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版本更新 · v2arXiv:2609.06355

Adaptive stabilization of a leaderless bearing-constrained formation with disturbances

Minh Hoang Trinh, Chuong Van Nguyen, Quoc Van Tran, Tuynh Van Pham

原文摘要

In this paper, we consider the problem of regulating and maintaining a target formation characterized by a set of bidirectional bearing constraints under disturbances. The agents in the formation are modeled by single integrators with deterministic bounded continuous disturbances of which both actual function and upper bound are unavailable for the control design. Due to the time-varying disturbances, the target formation is time-varying. We propose displacement/bearing-only based adaptive sliding mode control laws to uniformly asymptotically stabilize the time-varying target formation and reject the matched disturbances, and provide sufficient conditions to prevent possible collisions. A time-dependent adaptive design method is provided to convert the displacement-based control law to a bearing-only one. In addition, to alleviate chattering phenomena from sliding mode control, smooth adaptive control laws are introduced to guarantee uniform global boundedness of the desired formation. Finally, simulation results are given to support the analysis.

cs.MA · math.OC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.10292

Continual Instruction Tuning for LLM-ased Management of Continuum Resources

Lanpei Li, Antonino Vaccarella, Vincenzo Lomonaco, Massimo Coppola

原文摘要

Large language models (LLMs) are increasingly explored for resource management across the Cloud-Edge continuum. However, embedding LLMs directly inside latency-sensitive control loops places inference latency, execution jitter, and hallucination risks on the critical path. This study argues for a different use of LLMs: a continually instruction-tuned intent compiler that runs once per management intent or declared context change, above a fast, deterministic tactical controller that handles all low-latency runtime actions. We formulate intent compilation as a continual instruction tuning problem, because management intents arrive sequentially and accumulate while infrastructure and workload contexts drift. Unlike recent LLM-based management frameworks, our design scores the model's outputs by execution, so that catastrophic forgetting can be measured in the executed objective of the underlying controller. On a continuum simulator with a 2B backbone and parameter-efficient adapters, preliminary results show that naive sequential tuning forgets earlier intents in the executed objective, that replay of stored examples nearly prevents this forgetting, and that the tuned compact compiler performs on par with simpler translators while being more robust than a grammar parser to rephrased intents.

cs.DC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.10114

Policy Change for Treelike Monitors

François Hublet, Dhruv Nevatia, Joshua Schneider

原文摘要

We study the policy change problem that arises in the runtime verification of long-running systems. The online monitors typically used in this context are generally treelike, in that they maintain substates that monitor subformulae of the target policy. We consider when and how the policy can be changed while the monitored system is running by only exploiting the information stored in the monitor's state. This is relevant, for example, to account for new system functionality or changes in regulatory requirements. We formally define the policy change problem in a general setting, independent of any specific (treelike) monitor implementation. We then show that policy change for past-time metric temporal logic (pMTL) is decidable but has tight non-primitive recursive lower and upper bounds, while with discrete-time semantics it is EXPSPACE-complete.

cs.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13957

AVCC: Audio-Visual Conversation Corpus for Turn-Taking Prediction with a Human Benchmark

Haotian Qi, Gabriel Skantze

原文摘要

} Predicting who will speak next, and when, is a prerequisite for conversational agents and social robots that take turns without waiting for silence. Multiparty next-speaker prediction (NSP) has been studied on corpora with per-speaker sensors, each with its own event definitions. No public benchmark covers the single camera and microphone a robot has, and no model has been compared against how well people perform the same prediction. We turn AVCC, a 31-hour audio-visual corpus of two- and three-speaker online conversation recorded with a single static camera and microphone, into a public NSP benchmark. We add a speaker-disjoint seven-video test partition with 865 hold and 865 shift events at mutual-silence gaps, word-level forced-aligned transcripts for the whole corpus, and a human reference benchmark collected at the same events. We score an audio-only Role-Relative VAP model (RoleVAP) and the multimodal MuVAP model. Labels, transcripts, per-response human data, and the scripts that reproduce every number are released.

