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新论文 · v1arXiv:2611.06230

Homology Features for X-Ray Hyperspectral Images Segmentation

Fethi Harkat, Guillaume Gey, Valérie Perrier, Kévin Polisano

原文摘要

Characterizing Multi-Energy X-ray Transmission (XRT) hyperspectral images presents significant challenges for traditional classification frameworks due to high-dimensional redundancy and non-linear spectral overlaps. Although manifold learning techniques such as UMAP enhance spectral representation by preserving non-linear correlations, they do not explicitly utilize the geometric structure of the embedded manifold for segmentation. This study introduces a hybrid architecture that integrates topological features derived from persistent homology with convolutional and graph neural networks. The approach computes Vietoris-Rips persistent homology on UMAP-projected superpixel point clouds to capture H 1 cyclic structures that encode spectral heterogeneity. These topological descriptors are combined with pixel-level CNN features through a broadcasting mechanism that propagates superpixel-level graph representations to full spatial resolution. Experimental results on XRT multi-energy imagery demonstrate that the proposed hybrid model significantly outperforms both CNN-only and superpixel-pooling fusion baselines, suggesting that topological features provide complementary discriminative information to local spectralspatial descriptors.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06229

Bug2Scenario: Root-Cause-Preserving Test Scenario Amplification for Autonomous Driving Software

Yuchen Cao, Zhenyu Mao, Jacky Wai Keung, Yifei Wang, Yihan Liao, Jingyu Zhang

原文摘要

Simulation-based fuzzing exposes autonomous driving system (ADS) failures by repeatedly mutating and executing scenarios, but converting one failure into reusable regression scenarios remains difficult. Such variants test whether the mechanism persists after software changes, for example by varying weather or actor placement while retaining a collision's actor-route conflict. We present Bug2Scenario, a root-cause-guided post-fuzzing framework. From scenario data, traces, and oracle evidence, it extracts a root-cause pattern. An LLM proposes semantic variations over actors, missions, routes, and weather. Deterministic rules construct DriveFuzz-compatible scenarios. Execution evidence and manual review assess preservation. Separating these responsibilities prevents fluency, syntax, or recurring labels from masquerading as causal evidence. Under matched seeds, six LLM backbones, candidate budget, and execution pipeline, Bug2Scenario improves strict preservation by 6.7 percentage points (p=0.104; not significant) and boundary-inclusive preservation by 10.6 points (p=0.011). Feedback helps some initially unsuccessful seeds yield a strictly preserved candidate. Seed-level stopping then avoids further API calls and simulator runs. Remaining failures reflect actor-placement constraints, simulator instability, and incomplete or conflicting causal evidence. Bug2Scenario thus produces reliable ADS regression assets. Our implementation is available at https://github.com/YgcCoder/Bug2Sce.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.39855

Probabilistic Meshless Methods on Manifolds

Federico Pavesi, Noémie Jaquier, Antonio Candelieri

原文摘要

Probabilistic meshless methods solve partial differential equations (PDEs) by placing a Gaussian process prior on the solution and conditioning it on the PDE at a finite set of collocation nodes. The posterior provides both an approximation of the solution and a Bayesian quantification of its error. While these methods are well established on Euclidean domains, their extension to manifolds lacks, to our knowledge, any convergence analysis. We extend them to compact manifolds and study their convergence in two settings. First, for known manifolds whose Laplace-Beltrami eigenpairs are available, the prior can be evaluated exactly, but the PDE operator generally cannot be applied to it in closed form. We replace this operator by a standard generalized finite difference discretization on a point cloud sampled from the manifold, and show that the resulting method is consistent at the points of the cloud, at a rate governed by their fill distance. Second, for implicit manifolds, known only through a point cloud, both the prior and the operator must be approximated. We propose a new generalized finite difference scheme with a free stencil radius, and show that it retains the consistency of the standard construction while controlling the growth of its coefficients, and that an appropriate radius yields uniform convergence of the discrete Gram matrix. In both settings, we derive error bounds for the posterior mean which separate the collocation error intrinsic to the method from the error due to approximating the operator and, for implicit manifolds, the prior. Experiments on the circle, the sphere and the flat torus confirm our theoretical claims. On real-world cortical thickness data, our approach attains the accuracy of established discretizations, while conditioning on fewer than a tenth of the observations and providing a credible interval at every vertex.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06228

Separation Is a Riesz Energy: How Many Environments Does ICA Need?

Yuda Bi, Yubo Guo, Jiaxiao Cai, Xiaonai Li, Zhuolin Li, Vince D. Calhoun

原文摘要

Can independent components be recovered when all of them are Gaussian? Yes, if their variances change across environments; but how many environments, and how many samples as the number of components $d$ grows? We answer both for the joint diagonalization of $K$ covariances. Each component has a signature, its variances across the environments. Separating component $j$ costs the Riesz 2-energy $E_j$ of this signature, a sum, over the other components, of inverse pairwise Fisher informations. For Gaussian sources no estimator in $O(d)$ beats $cE_j/(n+E_j)$ at any $n$ and $d$, and above an explicit sample size a spectral estimator attains $E_j/n$ up to a constant, for every mixing and without the signatures (given a lower bound on the pairwise information). Since signatures live in $K-1$ dimensions, the rate obeys a dimension law: $d^2/n$, $d\log d/n$ and $d/n$ for $K=2$, $3$ and $K\ge4$ well-spread environments. What counts is the dimension the signatures span, not their number, and $\log d$ well-designed environments make $d\log d$ samples suffice. Projecting the covariances onto one linear combination pays $d^2/n$ at first order instead. In simulations up to $d=1000$ the predictions hold and joint diagonalization with $K\ge4$ attains the rate once $n/d$ is in the hundreds; on task fMRI the energy says which components are hard to separate.

