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版本更新 · v2arXiv:2610.01279

PickMoment: Continuous-Time Single-Image-to-Video via Learning Deblurring and Blur-to-Video

Junseong Shin, Hyeonsu Jo, Daehyun Kim, Tae Hyun Kim

原文摘要

Motion blur arises from the temporal integration of a continuous sharp signal over a finite exposure window, yet existing learning-based methods sidestep this physical model and predict only the sharp signal itself: most single-image deblurring methods recover a single frame at the exposure center, while blur-to-video methods predict a fixed set of frames. We introduce PickMoment, a continuous-time reformulation that directly learns the interval-mean blur over arbitrary sub-intervals of the exposure with a single deterministic model. Drawing an analogy to MeanFlow's average-velocity formulation, we train the model with three supervisions derived from the blur integral: an empirical reconstruction loss from available subframes, an additivity loss that enforces self-consistency across overlapping sub-intervals, and a sharp-frame loss anchored at the zero-interval limit. A single trained model unifies single-image deblurring, blur-to-video generation, and continuous-time pick-a-moment recovery as different queries to the same network, with no separate training for each task. Our PickMoment achieves state-of-the-art performance among generative-based deblurring methods on GoPro and HIDE while competitive against restoration-based methods on RealBlur, and the highest per-frame fidelity on GoPro-7 blur-to-video, all in a single forward pass without iterative sampling.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.03158

HackerSignal: A Multi-Source, CVE-Linked Corpus and Prospective Benchmark for Cyber Threat Intelligence

Benjamin M. Ampel, Sagar Samtani

原文摘要

Cyber threat intelligence (CTI) systems must link exploit code, advisories, and fix commits to the vulnerabilities they concern, and they must do so for vulnerabilities disclosed after training. HackerSignal is a public corpus and benchmark built for this prospective setting. It aggregates 7.45 million exact-deduplicated documents from 64 public sources across eight source layers (hacker forums, exploit archives, exploit question-and-answer sites, vulnerability references, advisories, bug-bounty disclosures, fix commits, and known-exploited-vulnerability records), spanning 1988--2026. All layers are keyed to a shared CVE identifier space with source provenance and release-mode governance. We define three benchmark tasks with temporal splits: (1) CVE linkage retrieval over 340,536 National Vulnerability Database descriptions, (2) exploit type classification into eight classes, and (3) temporal generalization, where training and test CVE sets do not overlap ($C_{\rm train} \cap C_{\rm test} = \emptyset$). Zero-shot dense and hybrid retrievers reach a mean reciprocal rank (MRR) of 0.71 on Task 1, and the top models are statistically tied under paired bootstrap tests. Cybersecurity-pretrained encoders lead exploit type classification (macro-F1 0.90). A two-annotator audit of 240 linkage labels finds 79.5% precision (Cohen's \k{appa} 0.94), with per-source precision from 20% to 100%. The hacker-community layer contributes 6.97 million posts but only about 2,000 CVE-linked records. A masked forum-post grounding pilot over these records reaches MRR 0.08, about nine times lower than the structured-source tasks. Grounding informal hacker discourse to vulnerabilities therefore remains an open problem that the release is designed to support. Code, data, and Croissant metadata are available at https://huggingface.co/datasets/BenAmpel/HackerSignal and https://github.com/BenAmpel/hackersignal.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08060

xDSL-CCPP: An alternative code generator for atmospheric modelling

Nick Brown, John Dennis

原文摘要

The Common Community Physics Package (CCPP) is a framework designed to simplify the exchange of physics packages between state-of-the-art atmospheric models. A core software component of CCPP is a code generator: a bespoke Python-based application that uses regular expressions to transform an input specification into Fortran source code. While the code generator initially provided a simple transformation capability, the demands of state-of-the-art atmospheric models have resulted in significant increases in its complexity. Furthermore, ongoing code modernisation efforts to enable GPU-based computing have placed additional demands on the code generator for which it was never designed. Domain Specific Languages (DSLs) have proven to be highly effective in High Performance Computing (HPC), offering the ability to simplify code modernisation efforts. However, traditionally the effort and specialist skills required in the development of a DSL have been a blocker to adoption. In recent years, however, there have been great advances in compiler technologies, and frameworks such as MLIR and xDSL have the potential to greatly lower this barrier to entry. In this paper, we present xDSL-CCPP, a new application-specific code generator based upon the latest MLIR and xDSL compiler technologies, for CCPP. Demonstrating a reduction in bespoke tooling code of around three times, from approximately 15,000 LoC to 5,000 LoC, we highlight the opportunities provided by these compiler frameworks and demonstrate how they significantly lower the barrier to development of HPC application-specific DSLs whilst maintaining application performance and promoting flexibility.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08054

NutriVisRAG: Multimodal Exemplar Grounding for Nutrient Estimation with Vision-Language Models

Lubnaa Abdur Rahman, Ioannis Papathanail, Maria Panagiotou, Nefeli Dimitropoulou, Margret Harðardóttir, Elisabeth J. den Brok, Bastiaan E. de Galan, Stavroula Georgia Mougiakakou

