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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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arXiv ↗PDF ↗
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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arXiv ↗PDF ↗
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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arXiv ↗PDF ↗
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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arXiv ↗PDF ↗
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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arXiv ↗PDF ↗
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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arXiv ↗PDF ↗
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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arXiv ↗PDF ↗
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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arXiv ↗PDF ↗
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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arXiv ↗PDF ↗
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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arXiv ↗PDF ↗
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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arXiv ↗PDF ↗
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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arXiv ↗PDF ↗
版本更新 · v3arXiv:2512.17581

Medical Imaging AI Competitions Lack Fairness

Annika Reinke, Evangelia Christodoulou, Sthuthi Sadananda, A. Emre Kavur, Khrystyna Faryna, Daan Schouten, Bennett A. Landman, Carole Sudre 等 37 位作者

原文摘要

Benchmarking competitions are central to the development of artificial intelligence (AI) in medical imaging, defining performance standards and shaping methodological progress. However, it remains unclear whether these benchmarks provide data that are sufficiently representative, accessible, and reusable to support clinically meaningful AI. In this work, we assess fairness along two complementary dimensions: (1) whether challenge datasets capture the diversity of real-world clinical data, and (2) whether they are accessible and legally reusable in line with the FAIR principles. To address this question, we conducted a large-scale systematic study of 249 biomedical image analysis challenges comprising 458 tasks across 19 imaging modalities. Our findings reveal limited representation across challenge datasets with respect to geographic location, imaging modalities, and problem types, raising concerns about how well current benchmarks reflect real-world clinical diversity. Despite their widespread influence, challenge datasets were frequently constrained by restrictive or ambiguous access conditions, inconsistent or non-compliant licensing practices, and incomplete documentation, limiting reproducibility and long-term reuse. Together, these shortcomings expose foundational fairness limitations in our benchmarking ecosystem and highlight a disconnect between leaderboard success and clinical relevance.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07994

Reviving the Discarded High-Resolution Feature for Transformer-Based Tiny Object Detection

Zehao Wang, Si Wu, Guiping Cao, Xin Li, Yaowei Wang

原文摘要

Tiny object detection remains challenging because tiny instances occupy only a few pixels, and their fine-grained details are easily lost during feature downsampling. Although the highest-resolution feature preserves rich local details for tiny objects, DETR-like detectors often discard it due to its large computational cost, while directly reintroducing this feature also brings dense background noise. To address this problem, we propose ReDHF, a novel framework that Revives the Discarded High-resolution Feature for tiny object detection. ReDHF has three cooperative components. Cross-Scale Deformable Fusion (CSDF) first constructs a semantically enhanced highest-resolution feature and injects its fine-grained details into the shallow feature under a moderate cost. Ellipse-Guided Shallow Supervision (EGSS) then guides the enhanced feature to learn more reliable classification and localization priors by applying geometry-adaptive auxiliary supervision. Full-Scale Query Interaction (FSQI) further enables decoder queries to access both low-level details and high-level semantic information, allowing them to aggregate richer cues for tiny objects. Extensive experiments show that ReDHF consistently improves multiple baselines across different DETR variants and achieves state-of-the-art performance on the evaluated benchmarks. The best ReDHF variants surpass their baselines by +1.4 AP on AI-TOD-v2, +1.6 AP on VisDrone, and +1.5 AP on SODA-D, with especially notable gains on tiny and small objects.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2511.08371

Multi-objective Hyperparameter Optimization with Expert Priors

Soham Basu, Nastaran Alipour, Frank Hutter, Danny Stoll

原文摘要

While Deep Learning (DL) experts often have prior knowledge about which hyperparameter settings yield strong performance, only few Hyperparameter Optimization (HPO) algorithms can leverage such prior knowledge and none incorporate priors over multiple objectives. As DL practitioners often need to optimize not just one but many objectives, this is a blind spot in the algorithmic landscape of HPO. To address this shortcoming, we propose the novel problem setting of multi-objective HPO with expert priors and introduce PriMO, the first HPO algorithm that can integrate user beliefs over multiple objectives. We show PriMO achieves state-of-the-art performance across 8 DL benchmarks in the multi-objective and single-objective setting. Further, we demonstrate that PriMO remains state-of-the-art at large scales by optimizing a vision-language model over three objectives.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2606.13657