cs.LG · cs.IT · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.23689

AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection

Mingi Choi, Gunhee Kim, Jisoo Kim, Taeksoo Kim, Taeyun Ha, Jongbin Lim, Hanbyul Joo

原文摘要

Scaling real-world dexterous-grasp data collection is limited by the sustained human effort required to operate the robot, verify grasp outcomes, and reset objects between trials. Simulation can generate grasp candidates at scale, but their success on hardware remains uncertain. We present AutoDex, a system that collects real-world dexterous-grasp data without continuous human operation. Given generated grasp candidates, AutoDex plans and executes grasps, labels their outcomes, and resets the object to continue collection across stable poses. Multi-view object tracking supports labeling under hand-object occlusion, while residual-torque collision monitoring and pose-directed reset enable the loop to operate without continuous human supervision. Using AutoDex, we collect over 3,500 grasp trials across 100 objects with Allegro and Inspire hands, recording both successes and failures together with synchronized visual observations and robot states. AutoDex reduces human effort by more than an order of magnitude, requiring an estimated 0.2 human-hours to collect 500 trajectories compared with 9.3 for teleoperation. As AutoDex collects trials, it builds a library of physically validated grasps for subsequent execution. In new object placements and obstacle configurations, retrieval from this library achieves 76% success, compared with 34% for candidates screened only in simulation. Code and data are available at https://snuvclab.github.io/AutoDex/.

cs.RO · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06226

Understanding the Perception of Transliterated Offensive Social Media Posts Among Banglish and Hinglish Users

Dhiman Goswami, Zhicong Lu, Sanchari Das

原文摘要

Effective content moderation depends on understanding not just what harmful content looks like, but how users perceive and respond to it. When offensive language is written in transliterated forms such as Banglish or Hinglish (Bangla or Hindi in Latin script), it may be interpreted very differently than in native scripts, with direct consequences for whether users report, ignore, or recognize it as harmful. Yet little is known about how script choice relates to user perception, or how these perceptions vary across demographic groups. To investigate these perceptions, we conducted a user study with $310$ participants ($193$ Hindi, $60$ Bangla, $57$ Bangla-Hindi bilingual speakers) who evaluated offensive posts in native scripts and transliterations. Participants provided offensiveness scores ($4.03$ for Banglish, $3.86$ for Hinglish) to transliterated posts, yet reporting fell from $20.9\%$ (native - Bangla, Hindi) to $18.2\%$ (transliterated - Banglish, Hinglish). Ignoring remained the dominant response (range $25.38\%-44\%$ overall). Women reported at higher probability than men for native-script ($23.6\%$ vs. $17.7\%$) and showed almost identical reporting probability in comparison to men for transliterated posts ($17.5\%$ vs. $17.9\%$), and younger participants ($<40$) rated Banglish content as more offensive than older participants ($\geq 40$), (Overall offensive score: $3.99$ vs. $3.78$). These findings identify transliteration as critical blind spot for moderation, underscoring the need for linguistically sensitive approaches to online safety.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.06233

Anchor PCA

Benedikt Seiter, Anya Fries, Julius von Kügelgen, Jonas Peters

原文摘要

Principal component analysis (PCA) is one of the most widely used unsupervised dimension reduction techniques. We study PCA for data from multiple related domains. Since principal components generally differ across domains, one way to obtain a shared low-rank embedding is to perform PCA on the pooled data. However, this approach can focus on spurious directions that exhibit high variation in only a few domains. To find a robust embedding that still explains most variance in unseen but similar domains, we propose instead to focus on shared directions of variation. To this end, we introduce Anchor PCA which trades off overall explained variance with agreement between the shared and domain-specific low-rank embeddings. Anchor PCA amounts to PCA on a modified target matrix and thus can be solved efficiently. Moreover, we show that Anchor PCA recovers a maximal invariant subspace and admits a minimax reconstruction interpretation under bounded domain-specific covariance inflations. On simulated and real-world data with temporal distribution shift, we demonstrate, respectively, that Anchor PCA recovers the maximal invariant subspace and yields embeddings that explain more variance on unseen domains than the pooling baseline and a worst-case alternative. Taken together, these findings establish Anchor PCA as a promising approach to robust unsupervised dimension reduction from multi-domain data.