原文摘要

Diet is a major determinant of health, yet accurate dietary assessment from free-living meal images remains challenging. Existing retrieval-based Vision-Language Model (VLM) approaches to nutrient estimation typically rely on textual food representations, while, to our knowledge, direct grounding on visually similar annotated meals has not been investigated. We present NutriVisRAG, a retrieval-augmented generation framework that uses a vision encoder to retrieve similar meals with known nutritional annotations and provide them as multimodal in-context exemplars for VLM nutrient estimation at inference time, without task-specific fine-tuning. We evaluate NutriVisRAG across four datasets, four VLMs, three retrieval encoders, and two prompting strategies, including free-living meals from adults with insulin-treated diabetes, where carbohydrate estimates are compared against two dietitians' annotations. Across all 96 configurations (4 datasets x 4 VLMs x 3 retrieval encoders x 2 prompting strategies), NutriVisRAG reduced the primary estimation error in 56, with improvements more frequent under whole-meal (35/48) than per-item prompting (21/48). Across the evaluated configurations, the lowest NutriVisRAG error was below the best Vanilla error in 13 of 16 VLM--dataset pairs. For GPT-5.4-mini, under whole-meal prompting, the best-performing retrieval encoder for each dataset reduced the primary error on all four datasets relative to Vanilla, with reductions of 1.7--35.7%. Overall, our results show that grounding in multimodal exemplars comprising images and respective annotations can improve nutrient estimation at inference time without task-specific fine-tuning, although the benefits remain configuration-dependent.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.17985

Gaussian Light Field Splatting: A Physical Prior-Driven Vision Transformer for Low-Light Image Enhancement

Yuhan Chen, Wenxuan Yu, Guofa Li, Fuchen Li, Kunyang Huang, Yicui Shi, Ying Fang, Wenbo Chu 等 9 位作者

原文摘要

Existing low-light image enhancement methods often suffer from local exposure imbalance and color distortion under complex, non-uniform illumination, while mainstream Vision Transformers lack explicit modeling of physics-based priors for illumination degradation. To address these limitations, we propose GLFS, a Gaussian Light Field Splatting-based Vision Transformer that incorporates continuous light-field priors from 2D Gaussian Splatting (2DGS) as an explicit physical bias into the Transformer self-attention mechanism to guide spatial gain estimation under complex non-uniform illumination. Specifically, GLFS represents scene illumination as a superposition of anisotropic Gaussian basis functions and introduces a physics-informed bias into the self-attention mechanism to adaptively infer a spatial gain field, enabling accurate and spatially uniform restoration under complex illumination conditions. To mitigate color shifts and structural degradation during enhancement, we further introduce a color vector angular loss and a luminance edge loss, with the former enforcing hue consistency and the latter improving the structural fidelity of local details. Extensive ablation studies and quantitative evaluations demonstrate that GLFS achieves competitive overall performance in illumination correction, structure preservation, and perceptual quality, validating the effectiveness of the continuous Gaussian light field prior for low-light image enhancement.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.01291

ODDR: One-Step Deshadow Diffusion via Reward Guidance

Junseong Shin, Kijun Kim, Minseong Kim, Dongjin Kim, Tae Hyun Kim

原文摘要

Recent advances in deep learning for shadow removal have significantly enhanced image quality and realism. However, most approaches rely on real-world paired datasets, which are costly to collect and often limited in scene diversity, leading to limited generalization. To address these limitations, we propose One-step Deshadow Diffusion via Reward guidance (ODDR), a new framework that achieves efficient and high-fidelity shadow removal without relying on real-world paired supervision. Our method begins with One-step Deshadow Diffusion (ODD), a baseline model trained on synthetic shadow data for efficient one-step shadow-free reconstruction. We further adapt ODD into ODDR using ShadowReward. In contrast to traditional, annotation-heavy approaches, ShadowReward is the first reward model for shadow removal trained entirely without human annotation. It learns to mimic human perceptual judgments by ranking synthetically generated images with controlled degradations, such as texture distortion and boundary artifacts. This reward-guided fine-tuning enables ODDR to close the synthetic-to-real domain gap. Extensive experiments show that ODD achieves strong performance without relying on real-world paired supervision, and ODDR further improves the results, narrowing the gap to fully supervised methods trained on real-world paired data while maintaining higher computational efficiency as a single-step model.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.14056

FacEDiT: Talking Head Video Editing via Facial Motion Infilling

Kim Sung-Bin, Joohyun Chang, David Harwath, Tae-Hyun Oh

原文摘要

Editing a local segment of a talking head video without reshooting the scene remains challenging and underexplored. Local edits, such as insertion, deletion, or substitution, require synthesizing a replacement segment while leaving the rest of the video unchanged. This involves variable-duration local rewriting while preserving identity, unedited regions, and boundary continuity, which standard speech-driven generation and lip synchronization methods do not directly address. Moreover, direct supervision is impractical, as it would require paired videos of the same person and scene that differ only in a local spoken segment. We instead formulate talking head video editing as facial motion infilling, a self-supervised pretext task that recovers masked facial motion from speech and surrounding motion context using ordinary video--speech pairs. Our key insight is that local video editing can be simulated during training by masking a motion span and reconstructing it from speech and visible motion context. Building on this formulation, we introduce FacEDiT, a mask-controlled talking head model with a local temporal attention bias and temporal smoothness regularization to improve lip--speech alignment and transition continuity. We also introduce FacEDiTBench, the first benchmark for talking head video editing, covering diverse edit types and lengths with dedicated evaluation metrics. Extensive experiments show that FacEDiT produces accurate, speech-aligned edits with strong identity preservation and seamless boundary transitions. Beyond editing, the same facial motion infilling model extends to portrait animation and lip synchronization by simply changing the mask pattern, establishing FacEDiT as a unified framework for talking head video editing and generation.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.02801

VIGOR: Zero-Shot Visual Generalization via Latent-Space Consistency in Model-Based Reinforcement Learning