cs.CL · cs.HC · cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13955

RB-LDC: Redundancy-Balanced Latent Coding for Noise-Robust Diffusion Latents

Hyunseok Jeong, Jaeho Jeon, Young-Sik Kim

原文摘要

Latent diffusion models decode approximately correct latents, making decoder sensitivity to perturbations relevant to image quality. After whitening, forward diffusion corrupts every direction at the same signal-to-noise ratio (SNR), despite unequal decoder sensitivities. We study an invertible linear layer between the encoder and diffusion process that shifts each direction's log-SNR. Its linear minimum mean-square-error (LMMSE) recovery risk has an exact spectral expression for any whitened latent distribution. Under an energy budget and a floor ensuring invertibility, minimizing decoder-sensitivity-weighted LMMSE risk integrated over the diffusion schedule yields a unique optimal allocation. The layer aligns with the decoder's expected pullback metric and equalizes marginal risk reduction across directions, computed by a deterministic spectral solver. At large budgets, the allocation follows a power law in sensitivity whose exponent is determined by the low-SNR tail of the loss weighting: one half for noise prediction and two thirds for data prediction under the cosine schedule, with a crossover on truncated schedules. We instantiate this principle as RB-LDC using Jacobian-based sensitivity estimates. On ImageNet at 256 x 256 resolution with SD-VAE and VA-VAE, trained RB-LDC improves reconstruction from noisy latents over budget-matched isotropic coding in all four metrics and five coding configurations, averaged over 15 SNRs and three pipeline seeds. It reduces average perturbed FID by 17.1 for VA-VAE block-local coding while preserving clean reconstruction. We also report intermediate SNRs where perturbed FID worsens. With a frozen diffusion model, RB-LDC yields lower FID point estimates in all 12 tested combinations of sampling progress and channel SNR under corruption during sampling.

cs.LG · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.21268

A Dynamic Survey of Soft Set Theory and Its Extensions

Takaaki Fujita, Florentin Smarandache

原文摘要

Soft set theory provides a systematic framework for parameterized decision-making by associating each parameter with a subset of a given universe, thereby enabling structured representations of uncertainty [1, 2]. Over the past decades, the theory has evolved into a broad range of extensions, including hypersoft sets, superhypersoft sets, TreeSoft sets, bipolar soft sets, and dynamic soft sets, while also establishing connections with various mathematical disciplines, particularly topology and matroid theory. This book presents a comprehensive survey of soft set theory and its principal extensions, with particular emphasis on parameter spaces, value spaces, representative mathematical constructions, and the underlying assumptions necessary for rigorous comparisons among different frameworks. It carefully distinguishes established definitions from the literature from exploratory formulations and critically examines the limitations of selected claims concerning representation and reduction. Building upon the first edition, this second edition introduces additional concepts and illustrative examples, addresses previously identified mathematical inconsistencies and typographical errors, and refines the overall structure and presentation to enhance mathematical rigor, clarity, and readability.

cs.AI · math.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13952

Geometry-Dependent Approximation and Stability Properties of Shallow ReLU$^k$ Networks on Bounded Euclidean Domains

Yuqing Liu, Tong Mao, Jinchao Xu

原文摘要

On bounded Euclidean domains, we study how hidden-parameter geometry affects shallow ReLU$^k$ networks through results on approximation rates, saturation-type lower bounds, Gram and stiffness matrix spectra, Bernstein and Marcinkiewicz--Zygmund inequalities, discrete least-squares recovery, and inverse approximation. Our analysis uses smooth localization and dyadic Fourier estimates. We quantify parameter geometry by the fill distance $h$ and antipodal separation distance $\underline h$. For the prescribed network space $L_n^k$, representative approximation estimates take the form \begin{equation*} \begin{aligned} \inf_{g_n\in L_n^k}\|f-g_n\|_{\mathcal L^2(Ω')}&\lesssim h^r\|f\|_{\mathcal H^r(Ω)},\\ \inf_{g_n\in L_n^k}\|f-g_n\|_{\mathcal L^2(Ω)}&\gtrsim \underline h^{\frac{d+2k+1}{2}}\|f\|_{\mathcal L^2(Ω')}. \end{aligned} \end{equation*} The upper bound exploits target regularity up to the critical order $\frac{d+2k+1}{2}$, while the lower bound applies asymptotically to fixed targets smoother than this order. The upper estimate holds on an interior subdomain $Ω'$, whereas the lower estimate concerns the larger domain $Ω$. We also obtain geometry-dependent spectral estimates that quantify conditioning and coefficient stability. Further results establish norm discretization and regularized recovery from function values, and infer target regularity from approximation rates under coefficient or separation conditions. Altogether, these results describe how parameter geometry governs approximation accuracy, the stability of coefficient representations, discrete recovery, and the regularity information contained in convergence rates.

cs.LG · math.NA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06625

Dynamic Shape Control of Soft Trunk Robots

Hehui Zheng, Marc Stampanoni, Roshan S. Kaundinya, Ronan Hinchet, George Haller, Robert K. Katzschmann

原文摘要

Controlling the shape profile of soft trunk robots extends their reach into complex regions of the workspace and enhances their use in practical applications. Recent advances in soft robot design have enabled soft trunk robots to achieve greater flexibility in conforming to complex shapes. Specifically, trunk robots whose geometries are designed as helicoids have been shown to enable extreme shape-changing capabilities. Their compliance makes them highly nonlinear, which in turn makes shape control difficult. On similar tendon-driven trunk robots, model-based controllers based on the theory of adiabatic spectral submanifolds (aSSMs) have achieved high accuracy in closed-loop end-effector tracking. We extend this approach by learning an aSSM model of the full body of a trimmed-helicoid soft trunk robot from data and use this in a model predictive control (MPC) scheme to demonstrate a range of shape control tasks on hardware. On closed-loop dynamic shape regulation, our method outperforms the available linear baselines by a factor of 10. In human-in-the-loop interactive demos, our method completes section-by-section shaping and balances a tennis ball for over 25 seconds, while the linear baselines fall short. A model-free reinforcement learning policy trained for 12 hours on hardware fails on the same balancing task. These results establish aSSM-based MPC as a practical route to full-body shape control on real soft hardware.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2509.23484

