Detecting personally identifiable information (PII) in user queries is critical for ensuring privacy in question-answering systems. Current approaches mainly redact all PII, disregarding the fact that some of them may be contextually relevant to the user's question, resulting in a degradation of response quality. Large language models (LLMs) might be able to help determine which PII are relevant, but due to their closed source nature and lack of privacy guarantees, they are unsuitable for sensitive data processing. To achieve privacy-preserving PII detection, we propose CAPID, a practical approach that fine-tunes a locally owned small language model (SLM) that filters sensitive information before it is passed to LLMs for QA. However, existing datasets do not capture the context-dependent relevance of PII needed to train such a model effectively. To fill this gap, we propose a synthetic data generation pipeline that leverages LLMs to produce a diverse, domain-rich dataset spanning multiple PII types and relevance levels. Using this dataset, we fine-tune an SLM to detect PII spans, classify their types, and estimate contextual relevance. Our experiments show that relevance-aware PII detection with a fine-tuned SLM substantially outperforms existing baselines in span, relevance and type accuracy while preserving significantly higher downstream utility under anonymization.
在用户查询中检测个人可识别信息（PII）对于确保问答系统中的隐私至关重要。目前的方法主要是在忽略某些PII可能对用户的提问具有上下文相关性的情况下对其进行屏蔽，这导致了响应质量的下降。大型语言模型（LLM）或许能够帮助判断哪些PII是相关的，但由于它们封闭源代码性质和缺乏隐私保障，这些模型不适合处理敏感数据。为了实现保护隐私的PII检测，我们提出了CAPID，一种实用的方法，通过微调本地拥有的小型语言模型（SLM），在将信息传递给LLMs进行问答之前过滤掉敏感信息。 然而，现有的数据集没有捕捉到训练此类模型所需的上下文依赖的相关性。为了解决这一问题，我们提出了一种合成数据生成管道，利用大型语言模型产生一个多样且领域丰富的数据集，涵盖多种PII类型和相关程度级别。使用这个数据集，我们将SLM微调以检测PII片段、分类其类型并估计上下文相关性。 我们的实验显示，基于微调后的SLM的相关性感知PII检测在片段准确性、相关性和类型准确性方面显著优于现有的基线模型，并且在匿名化后保持了更高的下游效用。
https://arxiv.org/abs/2602.10074
Human factors research has long focused on optimizing environments, tools, and systems to account for human performance. Yet, as humanoid robots begin to share our workplaces, homes, and public spaces, the design challenge expands. We must now consider not only factors for humans but also factors for humanoids, since both will coexist and interact within the same environments. Unlike conventional machines, humanoids introduce expectations of human-like behavior, communication, and social presence, which reshape usability, trust, and safety considerations. In this article, we introduce the concept of humanoid factors as a framework structured around four pillars - physical, cognitive, social, and ethical - that shape the development of humanoids to help them effectively coexist and collaborate with humans. This framework characterizes the overlap and divergence between human capabilities and those of general-purpose humanoids powered by AI foundation models. To demonstrate our framework's practical utility, we then apply the framework to evaluate a real-world humanoid control algorithm, illustrating how conventional task completion metrics in robotics overlook key human cognitive and interaction principles. We thus position humanoid factors as a foundational framework for designing, evaluating, and governing sustained human-humanoid coexistence.
人因研究长期以来一直致力于优化环境、工具和系统，以适应人类的表现。然而，随着类人机器人开始与我们共处工作场所、家庭和公共场所，设计挑战也随之扩大。现在我们必须不仅考虑人类因素，还要考虑到类人的因素，因为两者将共同存在于相同的环境中并相互互动。与传统机器不同，类人机器人引入了类似人类的行为、沟通和社会存在感的期望，这重塑了可用性、信任以及安全方面的考量。 在本文中，我们介绍了“类人人因”这一概念作为框架，该框架围绕四大支柱——物理层面、认知层面、社会层面和伦理层面构建而成。这些支柱旨在塑造类人的开发，使其能够与人类有效共存并协作。这个框架描述了人类能力和由人工智能基础模型驱动的一般用途类人能力之间的重叠和差异。 为了展示我们框架的实际效用，我们将该框架应用于评估一个现实世界的类人机器人控制算法中，展示了传统机器人任务完成指标如何忽视关键的人类认知和互动原则。因此，我们把“类人人因”视为设计、评估以及治理人类与类人长久共存的基础框架。
https://arxiv.org/abs/2602.10069
Out-of-distribution (OOD) detection is critical for the safe deployment of machine learning systems. Existing post-hoc detectors typically rely on model confidence scores or likelihood estimates in feature space, often under restrictive distributional assumptions. In this work, we introduce a third paradigm and formulate OOD detection from a diversity perspective. We propose the Vendi Novelty Score (VNS), an OOD detector based on the Vendi Scores (VS), a family of similarity-based diversity metrics. VNS quantifies how much a test sample increases the VS of the in-distribution feature set, providing a principled notion of novelty that does not require density modeling. VNS is linear-time, non-parametric, and naturally combines class-conditional (local) and dataset-level (global) novelty signals. Across multiple image classification benchmarks and network architectures, VNS achieves state-of-the-art OOD detection performance. Remarkably, VNS retains this performance when computed using only 1% of the training data, enabling deployment in memory- or access-constrained settings.
