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版本更新 · v2arXiv:2610.31661

OpenTac：面向可定制、可扩展与可迁移三维触觉感知的开放生态

OpenTac: An Open Ecosystem for Customizable, Scalable, and Transferable 3D Tactile Sensing

Ariel Slepyan, Geeta Chandra Raju Bethala, Jiafeng Gu, Anagha Rajendra Dangle, Yinzhe Zhou, Hongyuan Fu, Jade Sim, Tuhin Basu 等 15 位作者

AI 速读 基于摘要

作者提出一种可扩展的电阻式三维触觉皮肤及配套设计工具、仿真器与数据采集生态，并在多种机器人平台上演示其用于控制与策略学习。

cs.RO · eess.SP · eess.SY

arXiv ↗PDF ↗
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We present OpenTac, a novel sensor and a full-stack ecosystem for tactile skins that are highly customizable, scalable, and readily deployed in downstream robot control and learning applications. Our core contribution is twofold. First, we develop a scalable resistive tactile skin with a structured deformable overlay that converts tangential loading into spatially varying pressure. Differential responses across neighboring "taxels" support decoding of local shear vectors, and the common-mode response encodes normal force. Second, we provide a highly customizable sensor design tool that is able to automatically generate fabrication files for these tactile skins from 2D outlines and developable 3D surfaces, including from abstract descriptions such as text. We develop an ecosystem around the OpenTac sensor, including the CAD design tool, a sensor simulator, cross-sensor transfer, and fast/adaptive data acquisition. We demonstrate the broad applicability of the OpenTac-CAD sensor design tool by designing and fabricating more than 30 sensors across a variety of robot embodiments. We characterize the three-dimensional tactile fields produced by OpenTac with a commercial, high-precision force/torque sensor. We demonstrate the broad applicability of 3D tactile sensing across contact-rich feedback control and manipulation policy learning on four real-world robot embodiments. Additionally, adaptive acquisition enables kilohertz-rate partial readouts, and our readout circuitry enables high dynamic range (HDR) acquisition, measuring, for example, a 3.7 kg weight stack and a 20 g weight on the same sensor in the same frame. We provide all hardware, software, and result videos at opentac.github.io

版本更新 · v2arXiv:2611.06777

WISE: An Agentic Framework for Whole-page Multimodal Search Experience Attribution

Wenyi Xu, Renzhe Zhou, Songyang Li, Feiran Zhu, Ruoqi Zhao, Chenglei Dai, Xuanping Li, Kaiqiao Zhan 等 9 位作者

原文摘要

Algorithm-driven services, including search, recommendation, and advertising, rely on multi-stage model pipelines. While research prioritizes model optimization, post-deployment monitoring focuses on operational reliability. Yet operationally healthy systems may still produce poor-quality results, exposing a gap in existing evaluation and monitoring practices. However, a systematic problem formulation and benchmark for this task remain lacking. To bridge this gap, we formulate System Quality Diagnosis (SQD) for algorithm-driven services, a new task that extends quality evaluation beyond identifying poor outcomes to attributing their causes across system components. Focusing on industrial search, we propose WISE, an agentic framework for end-to-end quality diagnosis that discovers user-facing issues and traces their origins to underlying algorithmic components. To improve diagnostic reliability, we introduce ReTrace for constructing evidence-consistent trajectory supervision and Adaptive Harness-Policy Optimization (AHPO) for failure-guided optimization of the agent harness and model policy. To enable systematic evaluation, we construct WISE-BENCH, a real-world multimodal search benchmark that combines user-facing evidence, internal pipeline traces, and expert diagnosis annotations. Extensive experiments show consistent gains across model scales, with WISE achieving 75.20% end-to-end accuracy on Qwen3.5-35B-A3B. In production, WISE supports quality auditing, online quality assurance, and model optimization, reducing diagnosis costs and improving downstream relevance models.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.37918

Venus-DefakerOne2: A Vision-Forensics Foundation Model with Broader Coverage

Chenfan Qu, Yachao Liang, Mingqi Fang, Jiangwei Xie, Zhenming Wang, Baoyue Zhang, Changjiang Jiang, Zheng Chang 等 31 位作者