Accurate Predictions in Education with Discrete Variational Inference

Tom Quilter, Anastasia Ilick, Karen Poon, Richard Turner

原文摘要

One of the largest drivers of social inequality is unequal access to personal tutoring, with wealthier individuals able to afford it, while the majority cannot. Affordable, effective AI tutors offer a scalable solution. We focus on adaptive learning, predicting whether a student will answer a question correctly, a key component of any effective tutoring system. Yet many platforms struggle to achieve high prediction accuracy, especially in data-sparse settings. To address this, we release the largest open dataset of professionally marked formal mathematics exam responses to date. We introduce a probabilistic modelling framework rooted in Item Response Theory (IRT) that achieves over 80 percent accuracy, setting a new benchmark for mathematics prediction accuracy of formal exam papers. Extending this, our collaborative filtering models incorporate topic-level skill profiles, but reveal a surprising and educationally significant finding, a single latent ability parameter alone is needed to achieve the maximum predictive accuracy. Our main contribution though is deriving and implementing a novel discrete variational inference framework, achieving our highest prediction accuracy in low-data settings and outperforming all classical IRT and matrix factorisation baselines.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.13786

Projection-Free Evolution Strategies for Continuous Prompt Search

Yu Cai, Canxi Huang, Xiaoyu He

原文摘要

Continuous prompt search offers a computationally efficient alternative to conventional parameter tuning in natural language processing tasks. Nevertheless, its practical effectiveness can be significantly hindered by the black-box nature and the inherent high-dimensionality of the objective landscapes. Existing methods typically mitigate these challenges by restricting the search to a randomly projected low-dimensional subspace. However, the effectiveness and underlying motivation of the projection mechanism remain ambiguous. In this paper, we first empirically demonstrate that despite the prompt space possessing a low-dimensional structure, random projections fail to adequately capture this essential structure. Motivated by this finding, we propose a projection-free prompt search method based on evolutionary strategies. By directly optimizing in the full prompt space with an adaptation mechanism calibrated to the intrinsic dimension, our method achieves competitive search capabilities without additional computational overhead. Furthermore, to bridge the generalization gap in few-shot scenarios, we introduce a confidence-based regularization mechanism that systematically enhances the model's confidence in the target verbalizers. Experimental results on seven natural language understanding tasks from the GLUE benchmark demonstrate that our proposed approach significantly outperforms existing baselines.

cs.CL · cs.NE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13946

TreeAgent: Structure-Aware Memory Agent for Processing Long Hierarchical Data

Wei Gu, Weiwei Qi, Jiajun Tang, Tianhang Zheng, Bo Chen, Zhibo Zhang, Jinfei Liu, Kui Ren

原文摘要

Practical Web applications increasingly require agents to interpret long conversations or documents. For inputs exceeding the context windows of large language models (LLMs), memory agents process segments sequentially and retain past information in bounded memory. However, they typically divide inputs into flat token segments without preserving structure or hierarchy, causing three limitations for structured data such as API responses or HTML documents. First, fixed-length segmentation can split fields of the same record, breaking their relationships. Second, segments lack hierarchical information, obscuring their structural context and positions. Third, successive memory updates may discard details of earlier sibling records needed for reasoning on later segments. To address these limitations, we propose TreeAgent, a structure-aware agent that uses the input hierarchy to guide both segment-wise context processing and access to past information. Specifically, TreeAgent constructs segments from complete subtrees and groups of adjacent sibling subtrees to preserve local structural relationships. We provide theoretical guarantees on segment counts to guide token budget selection toward matching the segment count of fixed-length segmentation. We further attach structural paths to the segments for the agent to identify their ancestors and hierarchical positions. We also introduce residual memory links from the segments of low-level children to the segments of high-level parents, enabling direct information flows beyond successive memory updates. We evaluate TreeAgent with four representative memory agents on eight retrieval and aggregation tasks. The inputs are derived from real-world application data in JSON, HTML, YAML, and JavaScript and range from 8K to 1M tokens. TreeAgent improves the average score across the four datasets for all four agents, with 3.61% performance gain on average.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.12067

XposedMIA: Exploiting Explainable Artificial Intelligence (XAI) for One-Shot Membership Inference Attacks

Omar Hamdache, Serdar Kara, Kıvanç Kuzey Dikici, Abdullah Çağlar Öksüz, Anisa Halimi, Erman Ayday, Sinem Sav

