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版本更新 · v4arXiv:2607.18451

CANDOR: Chance-Calibrated Neighborhood Discordance in Frozen Encoders for Medical Imaging

Soroosh Tayebi Arasteh, Sven Nebelung, Daniel Truhn

原文摘要

In medical imaging, one frozen foundation encoder is often reused for many tasks. The encoder is usually chosen by the area under the receiver operating characteristic curve (AUROC) of a head trained on its features. The AUROC does not measure whether the encoder separates positive from negative images in its feature space. Chance-calibrated neighborhood discordance (CANDOR) measures this separation without training a head. Among images acquired the same way, a positive image is discordant when its k nearest negatives are more similar to it on average than its k nearest positives are. The discordance rate D is the share of discordant positives. When the neighbors come from positive and negative candidate sets of equal size, D has the chance level 1/2 for features without information about the label. With unequal candidate sets, all 11 chest encoders have rates above 1/2 (0.732 to 0.927) on the 12 chest findings of MIMIC-CXR and CheXpert. These rates increase as the finding becomes rarer. With equal sets, the same encoders have rates below 1/2 (0.277 to 0.463). On the chest test split, linear heads miss 0.301 of the discordant positives and 0.165 of the other positives. Across 22 encoders, 605,443 images, and 8 tasks in 7 domains, the best encoder of every task has a rate below 1/2. Because computing D takes one forward pass per image, a hospital can compare candidate encoders before it trains any head.

cs.LG · cs.AI · cs.CL · cs.CV

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新论文 · v1arXiv:2610.34432

On Detection Reliability for Small Object Segmentation

Olivier Rukundo

原文摘要

This study investigates whether predicted bounding boxes are reliable for determining where small-object segmentation should be performed without risking false negatives outside those boxes. The investigation leverages adenovirus particles as small objects, YOLO26x for detection, YO-LO26x-Sem for segmentation, and Mask R-CNN for both detection and segmentation. Segmenta-tion-model predictions were matched to ground-truth adenovirus masks or reference objects using their centroids, with ground-truth mask centroids preferred over ground-truth bounding-box centroids as references for matching. Matched predicted-mask centroids were considered as inside at least one detector-predicted bounding box or outside all such boxes, while ground-truth masks without matched predictions were recorded as having no matched segmentation. For the YOLO26 models, 34 reference objects (47.89%) had matched segmentation centroids inside at least one pre-dicted box, 8 (11.27%) had centroids outside all predicted boxes, and 29 (40.85%) had no matched predicted segmentation. For the Mask R-CNN model, the corresponding counts were 69 (97.18%), 0 (0.00%), and 2 (2.82%). These preliminary experiments demonstrated that restricting segmentation to detector-predicted bounding boxes risks excluding matched segmentation predictions outside those boxes, potentially introducing false negatives.

cs.CV

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新论文 · v1arXiv:2610.34283HF 社区推荐

ChronoReplay: One Answer Interface Is Not Enough for Streaming Spatial Intelligence

Yihao Hu, Yitong Lin, Pan Wang, Zhihao Wen, Hang Wang, Yiwen Zhao, Deyuan Liu, Yutong Li 等 11 位作者

原文摘要

Training a spatial VLM to answer which side an object lies on does not require it to preserve the metric displacement that determines that answer, while supervision for a distance estimate may leave categorical relations largely unconstrained. When post-training emphasizes one readout, improvements can therefore concentrate on that interface while transfer to others remains weak or even degrades; we call this observed specialization interface lock-in. ChronoReplay addresses it by replaying heterogeneous spatial supervision as chronologically ordered episodes, retaining source-native targets, and balancing supervision across families. Under the same observation access, a single ChronoReplay checkpoint improves OVO-S-Bench, OST-Bench, and UCS-Bench together, including a 13.41-point gain on OST-Bench, and achieves the highest three-benchmark mean among the supervision settings compared here. Same-data controls further separate the ingredients of this transfer: answer serialization redistributes gains across OST and UCS, family-balanced learning improves over ordinary joint SFT and natural turn weighting, and chronological replay exceeds a shifted-order control by about 2.41 OST points across two paired seeds. Adaptive frame selection then changes evidence access while keeping the trained model fixed, providing an additional inference-time gain at lower visual cost. Streaming spatial performance depends on what distinctions supervision preserves, how heterogeneous histories are organized during learning, and what causal evidence inference makes available at test time.

cs.CV

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新论文 · v1arXiv:2610.33645HF 社区推荐

PreView: Enhancing GUI Grounding with Preprocessed Views and Cues

Wenjun Hou, Kaishuai Xu

原文摘要

GUI grounding requires locating small controls in cluttered screenshots and identifying which control a natural-language instruction refers to. Cropping and enlargement can expose local detail, but the resulting image may still leave ambiguity about the intended target. We propose PreView, an auxiliary preprocessor that improves the inputs to an existing grounding model without further training that model. PreView prepares two complementary types of input features: task-relevant views for localization and candidate-specific cues for target understanding. A trained lightweight auxiliary model predicts and refines a center, crops the screenshot into a local view, and enlarges that view for zoomed-in grounding. It also prepares a visual cue (i.e., a red-circle marker with a location hint) and a textual cue describing the candidate's identity or function. The frozen grounding model uses the prepared inputs to predict the final click. Across five benchmarks, PreView improves the average grounding accuracy of Qwen3-VL-2B, OS-Atlas-4B, and Qwen3-VL-4B without further training these grounding models. Extensive analyses examine the contributions of views and cues, center refinement, and visual input budgets.