Dense Supervision, Sparse Updates: On the Sparsity and Geometry of On-Policy Distillation

Guo Yu, Wenlin Liu, Yulan Hu, Hao-Xuan Ma, Jun-Peng Jiang, Han-Jia Ye

原文摘要

On-policy distillation (OPD) has recently become a prominent post-training recipe by combining two desirable ingredients: on-policy student-generated trajectories and dense token-level teacher supervision. Yet how this hybrid training regime shapes a model remains poorly understood. We characterize the sparsity and geometry of OPD parameter updates across several language and vision-language model pairs and application settings. OPD updates are small and coordinate-sparse, while remaining distributed across layers and modules. This sparse support is operationally meaningful: masked training on the discovered subnetwork nearly recovers full-training performance. At the matrix level, the updates are numerically full-rank but spectrally concentrated. Their visible supports avoid coordinates emphasized by the source's principal structure and favor low-magnitude source coordinates, while the source singular-value spectra change little. Together, these findings show that OPD exhibits important weight-space signatures of on-policy post-training despite using dense teacher supervision.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08558

Minimum-Sufficient Knowledge and Computation for Wind Energy Predictive Analysis

Purbak Sengupta, Aparna S. varde, Emil Jørgensen Njor

原文摘要

Resource-constrained cyber-physical decision systems often apply fixed sensing, knowledge, and computational pipelines despite state-dependent information needs. We introduce Minimum-Sufficient Knowledge and Computation (MSKC), a resource-aware computing principle: select the least-costly route meeting decision-risk and deployment constraints, escalating only when cheaper routes are insufficient. As proof of concept, we isolate adaptive knowledge routing, the decision underlying Knowledge-Guided Machine Learning (KGML), which conventionally injects external knowledge unconditionally rather than only where needed. We instantiate this for wind ramp-event predictive analysis, an edge-oriented decision-support task, evaluating local, relational, and physics-informed routes with a state-aware gate on publicly available real data from two wind farms. We establish conditions for feasibility preservation, cost dominance, and strict expected savings over always-on routing. At a 35.9% relational-knowledge injection rate, adaptive routing is statistically indistinguishable from always-on injection on the reported metrics while reducing knowledge calls by 64%, supporting sustainable, Green-IT-aligned deployment. These results support MSKC's premise that uniformly maximal intelligence is unnecessary. This offers intelligent data analysis for big data streams: the analysis itself decides, per state, how much knowledge and computation the data warrant.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07988

Compression Through a Context Lens: Evaluating Adaptation, Quantization, and Distillation in Arabic Legal LLMs

Md. Rafiul Biswas, George Mikros, Wajdi Zaghouani

原文摘要

Model compression can facilitate the deployment of large language models in resource-constrained legal settings, but its effects on domain-specific performance may depend on the evaluation protocol. We investigate this issue using ArabLegalEval, a Saudi Arabic legal benchmark, under both with-context and no-context evaluation. The former provides supporting legal passages, while the latter assesses performance without external context. Removing the context produces accuracy gaps of up to 27 percentage points for the base model. We examine three stages: domain adaptation, quantization, and knowledge distillation across FANAR-9B, JAIS-8B, and Qwen2.5-7B. Four-bit quantization reduces model memory by 64-71%, with changes in no-context MCQ accuracy ranging from -3.2 to +1.6 percentage points. Distillation produces more variable results, with accuracy generally declining as student size decreases. A Qwen2.5-7B student distilled from FANAR achieves 76.6% no-context accuracy, compared with 77.2% for its teacher. Our findings suggest that no-context evaluation provides a useful complement to context-supported testing when assessing the effects of adaptation and compression on Arabic legal language models.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07984