stat.ML · cs.LG · stat.ME

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.05193

Factorized MLLM Embeddings for Composed Image Retrieval

WonJun Moon, Seongchan Kim, Dahun Kim, Seungryong Kim

原文摘要

Composed Image Retrieval (CIR) aims to retrieve a target image given a reference image and a modification text. Although recent approaches leveraging Multimodal Large Language Models (MLLMs) have shown significant progress, we argue that the MLLM's compositional understanding remains underutilized. This stems from directly mapping composed queries into a retrieval embedding via contrastive supervision alone, without explicitly resolving the intended visual modifications. In this work, we introduce Factorized MLLM Embeddings (FactEmbed), which decouples compositional query comprehension from building discriminative embeddings. Rather than directly projecting the composed query into a single embedding without internal reasoning, FactEmbed employs a set of factorized latents that simulate the intended visual modifications via an intermediate target imagination objective. This process encourages the latents to ground into target semantics before forming discriminative representations for retrieval. Subsequently, to propagate these factorized representations throughout the model for retrieval, we optimize a factor-level contrastive loss while distilling the decomposed representations into a deeper attention layer of the MLLM. Finally, for efficient inference, we retain the [EOS] token to establish a global-level embedding on top of these factorized latents, while we exploit the mean-pooled factorized latents to rerank a short candidate list at negligible cost, as both are produced by the same forward pass. Extensive evaluations demonstrate that FactEmbed outperforms prior works across CIR benchmarks.

cs.CV · cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.11722

EPIC: Efficient Predicate-Guided Inference-Time Control for Compositional Text-to-Image Generation

Sunung Mun, Sunghyun Cho, Jungseul Ok

原文摘要

Recent text-to-image (T2I) generators can synthesize realistic images, but still struggle with compositional prompts involving multiple objects, counts, attributes, and relations. We introduce EPIC (Efficient Predicate-Guided Inference-Time Control), a training-free inference-time refinement framework for compositional T2I generation. EPIC casts refinement as predicate-guided search: it parses the original prompt once into a visual program of object variables and typed predicates, then verifies each candidate image against this program using visual evidence extracted from that image. A candidate is judged to satisfy the prompt when all predicates pass; otherwise, unmet predicates guide the next step, routing localized failures to targeted editing and layout-level or persistent failures to resampling. On GenEval2, EPIC improves the prompt-level Soft-TIFA GM score from 34.16% for single-pass generation to 71.46%. With the same generator, editor, and image-model execution budget, EPIC outperforms the strongest baseline by 19.23 points. It also uses 31% fewer image-model executions, 72% fewer multimodal large language model (MLLM) calls, and 81% fewer MLLM tokens per prompt.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06223

Consistent Submodular Maximization with Full Foresight

Eric Balkanski, Paul Dütting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Ola Svensson, Morteza Zadimoghaddam

原文摘要

Online algorithms with bounded recourse face three intertwined obstacles: uncertainty about the future, computational limits, and a limited budget for changes. We ask a fundamental question that separates consistency from the other two: what does solution stability cost when the entire future is known and computation is unlimited? We answer this question for monotone submodular maximization, where an algorithm must maintain a solution of at most $k$ arrived elements, with a constant number of changes per step. We determine the optimal approximation thresholds: $3/4$ for deterministic algorithms and $ξ^\star \approx 0.7942$ for randomized algorithms. For every $\varepsilon>0$, we get within $\varepsilon$ of these thresholds with worst-case recourse polynomial in $1/\varepsilon$, independent of $k$ and the stream length. We prove matching impossibility results even for coverage functions. We attain the sharp bounds by optimally balancing two opposing forces: preparing for future arrivals and protecting the current solution. For randomized algorithms, we use addition robustness against prefixes of the known future, which we analyze tightly via an interval-selection problem. For deterministic algorithms, we introduce certificates, paths in a fractional relaxation that maintain high value, which we round into an update schedule.

cs.DS · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06221

FedXCrop: Privacy-Preserving Crop Disease Detection via Federated Learning with Post-Aggregation Explainability under Non-IID Farm Distributions

Kriti Saini, Manikandan P

原文摘要

Deep learning can classify crop diseases accurately from benchmark leaf images, but centralized training requires farms to share raw data, and predictions without explanations offer limited support for treatment decisions. We present FedXCrop, a federated crop disease classification pipeline that quantitatively evaluates explanations of the aggregated global model. A MobileNetV2 model trained centrally on PlantVillage achieved 99.38% test accuracy (standard deviation 0.05 over three seeds) on a fixed test set of 5,428 images across 38 classes. Federated models initialized with ImageNet weights were trained for 30 rounds across five clients. FedAvg achieved 98.81% accuracy with identically distributed clients and 98.81%, 98.59%, and 97.78% under Dirichlet label skew (alpha = 1, 0.5, and 0.1, respectively). FedProx was not significantly different from FedAvg at any heterogeneity level (McNemar p > 0.3). Grad-CAM and SmoothGrad explanations were evaluated on 380 test images. Both attributed more energy to the leaf than chance (0.70-0.78 versus 0.60), while Grad-CAM maps from federated and centralized models showed Spearman correlations of 0.74-0.76. Agreement decreased as heterogeneity increased, lesion localization was only modestly above chance, and a model-randomization test indicated that SmoothGrad maps depended substantially on image structure rather than learned parameters. In this setting, label skew reduced federated accuracy by 0.6-1.6 percentage points relative to centralized training. FedAvg largely preserved Grad-CAM behavior, while FedProx provided no measurable benefit.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06214

Riemannian Optimization-based Whole-body Obstacle Avoidance for Mobile Continuum Robots in Cluttered Environments