原文摘要

Machine learning (ML) services increasingly expose explainability outputs alongside model predictions. While such explanations are intended to improve transparency, they may also reveal information about the underlying training data. In this work, we show that the local surrogate geometry employed by Local Interpretable Model-agnostic Explanations (LIME) can be repurposed as a proxy for a sample's distance to the model's decision boundary. We extend this boundary-distance view to tabular counterfactual explanations and, for the first time, to visual counterfactual explanations for image classifiers. We present XposedMIA, a one-shot, explanation-based black-box membership inference attack (MIA) framework that converts the returned explanation into a proxy for the queried sample's distance to the model decision boundary. After a one-time calibration step on a small labeled calibration set of as few as 30 samples, our attack requires only a single query for each target sample. Unlike prior label-only attacks requiring iterative queries or shadow training, our method relies solely on the predicted label and its accompanying explanation. Our evaluation indicates that XposedMIA achieves high effectiveness, with success rates of up to 0.95 using only a single query per target, revealing that releasing explanations introduces an additional channel for membership leakage.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13945

Chem World: A Large-Scale Benchmark and Physics-Aware Framework for Trustworthy Molecular Property Prediction

Tianyou Bai, Huan Wang, Mingchen Gao, Tongrui Yang, Fangyue Lin, Pinze Ren, Zhenlin Zhao, Siming Dong

原文摘要

Molecular property prediction is fundamental to computational chemistry and drug formulation research, yet existing benchmarks remain fragmented across datasets, properties, and evaluation protocols, limiting systematic evaluation of model generalization. We introduce Chem World, a large-scale benchmark integrating 17 public datasets with over 800,000 molecular and chemical-system instances across 10 property tracks, covering single-molecule and multi-component systems, including drug solubility. Chem World provides a unified testbed for evaluating molecular representations and compositional modeling across diverse chemical properties. Building on the benchmark, we propose PACM (Physics-Aware Compositional Modeling), which combines molecular representations with composition-aware modeling and domain-specific physical constraints. Extensive experiments show that molecular language models provide strong and transferable representations, while PACM consistently improves prediction across diverse chemical systems. Chem World and our PACM-powered drug-property prediction Agent support generalizable chemical modeling and offer a foundation for AI-assisted biomedical research and drug formulation development.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13943

HERO-D3QN: Inter-Cluster Routing with Energy-Aware Soft Constraints in Wireless Sensor Networks

Weihao Wang, Heng Yang, Guangbo Chen, Shanshan Li, Jiulin Guo, Ajay Kaushik, Fangwei Li, Junrui Hao 等 9 位作者

原文摘要

Inter-cluster forwarding in wireless sensor networks can concentrate energy consumption on frequently selected relays, while hard energy thresholds restrict forwarding choices as residual energy declines. We propose Hierarchical Energy-balanced Routing Optimization based on a dueling double deep Q-network (D3QN), termed HERO-D3QN. Given the cluster structure, the method combines direction- and cost-based relay screening with energy-aware soft constraints. Relays below an adaptive energy threshold remain eligible if the other selection criteria are met. An enhanced state representation captures relay energy, progress toward the sink, potential forwarding load, and hotspot risk, and a multi-objective reward guides learning. In the comparison with D3QN using the same node locations, HERO-D3QN delayed first-node death (FND) from time step 629 to 1269 and reduced peak residual energy variance by 55.5%. In the ablation study, the full method achieved the latest FND and half-node death (HND) and the largest cumulative transmitted volume (170.52 Mbit) among the tested variants. Across five seed settings in which node locations and the random processes used in training and evaluation varied jointly, the coefficients of variation of FND, HND, and transmitted volume were 8.26%, 2.75%, and 3.88%, respectively. These results demonstrate the effectiveness of HERO-D3QN in delaying early node failure and reducing peak energy dispersion.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13942

Data4Cyber: Labeled Cyber-Physical Datasets for Distribution-Grid Management Systems Under Representative OT Attacks

Ömer Sen, Lennart Bader, Marawan Emara, Martin Serror, Benedikt Schwalm, Junichi Tsurumi, Tatsumi Oba, Tomohiro Izawa 等 9 位作者

原文摘要

Cyberattacks against power systems increasingly exploit industrial communication and control dependencies arising from distributed energy resources (DER), remote management, and market-based incentives. Progress on intrusion detection is constrained by a lack of public datasets that align operational technology (OT) network telemetry with physical measurements and control context. Data4Cyber is a labeled cyber-physical dataset recorded in a laboratory distribution management system (DMS) for self-consumption optimization with photovoltaic generation, battery storage, controllable loads, and substation metering. It captures synchronized process measurements, controller context, price incentives, and packet captures (Modbus/TCP and MQTT) across one benign baseline and six attack scenarios spanning protocol-semantic Modbus manipulation, man-in-the-middle (MitM) false-data injection, and MQTT price manipulation. Its fixed 1 s time base, phase-resolved labels, and state-oriented representation support temporally blocked stateful-detector evaluation. Baseline results show that high recall alone is insufficient: several detectors achieve sensitivity through alarm saturation. Context-driven attacks require process-aware interpretation rather than single-channel thresholding.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13941

PARDIG: Global Precipitation Estimates Learned from Sparse Satellite Observations