cs.CV · cs.CL

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版本更新 · v2arXiv:2610.31670HF 社区推荐

Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success

Yunfei Xie, Howard H. Qian, Shiyi Lan, Kaiyu Hang, Chen Wei

原文摘要

Robot foundation models are typically evaluated through costly closed-loop rollouts, motivating low-cost offline proxies for model capability. The Platonic Representation Hypothesis (PRH) suggests that as representations improve, modalities such as vision and language converge toward a shared statistical model of reality. We ask whether motion trajectories form another such modality: as embodied models become more capable, should their sensory representations converge toward the structure of physical motion? To test this, we propose Sensorimotor Alignment ($S^2$), a training-free offline metric that measures how well a model's sensory representation aligns with the geometry of physical motion. We find that $S^2$ correlates positively with downstream robot task success across model families, making it a low-cost offline proxy for prioritizing models before closed-loop evaluation. $S^2$ can also identify promising vision-language model (VLM) backbones before costly fine-tuning into vision-language-action models (VLAs). Beyond this correlation, $S^2$ offers representation-level explanations for phenomena observed in VLAs and world action models (WAMs). With $S^2$, we extend PRH to robotics and make sensorimotor alignment measurable, offering a low-cost, representation-level view of robot foundation models that closed-loop success rates alone cannot reveal.

cs.CV · cs.LG · cs.RO

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新论文 · v1arXiv:2610.32177HF 社区推荐

$Ψ$-0.6: Adaptive Streams of Thought in a Unified Vision-Language World Model

Khai Loong Aw, Klemen Kotar, Baihan Zhang, Yifan Liu, Lilian Chen, Atlas Kazemian, Rahul Venkatesh, Daniel Yamins

原文摘要

Humans reason about the physical world by flexibly combining different forms of inference as a problem unfolds. Achieving this flexibility is a major open challenge in AI: capabilities often remain separated across specialized models, limiting how they can be composed and jointly adapted to a task. We introduce $Ψ$-0.6, a unified vision-language world model that adaptively composes perception, language, geometry, and simulation within a shared reasoning process we call streams of thought. These streams interleave images, text, and explicit representations of geometry and motion, all of which the model can generate and interpret through a shared token interface. $Ψ$-0.6 surpasses leading open-weight models zero-shot on multi-view spatial reasoning, with further gains from reasoning through geometric intermediates. It also supports counterfactual simulation: generating and interpreting imagined scenes to determine, for example, whether moving an object would obstruct a pathway. Reinforcement learning from task feedback jointly optimizes the selection, generation, and use of intermediate representations, improving visual question-answering accuracy with gains that transfer to unseen benchmarks. $Ψ$-0.6 offers a path toward adaptive physical reasoning -- not only predicting the physical world but also choosing the representations through which to reason.

cs.AI · cs.CV · cs.LG

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新论文 · v1arXiv:2610.31566HF 社区推荐

CellTrack-Bench: Microscopic Cell Tracking Benchmark for Vision-Language Models

Ilan Theodoro, Jordão Bragantini, Loïc A. Royer

原文摘要

Large language models have shown outstanding performance on text-first tasks such as coding, math, and writing. Recent efforts are shifting models' training to include scientific problems that often live in highly specialized domains beyond text, such as protein structure and biological images. However, in these scenarios, their general-purpose accuracy compared with specialized algorithms remains in question. One such domain is cell tracking, which requires spatiotemporal awareness of cell dynamics. To evaluate this, we created a new benchmark, CellTrack-Bench, which assesses Vision-Language Models' (VLMs) cell-tracking-related performance against specialized trackers. The full benchmark draws from 15 standard cell-tracking datasets across four imaging modalities and comprises 1,537 problems corresponding to 9,041 cell-linking decisions. We selected a hard subset requiring greater reasoning effort by identifying failures shared across distinct algorithmic trackers. With this proposed dataset, we: (i) evaluated different environment harnesses for VLMs; (ii) benchmarked 9 frontier closed-source models and 13 open-weight models; and (iii) provided a detailed analysis of their failure modes. Code, the stored model outputs, and a pipeline that regenerates the benchmark from the source datasets are available at https://github.com/royerlab/celltrack-bench.

cs.CV

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新论文 · v1arXiv:2610.29003HF 社区推荐

T-DIG: Text-Conditioned Diversity for Image Generation

Simran Khanuja, Rohit Gandikota, Gaurav Parmar, Graham Neubig

原文摘要

Text-to-image (T2I) platforms typically return several images per prompt so users can explore different visual interpretations and choose one that matches their intent. However, T2I models are usually optimized for single-image quality which comes at a cost of multi-image diversity, limiting this exploration. Diversity is also contextual: one user wants variations in background, while another wants them in artistic style, and the same user may later want variations in clothing. The generated images should differ along whatever axis the user cares about. In this paper, we introduce T-DIG, a text-conditioned diversity framework for image generation and editing. T-DIG is an iterative framework comprising multiple modules that lets users type prompts, inspect default generations, request variations using natural language, and continue refining outputs until they're satisfied. At its core, T-DIG uses an LLM policy that produces K axis-conditioned prompts or edit instructions for a frozen generator or editor. We train this policy with GRPO using a text-conditioned diversity reward and alignment penalties. Because the image generator remains frozen, T-DIG applies to open-weight, distilled, and proprietary models. We also release the first dataset for text-conditioned prompt diversity in image generation, with the training and validation splits built from human-written axes and a test set drawn from real user interactions with T-DIG. T-DIG far exceeds baselines in improving text-conditioned diversity, while preserving base-prompt alignment across a variety of metrics and human judgements. We show that user-directed diversity exploration can be added to a wide range of image generation models, open and closed, without the need to access their weights. Website: https://image-study.org/t-dig/

cs.CV

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新论文 · v1arXiv:2610.33066HF 社区推荐