Duo Tang, Ping Deng, Sophie Armanini, Yajing Shen, Toshio Fukuda, Cosimo Della Santina, Peng Lu

原文摘要

Inspired by the coordination of global motion and local shape adaptation in snakes, elephant trunks, and octopus arms, we propose a unified planning and control framework for mobile continuum robots in cluttered and confined environments. Existing approaches often address high-level path planning and local motion control separately, limiting coordinated base-arm motion, kinematic consistency, and collision avoidance. Our framework integrates long-horizon trajectory planning with real-time geometric refinement under kinematic and environmental constraints. The global layer combines A* search with minimum-jerk planning to generate smooth reference trajectories. The local layer uses a geometry-consistent inverse kinematics solver and Riemannian optimization to coordinate mobile-base motion and continuum deformation while avoiding obstacles. We evaluate the framework through simulated pipe-traversal tasks with multiple baseline planners and real-world experiments involving navigation among dense obstacles and manipulation in confined spaces. Results demonstrate improved motion consistency, feasibility, and adaptability, supporting coordinated whole-body motion of mobile continuum robots in constrained environments.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.27202

Indirect Prompt Injection in the Wild: An Empirical Study of Prevalence, Techniques, and Objectives

Soheil Khodayari, Xuenan Zhang, Bhupendra Acharya, Giancarlo Pellegrino

原文摘要

As LLMs are increasingly integrated into systems that browse, retrieve, summarize, and act on web content, webpages have become an untrusted input vector for downstream model behavior. This enables site owners, contributors, and adversaries to embed instructions directly in web resources, i.e., indirect prompt injections. While prior work demonstrates such attacks in controlled settings, their prevalence, deployment, and real-world impact remain unclear. We present one of the first large-scale empirical analyses of indirect prompt injections in webpages and HTTP responses. Analyzing 1.2B URLs from 24.8M hosts, we identify 15.3K validated instances across 11.7K pages. These are not isolated cases: a small number of recurring templates account for most cases. We characterize their objectives, delivery mechanisms, visibility, persistence, and impact, revealing a heterogeneous ecosystem spanning disruptive prompts, reputation manipulation, content-protection directives, and AI-bot detection, targeting systems such as crawlers, search pipelines, customer-support agents, and hiring workflows. A key finding is that most instructions target machines rather than humans: about 70% appear in non-rendered HTML (e.g., headers, comments, metadata), and many visible cases are hidden via rendering techniques. To assess practical risk, we run 5,200 controlled experiments across 13 models and four webpage representations. Our results show compliance is limited but non-negligible, reaching up to 8% for smaller models on plain-text inputs, while structured representations reduce compliance by preserving structural cues. Overall, prompt-based interference is already present in the web ecosystem and represents a growing source of tension between LLM-driven automation and the sites it consumes.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06209

Sublinear Approximations for Spanning Tree Congestion

Sunny Atalig, Marek Chrobak, Petr Kolman, Hans Raj Tiwary

原文摘要

The spanning tree congestion of a connected graph \(G\) is the minimum, over all spanning trees \(T\) of \(G\), of the maximum number of edges of \(G\) crossing a cut induced by deleting an edge of \(T\). In the spanning tree congestion problem we are asked to compute a spanning tree with minimum congestion. We describe two sublinear-ratio approximation algorithms for the spanning tree congestion problem (STC): a simple $\Ot(n^{8/9})$-approximation, and a more involved $\Ot(n^{2/3})$-approximation, where the notation $\Ot$ hides factors polynomial in $\log n$.. Until now, no algorithm with sublinear ratio was known. We also prove that for any fixed $0<\varepsilon<1$, approximation of the spanning tree congestion within $2-\varepsilon$ is NP-hard. This improves the previous best known lower bound $6/5$.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03699

All Paths All at Once: A Quantum Monte Carlo and Neural Committor Approach to Reaction Pathway Search

Chien-Chai Chang

原文摘要

Locating competing reaction pathways is difficult when potential-energy gradients are unavailable. Here we present tReax_expedition, an energy-only approach combining coupled Monte Carlo exploration, stochastic quench labelling and restrained continuation. Exploration identifies connected states, while continuation resolves candidate channels and records sampled chain maxima. Single-search benchmarks on three two-dimensional potentials recovered an intermediate and competing routes without transition-state guesses. For eight of nine gas-phase reactions on GFN2-xTB, sampled chain maxima differed from independently refined saddle energies by less than 0.76 kJ/mol. Alanine dipeptide instead revealed incomplete conformational relaxation, showing that route connectivity can persist despite structural and energetic errors. Neural committor learning and local straddling-pair sampling connect the resulting paths to transition-region sampling, although molecular shooting diagnostics indicate limited committor calibration. These results show how energy-only exploration and continuation recover candidate pathways, while distinguishing route connectivity from geometric localization, barrier resolution and finite-temperature transition statistics.

physics.chem-ph · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2509.10127

Population-Aligned Persona Generation for LLM-based Social Simulation

Zhengyu Hu, Jianxun Lian, Zheyuan Xiao, Max Xiong, Tianfu Wang, Teng Xiao, Yuxuan Lei, Fengqing Jiang 等 13 位作者