Guy Shalev, Stephan Rasp, Boris Babenko, Andrew El-Kadi, Mohammed Alewi Hassen, Samier Merchant, Fred Zyda, Dominic Masters 等 17 位作者

原文摘要

Estimating precipitation at the global scale is vital for Earth system science and disaster response, yet observational coverage and measurement precision remain constrained. Existing solutions provide options that trade off desirable traits such as skill, latency, geographical coverage, temporal coverage, and spatial and temporal resolution. We present PARDIG, an artificial intelligence framework based on a spherical graph-mesh transformer architecture that produces near-real-time global precipitation estimates. PARDIG is trained on dense multi-modal inputs, including atmospheric state analyses and multi-spectral geostationary satellite imagery, to predict sparse, high-precision precipitation measurements from spaceborne radar. At inference, the trained model is applied densely across the globe at $0.1^\circ$ spatial and 15-minute temporal resolution, producing complete near-real-time precipitation fields from 2016 to the present, alongside a historical variant extending the record back to 1999. Evaluation against three independent radar networks (over the US, Europe, and Japan) and against over 36,000 surface stations worldwide shows that PARDIG consistently outperforms IMERG V07 (Final and Early), MSWEP V3, ERA5, and HRES across precipitation thresholds and regions, with the largest improvements at extreme intensities. Case studies of extreme precipitation events demonstrate that PARDIG successfully captures their spatial structure and timing. The improvements also extend to forecasting: WeatherNext 3, a global weather model, produces more skillful precipitation forecasts when trained to predict PARDIG. Both the historical and near-real-time PARDIG datasets are openly available under a CC BY 4.0 license.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.22549

ASPIRE: Make Spectral Graph Collaborative Filtering Great Again via Adaptive Filter Learning

Yunhang He, Cong Xu, Zhangchi Zhu, Hongzhi Yin, Wei Zhang

原文摘要

Graph filter design is central to spectral collaborative filtering, yet most existing methods rely on manually tuned hyperparameters rather than fully learnable filters. We show that this challenge stems from a bias in traditional recommendation objectives, which induces a spectral phenomenon termed low-frequency explosion, thereby fundamentally hindering the effective learning of graph filters. To overcome this limitation, we propose a novel adaptive spectral graph collaborative filtering framework (ASPIRE) based on a bi-level optimization objective. Guided by our theoretical analysis, we disentangle the filter learning objective, which in turn leads to excellent recommendation performance, spectral adaptivity, and training stability in practice. Extensive experiments show our learned filters match the performance of carefully engineered task-specific designs. Furthermore, ASPIRE is equally effective in LLM-powered collaborative filtering. Our findings demonstrate that graph filter learning is viable and generalizable, paving the way for more expressive graph neural networks in collaborative filtering. Our code is available at https://github.com/yhhe2004/ASPIRE.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.04785

Kierston & KierstonBench: Simulation of Allentown, PA and Evaluation of Language Models in Crime Investigation and Surveillance Tasks

Andrew Semenov, Svyatoslav Dorofeev

原文摘要

Large language models are candidates for collecting and analyzing records in criminal investigations and mass surveillance. Their capability and accuracy in this work cannot currently be measured. Real deployments lack the ground truth needed to score a model's conclusions, and no benchmark evaluates these capabilities in a simulated environment. We present Kierston, a city-scale simulation engine, and KierstonBench, a benchmark that tests these capabilities against known simulated events. Kierston models approximately 125,000 residents of Allentown, Pennsylvania, generating their daily activities and the telecommunications, financial, administrative and license-plate records that surveillance and investigative systems collect about them. These records derive from a common behavioral timeline and are distributed across sources representing separate providers. Models investigate through a restricted records interface, linking observations across sources without access to the underlying simulation. The benchmark comprises 32 task families covering record linkage, routine reconstruction, incident investigation and assessment of deceptive accounts. In an evaluation of sixteen language models, most could not perform records investigation and submitted identifications the records did not support. A small number of frontier models solved most of the tasks and abstained when evidence was insufficient. In some cases these models still fell for common deceptions and assumptions, and none replaced an investigator.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.11378

SegDis: Foreground Segmentation Followed by Type Discrimination for Salient and Camouflaged Object Detection

Dingning Luo, Shuo Dou, Tong Jiang, Zhenhua Duan, Jiachen Zhu, Xianfeng Li

原文摘要

Salient and camouflaged object detection (SOD and COD) both recover foreground support, yet most joint methods output only a binary mask and therefore cannot distinguish the type. USC12K distinguishes the two types, but its baseline USCNet still binds foreground existence to the type within a single normalization, so the failure source cannot be attributed. Our cross-task study shows that foreground localization transfers between SOD and COD, whereas type discrimination does not: a COD-only model recovers foreground on SOD images but its type judgement follows the training distribution rather than the foreground-background relation, so the two decisions require separate mechanisms. SegDis factorizes the output into an explicit foreground probability and a conditional type probability given foreground: FLR provides high-quality task-agnostic support, and RTA refines the SOD/COD assignment from the foreground-background relation without a task identifier. Because support is explicit, each ground-truth foreground pixel is classified as correct-type, type-swapped, or missed, so foreground-recovery and type-assignment errors are diagnosed separately. With PVT-v2-B2, SegDis attains 68.98% FG-mIoU and 78.35% mIoU on USC12K at about one-eighth of USCNet's parameters; CSCS is 5.75.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06706