Technohybridism: Recursive Semantic Normalization in AI-Mediated Societies

Charalampos M. Liapis, Epameinondas Panagopoulos

原文摘要

Language-generating AI systems are no longer only objects of linguistic interaction: their outputs increasingly re-enter the communicative and institutional practices from which their training material was drawn. Drawing on social theory, social epistemology, and the philosophy of language, this article develops a sociotechnical account of that re-entry. We introduce recursive semantic normalization: the process through which machine-generated formulations, once recurrently taken up, evaluated, reused, and institutionally retained, participate in the reproduction and transformation of semantic regularities. Under the heading of technohybridism, we argue that the relevant unit of analysis is the hybrid semantic system, a recursively coupled configuration of human practices, computational generation, and institutional uptake. First, we distinguish the concept from neighbouring accounts, including model collapse, looping effects, machine habitus, machine culture, and the epistemology of AI testimony. Second, we develop an analytical model that separates generation, selection, uptake, and persistence and distinguishes machine-attributable change from change that would have occurred anyway. Third, we analyse uptake as routinized, institutionally diffused, and socially stratified practice and state conditions under which machine-derived regularities become norms. Fourth, we identify social-epistemic consequences: the laundering of provenance that weakens epistemic vigilance, the erosion of the cognitive diversity on which collective inquiry depends, and the narrowing of shared hermeneutical resources. An evidential ladder for empirical research follows. The account attributes no normative authority to machine outputs; what changes is the field within which human and institutional uptake operates.

cs.CY · cs.AI · cs.HC

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2608.08600

Population-Scalable Multi-Agent World Modeling across Scene Geometries

Renjie Zhao, Yuxiang Wu, Mingyu Zhang, Jiaxin Li, Sisi Li, He Li, Yimin Sheng, Tianxi Tan 等 12 位作者

原文摘要

World models have recently achieved impressive progress in visual prediction and interactive generation, but extending them to multi-agent environments introduces a fundamental scalability challenge. Existing methods generally assume a fixed number of agents during training and inference, which ties the model to a pre-determined agent population and limits inference-time scalability. Our key insight is that cross-view consistency should arise from a shared world state whose evolution does not assume a predefined number of agents, while agent-specific observations should be generated by querying this state through a unified rendering interface. Based on this insight, we propose Khora, a scalable multi-agent world model that supports inference-time expansion to arbitrary numbers of agents without retraining. Our framework decouples world-state evolution from visual rendering and introduces a population-agnostic rendering mechanism for incorporating other agent information. This design maintains cross-view consistency through the shared world state rather than through dense interactions among observation streams inside the expensive video generator, enabling approximately linear practical scaling with the number of queried views. Qualitative experiments demonstrate that our approach generalizes to unseen numbers of agents while maintaining visual quality and multi-agent consistency. We further implement a real-time interactive system to demonstrate scalable open-world simulation.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07980

Solving Linear Programs in the Matrix Multiplication Time

Zhao Song

原文摘要

We give a randomized algorithm for feasible bounded-box linear programs with $n$ variables and $d\le n=O(d)$ linearly independent equality constraints. For additive accuracy $ε$, its expected running time is $O((n^ω+n^2\log n)\log(1/ε))$, where $ω$ is the matrix multiplication exponent. This answers the matrix-multiplication-time open question posted in Song's Ph.D. thesis [Son19] in this setting.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07979

Perception Over Geometry: Rethinking Gaussian Allocation in Mesh-based Splatting

Yuting Wang, Yang Zhao, Shanding Diao, Penglin Wang, Yuan Chen, Wei Jia, Ronggang Wang

原文摘要

3D Gaussian Splatting (3DGS) enables photorealistic novel-view synthesis but lacks explicit topology for geometric editing. Existing mesh-based methods enable geometric manipulation by associating Gaussians with triangular mesh faces, typically assigning a fixed number of Gaussians to each face. However, the representation capacity required for appearance reconstruction depends on local appearance complexity rather than merely on mesh tessellation density. To address these limitations, we propose P-MGS, a framework that separates appearance modeling from geometric control to achieve high quality rendering, compact representation, and flexible mesh editing. P-MGS introduces a perceptual complexity guided allocation strategy that distributes Gaussians according to surface appearance demand estimated from multiview observations. To preserve reconstruction flexibility, Gaussians are optimized without mesh constraints and subsequently associated with the original mesh through post hoc binding, retaining adaptive refinement while preserving mesh topology. During mesh editing, local affine deformation transfer updates Gaussian centers and covariances under mesh deformation, reducing misalignment and floating artifacts. Experiments demonstrate superior average novel view synthesis quality with fewer Gaussians and coherent mesh editing.

