinsong Ma, Ziye Ma

原文摘要

Recently, the detection of AI-generated content has been of particular importance in various fields of our society. For accountability purposes, many large language models (LLMs) have implicitly watermarked their outputs. Localizing such watermarks requires a piece of text to undergo an additional verification process. Existing localization methods typically rely on dense token-level scoring, making verification costly even when an LLM-generated watermark only sparsely appears. To tackle this problem, we design a new adaptive allocation strategy that first maintains broad document coverage and then progressively concentrates the detection budget on regions that exhibit stronger watermark evidence or are more likely to contain watermarked content. We then transform the statistical signals from the selected regions into e-values and employ the proposed token-weighted e-BH procedure to determine the localized watermark regions. The resulting framework is called BELL (Budgeted E-evidence Localization for LLM Watermarks). Theoretically, BELL can control the token-weighted false discovery rate (FDR) without independence assumptions across regions. Finally, extensive experimental results demonstrate that BELL consistently outperforms competing methods across multiple LLMs, datasets, and watermarking schemes.

cs.CR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2504.01491

How to Define the Quality of Data? A Feature-Based Literature Survey

Markus Matoni, Arno Kesper, Gabriele Taentzer

原文摘要

Data is generated in almost every digital interaction, and using it effectively requires high quality. But what exactly is data quality? The literature offers many, widely diverging answers. This article presents a systematic literature review that screened more than 8,700 records on data quality and identified 35 providing original, dimension-based definitions. Using feature-oriented domain analysis, a feature model of these heterogeneous definitions is derived and used to classify all identified publications. The classification reveals a heterogeneous landscape: despite an ISO standard and a few highly cited quasi-standards, no consensus exists on which quality dimensions to consider or how to define them. As definitions increasingly emerge for specific domains, contexts of use, and new types of data, the respective communities need to deliberately converge on how data quality is defined in their field.

cs.DB

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04960

An Agentic Framework for Quality-Aware and Scenario-Adaptive Image Compression

Zhenghao Chen, Lebin Zhou, Zicong Chen, Naifu Xue, Jiahao Li, Bin Li, Dong Xu, Yan Lu

原文摘要

Despite decades of progress, image codecs remain difficult to adapt to diverse real-world compression scenarios for two main reasons. First, most image codecs are optimized for a specific objective and require substantial redevelopment when the compression goal changes. Second, they typically assume clean inputs, whereas sensor-captured images often contain degradations that alter rate-quality behavior. To address these challenges, we propose Practical Image Coding Agents (PICA), a multi-agent framework that adapts existing codecs to diverse user requirements and potentially degraded inputs. PICA consists of four collaborative agents: a Perception agent that interprets user intent and analyzes image content, an Improvement agent that jointly considers bitrate and reconstruction quality to guide pre-coding restoration, a Coding agent that refines quality-control settings from global to regional levels, and an After-coding improvement agent that addresses residual degradations and compression-induced artifacts through restoration guided solely by image quality. To enable codec-agnostic regional adaptation, we introduce Agentic ROI Coding (AROIC), which treats the codec as a black box and allows the agent to automatically schedule, execute, evaluate, and reflect ROI coding plans without architectural modifications or codec-specific retraining. We integrate these four agents with a Scenario-Guided Quality-Rate (SGQR) evaluation strategy to enable more effective scenario-adaptive compression across heterogeneous codecs. Comprehensive experiments on clean, noisy, and blurred images across standard, perceptual, and machine-vision compression scenarios demonstrate that PICA consistently enhances the compression performance of both the conventional H.266/VVC codec and the neural Cheng2020 codec, outperforming competing compression methods on the Kodak, CLIC2020, and HEVC datasets.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04959

Lightweight graph transformers for clutter and target classification in automotive radar

Adrian Gheorghiu, Tunc Alkanat, Ashish Pandharipande

原文摘要

Radar is a core sensor modality for scene perception to achieve higher levels of autonomy in automotive driving. A common occurrence in automotive radars is clutter - detections of non-existent moving objects, that can adversely impact target detection and classification performance and subsequent driving actions. We propose lightweight tiny graph transformer network (TGTNet) models for classifying clutter from stationary and moving targets in the scene. Performance evaluation on the public RadarScenes show that our proposed TGTNet models achieve similar classification performance in precision and recall metrics in comparison to state of the art models, with one to two orders of magnitude lower model size and significantly faster inference.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04958

Shuffled OPSD: When Semantic Consistency Hurts On-Policy Self-Distillation?