ALTO: African and Levantine Tech-Facilitated Gender-Based Violence Corpus

Tianwei Zhang, Ivy Njoki Nyagwencha, Zahraa Dirani, Shalini Joshi, Scott A. Hale

原文摘要

Tech-facilitated gender based violence (TFGBV) is a pervasive online harm affecting women and gender minority groups through its many direct and covert forms. Current models and annotated datasets for gendered harm detection are dominated by data from, and models trained in, Global North languages. In collaboration with civil society organizations (CSOs) from Jordan, Francophone West Africa, and Kenya, we developed a community-led approach to crowdsource culturally and linguistically relevant instances of TFGBV in Levantine Arabic, African French, and Swahili. We annotated the crowdsourced data using active learning to develop a small but performant dataset (N=3022) called ALTO to improve TFGBV detection in the three languages. Through benchmarking and fine-tuning experiments, we show that existing harmful content detection models show poor TFGBV recall within ALTO and improve with fine-tuning. We found that models often miss covert harms across all languages, while error rates are markedly higher in Swahili. We report the experimental results and release the annotation taxonomy, dataset, and trained models.

cs.CL · cs.AI · cs.CY · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.07935

Towards Scalable Diagnostic-Driven Whole-Slide Image Retrieval for Pathology Archives

Jingsong Liu, Youssef Wally, Han Li, Weiwei Zhou, Jing Dai, Elisabeth Wetzer, Peter J. Schüffler

原文摘要

The growing adoption of digital pathology has rapidly expanded collections of hematoxylin-and-eosin whole-slide images, yet efficient navigation through these archives remains challenging, as metadata is often inconsistent or unavailable. Content-based image retrieval addresses this challenge by identifying similar cases directly from visual representations instead of manual annotations or metadata. Existing retrieval systems have demonstrated promising performance but largely rely on handcrafted indexing pipelines and predefined sampling strategies, constraining scalability and generalization. Here, we present CLEAR-WSI (Constant-Length Embedding for Automatic Retrieval of Whole-Slide Images) as a scalable, diagnostic-driven slide-retrieval framework, validated on both public and clinical data. Using a fixed-length slide embedding, CLEAR-WSI refines retrieved candidates with either a predicted diagnostic label or the ground-truth label, depending on the retrieval setting. Newly processed cases are subsequently added to the expanding memory bank, allowing the retrieval archive to grow over time. On a comprehensive benchmark consisting of two public and one private clinical datasets representing four retrieval tasks, CLEAR-WSI improves Precision@5 over the strongest evaluated baseline, with gains on CAMELYON16 (3.10), BRACS-1 (2.75), BRACS-2 (1.95), and X-HER2 (3.75). Additional ablation and latency studies further examine the contribution of each component and highlight its potential for scalable deployment.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13940

Tailoring Optimization Approaches with Domain Knowledge in Industry Process for Meta-heuristic Development

Jakub Cebula, Konrad Gmyrek, Paweł B. Myszkowski, Marko Đurasević, Łukasz P. Olech, Adam Paliszek-Saładyga

原文摘要

The paper presents an Industry Process for Meta-heuristic Development (IPMD) schema, a systematic methodology for applying meta- and hyper-heuristic applications to optimization problems. In this context, we present a case study of Balancing Non-dominated Tournament Genetic Programming (BNTGP) applied to solve two NP-hard multi-objective optimization problems: routing (Capacitated Vehicle Routing Problem with Time Windows) and scheduling (Multi-Skill Resource Constrained Project Scheduling Problem). The BNTGP method is based on a metaheuristic design. Its genetic programming-based representation makes BNTGP a hyper-heuristic, as it enables the exploration of a search space of heuristics for solving problems in general. The resulting heuristic can then be applied across a set of problem instances.

cs.NE · cs.AI

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新论文 · v1arXiv:2611.13938

Dual-Core Tensor Wheel Decomposition with Structure-Cached Acceleration for Tensor Recovery

Wenchao Xie, Qingsong Wang

原文摘要

Tensor wheel decomposition captures complex correlations in high-dimensional data through interactions between outer-ring factors and a high-order central core. However, updating these components requires the explicit construction and manipulation of large-scale matrices, which limits computational efficiency and incurs particularly high computational and memory costs for the central-core update. To address these limitations, we propose dual-core tensor wheel decomposition (DCTW), which factorizes the central core into two lower-order cores linked by a shared latent bond. Each core is coupled to a different group of outer-ring factors. We further propose a structure-cached acceleration strategy that avoids explicit large-scale matrix multi- plications and caches intermediate tensors generated during tensor network contractions for reuse across block updates. This reuse reduces redundant computation and avoids the explicit construction of large matrices. Complexity analysis and experiments show that the strategy based on structural caching accelerates optimization compared with explicit and implicit up- date schemes while limiting the increase in peak memory as the ranks grow. Experiments on color video completion and robust denoising show that DCTW matches or outperforms representative tensor network methods in reconstruction quality, computational efficiency, and scalability.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.11306