OvisOCR3 Technical Report

Yu Xia, Yinglun Li, Yuhui Chen, Jinlong Huang, Yuyang Wanyan, Shengze Shi, Yifei Song, Yue Duan 等 15 位作者

原文摘要

We introduce OvisOCR3, a 0.8B model for a broad range of OCR-related tasks. OvisOCR3 unifies document parsing, general OCR, key information extraction (KIE), document figure captioning, and cross-page table reconstruction in a single model. We train the model on real and synthetic data through continued pretraining and use domain-balanced mid-training to balance supervision across tasks with different data volumes and response lengths. Multi-teacher on-policy distillation transfers expertise from five domain-specific 4B teachers into the shared model, followed by mixed-task reinforcement learning with domain-specific rewards. We compare OvisOCR3 with general-purpose vision-language models and specialized OCR models on public and in-house benchmarks covering all five task families. In these comparisons, it achieves an overall score of 96.74 on OmniDocBench v1.6 and the highest overall scores on PureDocBench, Real5-OmniDocBench, and MDPBench. On OCRBench v2, it ranks first, scoring 65.9 and 61.3 on the English and Chinese splits, respectively. It also outperforms the baselines on OCRBench and all four KIE benchmarks. On DocFigCapBench, it achieves the highest localization F1 and a caption score close to that of Qwen3.8-Max. For cross-page tables, it leads in Relation F1 on CrossTabBench and truncated-table TEDS on MPDocBench-Parse. These results demonstrate that a compact 0.8B model can achieve strong performance across diverse OCR-related tasks. OvisOCR3 is available at https://huggingface.co/ATH-MaaS/OvisOCR3.

cs.CV · cs.LG

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新论文 · v1arXiv:2610.25276HF 社区推荐

Holo4D: Holistic 4D Reconstruction as Geometric Control for Video Diffusion

Yushi Lan, Zeren Jiang, Koichi Namekata, Xingang Pan, Chuanxia Zheng, Andrea Vedaldi

原文摘要

Synthesizing a monocular video from a new camera trajectory requires preserving the observed scene geometry and motion while completing regions that become newly visible. Feed-forward 4D reconstructors recover the structure of the observed scene, whereas video diffusion models provide a generative prior for completing unseen content. We introduce Holo4D, a video reshooting framework that uses reconstructed 3D trajectories as a holistic source-view 4D scaffold. We project these trajectories into the requested views and encode their geometry and source appearance to guide a pretrained video diffusion model. Together with the source video and target cameras, the scaffold grounds generation in reconstructed observations while leaving unsupported regions to the generative prior. Experiments demonstrate the value of trajectory-based conditioning for camera control and novel-view synthesis. The framework accommodates different 4D reconstructors without retraining the video generator, allowing it to benefit from future advances in source reconstruction. More details can be found in our project page.

cs.CV

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新论文 · v1arXiv:2610.19025HF 社区推荐

How Far Can Coding Agents Go in Visual Generation?

Hengguang Zhou, Yuanhao Ban, Jiaqi Feng, Xiaohuan Pei, Justin Cui, Cho-Jui Hsieh

原文摘要

Modern image generation largely relies on specialized generative models that leave the process of image construction implicit in learned parameters. We explore an alternative: general-purpose coding agents that construct images through executable programs. Our study of zero-shot drawing on AgentDrawBench, a 200-prompt benchmark, reveals a distinctive capability profile. Agents excel at preserving structure and satisfying precise visual constraints, achieving competitive results in domains where compositional accuracy matters more than photorealistic appearance, while remaining limited in perceptual richness. Their visual expressiveness varies with the drawing interface, and a qualitative study on reconstruction shows how procedural skills extracted from human demonstrations support different artistic media. We then examine code as an external visual language of thought: a structured, executable representation that agents can construct and inspect. We test it in visual question answering task and find reconstructed visual code retains substantial information for question answering and, in some domains, supports higher accuracy than the original image. These abilities raise a further question: do agents draw on a shared internal representation of scene content beyond a particular drawing program? We explore an internal visual language-of-thought hypothesis, in which a shared, compositionally structured representation supports visual operations across drawing languages. Cross-harness drawings and images assembled from independent single-pixel queries exhibit related layouts and tones. Agents also maintain self-generated room arrangements across viewpoints after visible drawings are removed. These results demonstrate that the coding agent would maintain a consistent and coherent world inside its imagine even before the harness gets involved and the generation starts.

cs.CV · cs.LG

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新论文 · v1arXiv:2610.10945Apple ML Research

GHARP：基于大规模重建先验的实时高斯头部动画

GHARP: Real-time Gaussian Head Animation from Large-scale Reconstruction Prior

Ali Benlalah, Sepehr Johari, Patricia Vitoria, Armin Kappeler, Artem Sevastopolsky, Alexander Jung, Gabriele Fanelli, Kevin Mader 等 17 位作者