出界检测（OOD，Out-of-distribution）对于机器学习系统的安全部署至关重要。现有的事后检测器通常依赖于模型置信度得分或特征空间中的似然估计，这往往需要严格的分布假设。在本工作中，我们引入了一种新的范式，并从多样性视角出发来定义OOD检测问题。我们提出了Vendi Novelty Score（VNS），这是一种基于Vendi Scores（VS）的OOD检测器，而VS是一组相似性为基础的多样性度量指标。VNS量化了测试样本如何增加在分布特征集中的VS值，提供了一种无需密度建模即可确定新颖性的原则方法。VNS具有线性时间复杂度、非参数性质，并自然地结合了类条件（局部）和数据集级别（全局）的新颖性信号。在多个图像分类基准测试及网络架构上，VNS实现了最先进的OOD检测性能。值得注意的是，在仅使用1%的训练数据进行计算的情况下，VNS仍能保持这种性能水平，从而可以在内存受限或访问受限的环境中部署。
https://arxiv.org/abs/2602.10062
Recent approaches in music generation rely on disentangled representations, often labeled as structure and timbre or local and global, to enable controllable synthesis. Yet the underlying properties of these embeddings remain underexplored. In this work, we evaluate such disentangled representations in a set of music audio models for controllable generation using a probing-based framework that goes beyond standard downstream tasks. The selected models reflect diverse unsupervised disentanglement strategies, including inductive biases, data augmentations, adversarial objectives, and staged training procedures. We further isolate specific strategies to analyze their effect. Our analysis spans four key axes: informativeness, equivariance, invariance, and disentanglement, which are assessed across datasets, tasks, and controlled transformations. Our findings reveal inconsistencies between intended and actual semantics of the embeddings, suggesting that current strategies fall short of producing truly disentangled representations, and prompting a re-examination of how controllability is approached in music generation.
最近的音乐生成方法依赖于分离表示，通常被标记为结构与音色或局部与全局特征，以实现可控合成。然而，这些嵌入的基本特性仍然未被充分探索。在这项工作中，我们使用一种基于探针任务的方法框架来评估一组用于可控生成的音乐音频模型中的此类分离表示，并且这种方法超出了标准下游任务的范围。所选模型反映了多样化的无监督分离策略，包括归纳偏差、数据增强、对抗目标以及分阶段训练流程。此外，我们还单独分析了特定策略的效果。我们的分析涵盖了四个关键维度：信息性（informativeness）、等变性（equivariance）、不变性（invariance）和分离度（disentanglement），这些特性在不同的数据集、任务及受控转换中被评估。研究发现表明，嵌入的预期语义与其实际语义之间存在不一致之处，这暗示现有的策略未能产生真正意义上的分离表示，并且呼吁重新审视音乐生成中的可控性方法。
https://arxiv.org/abs/2602.10058
Deep neural networks, especially transformer-based architectures, have achieved remarkable success in semantic segmentation for environmental perception. However, existing models process video frames independently, thus failing to leverage temporal consistency, which could significantly improve both accuracy and stability in dynamic scenes. In this work, we propose a Spatio-Temporal Attention (STA) mechanism that extends transformer attention blocks to incorporate multi-frame context, enabling robust temporal feature representations for video semantic segmentation. Our approach modifies standard self-attention to process spatio-temporal feature sequences while maintaining computational efficiency and requiring minimal changes to existing architectures. STA demonstrates broad applicability across diverse transformer architectures and remains effective across both lightweight and larger-scale models. A comprehensive evaluation on the Cityscapes and BDD100k datasets shows substantial improvements of 9.20 percentage points in temporal consistency metrics and up to 1.76 percentage points in mean intersection over union compared to single-frame baselines. These results demonstrate STA as an effective architectural enhancement for video-based semantic segmentation applications.