Qingyuan Zheng, Zhihao Dou

原文摘要

Knowledge distillation improves a student model's reasoning ability through supervision from a teacher. On-policy self-distillation (OPSD) uses the same model as both teacher and student under different contexts: the student generates reasoning trajectories from the question alone, while the teacher additionally receives a complete worked example and provides token-level supervision along those trajectories. Because the problem statement is shared, its content and ordering shape both student generation and teacher feedback. Preserving its semantic structure therefore seems natural, but does it always improve learning? We introduce Shuffled OPSD, which shuffles the words of the shared problem statement while keeping the teacher-only worked example intact. Across three mathematical reasoning benchmarks and three Qwen3 model scales, the method improves mean accuracy over both an intact-target control and Standard OPSD under matched training configurations. The largest gain over Standard OPSD is 3.52 percentage points on Qwen3-1.7B. Further controls show that preserving larger word blocks offers no advantage over word-level shuffling at the evaluated scales, whereas shuffling the teacher-only worked example reduces accuracy at every scale. Cross-problem mixing and target-removal experiments further distinguish the roles of problem content, word order, and teacher context. These results show that disrupting the shared problem statement can improve learning, while disrupting the teacher-only reference hurts performance. They challenge semantic coherence as a universally beneficial principle for OPSD and motivate closer study of how input structure shapes student generation and teacher supervision.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2411.14442

AI Ethics by Design: Implementing Customizable Guardrails for Responsible AI Development

Kristina Šekrst, Jeremy McHugh, Jonathan Rodriguez Cefalu

原文摘要

This paper explores the development of an ethical guardrail framework for AI systems, emphasizing the importance of customizable guardrails that align with diverse user values and underlying ethics. We address the challenges of AI ethics by proposing a structure that integrates rules, policies, and AI assistants to ensure responsible AI behavior, while comparing the proposed framework to the existing state-of-the-art guardrails. By focusing on practical mechanisms for implementing ethical standards, we aim to enhance transparency, user autonomy, and continuous improvement in AI systems. Our approach accommodates ethical pluralism, offering a flexible and adaptable solution for the evolving landscape of AI governance. The paper concludes with strategies for resolving conflicts between ethical directives, underscoring the present and future need for robust, nuanced and context-aware AI systems.

cs.CY · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04957

Safeguarding Multi-Agent Reinforcement Learning for Connected and Automated Vehicles with Recursively Feasible Control Barrier Functions

Jianye Xu, Yousef Sayed, Bassam Alrifaee

原文摘要

Multi-Agent Reinforcement Learning (MARL) shows promise in coordinating connected and automated vehicles but offers no safety guarantee. Although Control Barrier Functions (CBFs) can add this guarantee, their optimization problem can become infeasible in practice due to control bounds. Moreover, they often overapproximate vehicle and road geometry, which makes them conservative. This paper proposes the Backup-Horizon CBF (BH-CBF), which evaluates safety along a horizon until all vehicles stop. BH-CBF is recursively feasible under control bounds, and it considers the exact vehicle and road geometry. Based on BH-CBF, we derive a fully decentralized safety filter that only needs a single broadcast of vehicle states and routes per step and stays real-time in dense traffic. We use BH-CBF to safeguard a MARL policy over its life cycle: as a reward during pre-training, as a run-time filter at deployment, and as a filter in the training loop that keeps fine-tuning on unseen maps collision-free. In simulations on real-world maps of a roundabout and an intersection, BH-CBF avoids all collisions of unsafe MARL policies and keeps its safety guarantee at every step. Existing filters either collide, lose the guarantee, or fall back to full braking far more often. In recorded roundabout traffic with 1264 human-driven vehicles over 115 km, our safeguarded policies shorten the median travel time by 14% compared with the human drivers, without any collision.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04955

Deep Neural Operator Learning for stochastic heat equations and applications

Hengrong Du, Qi Feng, Weinan Wang

原文摘要

In this paper, we establish a universal approximation theorem for solution operators of semilinear stochastic heat equations in spatial dimension \(d\ge2\), extending deep neural operator approximation results to SPDEs with distribution-valued Gaussian forcing. A smooth Fourier cutoff reduces the forcing to finitely many measurements, while Besov and Schauder estimates control the resulting solution error. The finite-dimensional solution map is then approximated by ReLU branch networks together with grid-based trunk functions, yielding explicit bounds on the numbers of forcing measurements and trunk functions and approximation in every finite stochastic moment. For linear equations, the reduction coincides with truncation of the first Wiener chaos. Lastly, as an application, we prove a high-probability local-in-time approximation theorem for stochastic Navier--Stokes equations, with finite measurements of both the initial condition and forcing and an explicit sensing rate.