原文摘要

Recent advances in large language models (LLMs) have enabled large-scale, high-fidelity social simulations, creating new opportunities for computational social science. However, constructing persona sets that faithfully reflect real-world population diversity remains a key challenge. Existing studies often emphasize agentic frameworks and simulation environments, while paying less attention to persona generation and the biases introduced by unrepresentative persona sets. In this paper, we propose a systematic framework for synthesizing high-quality, population-aligned persona sets for LLM-driven social simulation. Our approach begins by leveraging LLMs to generate narrative personas from long-term social media data, followed by rigorous quality assessment to filter out low-fidelity profiles. We then apply importance sampling to achieve global alignment with reference psychometric distributions, such as the Big Five personality traits. To address the needs of specific simulation contexts, we further introduce a task-specific module that adapts the globally aligned persona set to targeted subpopulations. Extensive experiments demonstrate that our method significantly reduces population-level bias and enables accurate, flexible social simulation for a wide range of research and policy applications.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06207

Performance Portability of Out-of-Kernel Optimizations on GPUs: A Molecular Docking Case Study

Gianmarco Accordi, Leonardo Beltrame, Theresa Pollinger, Jens Domke, Davide Gadioli, Gianluca Palermo

原文摘要

Achieving performance portability is challenging for irregular scientific applications built as repeated multi-kernel GPU pipelines, because throughput depends on out-of-kernel decisions as much as on kernel efficiency. Using molecular docking as a representative case study, we propose an out-of-kernel tuning methodology that treats batching policy, worker concurrency, and per-worker memory allocation as explicit optimization parameters, replacing backend-dependent occupancy-driven choices with a more portable execution strategy based on explicit resource bounds. We implement this methodology in the muDock mini-app under a manager/worker execution model where multiple host workers share a GPU through independent streams. We then evaluate the methodology on NVIDIA, AMD, and Intel GPUs across CUDA, HIP, and SYCL implementations. Across all evaluated platform/backend pairs, the memory-bounded formulation exceeds the peak throughput of the occupancy-guided baseline. The fully tuned methodology improves the end-to-end throughput on all tested configurations, achieving an average speedup of 1.53x over the baseline.

cs.DC · cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06206

Loop Before You Leap: What Recurrent Depth Can and Cannot Buy in Data-Limited Pretraining

Daichi Hotta, Yohei Kobashi, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo

原文摘要

Multimodal models have often scored below text-only baselines in the BabyLM Challenge (Klerings et al., 2024; Takmaz et al., 2025). We test whether additional recurrent computation helps close this gap. Recurrence increases computation by reusing layers without adding parameters or training data. Using a single implementation, we compare standard and looped Transformers under a 100M-word budget, with three seeds per main condition. Each image is represented by one feature vector from a frozen DINOv2 encoder, used as a prefix token. Caption words count toward the budget, so multimodal training also changes the language data. In a pre-registered text-only comparison, a 12-layer stack repeated four times exceeds a standard 48-layer Transformer by 5.4 BLiMP percentage points at matched inference compute. However, multimodal training lowers BLiMP accuracy by about 3.8 points in both architectures, with no clear mitigation from recurrence. An exploratory loop-side no-image control, trained with the multimodal language data and batching schedule, scores 5.7 points below the text-only baseline. Adding the visual prefix raises accuracy by 1.85 points on average, but this contrast is uncertain with three seeds. These results suggest that changes in language data and its presentation are a major source of the drop. Diagnostic tests show that the models use visual information; its contribution to grammatical generalization remains uncertain under this single-vector interface.

cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06204

CoSafe: Cooperative Runtime Safety Assurance for Mixed-Traffic Autonomous Driving

Dingji Wang, You Lu, Haowen Jiang, Yanggang Chen, Yuyang Bi, Bihuan Chen, Shenglin Bao, Xin Peng

原文摘要

With the rapid development of autonomous driving systems (ADSes), real-world roads are evolving into mixed-traffic environments where autonomous vehicles (AVs) and other traffic participants (e.g., human-driven vehicles (HDVs), cyclists, and pedestrians) coexist and interact with each other. However, current ADSes still incur various safety violations in mixed traffic scenarios, raising a strong demand for runtime safety assurance frameworks that can continuously monitor hazards and prevent potential violations. Existing ego-centric assurance approaches are insufficient for mixed traffic, where violations often arise from locally reasonable yet jointly incompatible decisions among multiple vehicles and cannot be reliably resolved through independent takeover. To bridge this gap, we propose CoSafe, a modular and ADS-agnostic cooperative runtime safety assurance framework for mixed-traffic autonomous driving to extend runtime safety assurance from ego-centric takeover to scene-level cooperative intervention. CoSafe leverages vehicle-to-everything (V2X) communication to enable roadside units (RSUs) to monitor scene-level hazards and jointly mitigate hazards involving multiple vehicles. We integrate CoSafe with seven state-of-the-art ADSes, and our evaluation on the InterDrive benchmark demonstrates that CoSafe improves the driving score of AVs by 70.03% on average, reduces safety violations by 87.80%, and accurately identifies unsafe AVs with an F3 score of 0.930, while satisfying real-time constraints.

cs.SE · cs.AI · cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.06327

Query-Oblivious Coresets for Softmax Attention: Improved Bounds and Efficient Constructions