Mingyu Park, Samyeul Noh, Hyun Myung, Donghwan Lee

原文摘要

Model-based reinforcement learning (MBRL) achieves strong sample efficiency by planning in learned latent dynamics but degrades substantially under unseen visual distractions such as background, lighting, or camera changes. Unlike model-free RL, where encoder perturbations affect only single-step predictions, MBRL suffers from a two-level vulnerability: distractions push encoder outputs out of distribution, and these errors compound through recursive latent rollouts. We propose visual generalization via latent-space consistency in model-based RL (VIGOR), which enables zero-shot generalization to unseen distractions while retaining its backbone's sample efficiency. VIGOR comprises three interdependent components: (i) asymmetric weak-to-strong augmentation, which pairs weak-only and weak-to-strong latent views within a batch; (ii) dynamics-level consistency, which enforces augmentation-invariant transition predictions via direct latent regression; and (iii) encoder-level stabilization, which prevents encoder drift under the cross-augmentation supervision of (ii). On the DeepMind Control Suite (DMC) and Robosuite, VIGOR instantiated on TD-MPC2 outperforms state-of-the-art model-free and model-based baselines in zero-shot generalization, surpassing the second-best baseline by 3.4% on DMC and 43.6% on Robosuite, with sample efficiency comparable to its backbone and fewer environment interactions than the other baselines. Ablations show that VIGOR is augmentation-agnostic: augmentations from distinct perturbation families preserve strong generalization, confirming that latent-space consistency, not the augmentation choice, drives robustness. A second instantiation on DreamerV3, a structurally distinct recurrent state-space model, also improves zero-shot generalization on both benchmarks, indicating backbone-agnostic benefits. Code and videos: https://vigor-gen.github.io/.

cs.AI · cs.LG · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.13624

Instruction-Tuning Large Language Models with Medical Classification Data Improves ICD Coding of German Tumor Diagnoses

Stefan Lenz, Lakisha Ortiz Rosario, Georg Vollmar, Arsenij Ustjanzew, Fatma Alickovic, Thomas Kindler, Torsten Panholzer

原文摘要

Accurate coding of tumor diagnoses with ICD-10-GM and ICD-O-3 is essential for structured cancer documentation in Germany. Smaller open-weight LLMs are appealing for privacy-preserving automation but often struggle with coding accuracy in German-language contexts. This study investigates whether instruction-based fine-tuning on public datasets improves the coding accuracy of open-weight LLMs for German tumor diagnosis texts. The evaluation uses coded diagnoses from the local tumor documentation system as test data. In a systematic data quality assessment, the upper limit for ICD-10 coding performance was estimated at 60-79% for exact and 81-94% for partial (three-character codes only) derivation. As training data, over 500,000 question-answer pairs were created based on the ICD-10-GM, ICD-O-3, and OPS catalogues. Eight open-weight models from the Qwen, Llama, and Mistral families (7-70 B parameters) were fine-tuned. ICD-10-GM accuracy rose from 1.4-24% to 41-58%, and partial accuracy from 31-74% to 73-83%. The accuracy of ICD-O-3 topography coding also improved but started and remained considerably lower with an exact accuracy of 22-40% and a partial accuracy of 56-67% after fine-tuning. Malformed code outputs dropped to 0% for all models. Tumor-diagnosis recognition reached 99%. Accuracy correlated positively with model size, but gaps between small and large models narrowed after fine-tuning. The reasoning mode in Qwen3 generally yielded a lower performance than fine-tuning and was over 100 times slower. Our findings highlight the potential of leveraging public catalogues to build instruction datasets that improve LLMs in medical documentation tasks. The complete training dataset and the best-performing checkpoints of the fine-tuned models are available from https://huggingface.co/datasets/stefan-m-lenz/ICDOPS-QA-2024.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.24464

Kineo: Calibration-Free Metric Motion Capture From Sparse RGB Cameras

Charles Javerliat, Pierre Raimbaud, Guillaume Lavoué

原文摘要

Markerless multi-view motion capture is often constrained by the need for precise camera calibration, limiting accessibility for non-experts and in-the-wild captures. Existing calibration-free approaches mitigate this requirement but suffer from high computational cost and reduced reconstruction accuracy. We present Kineo, a fully automatic, calibration-free pipeline for markerless motion capture from videos captured by unsynchronized, uncalibrated, consumer-grade RGB cameras. Kineo leverages 2D keypoints from off-the-shelf detectors to simultaneously calibrate cameras, including Brown-Conrady distortion coefficients, and reconstruct 3D keypoints and dense scene pointmaps at metric scale. A confidence-aware spatio-temporal keypoint subsampling strategy, combined with graph-based global optimization, enables robust calibration at a fixed computational cost independent of sequence length. We further introduce a pairwise reprojection consistency score, a heuristic measure of the agreement between views, which we use to weight bone lengths in metric scale recovery. Evaluations on EgoHumans and Human3.6M demonstrate substantial improvements over prior calibration-free methods. Compared to previous state-of-the-art approaches, Kineo reduces camera translation error by approximately 83-85%, camera angular error by 86-92%, and world mean-per-joint position error (W-MPJPE) by 83-91%. Kineo is also efficient in real-world scenarios, processing multi-view sequences faster than their duration in a specific configuration (e.g., 36 min to process 1 h 23 min of footage). The full pipeline and evaluation code are openly released to promote reproducibility and practical adoption at: https://liris-xr.github.io/kineo/

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.00511

Large Language Models for Multi-Lingual Equivalent Mutant Detection: An Extended Empirical Study

Honglin Shu, Zhao Tian, Dong Wang, Junji Yu, Jiazhe Zhang, Xuejie Cao, Junjie Chen, Yasutaka Kamei