cs.LG · math.AP · math.PR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.10867

Deep Learning of Compositional Targets with Hierarchical Spectral Methods

Hugo Tabanelli, Yatin Dandi, Luca Pesce, Florent Krzakala

原文摘要

Why depth yields a computational advantage over shallow methods remains a central open question in learning theory. We study this question in a controlled high-dimensional Gaussian setting, focusing on compositional target functions. We analyze their learnability using an explicit three-layer fitting model trained via layer-wise spectral estimators. The target combines polynomial latent features with a scalar readout, and its compositional structure allows learning to proceed in stages. Recovering an intermediate representation reduces the problem to lower-degree spectral estimation tasks, as in multi-index models. Our analysis establishes Gaussian comparison bounds for projected moments when $0<\varepsilon<1/3$, and motivates separations in sample complexity between two- and three-layer learning strategies, conditional on a conjectured spectral recovery step.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.04183

Optimal Zonotope Containment via Adaptive Gaussian Sampling

Friedrich Eisenbrand, Thomas Rothvoss, Matteo Russo, Ruben Skorupinski

原文摘要

We investigate the convex-body containment problem $\max\{s >0 : s Z \subseteq Q\}$, where the outer body $Q \subseteq \mathbb{R}^d$ is described by a membership oracle and the inner body $Z \subseteq \mathbb{R}^d$ is a zonotope. Our main result is a sampling-based $O(\sqrt{d/\log d})$-approximation algorithm for this problem that matches the lower bound of $Ω(\sqrt{d/\log d})$ by Khot and Naor in the oracle model. Starting from approximate Lewis position, the algorithm samples Gaussian directions and adaptively shortens and repositions the generators. Its analysis combines preservation of the support in a fixed direction with contraction of the corresponding functional after renormalization. Crucially, the algorithm works directly with the input generators and does not use zonotope sparsification. We complement this result with a \emph{universal} $Ω(\sqrt{d/\log d})$ lower bound holding for \emph{all} full-dimensional zonotopes when the number of oracle queries is polynomial in $d$. Finally, we consider containment problems $\max\{s >0 : s K \subseteq Q\}$, for general convex bodies $K \subseteq \mathbb{R}^d$. A result of Nasz{ó}di on approximating $K \subseteq \mathbb{R}^d$ by a polytope implies a $Θ(d/\log d)$ approximation algorithm in polynomial time when the centroid of $K$ is the origin. We show the tightness of this approximation factor in the oracle model via a reduction to the circumradius computation. Our lower bound holds for centrally symmetric convex sets, implying that Barvinok's optimal $O(\sqrt{d})$-approximation of a centrally symmetric convex body by a polytope with a polynomial number of vertices cannot be computed in polynomial time.

cs.DS · math.MG

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2609.07979

The Stretch Factor of Planar Delaunay Triangulations Is Nearly Determined

Guanlin Mo, Kangke Cheng, Hu Ding

原文摘要

The Delaunay triangulation is a classical geometric structure on a finite set of points in the plane. Its stretch factor is the largest ratio of the shortest-path distance along its edges to the Euclidean distance between the same two points. Determining the worst-case stretch factor of planar Delaunay triangulations is a long-standing open problem. We prove a uniform upper bound of 1.5949, improving the previous bound of 1.998 and narrowing the gap to the best known lower bound of 1.5932 from 0.4048 to less than 0.0017, nearly pinpointing the worst-case stretch factor. A central difficulty is that the line segment between two points may cross arbitrarily many triangles. Our progress accounting framework addresses this by converting the global bound into a family of local inequalities, each involving only a fixed number of real parameters regardless of the number of triangles crossed. Two further geometric lemmas make computer-assisted verification tractable: the pencil lemma decomposes each local transition into two geometrically independent stages, reducing verification over infinitely many possible next disks to a single differential condition; and the corner lemmas handle degenerate boundary cases where direct verification would fail. With these reductions, the proof ends with a finite verification of an explicit potential in exact rational arithmetic and outward-rounded interval arithmetic. Numerous geometric network constructions use the Delaunay stretch factor as a black box, so the tighter bound directly improves stretch-factor guarantees for a range of downstream applications, including bounded-degree plane spanners and low-weight spanners.