原文摘要

Real-world forgery is inherently heterogeneous: it manifests not only as digital manipulation but also as physical spoofing, and is carried not only by images but also by videos. Despite substantial progress, a unified anti-forgery foundation model covering such a broad threat spectrum remains absent. In this paper, we present Venus-DefakerOne2, which substantially extends the forensic capability boundary along both the forgery-form and media-type dimensions. Trained on tens of millions of open-source and in-house samples, it incorporates two key designs. Tide-Style-Tuning alternates LoRA and full-parameter fine-tuning to balance computational efficiency, generalization, and hard-sample fitting while mitigating catastrophic forgetting. Entropy-based Test Adaptation determines the optimal prediction modality by analyzing prediction entropy. Venus-DefakerOne2 achieves new state-of-the-art performance across a comprehensive suite of benchmarks, including DeepFake faces (96.7%), AI-generated images (93.3%), tampered documents (87.6%), manipulated natural images (89.6%), physical spoofs (93.1%), and AI-generated videos (96.0%). It significantly outperforms both specialized expert models and its predecessor, DefakerOne, establishing itself as a powerful and versatile foundation model for binary-decision forensic tasks.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01743

VeriBase: Automated Construction of Theorem Libraries for Software Verification

Zihao Zhang, Zenan Li, Taolue Chen, Zhe Zhou, Tao Xie, Yuan Yao

原文摘要

Software verification is typically more challenging than coding. Software developers routinely build on mature libraries and APIs, but turning successful verification solutions into reusable libraries still requires substantial expert effort. In this paper, we propose VeriBase, a framework for automatically constructing software-verification libraries by jointly generalizing code and its associated theorems from successful verification solutions. The key challenge is to generalize implementations together with the theorem statements and proofs that depend on these implementations, and reconcile overlapping components across verification tasks. To this end, VeriBase treats code and its associated theorems as declaration families, and designs a three-phase pipeline, which proves seed tasks, generalizes code and associated theorems, and harvests the resulting families into shared interfaces. VeriBase updates dependent declarations as interface change and uses Lean to check the resulting code and proofs. We evaluate VeriBase across four benchmarks with three backend LLMs. The results show that the general-purpose library built from independent 4,191 programs increases the average task success rate by up to 17.0% and meanwhile reduces the proof completion time by up to 21.4%. When VeriBase constructs a benchmark-specific library, further improvement can be observed.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.16879

Towards Generalized Image Manipulation Localization via Score-based Model

Yunfei Wang, Bo Du, Zhe Yang, Xin Liu, Zhiyu Lin, Tianxin Xu, Ji-Zhe Zhou

原文摘要

With the rapid evolution of synthetic media, Image Manipulation Localization (IML) has emerged as a critical component in multimedia forensics for ensuring the integrity of digital content. However, generalization remains a core challenge, as existing discriminative methods typically learn a fixed decision boundary that tends to overfit to specific training artifacts and fails to adapt to unseen manipulation types. To address this, we propose DiffIML, a novel framework that introduces score-based generative modeling to IML. Diverging from the direct estimation of hard boundaries, DiffIML approximates the score function, the gradient of the log-likelihood, to capture the intrinsic geometric topology of mask distributions. This paradigm leverages structural priors to iteratively recover coherent masks from noise, thereby circumventing the brittleness associated with discriminative models. Under this formulation, diffusion models serve as an effective numerical solver for the learned score function.To ensure practicality, we respectively resolve the efficiency and stability bottlenecks of standard diffusion by: (1) utilizing a Lightweight Mask-Specific VAE for fast latent-space process and a decoupled architecture with a lightweight denoising UNet, (2) edge supervision and error prior to mitigate error accumulation during sampling. Extensive experiments of two distinct protocols on eight non-generative and three generative benchmarks demonstrate that DiffIML consistently outperforms state-of-the-art methods, yielding remarkable generalization improvements on diverse unseen datasets. The code is publicly available at https://github.com/scu-zjz/DiffIML.

cs.CV

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arXiv ↗PDF ↗