原文摘要

Mutation testing is a powerful technique for ensuring software quality. However, the presence of equivalent mutants introduces unnecessary costs and biases, limiting its practical effectiveness. Although numerous equivalent mutant detection (EMD) methods have been proposed, they often face distinct challenges: pure-code analysis methods can be limited by their reliance on specific compiler infrastructures, while existing machine-learning approaches remain constrained by scarce training data and limited generalization to unseen mutants. Large language models (LLMs) have recently demonstrated remarkable performance across diverse code-related tasks by better capturing program semantics. Yet their potential for EMD remains largely unexplored, particularly in the multi-lingual context. This paper presents the first comprehensive empirical study on LLMs for EMD, using 3,302 Java and 1,088 C mutant pairs to benchmark against state-of-the-art methods, explore strategy variations, assess efficiency, and evaluate cross-lingual generalization. Experimental results show that LLM-based approaches achieve higher F1-scores than the evaluated traditional methods, with fine-tuned code embedding yielding the highest detection accuracy among the tested strategies. Moreover, LLM-based approaches strike a practical balance between effectiveness and efficiency with inference times comparable to existing machine-learning models. Importantly, fine-tuned LLMs demonstrate measurable generalization across programming languages. These findings establish LLMs as a viable and efficient approach for tackling the longstanding challenge of equivalent mutant detection, offering new directions for advancing mutation testing in practice.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.08341

AnyDexRT: Calibration-Efficient Dexterous Hand Retargeting with Few-Shot Human Guidance

Chenxi Wang, Ying Feng, Hongjie Fang, Shangning Xia, Lixin Yang, Chuan Wen, Cewu Lu

原文摘要

Teleoperation is a key interface for controlling dexterous robotic hands and collecting demonstrations for imitation learning. Its effectiveness largely depends on kinematic retargeting, which maps operator hand motions to feasible and intuitive robot hand motions. Existing methods often require hand-crafted objectives, precise calibration, or global shape matching between human and robot hand spaces, making them sensitive to hand-specific tuning and less reliable across different dexterous hands. We propose AnyDexRT, a calibration-efficient retargeting method for intuitive dexterous teleoperation across human-like dexterous hands. AnyDexRT combines self-supervised fingertip correspondence learning with few-shot human guidance to anchor the mapping in task-relevant regions, and further refines pinch-related poses using a contact classifier. Experiments on diverse dexterous hands and real-world teleoperation tasks show that AnyDexRT improves retargeting quality, reduces manual tuning, and provides more intuitive and efficient control than prior retargeting methods. Project website: https://chenxi-wang.github.io/projects/anydexrt

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2505.07741

BBR's Sharing Behavior with CUBIC and Reno

Fatih Berkay Sarpkaya, Ashutosh Srivastava, Fraida Fund, Shivendra Panwar

原文摘要

TCP BBR's behavior has been explained by various theoretical models, and in particular those that describe how it co-exists with other types of flows. However, as new versions of the BBR protocol have emerged, it remains unclear to what extent the high-level behaviors described by these models apply to the newer versions. In this paper, we systematically evaluate the most influential steady-state and fluid models describing BBR's coexistence with loss-based flows over shared bottleneck links. Our experiments, conducted on a new experimental platform (FABRIC), extend previous evaluations to additional network scenarios, enabling comparisons between the two models and include the recently introduced BBRv3. Our findings confirm that the steady-state model accurately captures BBRv1 behavior, especially against single loss-based flows. The fluid model successfully captures several key behaviors of BBRv1 and BBRv2 but shows limitations, in scenarios involving deep buffers, large numbers of flows, or intra-flow fairness. Importantly, we observe clear discrepancies between existing model predictions and BBRv3 behavior, suggesting the need for an updated or entirely new modeling approach for this latest version. We hope these results validate and strengthen the research community's confidence in these models and identify scenarios where they do not apply.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08039

HITCH: Teaching a Vision-Language Model to Operate a Humanoid through a Whole-Body Harness

Qingzhou Lu, Chenxin Liu, Xinming Wang, Liu Cao, Yanjiang Guo, Mengdi Xu, Jianyu Chen

原文摘要

Enabling vision-language models (VLMs) to control humanoid robots requires both reliable whole-body interfaces and knowledge of how to use the body that carries over across tasks. We present HITCH (Harness Interfaces To Command Humanoids), a harness in which perception returns the 3D position of any point the VLM selects in its image, and execution turns commands for the pelvis, torso, hands, walking, and gaze into whole-body motion with a learned tracker. We find that both measuring the scene and executing commands accurately raise task success. We then fine-tune a VLM on trajectories in which teacher policies solve five base tasks through HITCH, so that it learns how to operate the humanoid. Without task-specific trajectories, the fine-tuned model completes 51% of SIMPLE episodes and 42.5% of HumanoidArena episodes, and adding five trajectories per SIMPLE task raises its SIMPLE success to 95%. On a real G1, it carries a box and walks to a goal without real training data.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08035

Should We Pay This Much for Robustness? Efficient Proxy Certificates with Marginal Guarantees