cs.CG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04952

Artifact-to-Capability: Diagnosing Breaks in Capability Formation of Self-Evolving Agents in a Controlled Environment

Ziqian Ban, Haoxiang Yang, Yanfei Zhang, Shixin Liang, Haoran Yu, Xu Lin, Shaobin Zhao, Xuran Li 等 9 位作者

原文摘要

Self-evolving agents write interaction experience into artifacts and reuse them in later tasks. Most work supports such methods with three proxy signals: artifacts accumulate, behavior changes once they are loaded, and metrics rise. We call this signal-based evaluation. What these signals are meant to indicate, however, has not been defined. To fill this gap, we define capability formation from artifacts as five sequentially dependent properties, two of which form a checkpoint for capability formation, and introduce three-scale diagnosis, which judges these properties at the state, game, and lineage levels. In freestyle Gomoku, Reflexion, Voyager, and DGM evolve with the same base model and all show the three signals, yet each exhibits a distinct capability-formation break. Reflexion shows a legalization gain: its pass rate rises only because more of its moves are legal. Voyager shows a compositional override: components that are sound on their own combine to miss all 20 available one-move wins. DGM shows loss-only drift: after passing the checkpoint, later versions only lose passing states. These counterexamples show that signal-based evaluation cannot determine whether a capability has formed.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.29069

Rethinking AI Cloud Infrastructure for Agentic Serving Systems with the Aries Experimentation Framework

Jooyoung Park, Leonid Kondrashov, Boxi Zhou, Hongrui Liu, Zonghao Liu, Chengzhi Lu, Riccardo Mancini, Esha Choukse 等 17 位作者

原文摘要

Autonomous agents challenge conventional LLM serving with long-lived, cross-component execution and evolving system state. These characteristics complicate controlled systems experimentation and attribution of end-to-end behavior across the serving stack. We present Aries, a full-stack experimentation framework that separates task specifications from execution configurations, reconstructs cross-component agent trajectories with correlated system telemetry, and exposes stateful tool execution through a consistent interface across heterogeneous sandbox substrates. We use Aries to conduct reproducible experiments on open agent harnesses and benchmarks. We complement these experiments with production traces from a commercial platform, grounding low-level systems research in observed production behavior. Our results show that (1) token-centric metrics miss non-inference bottlenecks, (2) retaining additional context yields diminishing accuracy benefits while reducing serving capacity, and (3) tool sandboxes alternate between long idle periods and short resource bursts, while current snapshot-based state management makes aggressive suspension costly. We then discuss the vision for agent-native serving systems designed around trajectory-level metrics, adaptive context management, and elastic sandbox resource management.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04950

DPAGait: Dynamic Pose-Knowledge-Aware Network for Robust Gait Recognition in the Wild

Zenan Lin, Junduan Huang, Kun Liu, M. Saad Shakeel, Wenxiong Kang

原文摘要

Gait biometrics aims at identity recognition by appearance or pose features captured during unconstrained human walking. Existing methods predominantly rely on binary silhouette images, inherently characterized by low information entropy, and fail to fully utilize the dynamic information in gait sequences, limiting their ability to handle cross-time variations effectively. Consequently, these methods encounter significant challenges in the wild. They are susceptible to variations in clothing, carried objects, and viewpoints, as well as dynamic environmental conditions, all of which lead to substantial degradation in recognition accuracy and robustness. To overcome these challenges, we propose a pose-knowledge-aware approach specifically designed for robust gait recognition in the wild. First, we propose a novel Dynamic Pose-Parsing enhanced Silhouette gait representation paradigm that substantially enhances the information entropy and effectively captures fine-grained behavioral cues by integrating pose-parsing cues with traditional silhouettes. Subsequently, we develop a dual-branch Dynamic Pose-Knowledge-Aware Gait network. The framework consists of two complementary branches: (1) a 2D dynamic pose-aware branch that extracts pose-enhanced features from our proposed DPPS representations, and (2) a 3D Skinned Multi-Person Linear branch that leverages the 3D SMPL model to capture rich and comprehensive 3D dynamic pose information. Finally, the features are integrated to facilitate accurate and robust gait recognition. Extensive experiments on four challenging in-the-wild gait datasets demonstrate the effectiveness and generalizability of the proposed DPPS representation. The complete framework is further evaluated on SUSTech1K, CCPG, and Gait3D, where SMPL information is available or can be consistently estimated, achieving competitive performance under diverse real-world conditions.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04949