Ofek I. Cohen

原文摘要

A query-oblivious coreset for a softmax-attention head is a subset of the key--value pairs whose attention output is within $\varepsilon$ of the full one for every query in a ball. With $ρ$ the query radius times the centred key radius, we build such coresets of size $O(e^ρ(\sqrt{d_v}+\sqrt{d_k\log(1+ρ)})/\varepsilon)$ in randomised polynomial time, improving the existential $O(\sqrt d\,e^{ρ+\frac12\logρ+o(\log\logρ)}/\varepsilon)$ of Liberty, Andoni and Kleiner to within $\sqrt{\log(1+ρ)}$ of their $Ω(\sqrt d\,e^ρ/\varepsilon)$ lower bound: a spherical lift of both balls into one exponential-kernel instance admits the Bozzai--Rothvoss chaining bound, and Chevet's inequality splits key from value dimension. A sampling cap $O(e^{2ρ}/\varepsilon^{2})$ completes the envelope, and in fixed dimension Tai's diameter-free bound removes the logarithm. A census of every head of Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct finds $ρ\ge24.100$, so every whole-ball bound with a factor $e^ρ/\varepsilon$ prescribes a coreset larger than the cache: the algorithmic contribution is asymptotic on these models.

cs.DS · cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.12575

Discovering Semantic Structures in Psychological Scales: A Response-Free Front-End to Efficient Simplification

Bo Wang, Yuxuan Zhang, Yueqin Hu, Hanchao Hou, Kaiping Peng, Shiguang Ni

原文摘要

Psychological scale refinement traditionally relies on response-based methods such as factor analysis, item response theory, and network psychometrics to uncover latent constructs and optimize item composition. Although rigorous, these approaches require large samples and may be constrained by data availability and cross-cultural comparability. Recent advances in natural language processing suggest that the wording of scale items contains construct-relevant semantic organization, offering a complementary response-free perspective. We introduce a topic-modeling framework that operationalizes item-level semantic structure for systematic scale simplification. Items are encoded using contextual sentence embeddings and grouped through density-based clustering to identify semantic topics without predefining their number. Within each cluster, class-based term weighting identifies representative keywords that summarize each topic and support an interpretable semantic pre-structure of the item pool. Semantically related clusters are hierarchically merged, and representative items are selected using structure-aware membership criteria within an integrated reduction pipeline. We evaluated the framework on three widely used instruments: DASS, IPIP, and EPOCH, covering negative affect, personality traits, and well-being. Results showed that the identified semantic topics often formed factor-like groupings aligned with established constructs. Representative items selected through semantic membership probabilities reduced item counts by 60.5% on average while maintaining acceptable psychometric properties and preserving factor correlation patterns. Our framework formalizes semantic topic structure as an inspectable front-end for scale construction and simplification. To facilitate adoption, we provide a visualization-supported tool enabling one-click semantic analysis and structured simplification.

cs.CL · cs.LG

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新论文 · v1arXiv:2611.06203

BiCG-Net: Target-Specific Image Quality Assessment via Observation Modeling and In-Image Differencing

Yibu Yang, Tingqiong Cui, Liming Lei, Hao Xiong, Jun Xu, Siyuan Liu, Guanghui Huang

原文摘要

We estimate the quality of a specific target region (e.g., a train body) in an image, rather than the whole image. This differs fundamentally from whole-image No-Reference Image Quality Assessment (NR-IQA): when the target occupies a small fraction of the frame, the whole-image score is dominated by background, and no public benchmark provides target-level human Mean Opinion Score (MOS). We estimate target quality via nested sampling - a sequence of patches expanding outward from the target box - and infer quality from the relative structure of this sequence. This motivates BiCG-Net, a student network that distills an 11-teacher ensemble through a linear observation model and an in-image differencing mechanism, which provably cancels the target term and teacher-specific bias. We train and test the student independently on each dataset, and evaluate zero-shot transfer across benchmarks. On CSIQ and LIVEC, the student reaches PLCCs of 0.9009 and 0.9133 against human MOS, exceeding the best single teacher and the teacher consensus. On a self-built MOS-free railway dataset, the student reaches a PLCC of 0.9855 against a weak label. The student exceeds its teachers because it learns a new mapping from the relative structure of teacher readings to the ground-truth label, rather than mimicking them. Residual analysis on CSIQ and LIVEC shows the prediction residual is significantly correlated with background contamination, teacher dispersion, and label extremity, demonstrating the student extracts information the consensus does not contain. Under CSIQ-to-LIVEC transfer, the frozen student reaches a PLCC of 0.8416, exceeding the consensus by 0.1947. We position BiCG-Net as an offline target-level quality auditing and label-generation engine; its 39,482-parameter student is compact by design, acting as a unifying layer mapping heterogeneous teacher readings onto a common quality scale.