AI 速读 基于摘要

GHARP 将头部动画解耦为离线身份建模与实时表情残差预测两阶段，并引入身体对齐网络缓解身体与衣物区域的不确定性，作者报告在 Ava-256 上达到先进质量且推理显著加速。

cs.CV · cs.LG

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We present GHARP (Real-time Gaussian Head Animation from Large-scale Reconstruction Prior), a method that animates 3D human heads in real time from a few input images of a subject and a driving expression signal. We decouple the problem into an identity stage that builds a representation of the subject's geometry and appearance offline, and an animation stage that predicts expression-dependent residuals on top of it at runtime. This separation offers a favorable trade-off with respect to fidelity, quality and runtime: the identity stage can be expensive while the animation stage runs a lightweight network, optimized for mobile devices. Our method performs animation in a semantically structured latent space of a pretrained reconstruction model, where expression changes remain spatially contained, making residual prediction efficient. This reconstruction prior provides a consistent spatial layout, allowing fusion of multiple input views into a compact, fixed-size canonical Gaussian representation. While this two-stage design improves the runtime-quality trade-off, it still inherits a problem common to all expression-driven avatar methods: expression codes describe only the face and thus omit body pose and clothing position, making these regions underspecified in the input. The animation network faces an ill-posed mapping and resorts to averaging over conflicting body appearances, producing blur and temporal flicker. We address this with a body alignment network that learns to align the person's body in the target image with the input reference images, removing the ambiguity from the training signal. Our method achieves state-of-the-art quality on the Ava-256 benchmark while running up to 13x faster on an A100 GPU with 8x fewer Gaussians.

首次观测 · 记录 #1软件学报:jos:7720软件学报

预测不确定性感知的测试集选择方法

高玉鑫, 黎浩坤, 张书海, 陈卓琨, 马钟, 魏璐, 许守恺, 游增 等 11 位作者

原文摘要

随着深度学习模型在嵌入式与边缘设备中的广泛应用, 传统依赖完整测试集的模型评估方法因其高昂的计算开销与资源消耗, 难以满足低延迟且高效率的部署需求. 现有数据集选择方法往往难以兼顾评估精度与高压缩比, 导致性能估计偏差显著. 为此, 提出一种预测不确定性感知的测试集选择(prediction-uncertainty-aware test set selection, PUA-TSS)方法, 旨在通过构建高信息量的代理测试集, 实现对模型性能的高效准确评估. 该方法首先利用模型预测输出的信息熵量化样本不确定性, 对测试样本进行任务驱动排序; 在此基础上, 采用等间隔均匀采样策略, 保留关键分布特征, 尤其缓解长尾场景下稀有类别的丢失问题, 从而在低采样率下维持评估的敏感性与一致性. 在图像分类和目标检测以及分割等多个任务上的基准数据集及多种模型架构上的实验表明, 所提方法在仅保留5%测试样本的情况下, 代理集评估结果与完整测试集评估的平均绝对偏差分别控制在0.71个百分点 (分类)、2.90个百分点 (检测)以及0.40个百分点 (分割)以内, 显著优于随机采样与现有数据集压缩方法. 进一步地, 在评估不同位宽的量化模型时, 性能评估误差稳定控制在5个百分点以内, 且无需为各模型变体重复构建代理集或执行完整评估. 实验结果验证了PUA-TSS方法在大幅降低模型评估计算成本的同时, 具备高精度与强鲁棒性以及良好泛化能力, 为资源受限环境下的模型评估提供了一种有效解决方案.

cs.* · cs.CV

软件学报 · 2026 · 正式发表

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首次观测 · 记录 #1软件学报:jos:7673软件学报

基于数据重建的净标签数据投毒攻击方案

张文博, 李雄, 张文琪, 谢勇, 陈厅, 禹继国, 张小松

原文摘要

人工智能系统依赖大规模、开放式的训练数据采集, 这为数据投毒攻击提供了可乘之机. 攻击者能够通过渗透数据供应链等方式操控训练数据, 破坏深度学习模型的可用性. 相较于基于标签翻转的脏标签数据投毒攻击, 净标签攻击虽具有更强的隐蔽性, 但在实现时面临更大挑战: 一方面, 需要精确操控样本特征而非直接篡改其标签; 另一方面, 还需确保毒化样本在视觉上与原始样本保持高度一致以规避检测. 虽然现有的净标签攻击方案已取得一定成效, 但往往因假设宽松, 难以在实际场景中有效实施, 且隐蔽性和计算效率仍有提升空间. 为此, 提出一种基于数据重建的深度学习净标签数据投毒攻击方案CPDR, 该方案重构原用于图像分割任务的Attention U-Net模型的损失函数, 将其转换为一个包含重建损失、投毒效果损失和模型更新一致性损失的多任务学习重建模型, 通过数据重建生成隐蔽的毒化样本, 并在黑盒假设下实现有效的投毒攻击. 在集中式学习和联邦学习这2种场景、2个数据集和4个目标模型上的实验表明, CPDR能在几乎不影响深度学习主任务表现的情况下降低模型在目标类别上的分类准确率. 在集中式学习下, CPDR对目标类别准确率的平均降幅高于VagueGAN方案2.33个百分点, 高于Adversarial Poison方案2.42个百分点; 在联邦学习下, 其优势进一步扩大, 分别高于上述对比方案4.44和4.10个百分点. 此外, CPDR生成的毒化样本与原始样本视觉差异微小, 难以辨别和检测, 实现了隐蔽性和攻击效果的良好平衡.