HERIT: Enabling Global Access to Hanja-to-English Historical Translation by Mitigating Temporal Bias

Dayeon Jeong, Kyeongpil Kang

原文摘要

Historical archives are invaluable for multidisciplinary research but remain inaccessible globally due to severe language barriers. While manual translation is prohibitively expensive and time-consuming, simply applying existing machine translation models is inadequate due to unique linguistic nuances and the temporal bias inherent in scarce human-labeled corpora. To address these challenges, we propose HERIT, a systematic domain-adaptation pipeline that leverages retrieval-augmented generation (RAG) to generate high-quality pseudo-labeled data from abundant Hanja-Korean corpora. This approach substantially expands the training dataset, mitigating both data scarcity and temporal overfitting across different historical periods. Extensive automated and human evaluations demonstrate that HERIT consistently outperforms baselines. Finally, we translate previously untranslated portions of the archives using our model, thereby enabling global access to these resources for researchers.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04948

PATTS: Parallel-Aware Test-Time Scaling for Thought-Image Generation in Multimodal Discrete Diffusion LLMs

Yasong Dai, Zeeshan Hayder, David Ahmedt-Aristizabal, Hongdong Li

原文摘要

Test-time scaling (TTS) improves generative models by allocating additional inference-time computation to sampling, search, and verification. Existing image generation approaches, however, typically rank completed images or image-centric intermediate states. We study TTS for parallel multimodal discrete diffusion language models, in which reasoning tokens and image tokens are decoded jointly within a single generation trajectory. We first diagnose a limitation of conventional final-image selection: on GenEval, a CLIP-style image-prompt score is weakly aligned with within-prompt compositional correctness, leaving substantial headroom between verifier selection and oracle selection. We then propose PATTS, a novel parallel-aware TTS framework that searches over intermediate thought-image states using cross-modal verifier signals. PATTS uses a state-dependent reliability weight, derived from model-internal trajectory diagnostics, to modulate the influence of external semantic verification during generation. In the fixed N=32 evaluation on MMaDA-Parallel, the PATTS reaches a GenEval score of 0.844, compared with 0.628 without TTS, 0.787 for Best-of-N, and 0.810 for FKD. These results show that reliability-aware trajectory verification can improve test-time selection for parallel thought-image co-generation under the evaluated search budget. Our code release is at https://github.com/Ringo-Star/PATTS.git

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.26788

Quantum Advantage for Distributed Symmetry Breaking

Maxime Flin, Longcheng Li, Jukka Suomela

原文摘要

We present a distributed quantum algorithm that $3$-colors cycles in $O(1)$ rounds, with high probability. It follows that all locally checkable labeling problems (LCLs) that have round complexity $O(\log^* n)$ in the classical LOCAL model can be solved in $O(1)$ rounds in the quantum-LOCAL model, with high probability; this includes problems such as maximal independent set and maximal matching in bounded-degree graphs. This presents the first natural examples of graph problems with an asymptotic distributed quantum advantage for the LOCAL model; all prior examples that separate LOCAL and quantum-LOCAL are artificial problems constructed merely for the sake of demonstrating quantum advantage.

quant-ph · cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04947

Linear Equivalence of Full Weight Spectrum Cyclic Orbit Codes

Minjia Shi, Ferruh Ozbudak

原文摘要

We determine the linear-equivalence classes within the two families of full weight spectrum one-orbit cyclic subspace codes. This solves the second open problem posed by Castello, Polverino and Zullo [J. Combin. Theory Ser. A 212 (2025) 106005]. For the first family, the criterion is a fractional linear transformation over $\F_q$ followed by a power of $q$-Frobenius. For the second family, it is an affine transformation over $\F_{q^2}$ together with a power of $q$-Frobenius. We use polynomial spaces over finite fields, the linear-isometry theorem for cyclic orbit codes, and trace duality. The arguments include parameters belonging to proper subfields of the ambient field.

cs.IT · cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04946

How Well Does Webcam Eye Tracking Work "in the Wild"? A Large-Scale Validation of Chiasm and a Comparison with WebGazer