cs.CV

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新论文 · v1arXiv:2611.06196

Beyond Terminal Constraints: Reliable Robot Control with Learned Safe Sets

Gianni Lunardi, Elias Fontanari, Matteo Saveriano, Andrea Del Prete

原文摘要

Ensuring safety in advanced robot control methods such as Model Predictive Control (MPC) and Reinforcement Learning remains an open challenge. A common approach relies on control-invariant sets, yet their exact computation for nonlinear systems is generally intractable, and numerical approximations compromise theoretical guarantees. In this work, we show that safety does not need to rely exclusively on control invariance. We introduce novel classes of safe sets, each broader than classical control-invariant sets, and analyze corresponding MPC schemes that guarantee recursive feasibility by constraining different nodes of the prediction horizon rather than only the terminal state. Furthermore, when the safe set is an inner approximation of a suitable control-invariant set, our methods can ensure safety through a safe-abort strategy, even in the absence of recursive feasibility. We validate our approaches in extensive simulations on a 6-joint robot manipulator, showing fewer constraint violations than state-of-the-art methods while maintaining comparable tracking performance and computational efficiency. Hardware experiments further demonstrate the practicality of our schemes for real-world robotic applications.

cs.RO

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版本更新 · v3arXiv:2610.21742

Sparse Federated Echo State Networks for Time-Series Classification via Smooth-L1 Regularization

Gengcheng Lyu, Manuel Röder, Frank-Michael Schleif

原文摘要

Echo State Networks (ESNs), as lightweight implementations of reservoir computing, provide efficient solutions for temporal data processing in resource-constrained environments. While standard ESNs offer computational efficiency, their dense readout layers inflate the stored model and the per-sample inference cost. This paper introduces a Smooth-L1 regularized ESN (SL1ESN) that promotes sparsity in the readout layer while preserving differentiability for gradient-based optimization. The method employs a twice-differentiable Smooth-L1 approximation compatible with Newton's method, which we exploit both for centralized training and for a second-order (Newton-step) federated aggregation scheme. Under a single fixed shrinkage threshold shared by every dataset and both settings, experiments on seven time-series classification benchmarks show that SL1ESN learns readouts that are 77-95% sparse in the centralized setting and 75-95% sparse in the federated one. On five of the seven benchmarks, the mean accuracy of the sparse readout over ten seeds differs from that of the stronger dense baseline by at most 0.7 points in the centralized setting and 1.6 points in the federated one, differences of the same order as the seed-to-seed variation, while on the remaining two it trails by 2.4-5.8 points. The second-order federated update converges in only a few rounds. The resulting sparsity compresses the stored model, the per-sample inference cost, and the server-to-client broadcast by a comparable factor. These results demonstrate the practical value of sparse readouts for edge computing and IoT deployments.

cs.LG

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新论文 · v1arXiv:2611.06195

Annotator-Free Object Detection via Foundation Model Collaboration

Yasiru Ranasinghe, Vishal M. Patel

原文摘要

Object detection research remains constrained by high annotation costs and the closed-set assumptions of supervised models, limiting scalability to novel categories. We present a target-dataset annotation-free object detection framework that eliminates the need for human labeling on the target dataset by leveraging the complementary strengths of pretrained foundation models, including segmentation and vision-language systems, combined with a self-refining training loop. To further reduce reliance on expensive LVLM queries, we introduce a landmark-based clustering mechanism that amortizes labeling across image clusters, improving label consistency while lowering computational cost. Our method autonomously generates segmentation proposals, assigns semantic labels through multimodal reasoning, and iteratively improves both localization and classification using a lightweight open-set detector. This modular pipeline enables adaptation to completely unseen object classes and domains without any human-provided target-dataset annotations, while maintaining compatibility with existing detection architectures. Evaluations on COCO, LVIS, ODinW-35, and Roboflow-100 demonstrate substantial performance gains over state-of-the-art open-vocabulary and long-tail detection methods, achieving 60.8 AP on COCO and 47.4 AP on LVIS under zero-shot settings. Beyond strong benchmark results, our approach provides a scalable foundation for real-world detection systems in unknown environments, where annotation is infeasible.

cs.CV

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新论文 · v1arXiv:2611.06191

GPUMAP: GPU-Accelerated Progressive Uniform Manifold Approximation and Projection

Myeongwon Jung, Jaemin Jo

原文摘要

Dimensionality reduction (DR) techniques are fundamental to the analysis of high-dimensional data, yet their substantial computational cost limits their use in interactive settings. To address this limitation, we present GPU-accelerated Progressive Uniform Manifold Approximation and Projection (GPUMAP), an efficient progressive DR algorithm designed for interactive use. We identify three atomic operations within UMAP and restructure them into a progressive pipeline. As part of this adaptation, we introduce modifications to UMAP, including operation queues, instance-wise local join, and scheduling strategies. We further accelerate computation by leveraging GPU parallelism, enabling interactive analysis of million-scale datasets on commodity GPU hardware. Finally, we employ our implementation to systematically study the behavior of progressive DR, investigating how projections evolve over time relative to the ground-truth projection and how biased data streams influence the quality and stability of the resulting projections.

cs.LG · cs.GR · cs.HC

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版本更新 · v2arXiv:2610.29311

Interpretable Cutting Plane Selection for Mixed-Integer Linear Programming Using Genetic Programming