Soroush H. Zargarbashi, Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski

原文摘要

Modern classifiers remain sensitive to small input perturbations, while strong model-agnostic robustness certificates such as randomized smoothing are often too expensive for deployment, requiring an extensive number (e.g. 2000) of model forward passes per input. We propose a framework that delegates certification to a cheap proxy function while statistically controlling its deviation from the original sound certificate. Given an exchangeable unlabeled calibration set, we tune the proxy via robust conformal risk control to guarantee a bounded marginal (rather than pointwise) false-approval rate: the probability, over future inputs, that the proxy accepts an input whose prediction is flipped due to perturbation. The resulting proxy certificate can be used either as a standalone marginal certificate or as a fallback mechanism that invokes the expensive certificate only when the proxy rejects. We provide both single-sample and sample-heavy calibration recipes. Empirically, as a standalone certificate our method reduces the test-time certification cost to a single model forward while retaining much of the certified performance of randomized smoothing at small radii, and as a fallback it recovers the certified ratio of randomized smoothing at a more modest speedup. This enables cheap yet effective robustness guarantees for larger architectures such as vision transformers and language models.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08032

Reproducing Kernel (Quasi-)Banach Spaces with the $\ell^p$ Coefficient Norms

Tianfang Wang, Guohui Song, Haizhang Zhang

原文摘要

This paper advances the theory of reproducing kernel Banach spaces (RKBSs) by investigating the general $\ell^p$ regularization framework, with a particular focus on the non-convex regime $0<p<1$. While our preceding work established the foundation for RKBSs with the $\ell^1$ norm, the pursuit of stronger sparsity---closer to the $\ell^0$ pseudo-norm---naturally motivates this exploration into $\ell^p$ quasi-norms. For $0<p<1$, we construct reproducing kernel quasi-Banach spaces (RKQBSs) with $\ell^p$ quasi-norms and establish existence and sparsity results for interpolation and regularized learning under appropriate assumptions. We characterize the linear and relaxed representer theorems in terms of a generalized Lebesgue constant and derive bounds under kernel decay, sample spacing, and stability assumptions. For $p>1$, we construct RKBSs using a fixed countable dictionary, establish minimizer existence, and identify the optimal relaxed representer factor through an off-sample coefficient operator. These results reveal fundamental differences between the regimes $0<p<1$, $p=1$, and $p>1$. Numerical experiments compare the accuracy and sparsity of solutions for these three cases.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08023

Precision - Preserving Subtensor Inference for a 22B Joint Audio - Video Generator under a Fixed GPU Working Set

Hanyue Shen, Xinling Liao

原文摘要

Complete-block offloading reduces model residency but requires each transferred block to fit the GPU budget. We present Precision-Preserving Subtensor Inference (PSI), a fixed-arena runtime that combines sub-block weight streaming with separately managed intermediate state for the BF16 LTX-2.5 22B joint audio-video transformer. The contribution is a construction whose managed GPU footprint is determined by reusable pages, live state, and bounded workspace, together with controls that separate resource use, transport correctness, and numerical fidelity. Its partitioned operators preserve the dense real-arithmetic construction under stated assumptions. Across nine matched full-prompt cases on RTX A6000, modified PSI reduces sampled transformer-process peak memory from 8,578 to 360 MiB relative to official MATH, while mean time for the eleven transformer invocations rises from 247.11 to 349.00 s. Its managed arena is 80 MiB. Complete native generation and a complete-block FLASH path both succeed under a fixed 3 GiB available-memory cap. The matched A6000 comparison and separate DGX Spark service suite both achieve 100% success in complete audiovisual decoding. FFN reuse lowers mean block time by 4.0% with unchanged BF16 outputs; a fixed-arithmetic one-token transport control preserves all saved states bitwise. Targeted bias and attention changes improve local and one-token numerical comparisons, while mean endpoint error increases across nine full-prompt cases. The construction enables BF16 audiovisual inference with an explicit transformer-memory budget and makes numerical fidelity a separate, testable property of the implementation.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08021

GTAFL: Addressing Test-Agnostic Long-Tailed Federated Learning in Medical Image

Guangyu Chen, Jingyun Zeng, Hui Jiang

原文摘要

In healthcare, protecting patient privacy is crucial due to the sensitivity of medical data and its extensive accessibility. Federated Learning (FL) offers a decentralized and privacy-preserving training paradigm, making it an ideal solution for healthcare applications. A critical challenge in healthcare is that real-world medical data often exhibits long-tailed distributions in local and global views. Existing methods addressing long-tailed FL problem typically assume that the model will be evaluated on uniform test data distribution. However, practical test data in healthcare systems is often agnostic and unpredictable, leading to potential model failures in real-world scenarios. In this paper, we introduce a novel task termed Test-Agnostic Long-Tailed Federated Learning and propose GTAFL, a comprehensive framework to address this challenge. During the training stage, GTAFL employs adaptive re-sampling, expert classifier retraining, and self-supervised learning to correct biased classifiers and distorted feature spaces caused by long-tailed training distributions. During the inference stage, an ensemble mechanism combines retrained expert classifiers to handle test data with unknown distributions. Extensive experiments on CIFAR10 and two medical datasets manifest that our framework outperforms other state-of-the-art methods. The code is available at https://github.com/cgy-lavine/GTAFL

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2508.05844

Online Budget Allocation with Censored Semi-Bandit Feedback

François Bachoc, Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni

原文摘要

We study a stochastic budget-allocation problem over $K$ tasks. At each round $t$, the learner chooses an allocation $X_t \in Δ_K$. Task $k$ succeeds with probability $F_k(X_{t,k})$, where $F_1,\dots,F_K$ are nondecreasing budget-to-success curves, and upon success yields a random reward with unknown mean $μ_k$. The learner observes which tasks succeed, and observes a task's reward only upon success (censored semi-bandit feedback). This model captures, for instance, splitting payments across crowdsourcing workers or distributing bids across simultaneous auctions, and subsumes stochastic multi-armed bandits and semi-bandits. We design an optimism-based algorithm that operates under censored semi-bandit feedback and assumes access to an exact maximization oracle. Our main result is an instance-dependent bound showing that in diminishing-returns regimes, the regret of this algorithm scales polylogarithmically with the horizon $T$ without any ad hoc tuning. For general nondecreasing curves, we prove that the same algorithm (with the same tuning) achieves a worst-case regret upper bound of $\tilde O(K\sqrt{T})$. Finally, we establish a matching worst-case regret lower bound of $Ω(K\sqrt{T})$ that holds even for full-feedback algorithms, highlighting the intrinsic hardness of our problem outside diminishing returns.