Artem V. Belopolsky, Florian Perdreau

原文摘要

Webcam eye tracking can greatly accelerate data collection in basic and applied research by reaching large, diverse samples of participants online. However, its spatial performance is considered well below that of hardware-based systems, limiting its use. We evaluated the first-generation Chiasm webcam eye tracker in a large online study ($N = 505$) spanning diverse participants, devices, and recording conditions. Chiasm achieved a mean spatial accuracy of 2.39 cm ($\approx 2.28^\circ$ at 60 cm), spatial precision (SD) of 1.05 cm ($\approx 1.00^\circ$ at 60 cm), and sample-to-sample precision (RMS-S2S) of 1.29 cm ($\approx 1.23^\circ$ at 60 cm). For the 91% of participants who met a 3 cm calibration criterion, spatial accuracy was 2.15 cm ($\approx 2.05^\circ$ at 60 cm). After three minutes of a task that encouraged head movement, spatial accuracy declined by 0.59 cm, while the gaze center at each target shifted by a mean distance of only 0.37 cm. On the validation block, the offset was 0.30 cm larger for men than for women. Age, region of residence, face luminance, and Michelson contrast showed no reliable relationship with spatial accuracy. A higher eye aspect ratio, a more open eye, was associated with a smaller offset. Wearing glasses decreased spatial accuracy on every validation block. In a comparison between a random subsample ($N = 101$) and a separate sample tested with the open-source tracker WebGazer ($N = 101$) using an identical protocol, Chiasm showed better spatial accuracy (2.18 vs. 4.51 cm) and spatial precision (SD; 1.01 vs. 1.55 cm). WebGazer showed better sample-to-sample precision (RMS-S2S; 0.79 vs. 1.26 cm), which likely reflects conservative, over-smoothed estimates rather than superior stability. These results suggest that Chiasm is narrowing the gap between webcam and hardware-based eye tracking, making large-scale gaze research "in the wild" a reality.

cs.CV

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新论文 · v1arXiv:2611.04944

Local Visibility Roadmaps in Continous Multi-agent Path Finding

Kristýna Janovská, Pavel Surynek

原文摘要

We address the problem of smooth continuous multi-agent path finding (SC-MAPF), where agents move in a continuous environment along smooth curves in continuous time. The paths for individual agents are searched in local visibility roadmaps that consider obstacles and other agents. A new algorithm is proposed in this work, which combines the previous algorithm CE-CBS for high-level search with any-angle path finding for local single-agent planning, where agents are to avoid obstacles, both static and other agents. The new algorithm is tested and compared to CE-CBS, the algorithm building individual paths using RRT$^*$, and SMT-CCBS algorithm, an algorithm for MAPF$_R$. Experimental results show various advantages of the new algorithm over CE-CBS and SMT-CCBS in comparable settings.

cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04943

Learning Where to Look: Observation Policy Optimization for Thinking with Images

Junfeng Wang, Jiawei Liu, Yongchao Xu, Tao Jiang, Jiangbo Ai, Jin Zhang, Chong Wang, Qixing Zhang

原文摘要

Complex visual question answering often requires vision-language models to think with images, i.e., actively acquire local visual evidence by zooming into informative regions of high-resolution images. Our error analysis shows that many failures in this setting originate from incorrect observation actions, where the model fails to localize key regions or obtain crops that contain sufficient evidence for answering. We further observe that bounding-box tokens exhibit substantially higher generation entropy than ordinary language tokens, indicating high uncertainty when the model decides where to look. However, existing reinforcement learning methods based on final-answer rewards provide only trajectory-level feedback, making it difficult to assign precise credit to the observation actions. We propose Observation Policy Optimization (OPO), an RL algorithm for optimizing observation actions in tool-augmented thinking with images. OPO consists of two core modules. First, Uncertainty-Guided Observation Branching (UOB) treats each image zoom-in tool call as an observation event and selectively branches at high-uncertainty, low-redundancy events during rollout, enabling efficient exploration of alternative cropping decisions and their resulting visual evidence. Second, Evidence-Localized Advantage Attribution (ELAA) compares sibling branches using local evidence-sufficiency rewards and assigns credit only to the corresponding observation-action tokens. By decoupling observation-action optimization from trajectory-level outcome supervision, OPO improves visual evidence acquisition and further enhances visual reasoning performance. Experiments show that OPO consistently improves Qwen3-VL models across scales and achieves competitive performance against larger models and tool-augmented baselines. Code is publicly available.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04941