cs.GR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07976

QRAE: Query-Relevant Attention Entropy for Context Compression in DeepSearch Agents

Yingjie Tan, Haiquan Qiu, Quanming Yao, Manyi Zhong, Xianzhi Yu, Yaqing Wang

原文摘要

DeepSearch LLM agents repeatedly search and carry retrieved sources into subsequent reasoning steps. This expanding context increases token consumption and latency, although many results do not contribute to later reasoning. We propose Query-Relevant Attention Entropy (QRAE), an online context compression method that uses sparse evidence-assessment heads to decide which intact retrieved chunks should persist across reasoning steps. It scores bounded candidate batches through paired prefills: a query-free control suppresses shared attention responses, while normalized entropy measures the breadth of the positive response within each chunk. High-scoring chunks enter a persistent evidence whitelist and are included in the reasoning context. Uncertain chunks remain outside the reasoning context but are retained in a candidate cache, where they can be retrieved for later reevaluation. QRAE requires neither an additional generative compression pass nor modification of the attention operator. Across multiple DeepSearch benchmarks and reasoning models, QRAE improves task success by an average of 2.35 percentage points and reduces end-to-end latency and reported token usage by an average of 30% and 45%, respectively. These benefits generalize to a model with nearly 100B parameters.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07977

Stealthy Malicious Interference Detection in UAV Formations: A Power-Domain Signaling Game Approach

Jianle Ba, Shaolong Guo, Yuntao Wang, Xin Wu, Zhou Su, Zhendong Li

原文摘要

Reliable intra-swarm communication is fundamental to cooperative sensing, control, and mission execution in unmanned aerial vehicle (UAV) swarms. In open low-altitude shared-spectrum environments, coexisting UAVs may cause benign interference through spectrum reuse and signal leakage, while jamming UAVs may deliberately degrade critical intra-swarm links. Since a jamming UAV can adjust its transmit power to disguise signal-to-interference-plus-noise ratio (SINR) degradation as link fluctuation in benign interference, stealthy jammer UAV detection remains a challenge. In this paper, we propose a novel belief-driven stealthy jammer UAV detection scheme from the perspective of Bayesian security game. To capture stealthy interference in shared-spectrum UAV swarms, the interfering UAV is modeled as a hidden benign or jamming type, whose transmit power strategy induces the SINR observed by the defender. The defender updates its posterior belief from the observed SINR and decides whether to maintain normal operation or trigger defensive power boosting. We characterize pure-strategy perfect Bayesian equilibrium (PBE) conditions under separating structures with distinguishable type and pooling structures with indistinguishable observations. Numerical results show that the proposed scheme improves the defender's expected utility compared to the conventional schemes.

cs.CR · cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.02851

Streaming Persistent 3D Gaussian Proxies for View-Flexible Remote Rendering

Matti Siekkinen, Teemu Kämäräinen, Ahmad Alhilal

原文摘要

Cloud rendering executes demanding real-time graphics on a remote server, but conventional video transmission constrains the displayed image to the server-rendered viewpoint. Image-space warping can compensate for client motion during transport and display latency, although its accuracy decreases with pose offset. We investigate an alternative approach in which the server constructs a persistent, client-renderable 3D Gaussian proxy online from game-engine render buffers and progressively transmits independently renderable spatial tiles. Our Unity prototype supports continuous optimization and expansion, approximate relighting, and engine-tracked rigid-object motion, while the client renders locally from its current viewpoint. We evaluate four scenes under controlled pose offsets and encoded walkthroughs, comparing the proxy with stale server-rendered frames, depth-assisted warping using HEVC with one, three, or five reference views. The proxy maintains stable quality as pose offset increases. At 50 Mbit/s, it achieves higher SSIM than all evaluated warping configurations at 367 ms on all four scenes and at 183 ms on three of the four scenes. The results identify complementary operating regimes: image warping is effective at small pose offsets, conventional video is rate-efficient when only the server-rendered viewpoint is required, while the persistent proxy provides robust viewpoint flexibility when the original renderer cannot be executed at the client.

eess.IV · cs.GR · cs.MM

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arXiv ↗PDF ↗