cs.GT · cs.LG · stat.ML

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新论文 · v1arXiv:2611.08016

Learning Dynamics of On-Policy Distillation

Guan Zhe Hong, Sumeet Ramesh Motwani, Liang Chen, Yangchen Pan, Junchi Yu, Jialin Yu, Philip Torr, Yi Ren

原文摘要

On-policy distillation (OPD) is emerging as an important paradigm for language-model post-training, combining dense teacher supervision with training on student-generated trajectories. This creates a feedback loop where each update changes the student and thereby the training distribution that generates subsequent updates. Yet, how this feedback shapes learning and generalization remains poorly understood. We conduct a mechanistic analysis of OPD's learning dynamics, distinguishing the two feedback loop stages: conversion of the distillation signal into parameter updates, and broadcast through which these updates change predictions across prefixes and reshape the rollout distribution. This framework helps us understand both limited student responsiveness despite substantial teacher-student disagreement, and degeneration when updates that improve local teacher agreement also increase the probability of unsuccessful continuations, exposing the student to unreliable supervision in subsequent rollouts. Finally, we show that offline distillation can prepare a student for OPD without significantly improving immediate held-out accuracy. While OPD alone may degrade generation, it can yield substantial accuracy gains after sufficient offline preparation. Together, our results connect teacher supervision, student geometry, and the evolving rollout distribution in determining the learning dynamics of OPD and its effects on downstream performance.

cs.LG · cs.CL

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版本更新 · v4arXiv:2607.04364

RL Forgets! Towards Continual Policy Optimization

Mao-Lin Luo, Zhe-Xu Wang, Zi-Hao Zhou, Bo Ye, Jian Zhao, Min-Ling Zhang, Tong Wei

原文摘要

Continual post-training is becoming a central paradigm for adapting vision-language models to evolving tasks. Recent studies report that reinforcement learning is less prone to forgetting than supervised fine-tuning, motivating the view that RL is inherently resistant to forgetting. However, this view remains insufficiently validated, as existing evidence is largely drawn from outdated or homogeneous benchmarks. We revisit this assumption by introducing MRCL, a Multimodal Reasoning Continual Learning benchmark built from recent and diverse multimodal reasoning tasks. Experiments on MRCL show that standard reinforcement learning still suffers from catastrophic forgetting during continual post-training. We trace the failure to an objective mismatch. The KL regularization used in common policy optimization methods is evaluated on current-task data, whereas forgetting is caused by behavioral drift on prior-task distributions. To address this problem, we propose Continual Policy Optimization (CPO), a replay-free method grounded in a prior-task behavioral KL objective. CPO derives a local Fisher surrogate from the historical KL objective and uses parameter movement as a gradient-free proxy for Fisher sensitivity, enabling sparse regularization with negligible additional overhead. Experiments on three model scales and comparisons with multiple RL baselines show that CPO consistently reduces forgetting while maintaining effective adaptation and preserving broader pretrained capabilities. The implementation code is available at https://github.com/MaolinLuo/CPO.

cs.LG

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新论文 · v1arXiv:2611.08013

Development of Anthropomorphic Arm for collaborative and Home Service Robot CHARMIE

Hafiz Adil, Tiago Ribeiro, Antonio Ribeiro, Carolina Lopes, Joana Coelho, Gil Lopes, Fernando Ribeiro

原文摘要

Service Robots are becoming a reality and are progressing by leaps and bounds. Alongside this avenue of robotics is also the field of prosthetics, which is evolving rapidly. These topics are hot cakes in the industry and are widely developed. Some have one quality, and some have others, but are making a shoreline towards success. The development of arm and hand as individual or as a package is becoming common as more and more advanced collaborative robots come to the surface and join their human counterparts in the real world. This dissertation presents an open-source 3D-printed arm which was assembled and programmed on two fronts. The first is the control of the hand by utilising teleoperation with a camera and ML-based hand pose estimation. Secondly, it is the control via kinematics utilising the Denavit-Hartenberg method to define the motion and to find the position of the end effector in 3D space.

cs.RO

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新论文 · v1arXiv:2611.08012

Symmetry-Aware Surrogate-Assisted Testing of UAVs

Anas Shrinah, Chris Bennett, Kerstin Eder

原文摘要

We investigate a symmetry-aware surrogate-assisted testing approach for UAV autopilots. The proposed approach exploits geometric symmetries between flight missions by trans- forming obstacle configurations into a shared canonical coordi- nate frame. This enables a neural-network surrogate trained on a single reference mission to be reused across arbitrary missions, reducing the amount of high-fidelity simulation data required for training. The surrogate model is trained to predict the minimum clearance distance between a UAV and surrounding obstacles and is used to guide test generation towards safety-critical scenarios. We evaluate this approach using the Aerialist UAV test bench and benchmark its performance against techniques from the 2024 to 2026 UAV Testing Competitions. Results show that the approach achieves an average critical-test-case hit rate of 42% across the evaluated missions, compared with approximately 10% for the constrained-random baseline used to generate the training data. This approach also achieves high input diversity and competitive benchmark performance. However, our findings reveal limitations in transferring surrogate predictions across missions with differing flight-path characteristics, highlighting the boundaries of geometric canonicalization. To support reproducibility and future research, we provide the trained surrogate model and a dataset comprising more than 2,500 high-fidelity simulations.