cs.* · cs.AI · cs.CV

软件学报 · 2026 · 正式发表

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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.11ACL Anthology · ACL 2026

IDP Accelerator: Agentic Document Intelligence from Extraction to Compliance Validation

Md Mofijul Islam Amazon, Md Sirajus Salekin Amazon, Joe King Amazon, Priyashree Roy Amazon, Vamsi Thilak Gudi Amazon, Spencer Romo Amazon, Akhil Nooney, Bob Strahan Amazon (AWS) 等 10 位作者

原文摘要

Understanding and extracting structured insights from unstructured documents remains a foundational challenge in industrial NLP. While Large Language Models (LLMs) enable zero-shot extraction, traditional pipelines often fail to handle multi-document packets, complex reasoning, and strict compliance requirements. We present IDP (Intelligent Document Processing) Accelerator, a framework enabling agentic AI for end-to-end document intelligence with four key components: (1) DocSplit, a novel benchmark dataset and multimodal classifier using BIO tagging to segment complex document packets; (2) configurable Extraction Module leveraging multimodal LLMs to transform unstructured content into structured data; (3) Agentic Analytics Module, compliant with the Model Context Protocol (MCP) providing data access through secure, sandboxed code execution; and (4) Rule Validation Module replacing deterministic engines with LLM-driven logic for complex compliance checks. The interactive demonstration enables users to upload document packets, visualize classification results, and explore extracted data through an intuitive web interface. We demonstrate effectiveness across industries, highlighting a production deployment at a leading healthcare provider achieving 98% classification accuracy, 80% reduced processing latency, and 77% lower operational costs over legacy baselines. IDP Accelerator is open-sourced with a live demonstration available to the community.

cs.CL · cs.AI · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.14ACL Anthology · ACL 2026

Dash-M5H: An Interactive Dashboard for Multi-Modal, Multi-Model Mental Health Assessment

Raymond Alavo University of Notre Dame, Xinyuan Zhang University of Notre Dame, Gemza Ademaj, Junhui Cai University of Notre Dame, Hyeokhyen Kwon Emory University, Robert Cotes Emory University, Gari D. Clifford Emory University and Georgia Institute of Technology, Ahmed Abbasi University of Notre Dame

原文摘要

We present Dash-M5H, an interactive dashboard for multi-modal, multi-model mental health assessment that helps clinicians and researchers jointly inspect multimodal behavioral data with multi-model signal outputs of recorded clinical interviews. Guided by signal detection and integrated sensemaking theories, Dash-M5H synchronizes transcript text, audio, and facial behavior (action units and gaze) to support overview-to-detail evidence tracing; and it integrates extracted signals (e.g., sentiment and facial activity) with a clinically grounded VLM prediction pipeline that produces DSM-5-aligned depression predictions. Dash-M5H (https://dash-m5h.io) is implemented in a lightweight, browser-based stack (Quarto + Observable JS + D3), supports local data import and time-synced clinical annotation with export. We demonstrate Dash-M5H through a depression screening scenario, evaluate its note-taking and screening capabilities through a user experiment, and release a live demo (https://youtu.be/w3qCJ02k6bw) and code (https://github.com/nd-hal/M5H-Dashboard-VLM) to facilitate reproducible evaluation.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.23ACL Anthology · ACL 2026

GovScape: A Public Multimodal Search System for 70 Million Pages of Government PDFs

Ying-Hsiang Huang University of Washington, Claire Gong Department of Computer Science, University of Washington, Shreya Shaji, Alison R Yan, Leslie Harka University of Washington, Albert Du University of Washington, Anjali Shubha Gopal, Samuel J Klein Knowledge Futures 等 13 位作者

原文摘要

Efforts over the past three decades have produced web archives containing billions of webpage snapshots and petabytes of data. The End of Term Web Archive alone contains millions of PDFs produced by the federal government. While preservation with web archives has been successful, significant challenges for access and discoverability remain. In this paper, we introduce GovScape, a public search system that supports multimodal searches across 10,015,993 federal government PDFs from the 2020 End of Term crawl (70,958,487 total PDF pages) – to our knowledge, all renderable PDFs in the 2020 crawl that are 50 pages or under. GovScape supports four primary forms of search: in addition to providing (1) filter conditions over metadata facets including domain and crawl date and (2) exact text search against the PDF text, we provide (3) semantic text search and (4) visual search against the PDFs across individual pages, enabling users to structure queries such as “redacted documents” or “pie charts.” We detail GovScape’s search affordances, embedding pipeline, system architecture, and open source codebase. Significantly, the total estimated compute cost for GovScape’s pre-processing pipeline for 10 million PDFs was approximately 1,500, equivalent to 47,000 PDF pages per dollar spent on compute, demonstrating the potential for immediate scalability. We evaluate GovScape by (1) analyzing 1,679 search queries and (2) benchmarking vector and keyword index efficiency using these queries. GovScape can be found at https://www.govscape.net.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.25ACL Anthology · ACL 2026

BabelDOC: Better Layout-Preserving PDF Translation via Intermediate Representation

Yang Qi, Xiangyao Ma funstory.ai, Xiao Wang, Hao Wang Shanghai University, Rui Wang Shanghai Jiao Tong University