ScrapeBench: Evaluating Legal Compliance of AI Agents in Website Scraping

Joseph Marvin Imperial, Daniel Slate, Noam Kolt

原文摘要

AI agents deployed on the open web are becoming more capable of performing actions that may breach contractual provisions and violate computer security laws. In this paper, we introduce ScrapeBench, the first agentic benchmark for empirically evaluating the legal compliance of AI agents with website scraping policies, namely terms of service (ToS) and $\texttt{robots.txt}$ directives. Across widely used closed-weight and open-weight AI agents, we find that most agents exhibit low compliance rates (< 5%) when instructed to scrape websites in our sample of 1,626 popular websites that explicitly prohibit scraping. In addition, we observe that even where AI agents actively check the applicable ToS and $\texttt{robots.txt}$, they continue to exhibit low compliance rates. Conversely, in our sample of 269 websites that do not prohibit scraping, we observe that agents sometimes refuse to scrape despite it being lawful, such as Claude Opus 4.6 exhibiting an over-refusal rate of 98.5%. Our human baseline experiments (n = 180 participants) suggest that humans exhibit compliance rates comparable to AI agents but over-refuse at much higher rates.

cs.CL · cs.AI

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版本更新 · v2arXiv:2502.15118

Do we really need the Rademacher complexities?

Daniel Bartl, Shahar Mendelson

原文摘要

We study the fundamental problem of learning with respect to the squared loss in a convex class. In the subgaussian setting, the sharpest known sample complexity bounds are characterised via fixed points associated with the limiting gaussian process. In more general settings (for example, heavier-tailed scenarios) the state-of-the-art bounds rely on localised Rademacher complexities, which are often difficult to control and can be considerably larger than their limiting gaussian counterparts. We show that if one has access to coarse information on the covariance structure of the underlying class, it is possible to derive an upper bound on the sample complexity that matches the state-of-the-art rates in the gaussian setting---even in heavy-tailed regimes. As a consequence, under rather minimal assumptions, learning problems that share the same $L_2$-structure---even those involving heavy-tailed distributions---admit the same upper bound on the sample complexity as if the problem were fully gaussian. This constitutes the first universality result for general convex learning problems. The proof is based on a novel learning procedure, and its performance is studied by combining optimal mean estimation techniques for real-valued random variables with Talagrand's generic chaining method.

math.ST · cs.LG · stat.ML

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新论文 · v1arXiv:2611.13935

MEOW: Medical Open-World Interactive Segmentation

Philip Matthias Winter, Maria Wimmer, Bernhard Schäfl, David Major, Dimitrios Lenis, Astrid Berg, Theresa Neubauer, Ariharasudhan Muthusami 等 11 位作者

原文摘要

In open-world recognition, machine learning models not only make predictions on unknown structures ("generalized open-set"), but also continually learn and improve from those predictions over time. Interactive segmentation, despite its access to user feedback, has so far remained confined to the open-set setting. In this work we introduce MEOW, the first framework that turns non-interactive segmentation backbones into interactive open-world learners without updating learnable model parameters. MEOW combines parameter-free transductive reasoning with memory consolidation to accumulate knowledge and thereby improve future performance. Focusing on medical data, we show that MEOW consistently improves its own predictions on classes never seen during training, and transfers zero-shot to unfamiliar domains, including micro-CT data and other imaging modalities.

cs.CV · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.10285

FAIDER: Fairness-Aware Item-Level Decoding for Large Language Model Recommenders

Ziwei Wu, Jingrui He

原文摘要

Large language model (LLM) recommenders can produce semantically different recommendations when a user's protected attribute changes, even if the interaction history remains fixed. Controlling this disparity is challenging when model weights and full-vocabulary logits are inaccessible. We propose FAIDER (Fairness-Aware Item-level DEcoding for Recommendation), which incorporates fairness into list construction by selecting complete item candidates rather than manipulating token-level logits or repeatedly repairing finished outputs. The central idea is to separate the operational criterion for a violation from how strongly generation is constrained. Conformal-inspired empirical calibration fixes a cross-group semantic-disparity threshold, while online decoding applies either hard filtering or a soft penalty adjusted through dual-inspired feedback to target a prescribed violation rate. Separately, we establish a pathwise guarantee: under a uniform enforceability assumption, the soft decoder's empirical violation rate exceeds its target by at most $O(1/T)$. Experiments across recommendation benchmarks and LLM backbones show that Soft FAIDER improves both measured semantic fairness and ranking quality over the evaluated prompt-repair baselines. It also generally achieves a more favorable fairness-utility balance than the hard variant, indicating that stricter per-recommendation constraints do not necessarily yield lower measured disparity.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13933