深度神经网络，特别是基于变压器的架构，在环境感知中的语义分割方面取得了显著的成功。然而，现有的模型处理视频帧时是独立进行的，从而未能利用时间一致性，而这在动态场景中可以大幅提高准确性和稳定性。在此项工作中，我们提出了一种空间-时间注意（STA）机制，它扩展了变压器注意力块，以纳入多帧上下文，从而使视频语义分割具备稳健的时间特征表示能力。我们的方法将标准的自注意力处理方式修改为能够同时处理时空特征序列，同时保持计算效率，并且只需要对现有架构做出最小改动。STA在各种Transformer架构中具有广泛的适用性，并且无论模型是轻量级还是大规模，在所有情况下均能有效运行。在Cityscapes和BDD100k数据集上的全面评估显示，与单帧基线相比，时间一致性指标提高了9.20个百分点，平均交并比（mean Intersection over Union）最高提升了1.76个百分点。这些结果表明STA作为一种架构增强手段，在基于视频的语义分割应用中具有有效性。
https://arxiv.org/abs/2602.10052
Current instance segmentation models achieve high performance on average predictions, but lack principled uncertainty quantification: their outputs are not calibrated, and there is no guarantee that a predicted mask is close to the ground truth. To address this limitation, we introduce a conformal prediction algorithm to generate adaptive confidence sets for instance segmentation. Given an image and a pixel coordinate query, our algorithm generates a confidence set of instance predictions for that pixel, with a provable guarantee for the probability that at least one of the predictions has high Intersection-Over-Union (IoU) with the true object instance mask. We apply our algorithm to instance segmentation examples in agricultural field delineation, cell segmentation, and vehicle detection. Empirically, we find that our prediction sets vary in size based on query difficulty and attain the target coverage, outperforming existing baselines such as Learn Then Test, Conformal Risk Control, and morphological dilation-based methods. We provide versions of the algorithm with asymptotic and finite sample guarantees.
当前的实例分割模型在平均预测性能方面表现出色，但缺乏原则性的不确定性量化：其输出未进行校准，并且无法保证预测掩码接近真实目标。为解决这一局限性，我们引入了一种符合预测算法，用于生成实例分割的自适应置信集。给定一幅图像和一个像素坐标查询，我们的算法会为此像素生成一组包含实例预测结果的置信集，并提供了一个可证明的概率保证：至少有一个预测具有较高的交并比（IoU）与真实目标实例掩码相匹配。 我们将该算法应用于农业领域边界划分、细胞分割以及车辆检测中的实例分割示例。实验证明，我们的预测集合根据查询难度的变化而变化，并能达到预定的覆盖率，优于现有的基准方法，如“Learn Then Test”、“Conformal Risk Control”和基于形态学膨胀的方法。 我们提供了该算法的不同版本，既有渐近保证也有有限样本保证。
https://arxiv.org/abs/2602.10045
Head Magnetic Resonance Imaging (MRI) is routinely collected and shared for research under strict regulatory frameworks. These frameworks require removing potential identifiers before sharing. But, even after skull stripping, the brain parenchyma contains unique signatures that can match other MRIs from the same participants across databases, posing a privacy risk if additional data features are available. Current regulatory frameworks often mandate evaluating such risks based on the assessment of a certain level of reasonableness. Prior studies have already suggested that a brain MRI could enable participant linkage, but they have relied on training-based or computationally intensive methods. Here, we demonstrate that linking an individual's skull-stripped T1-weighted MRI, which may lead to re-identification if other identifiers are available, is possible using standard preprocessing followed by image similarity computation. Nearly perfect linkage accuracy was achieved in matching data samples across various time intervals, scanner types, spatial resolutions, and acquisition protocols, despite potential cognitive decline, simulating MRI matching across databases. These results aim to contribute meaningfully to the development of thoughtful, forward-looking policies in medical data sharing.