原文摘要

As global cross-lingual communication intensifies, language barriers in visually rich documents such as PDFs remain a practical bottleneck. Existing document translation pipelines face a tension between linguistic processing and layout preservation: text-oriented Computer-Assisted Translation (CAT) systems often discard structural metadata, while document parsers focus on extraction and do not support faithful re-rendering after translation. We introduce BabelDOC, an Intermediate Representation (IR)-based framework for layout-preserving PDF translation. BabelDOC decouples visual layout metadata from semantic content, enabling document-level translation operations such as terminology extraction, cross-page context handling, glossary-constrained generation, and formula placeholdering. The translated content is then re-anchored to the original layout through an adaptive typesetting engine. Experiments on a curated 200-page benchmark, together with human evaluation and multimodal LLM-as-a-judge evaluation, show that BabelDOC improves layout fidelity, visual aesthetics, and terminology consistency over representative baselines, while maintaining competitive translation precision. The open-source toolkit and its interactive downstream applications have garnered over 7.8k stars on GitHub https://github.com/funstory-ai/BabelDOC. A demonstration video is available at https://youtu.be/chwrlApH7a4.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.31ACL Anthology · ACL 2026

DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis

Zhichao Shi UCAS, Cehao Yang The Hong Kong University of Science and Technology, Hao Zhou, Xiaojun Wu The Hong Kong University of Science and Technology and IDEA Institution, Huajie Li International Digital Economy Academy, International Digital Economy Academy, Xuhui Jiang International Digital Economy Academy, International Digital Economy Academy, Chengjin Xu International Digital Economy Academy, Yuanzhuo Wang Chinese Academy of Sciences 等 9 位作者

原文摘要

Synthetic data has emerged as a crucial solution to the data scarcity bottleneck in large language models (LLMs), particularly for specialized domains and low-resource languages. However, the broader adoption of existing synthetic data tools is severely hindered by convoluted workflows, fragmented data standards, and limited scalability across modalities.To address these limitations, we develop DataArc-SynData-Toolkit, an open-source framework featuring: (1) a configuration-driven, end-to-end pipeline equipped with an intuitive visual interface and simplified CLI for exceptional usability; (2) a unified, quality-controllable synthesis paradigm that standardizes multi-source data generation to ensure high reusability; and (3) a highly modular architecture designed for seamless multimodal, multilingual, and multi-task adaptation.We apply the toolkit in multiple application scenarios. Experimental results demonstrate that our toolkit achieves an optimal balance between generation efficiency and data quality. By offering an end-to-end and visually interactive pipeline, DataArc-SynData-Toolkit significantly lowers the technical barrier to synthetic data generation and subsequent model training, accelerating its practical deployment in real-world applications.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.35ACL Anthology · ACL 2026

MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing

Yang Liu, Jinxuan Cai, Yishen Li, Qi Meng Beijing University of Posts and Telecommunications, Zedi Liu, Xin Li Free Org., Chen Qian Shanghai Jiaotong University, Chuan Shi Beijing University of Post and Telecommunication 等 9 位作者

原文摘要

Large language model-based (LLM-based) multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration. MAS workflows can be naturally modeled as directed computation graphs, where nodes execute agents or sub-workflows and edges encode dependencies and message passing. However, implementing complex graph workflows in current frameworks still requires substantial manual effort, offers limited reuse, and makes it difficult to integrate heterogeneous external context sources. To overcome these limitations, we present MASFactory, a graph-centric framework for orchestrating LLM-based MAS. It introduces Vibe Graphing, a human-in-the-loop approach that compiles natural-language intent into an editable workflow specification and then into an executable graph. In addition, the framework provides reusable components, skill support, multimodal message handling, and pluggable context integration, as well as a visualizer for topology preview, runtime tracing, and human-in-the-loop interaction. We evaluate MASFactory on seven public benchmarks, validating both reproduction consistency for representative MAS methods and the effectiveness of Vibe Graphing. Our code (https://github.com/BUPT-GAMMA/MASFactory, licensed under Apache-2.0) and video demonstration (https://youtu.be/ANynzVfY32k) are publicly available.

cs.CL · cs.AI · cs.MA · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.37ACL Anthology · ACL 2026

Potato 2.0: A Comprehensive Annotation Platform with AI-in-the-Loop Support

David Jurgens University of Michigan - Ann Arbor, Michael Chen, Lina Iyer

原文摘要

Annotated data remains essential for training and evaluating NLP systems. Large language models have broadened the kinds of data researchers need, including multimodal and agentic system data. Here, we introduce Potato 2.0, a major update to our open source annotation platform designed for easy deployment, customization, and fully reproducible and shareable annotation designs. Potato offers broad support for many types of annotations in NLP, including 39 different types of annotation tasks, support for text, audio, image, and video modalities, or mixtures thereof. Potato 2.0 includes robust support for labeling agentic system outputs through reading common trace formats, or live interaction and annotation with agents in multiple settings, such as chatting, web-browsing, and coding. Potato also includes multiple AI-assistance features to help annotators more easily label data. Finally, Potato introduces a new agentic AI-in-the-loop workflow where a single human annotator collaborates with an LLM through iterative prompt refinement, uncertainty-driven instance selection, and progressive autonomy—enabling efficient dataset creation without a large annotation team.

cs.CL · cs.AI · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.38ACL Anthology · ACL 2026

mllm-shap: A Shapley Value Explainability Platform for Text-Audio Multimodal Large Language Models

Jakub Muszyński CERN, Paweł Pozorski Warsaw University of Technology, Maria Ganzha Warsaw University of Technology