cs.RO · cs.SE

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新论文 · v1arXiv:2611.08009

A Unified Fractal Impedance Control Framework

Adnan Takriti, Mahmoud Sadeq, Quentin Rouxel, Daniele Cafolla, Michael Mistry, Carlo Tiseo

原文摘要

Physical human--robot interaction requires controllers capable of maintaining intrinsically bounded interactions with the environment. The Fractal Impedance Controller (FIC) has been proven to guarantee these safety characteristics by design. This paper introduces the robotic implementation and experimental validation of its most advanced architecture: the Harmonic-Passive Motion Paradigm (H-PMP), an evolution of the Passive Motion Paradigm designed to mimic human-like interaction. Furthermore, we present a unified framework for FIC deployment, supported by an open-source software suite featuring ROS2, MuJoCo, and physical hardware integrations. Through three experiments conducted on a Kinova Gen3 arm (trajectory tracking, a wall press with sudden loss of contact, and learning-by-demonstration) we compare the H-PMP against earlier FIC architectures. The results demonstrate that the H-PMP achieves a seamless trade-off between admittance and impedance behaviours. This makes it ideal for deployment in unstructured environments, enabling the robot to transition between interaction modes with no computational overhead.

cs.RO · eess.SY

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版本更新 · v2arXiv:2610.09502

RT-DETR-World: Transferring Rich LLM Semantics to Real-Time Open-Vocabulary Detection

Yupeng Zhang, Ziyi Zhao, Juntao Cheng, Sheng Wang, Ningnan Guo, Ruize Han, Liang Wan

原文摘要

Open-vocabulary detection (OVD) recognizes categories unseen during training through textual category queries, yet achieving strong generalization with real-time efficiency remains challenging. Beyond vocabulary scaling, zero-shot generalization may benefit from reusable visual--semantic cues learned from seen data, including attributes, actions, states, and contextual relations. Existing real-time OVD methods primarily emphasize vocabulary coverage and efficient region/query--text matching; under strict efficiency constraints, compact detectors may struggle to absorb rich instance semantics and scene context. We propose RT-DETR-World, a compact DETR-style detector that transfers the rich semantics conveyed by descriptions during training while retaining lightweight query--text matching at inference. We construct GroundingCapv2 with three levels of supervision: category names for standard OVD, object descriptions conveying instance-level semantics, and image descriptions conveying object relations and scene context. These descriptions serve only as training-time semantic supervision. To help the compact detector absorb these semantics, we propose Dual-Path Description Alignment (DDA), combining a deployment-consistent MiniLM pathway with a training-only LLM teacher. MiniLM provides query--category supervision and object-description alignment, while offline teacher features supervise matched queries and global visual representations at the object and image levels, respectively. All teacher features are precomputed, and the teacher-side modules are removed after training. We further propose Relation-Aware Negative Relaxation (RNR), which uses teacher-derived semantic similarities to relax related negatives while preserving exact positives. Experiments demonstrate competitive zero-shot accuracy and a favorable accuracy--efficiency trade-off. The code will be released.

cs.CV

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新论文 · v1arXiv:2611.08006

Automatic keyphrase extraction and annotation: modern theoretical approaches and practical solutions for text and speech

Daria Guseva, Olga Mitrofanova

原文摘要

The exponential growth of textual and audiovisual information has made the task of automatic keyphrase extraction (KE) increasingly significant. This article provides a comprehensive analysis of contemporary theoretical approaches and practical solutions for KE across both text and speech modalities. The primary contribution of this work is its systematic synthesis of these often-disparate research strands into a unified analytical framework, highlighting the evolution of the field from statistical methods towards large language models (LLMs) and end-to-end speech processing. We examine the stages of KE, the characteristics of keyphrases in written and spoken language, and terminological nuances. Various methods for automatic KE are discussed and analyzed in detail: statistical, hybrid, machine learning-based, and structural. The review dedicates substantial attention to emerging paradigms, including keyphrase generation using LLMs, and provides a detailed overview of methodologies and challenges in automatic corpus annotation. Furthermore, we specifically analyze current directions and inherent difficulties in KE for spoken language, comparing transcript based and end-to-end acoustic approaches. This synthesis leads us to conclude that the field is moving towards a more integrated, context-aware paradigm. Future progress will depend on addressing key challenges such as data scarcity for low-resource languages, effective multimodal fusion, and the nuanced evaluation of generative KE systems.

cs.CL · cs.HC

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新论文 · v1arXiv:2611.08005

Deformable Contact-Aware 3D Object Placement

Songhe Wang, David Miller, Charles A. Kamhoua

原文摘要

Current language-guided 3D object placement typically predicts a rigid 6-DoF pose, ignoring the physical reality that indoor placements are dictated by contact. Consequently, predicted poses often float, interpenetrate supports, or fail to deform soft surfaces. We propose Deformable Contact-Aware Placement (DCAP), a framework that reframes placement as a drop-to-equilibrium process. Rather than predicting a static pose, DCAP constructs a semantic release state that specifies the drop location, object scale, and deformable components, allowing a coupled rigid-deformable simulator to settle the object under gravity and friction. This explicitly separates semantic intent from physical realization. Evaluated on a benchmark of 2,063 prompt-asset-scene cases, DCAP significantly outperforms static baselines. On standard placements, it improves VLM Physics & Naturalness by 21.2 points and human preference by 26.5 points over the strongest raw rigid baseline, while reducing mean penetration depth by 72.3%. Compared to simulated rollout baselines, DCAP reduces residual speed by 72.1%. Ultimately, our results prove that physically accurate 3D placement requires modeling semantic intent and physical realization as strictly separate processes.