The Case for AI-Enabled Gate Monitoring as Farm Security in the UK: A PRISMA-Guided Review of the Evidence on Rural Crime, Livestock Predation and Farm Access

Steve Symes, Paul Fergus, Mark Carr, Omar McIver, Arwyn Jones

原文摘要

Farm security in the UK rests on thinner evidence than its cost suggests. We ran a PRISMA guided review: twelve searches of Web of Science and Scopus on 2nd -4th November 2026 returned 1,183 records (761 unique), and grey-literature and hand-searches added 35 assessed sources. We synthesised 96 database records and 24 grey sources narratively. UK peer-reviewed evidence is small (16 records), and the main UK survey of crime and farmer mental health has 80 responses. Cost estimates rest on insurer claims: NFU Mutual put UK rural crime at £41.5 million in 2025, with livestock theft up almost 30% to about £4.5 million and dog attacks on livestock costing £1.95 million. The Home Office states that agricultural crime cannot be separately identified in its central data. We found no independent evaluation of cameras, sensors, trackers or marking; light and sound deterrents lost effect within one to five months in a systematic review of anti-predator interventions; and no study examined how often farm gates are left open or what follows. Camera-trap AI papers report accuracy on ecological datasets, none at a farm gate in the records we found, and in one comparison machine learning detected 41% of wildlife against 81% for analysts. We argue, as an inference from these gaps and not as a demonstrated effect, that continuous gate-level AI-mediated monitoring, as implemented in Fieldmark AI, would be highly beneficial for UK farm security. It would create the farm-level, time-stamped record that no official source holds, verify events that periodic human checks miss, resolve whether foxes, dogs or people were responsible, and give every other security measure something to be tested against. No study has yet shown that it reduces loss, and we set out the evaluation needed to show it.

cs.CV · cs.AI

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新论文 · v1arXiv:2611.13931

TAPCO-G: LLM-Guided Design-Adaptive Timing and Power Co-Optimization with Graph-Based Physical Priors

Wuqian Tang, Zixiao Wang, Che-Chien Lin, Fong-Hua Fu, Laura Angélica Shion-Korsak, Xinyun Zhang, Bei Yu, Chun-Yao Wang

原文摘要

Post-placement timing and power co-optimization is critical to meeting setup timing targets and reducing power within placement constraints. A cell replacement can improve timing along one path while increasing power or reducing slack on others. Conventional optimizers follow fixed rules for scheduling and accepting repairs, limiting their ability to address design-specific bottlenecks. A large language model (LLM) can propose changes to these rules, but on its own it cannot reliably estimate how a cell replacement affects endpoint slack and power. TAPCO-G combines LLM-guided optimizer adaptation with graph-based physical priors to evaluate candidate cell replacements. The outer loop tunes tool settings using physical-design feedback and revises executable repair policies when tuning stalls. The inner loop anchors delay estimates to static timing analysis snapshots, accounts for upstream driver-load changes, and propagates delay shifts to timing endpoints. It combines these estimates with coarse-to-fine power screening to prioritize candidates for physical trials. TAPCO-G produced placement-legal, logic-equivalent implementations and outscored CERBERUS on all eight ASAP7 benchmarks from the MLCAD 2026 Contest, with a mean quality score gain of 5.516 points, and scored 21.648 points higher than TAPCO on average. These results demonstrate the effectiveness of the proposed co-optimization framework.

cs.AR · cs.AI

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新论文 · v1arXiv:2611.13929

ReACT: Reflection-Consistent Modulation and Cross-Load Consistency for Humanoid Velocity Tracking Under Asymmetric Loads

Xianghui Wang, Aoran Fu, Liyou Luo, Shanze Wang, Xiaoyu Shen, Dan Zhang, Wei Zhang

原文摘要

In recent years, humanoid loco-manipulation has progressed to whole-body grasping, lifting, and transport. A basic requirement underlying these tasks is carrying the object to its destination, which depends on the locomotion controller accurately executing velocity commands from the navigation policy. However, carrying an object in one hand introduces a persistent asymmetric upper-body load that can degrade velocity tracking. Persistent tracking biases can accumulate into position and heading deviations, increasing the risk of collisions and navigation failure. To address this problem, we present ReACT, a framework for accurate velocity tracking under asymmetric upper-body loads. Reflection-consistent disturbance modulation uses the estimated wrench to modulate actor features for load compensation while preserving action correspondence under reflected observations. Cross-load tracking consistency training compares loaded and unloaded conditions under the same velocity commands to promote consistent velocity tracking across loading conditions. Simulation experiments on a Unitree G1 humanoid robot show improved velocity tracking and reduced directional bias under varying asymmetric loads. Real-world experiments demonstrate collision-free goal reaching in all 40 tested navigation trials under unilateral hand loads with a fixed mapless navigation policy.

cs.RO

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