Gwen maudet, Mathis Da Cunha, Grégoire Danoy

原文摘要

Cutting planes (cuts) remove fractional solutions from mixed-integer linear programs (MILPs) without excluding any feasible integer solutions, making them a key ingredient in modern MILP solvers. Current state-of-the-art solvers rely on fine-tuned handcrafted cut strategies, which, despite their effectiveness, remain inherently static and can be suboptimal. More recently, learning-based approaches have been proposed to design adaptive cut strategies; however, most of these methods are based on neural networks, offering limited interpretability and providing no guarantees of convergence. In this paper, we propose \emph{Genetic Programming and Statistics-based Cut Selection} (GPSCS), the first learning-based framework that explicitly generalizes handcrafted cut selection heuristics into a fully interpretable policy. First, a genetic programming model evolves a symbolic scoring function that ranks candidate cuts using features derived from the cuts and the solving context. Second, a lightweight statistics-based mechanism adaptively determines the number of top-ranked cuts to add at each separation round by detecting significant score dropouts. We evaluate GPSCS within the SCIP solver on seven classes of synthetic and real-world benchmark instances, with a strong emphasis on generalization: evaluation instances are on average 41 times harder than those used for training. Experimental results show that GPSCS improves average solving time by $6\%$ compared to SCIP's default cut selection strategy and achieves a two-fold speedup over both a recent neural-network-based approach and a no-cut baseline, demonstrating strong robustness and generalization across heterogeneous MILP families.

cs.LG · math.OC

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新论文 · v1arXiv:2611.06188

ProGram: A Grammar for Progressive Computation Pipelines

Seunghoon Jung, Jaemin Jo

原文摘要

Progressive Visual Analytics (PVA) systems have demonstrated the effectiveness of time-bounded, incremental computation for exploring large-scale data. However, our survey reveals that existing PVA implementations are often difficult to reuse and extend, as their progressive behaviors are typically realized through ad hoc, hard-coded mechanisms distributed across servers and clients. To address this limitation, we present ProGram, a grammar for progressive computation pipelines. Informed by a comprehensive analysis of prior concepts and design practices in existing PVA systems, ProGram is designed to support core progressive features while reducing implementation burden. Furthermore, we extend the design space by introducing novel mechanisms, such as lockstepping, forking, and temporary prioritization, to better support diverse usage scenarios. Finally, we demonstrate the expressiveness and coverage of ProGram by reproducing and extending existing progressive visualization systems, presenting a gallery of progressive computation pipelines, and discussing the grammar coverage.

cs.HC

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版本更新 · v3arXiv:2607.08124

TTHE: Test-Time Harness Evolution

Jun Nie, Yonggang Zhang, Jun Song, Qianshu Cai, Dahai Yu, Yike Guo, Xinmei Tian, Bo Han

原文摘要

The behavior of an LLM agent is determined not only by the underlying model, but also by its harness: the executable program that constructs context, invokes tools, verifies intermediate results, and recovers from failures. Existing approaches typically optimize harnesses on training or development data and freeze the resulting programs at test time, limiting adaptation to shifts in task distributions, failure modes, and tool interactions. We ask whether the harness can instead be optimized during evaluation using unlabeled execution traces. We introduce Test-Time Harness Evolution (TTHE), which treats the executable harness as the state of test-time adaptation. On each unlabeled test batch, TTHE iteratively evolves a population of candidate harnesses through agentic proposers that reason over execution traces and explore diverse edit directions. A judge selects a candidate using execution traces and execution-derived proxy signals. The selected harness determines the current batch's outputs and initializes adaptation on the next. Crucially, the solver, proposers, and judge operate through different harnesses around the same frozen LLM, without weight updates, access to final-answer labels, or a separately trained adaptation model. Across six execution-grounded benchmarks spanning text-to-SQL, competitive programming, software engineering, data-science coding, terminal-based tasks, and agentic tool use, TTHE improves fixed ReAct-style baseline harnesses through persistent, inspectable program adaptation. These results recast test-time adaptation for LLM agents as evolution over executable control programs and identify execution-derived proxy reliability as a central challenge for robust unsupervised agent improvement.

cs.SE · cs.LG

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新论文 · v1arXiv:2611.06185

Editing Large Language Models with Geometry-Aware Regularization

Bingqing Liu, Wei Liu, Jun Wang, Xiaobo Sun, Yuhua Li, Ruixuan Li

原文摘要

Knowledge editing has become a key technique for updating knowledge in large language models. However, existing methods neglect the geometric structure of the knowledge representation space, leading to two key limitations: they cannot distinguish which pre-trained knowledge should be preferentially preserved (core vs. non-core) nor which new edits are inherently difficult (hard vs. easy). To address this, we propose GEdit, a geometry-aware framework built on a Low-Rank Plus Diagonal (LR+D) factor model that captures the intrinsic low-rank structure of Transformer FFN hidden representations. GEdit features two complementary mechanisms. First, the low-rank regularization applies targeted protection on a low-rank core knowledge subspace. Second, the adaptive regularization dynamically adjusts the regularization strength based on each edit request's editability: relaxing constraints for hard edits to ensure success, and tightening them for easy edits to better preserve pretrained knowledge. Extensive experiments on GPT2-XL, GPT-J, and LLaMA-3-8B under large-scale editing scenarios, including both mini-batch and fully sequential editing settings with up to 10,000 continuous updates, show that GEdit consistently and significantly outperforms state-of-the-art baselines in both editing success and knowledge preservation.

cs.CL · cs.AI

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