Residual Attribute Leakage Is an Interface: Auditing Privacy Evaluation After Text De-identification

Tuan Thanh Duong, Joongmin Shin, Jeongho Yoon, Junyoung Son, Heuiseok Lim

原文摘要

A privacy evaluation of de-identified text asks a language model (the adversary) to guess a person's protected attribute. We introduce a prior-normalised clipped leakage score of its confidence gain on the true value and audit the conclusions drawn from it. Each verdict depends on an evaluation interface (the queried person, answer options, prior, adversary model and scoring rule), which we vary on fixed releases of three corpora. In a stress test, an editor that writes the opposite-sex form at the same spans a redaction editor would redact misleads the adversary about most people, yet ranks first among editors scored on the same MedQA entries under this score, as under attack accuracy and proper scoring rules read as privacy. A relabelling-invariant reading of AUC ranks that editor sixth of eight, and a rule-aware adversary recovers the attribute from the same text. On ordinary releases most verdict changes are gains or losses of separability; of six choices varied over all pairs, only the adversary reverses comparisons (5 of its 124 changes), and the query can target the wrong person. SACC, a traceable editor we build as the instrument, lets us ablate two editing principles and show where its advantage does not hold.

cs.CL · cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04940

HTNN-MSC: Multi-view Subspace Clustering with a Hyperbolic Tangent Tensor Low-Rank Surrogate

Yiwen Dou, Yiqiang Ding

原文摘要

High-order dependencies among views can be modeled by tensor singular value decomposition (t-SVD) in multi-view subspace clustering. However, the tensor nuclear norm often used with this framework assigns the same asymptotic marginal penalty to singular values of different magnitudes, which may excessively shrink dominant components that encode cluster structure. We propose HTNN-MSC, which replaces this norm with a bounded hyperbolic tangent tensor rank surrogate. The surrogate is strictly concave on the positive half-axis and has a Lipschitz continuous derivative. Its marginal penalty decreases as singular values grow. We develop an alternating direction method of multipliers solver in which the tensor subproblem decomposes into scalar frequency-domain shrinkage problems. Safeguarded multi-start Newton iterations and candidate-objective comparison implement the resulting hyperbolic tangent thresholding operator. The shape parameter is scaled by the input spectrum. We also establish o-minimal definability of the augmented objective. Under explicit boundedness, descent, relative-error, and exact-block conditions, the Kurdyka-Lojasiewicz framework guarantees convergence of the full sequence to a KKT point. On 23 datasets, HTNN-MSC achieves a mean clustering accuracy of 0.8404 and an average accuracy rank of 2.78 against nine representative methods. Friedman tests and Holm-adjusted Wilcoxon comparisons reveal significant overall differences, although several pairwise differences remain nonsignificant. HTNN-MSC records 19 wins, one tie, and three losses against the convex t-SVD-MSC skeleton. A same-skeleton ablation also shows that the proposed surrogate does not uniformly outperform alternative nonconvex penalties.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.41290

SAT-ACT: Learning CDCL Branching from Outcome-Derived Action Preferences

Zhenchao Sun, Chongyang Tao, Ping Lu, Tongkai Lu, Jianling Gao, Shuai Ma

原文摘要

Learning-based approaches have increasingly been explored for SAT solving, with particular attention to improving branching decisions in conflict-driven clause learning (CDCL) solvers. These approaches derive branching guidance either from static structural properties of SAT formulas or from dynamic signals obtained through solver feedback and execution traces. However, these forms of supervision provide limited evidence about state-dependent branching effects, while different outcome measures may favor different branching choices. To address these limitations, we propose SAT-ACT, an outcome-derived learning framework for CDCL branching. We design three complementary outcome comparison criteria to extract action preferences from independent branching action rollouts initiated at the same solver state. The criteria identify preferences supported by the outcomes and leave comparisons unresolved when the evidence is insufficient or conflicting, instead of forcing an ordering using a predefined scalar metric. A state-conditioned network learns branching priorities from the extracted preferences and uses them to guide CDCL search directly without online action rollouts. We further introduce non-dominated heuristic action supervision, which retains the solver-selected action as a training target only when it remains non-dominated under these preferences. Solver-level experiments show that SAT-ACT consistently reduces CDCL search costs on three SAT families and improves PAR-2 on the SAT Competition benchmarks.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04939