原文摘要

We present mllm-shap, an open-sourcePython platform for researchers and ML practitioners that extends Shapley value (SV)explainability from text-only large languagemodels to multimodal LLMs (MLLMs) thatjointly process text and audio. Buildingon the token-level SV framework introducedby TokenSHAP, mllm-shap addresses threechallenges absent in the text-only setting:(1) modality-aware coalition masking thathandles the coexistence of text tokens anddense audio encoder frames within a single input, (2) multi-turn conversation tracking withper-token role and modality metadata, and(3) audio token grouping via phonetic alignment that reduces the coalition space by 10–50 times. The platform ships as a pip-installablepackage implementing five SV estimation strategies – including a Complementary Contributions estimator with Neyman-optimal allocation that outperforms Monte Carlo baselines – together with an interactive web GUIfor real-time attribution visualization. Toour knowledge, mllm-shap is the first publicly available framework for complete, reproducible SV-based explainability of text-audioMLLMs. The package is MIT-licensed withfull source code on GitHub and a demonstration video included as supplementary material.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.62ACL Anthology · ACL 2026

The olmOCR Project: Building Fully Open OCR using VLMs

Jake Poznanski Allen Institute for Artificial Intelligence, Kyle Lo Allen Institute for Artificial Intelligence, Luca Soldaini Microsoft AI

原文摘要

We present olmOCR, a fully open OCR system developed through iterative public releases and community feedback. The system combines a 7B vision-language model trained in two stages: supervised finetuning on 260K diverse PDF pages, followed by reinforcement learning with visual unit tests over synthetic documents. Visual unit tests are binary checks of structural fidelity, including tables and equations, and serve both as an interpretable evaluation framework and as direct optimization targets. We also introduce olmOCR-Bench, a benchmark of 1.4K challenging PDFs evaluated via visual unit tests, on which olmOCR achieves state-of-the-art performance among open systems and proprietary APIs at a fraction of the cost. We have deployed olmOCR at scale to 100M+ PDFs to curate pretraining data for Olmo 3. We share lessons from our open development process and release all models, data, and code across two major releases.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.78ACL Anthology · ACL 2026

Spectra: A Mechanistic Interpretability Library for Vision-Language Models

Clement Neo Nanyang Technological University and Apart Research, Yongsen Zheng Nanyang Technological University, Kwok-Yan Lam Nanyang Technological University, Luke Ong Nanyang Technological University

原文摘要

Vision-Language Models (VLMs) have become increasingly important in AI applications, yet interpretability tools for these models lag behind those available for text-only language models. While libraries like TransformerLens have enabled significant progress in understanding language models, existing tools for VLMs are limited to basic activation probing and saving. We introduce Spectra, a library specifically designed for mechanistic interpretability of VLMs that provides unified abstractions for activation patching, attention pattern analysis, and meta-functions across diverse VLM architectures. Built on HuggingFace’s Transformers, our library handles architecture-specific complexities through per-checkpoint configurations while maintaining a simple, high-level interface. We demonstrate the library’s capabilities by performing interpretability experiments on a counting task, showing how researchers can easily perform experiments that were previously cumbersome to do. The library currently supports Qwen2.5-VL, Qwen3-VL, LLaVA 1.5 and SmolVLM, with a design that facilitates extension to additional architectures. The library can be found at github.com/clemneo/vlm-spectra.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.80ACL Anthology · ACL 2026

Praat++: Multimedia Annotation System for Speech and Vocalization

Weiran Zhang University of Texas at Arlington, Kenny Q. Zhu University of Texas at Arlington

原文摘要

High-quality time-aligned annotation is fundamental to speech processing and animal vocalization research, yet precise boundary localization and consistent labeling remain challenging in collaborative settings. We present Praat++, a web-based multimedia annotation system designed for collaborative, video-informed, and AI-assisted timeline labeling of audio and video data. The system tightly synchronizes waveform, spectrogram, pitch, intensity, and time-aligned video playback with fine-grained region-based editing, enabling precise boundary refinement and improved label accuracy within a unified interface. Praat++ further incorporates role-aware workflow management and human-in-the-loop AI-assisted pre-annotation to enhance inter-annotator consistency and reduce labeling time. Through real-world multimodal speech and animal vocalization annotation scenarios, we demonstrate that Praat++ provides an integrated infrastructure for improving annotation quality and efficiency in dataset construction workflows. The demo video (https://www.youtube.com/watch?v=YboCoBRF5lg), website (https://redgiant.uta.edu/praat) and source code (https://github.com/UTA-ACL2/PraatPlusPlus) are now publicly available.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.82ACL Anthology · ACL 2026

OpenGlass: A Sensing-Computing Split Architecture for Local MLLM-Driven Real-Time Visual Assistance

Mengzhang Li, Yuan Yao National University of Singapore

原文摘要

We present OpenGlass, an open-source, privacy-oriented, local-first system for low-latency multimodal visual assistance, with a primary focus on blind and low-vision users. Cloud MLLM assistants offer strong visual understanding, but often require uploading first-person visual data and can suffer multi-second network delays; wearable glasses are ideal for sensing, but cannot host large models under tight compute and power budgets. OpenGlass addresses this gap with a sensing-computing split: an ESP32-based glasses-side unit captures visual context, while a nearby consumer-grade device performs local MLLM inference and local speech output, reducing cloud reliance and keeping raw egocentric visual data on user-controlled devices by default. We evaluate response quality, query-ready-to-audio latency, safety-aware abstention, and auditable logs. Under real ESP32 Wi-Fi capture, OpenGlass reaches 993 ms median user-to-audio latency with resized payloads and 1625 ms with raw 1280\times720 payloads; 97.5% and 93.3% of trials fall below 2 s, respectively. OpenGlass is a user-initiated visual-assistance reference platform for obstacle/hazard awareness, sign/object queries, and image-quality self-checking, rather than a certified navigation aid. We release source code, hardware instructions, prompts, evaluation data, and logs.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.12ACL Anthology · ACL 2026