cs.CV

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新论文 · v1arXiv:2611.08003

When GeoFMs Meet VHR: Benchmarking Foundation Models on Very High Resolution Earth Observation Imagery

Kshitij Raj Sharma, Pierre Adorni, Minh-Tan Pham, Dirk Tiede, Sébastien Lefèvre

原文摘要

Geospatial foundation models are a widely adopted starting point for remote sensing tasks. Most are pretrained on Sentinel-1/2 and Landsat imagery at a ground sampling distance (GSD) of 10m or coarser. Many applications instead rely on very high resolution (VHR) imagery, where individual buildings, roads, and amenities are resolved. We study which pretraining regimes transfer well to this scale. We introduce VHR-Bench, a benchmark suite of ten datasets at 1.5m GSD or finer spanning five task families, eight application domains, and both satellite and aerial platforms. We evaluate twelve encoders in three pretraining tiers with frozen backbones at two training-data fractions. We find that both pretraining on large natural-image collections and VHR-specific pretraining transfer effectively to VHR tasks, with complementary strengths. SkySense leads when labels are scarce; at the full label fraction the web-pretrained DINOv3 ViT-L edges ahead, with the two close together there. Encoders pretrained only on Sentinel-scale imagery transfer least well under RGB input. Guided by these complementary results, we continue the self-supervised pretraining of DINOv3 ViT-L on 1.02 million held-out VHR tiles and evaluate the resulting encoder without changing the downstream protocol. The continued model gives a clear improvement over the released backbone when labels are scarce and a modest one at the full fraction, at no increase in inference parameters or compute. These findings support a generalize-then-specialize strategy: already strong, broad natural-image pretraining can be further improved by a short VHR continuation stage while preserving its general visual features.

cs.CV

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新论文 · v1arXiv:2611.07998

Towards Multiscale Molecular Representation Learning through Geometry-Based Fragmentation for Polymer Property Prediction

Makoto Takamoto, Chaitanya Sampara, Viktor Zaverkin, Daniel Oñoro-Rubio, Mathias Niepert

原文摘要

It is well established that molecular substructures can contribute unequally to chemical and physical properties, with specific motifs or side chains often playing dominant roles. Fragment-based representations have therefore emerged as an effective strategy for incorporating chemically meaningful inductive biases, as exemplified by molecular fingerprints and fragment-based attention mechanisms. However, many existing fragmentation-based machine learning methods rely on predefined chemical rules, which may yield suboptimal fragmentation for previously unseen or chemically unfamiliar structures and often overlook the importance of multiscale molecular representations. To address these limitations, we formulate molecular fragmentation as a radius-constrained optimization problem inspired by the k-center formulation, where the number of clusters is not fixed a priori but emerges from the specified radius, and propose a geometry-based fragmentation method that partitions arbitrary molecular or material structures using largely geometric information, without relying on predefined chemical fragmentation rules. The method provides flexible control over fragment size, enabling the construction of multiscale fragment representations for scale-adaptive modeling. Building on this framework, we develop a geometric fragment-based attention mechanism that integrates multiscale structural information and incorporate it into a multimodal machine learning model combining p-SMILES representations with three-dimensional (3D) conformational information. Experiments on a benchmark polymer dataset show that incorporating 3D conformational information improves prediction accuracy for several properties over models based exclusively on p-SMILES representations. Explainable artificial intelligence analyses further provide insights into the physical relevance of the learned representations.

cs.LG · cond-mat.mtrl-sci · physics.chem-ph

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新论文 · v1arXiv:2611.07996

SEER: A Scalable Event-Driven Runtime for Parallel Execution of Multi-Agent Reinforcement Learning Environments

Jiming Su, Feng Zhu, Yuhao Xiao, Miao Zhang

原文摘要

Multi-agent reinforcement learning (MARL) is increasingly used in systems where many entities interact in a shared environment. In these workloads, policy inference can be batched on GPUs, but the CPU-side environment path still updates entity states, resolves local interactions, and builds observations inside each $\mathrm{env.step}$. This path can dominate latency in interaction-intensive workloads after policy batching. Existing vectorized environment interfaces and general event-driven runtimes do not directly address the fine-grained cross-entity work inside one bounded MARL step while preserving the step-level semantics expected by learning systems. We present SEER (Scalable Event-Driven Environment Runtime), a runtime for MARL environment execution under an explicit event-locality contract. SEER models each environment step as a bounded stream of timestamped events. If direct state updates are entity-local and cross-entity effects are represented as ordered future events, SEER can run independent events in parallel while preserving serial-equivalent boundary outputs. To make this contract practical, SEER uses a CAS-based dual-layer event queue: local event queues preserve per-entity causal order, while a parallel executive list exposes ready work to global worker threads. SEER further uses STPA, a preflight-guided thread-pool selection mechanism that combines short runtime traces with static workload and hardware features. We evaluate SEER on two parameterized MARL workloads: compute-intensive UAV swarm control (Aircraft) and communication-intensive wireless management (PCS). On Aircraft, SEER reduces inference latency by 16.2\% over the baseline. On PCS, SEER reduces environment-execution latency by 46.9\%, 19.5\%, and 64.9\% over ROSS-T, NBSE, and ROOT-Sim, respectively.

cs.DC · cs.MA

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