Robustness Verification against Patch Attacks via Group-Lasso Semidefinite Relaxation

Yukina Nakagawa, Takami Sato, Ken Kobayashi, Kazuhide Nakata

原文摘要

Robustness verification is essential for ensuring the safety and security of neural networks, but current methods focus heavily on norm-ball attacks, which do not adequately model typical real-world threats that present physical perturbations, known as patch attacks. A naive approach requires computationally prohibitive enumeration of all possible patch positions. To address the challenge, we propose a novel neural network verification method specialized to the patch-attack threat. Instead of enumerating patch locations, we first model the patch attack with group-wise sparsity and formulate its verification methods as a mixed-integer semidefinite program (MISDP) by applying SDP relaxation to the neural network. We then propose three relaxations, including Group-Lasso relaxation, that remove the integer variables while preserving the sparse, group-wise structure of the original attack, enabling substantially efficient verification. Our empirical analysis demonstrates that the proposed relaxations require less than 3% of the computation time of the original MISDP, while the Group-Lasso relaxation incurs only a 0.34\% average increase in the objective value over the original formulation. Furthermore, Group-Lasso achieves a verified rate of 91%, compared with under 70% for prior methods.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.12676

WoodBR-Cross: A public wood-image dataset and individual-tree-disjoint benchmark for species identification

Daniel Casanova, Alexandre Bahia Gontijo, Arnaldo Candido Junior, Silvana Nisgoski, Pedro Luiz de Paula Filho

原文摘要

Wood identification supports timber inspection and species conservation. However, benchmarks can overestimate generalization when photographs of the same tree occur in training and test sets. We present WoodBR-Cross: 4,875 transverse-surface microscope images, 38 operational taxonomic classes and 303 catalogue-linked trees. A species subset contains 3,406 images, 20 classes and 214 trees. Task A evaluates all classes using duplicate-aware image partitions. B1 partitions images of the species subset, allowing different photographs of one tree across partitions. B2 keeps each tree's photographs together, testing recognition of unseen trees on the same subset. We benchmark three backbones and baselines across three folds and five training seeds. Validation-selected ConvNeXt-Tiny achieves B2 tree-level macro-F1 of 0.8621 +/- 0.0317; frozen DINOv2 features yield 0.9000 +/- 0.0722. Values are means +/- SD across three folds; fine-tuned scores are seed-averaged within folds. This resource supports reproducible, group-aware evaluation.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.27877

Gaze2HOI: Gaze-Aware Intention Disambiguation for Egocentric Hand-Object Interaction Forecasting

Chuanyang Ma, Chenye Wang, Yixuan Wang, Huiling Zhou, Man Zhang, Xingqun Qi, Muyi Sun, Caifeng Shan 等 9 位作者

原文摘要

Anticipating how users will interact with their surroundings is essential for proactive systems that provide timely assistance in extended reality and embodied applications. Prior work typically forecasts hand-object interactions directly from historical observations, leaving intention and contact geometry implicit. However, similar observation histories can precede semantically or geometrically distinct futures. Inspired by behavioral evidence that gaze can provide early cues for underlying intention, we propose Gaze2HOI, a gaze-aware framework that formulates hand-object interaction forecasting as a hierarchical problem comprising intention inference, contact grounding, and coupled hand-object motion generation. Specifically, Gaze2HOI first weights gaze cues by fixation strength and recency, and uses them to modulate the visual representations of a vision-language model (VLM) through Temporal Gaze Saliency Modulation (TGSM), thereby reducing semantic ambiguity in intention inference. Subsequently, an Anticipatory Contact Grounding (ACG) module maps these structured intentions into explicit contact, support, and placement fields, effectively mitigating geometric ambiguity. Driven by these explicit conditions, a diffusion-based model jointly generates the six-degree-of-freedom (6-DoF) object trajectories and future articulated motion of both hands. To evaluate Gaze2HOI, we further introduce EgoAIM, a phase-aware benchmark that organizes egocentric hand-object interactions into four phases defined by evolving hand-object relations and provides fine-grained intention annotations. Extensive experiments demonstrate that Gaze2HOI achieves state-of-the-art performance in both intention inference and motion forecasting, even approaching oracle-level end-to-end performance in specific interaction phases. Project page: https://gaze2hoi.github.io/.

cs.CV

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