头部磁共振成像（MRI）通常在严格的监管框架下收集和共享用于研究。这些框架要求在分享数据前移除潜在的身份标识符。然而，即使去除了颅骨信息后，脑实质中仍然包含独特的特征，这些特征可以在不同数据库中的同一参与者的其他MRI图像之间进行匹配，从而构成隐私风险，特别是当有额外的数据特性可用时。现有的监管框架通常规定需要基于某一合理水平的评估来评定此类风险。 先前的研究已经表明，通过脑部MRI可以实现参与者之间的关联，但它们依赖于训练基或计算密集型方法。在这里，我们展示了使用标准预处理后进行图像相似性计算的方法，可以将去除了颅骨信息后的T1加权MRI与个人匹配起来，即使在有其他身份标识符可用的情况下也可能导致重新识别的问题。 我们在跨不同时间间隔、扫描类型、空间分辨率和采集协议的数据样本中实现了近乎完美的链接准确性，即便考虑到潜在的认知衰退。这些结果模拟了跨数据库进行MRI匹配的情况，并且旨在为医疗数据共享的发展提供有意义的贡献，制定更加周到前瞻性的政策。
https://arxiv.org/abs/2602.10043
Recent studies have demonstrated that incorporating Chain-of-Thought (CoT) reasoning into the detection process can enhance a model's ability to detect synthetic images. However, excessively lengthy reasoning incurs substantial resource overhead, including token consumption and latency, which is particularly redundant when handling obviously generated forgeries. To address this issue, we propose Fake-HR1, a large-scale hybrid-reasoning model that, to the best of our knowledge, is the first to adaptively determine whether reasoning is necessary based on the characteristics of the generative detection task. To achieve this, we design a two-stage training framework: we first perform Hybrid Fine-Tuning (HFT) for cold-start initialization, followed by online reinforcement learning with Hybrid-Reasoning Grouped Policy Optimization (HGRPO) to implicitly learn when to select an appropriate reasoning mode. Experimental results show that Fake-HR1 adaptively performs reasoning across different types of queries, surpassing existing LLMs in both reasoning ability and generative detection performance, while significantly improving response efficiency.
最近的研究表明，在检测过程中加入链式思维（Chain-of-Thought，CoT）推理可以提高模型识别合成图像的能力。然而，过长的推理过程会带来巨大的资源开销，包括令牌消耗和延迟问题，并且对于明显伪造的处理来说这是不必要的浪费。为了解决这个问题，我们提出了Fake-HR1，这是一个大规模混合推理模型，在我们的知识范围内，它是第一个能够根据生成式检测任务的特点自适应地判断是否需要进行推理的模型。为了实现这一目标，我们设计了一个两阶段训练框架：首先进行混合微调（Hybrid Fine-Tuning，HFT）以完成冷启动初始化，然后使用带有混合推理分组策略优化（Hybrid-Reasoning Grouped Policy Optimization，HGRPO）的在线强化学习来隐式地学习何时选择合适的推理模式。实验结果表明，Fake-HR1能够在不同类型的查询中自适应地执行推理，并且在推理能力和生成检测性能方面都超越了现有的大型语言模型，同时显著提高了响应效率。
https://arxiv.org/abs/2602.10042
Forestry cranes operate in dynamic, unstructured outdoor environments where simultaneous collision avoidance and payload sway control are critical for safe navigation. Existing approaches address these challenges separately, either focusing on sway damping with predefined collision-free paths or performing collision avoidance only at the global planning level. We present the first collision-free, sway-damping model predictive controller (MPC) for a forestry crane that unifies both objectives in a single control framework. Our approach integrates LiDAR-based environment mapping directly into the MPC using online Euclidean distance fields (EDF), enabling real-time environmental adaptation. The controller simultaneously enforces collision constraints while damping payload sway, allowing it to (i) replan upon quasi-static environmental changes, (ii) maintain collision-free operation under disturbances, and (iii) provide safe stopping when no bypass exists. Experimental validation on a real forestry crane demonstrates effective sway damping and successful obstacle avoidance. A video can be found at this https URL.
林业起重机在动态、无结构的户外环境中运行，其中同时避免碰撞和负载摆动控制对于安全导航至关重要。现有的方法分别解决了这些挑战，要么集中在预定义的安全路径上的摆动减缓上，要么仅在全球