DocSplit: A Comprehensive Benchmark Dataset and Evaluation Approach for Document Packet Recognition and Splitting

Md Mofijul Islam, Md Sirajus Salekin, Nivedha Balakrishnan, Vincil C. Bishop III, Niharika Jain, Spencer Romo, Bob Strahan, Boyi Xie 等 9 位作者

原文摘要

Document understanding in real-world applications often requires processing heterogeneous, multi-page document packets containing multiple documents stitched together. Despite recent advances in visual document understanding, the fundamental task of document packet splitting, which involves separating a document packet into individual units, remains largely unaddressed. We present the first comprehensive benchmark dataset, \textit{DocSplit}, along with novel evaluation metrics for assessing the document packet splitting capabilities of large language models. \textit{DocSplit} comprises five datasets of varying complexity, covering diverse document types, layouts, and multimodal settings. We formalize the \textit{DocSplit} task, which requires models to identify document boundaries, classify document types, and maintain correct page ordering within a document packet. The benchmark addresses real-world challenges, including out-of-order pages, interleaved documents, and documents lacking clear demarcations. We conduct extensive experiments evaluating multimodal LLMs on our datasets, revealing significant performance gaps in current models’ ability to handle complex document splitting tasks. The \textit{DocSplit} benchmark datasets and proposed novel evaluation metrics provide a systematic framework for advancing document understanding capabilities essential for legal, financial, healthcare, and other document-intensive domains. We release the datasets and evaluation code to facilitate future research in document packet processing.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.126ACL Anthology · ACL 2026

Multi-Task Reinforcement Learning for Enhanced Multimodal LLM-as-a-Judge

Junjie Wu, Xuan Kan, Zihao He, Shunwen Tan, Bo Pan, Kaitai Zhang

原文摘要

Multimodal Large Language Models (MLLMs) have been widely adopted as MLLM-as-aJudges due to their strong alignment with human judgment across various visual tasks. However, most existing judge models are optimized for single-task scenarios and struggle to generalize to diverse contexts, which is a critical requirement for reliable evaluation. To address this limitation, we propose Multi-Task Reinforcement Learning for MLLM-as-a-Judge (MT-RL-Judge), a framework that jointly optimizes the judge model across multiple tasks, leveraging the generalization capabilities of RL. Experimental results against several strong baselines demonstrate that MT-RL-Judge outperforms strong baselines in both judgment consistency and correlation with human preferences. Furthermore, our approach exhibits robust generalization on out-of-distribution tasks, further validating its effectiveness.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.134ACL Anthology · ACL 2026

MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A

Hanoz Bhathena, Parin Rajesh Jhaveri, Rohan Mittal, Prateek Singh, Aymen Kallala, Rachneet Kaur, Yiqiao Jin, Zhen Zeng 等 10 位作者

原文摘要

Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and for answer generation. While efficient, this trend often neglects explicit handling of the rich, structured information in complex enterprise documents, instead depending on pre-trained embeddings or vision-language models to implicitly capture such structure. In this work, we take a more direct approach: MM-BizRAG proactively extracts and represents document structure via a document structure-aware split that dynamically routes documents through orientation-specific ingestion pipelines, applying explicit layout-aware parsing for vertically structured documents (e.g., reports) and holistic page-level representations for horizontally structured documents (e.g., slide decks). A unified LLM-driven artifact transformation pipeline with placeholder-based positional alignment preserves natural reading order, while inference-time multimodal assembly decouples retrieval representations from generation context, enabling richer, more grounded answers without any finetuning requirement. Through experiments on a large, heterogeneous enterprise dataset and two public benchmarks (SlideVQA and FinRAGBench-V), MM-BizRAG consistently outperforms state-of-the-art vision-centric baselines by up to 32% points, with especially strong gains on report-style layouts. Furthermore, we introduce FastRAGEval, a single-call LLM Judge metric for fine-grained generative recall that halves RAGChecker’s cost while achieving stronger human alignment.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.143ACL Anthology · ACL 2026

MTIVE: Multi-Task Image Verification Engine Using Vision-Language Models for E-commerce

Yu-Tong Cao, Vishnu Prabhakaran, Arunita Das, Purav Aggarwal, Anoop Saladi

原文摘要

Vision-language models show promise for e-commerce automation but struggle with noisy real-world images and multi-task requirements. We introduce MTIVE, a curriculum learning framework that progressively adapts base models through three stages: continued pre-training on large-scale e-commerce datasets with contrastive learning and diverse dialogue templates, instruction tuning on synthetic data, and modular task-specific expert training. Our architecture uses frozen base weights with stacked LoRA adapters—shared modules for domain knowledge and lightweight task-specific experts—enabling continual learning without catastrophic forgetting. MTIVE outperforms open-source and proprietary baselines in both standard and continual learning settings.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表