The Public Discourse Corpus (PDC) is the first dataset of public‑figure interview speech annotated for affective valence and epistemic modality. It contains 998 videos from 100 speakers across seven professional domains, yielding 186,642 sentences (3.1 million words). A key methodological contribution is Target Speaker Participation (TSP), a five‑category annotation taxonomy with documented inter‑annotator reliability (κ = 0.616), and an audio‑first diarization pipeline that separates target‑speaker turns from interviewer and third‑party speech. The corpus, annotation tools, validation sample, and processing pipeline are released as open source.
By Bo Chen
The study investigates how document segmentation and chunk representation affect retrieval-augmented generation (RAG) for chemistry texts. Using the ChemQuests corpus, the authors benchmark 41 embedding models and evaluate them across five chunking strategies, seven chunk sizes, and various overlap settings. They find that embedding choice has the largest impact, with models like E5, BGE, and Nomic performing best, and recommend medium-to-large chunks with fixed-token, recursive-token, or hierarchical-section chunking and low overlap for effective chemistry-aware RAG.
By Mahmoud Amiri, Thomas Bocklitz
FreqDINO++ is a frequency‑guided multi‑task routing vision foundation model designed for universal ultrasound analysis. It introduces a Multi‑task Routing Adapter for efficient task‑common and task‑specific integration, a Frequency‑aware Feature Enhancer to capture multi‑scale frequency characteristics, and a Task‑aligned Collaborative Decoder that promotes collaboration between dense and global prediction tasks. Experiments on large‑scale multi‑task and external single‑task ultrasound benchmarks show that FreqDINO++ outperforms strong baselines and recent foundation models across 27 diverse clinical task scenarios, with promising generalization to unseen data.
By Qing Xu, Yixuan Zhang, Yue Li, Xiangjian He, Qian Zhang, Mainul Haque, Rong Qu, Wenting Duan, Jieyun Bai, Zhen Chen
PART is a transformer-based framework that performs 3D part retrieval and assembly by selecting appropriate parts from a library and predicting their 6‑DoF poses to reconstruct a target shape. It tackles the combinatorial search space, variable‑length outputs, and continuous pose estimation by formulating the task as set prediction and jointly training with a segmentation‑enhanced optimization module. The authors also introduce a large‑scale dataset of over 80,000 shapes and demonstrate that PART generalizes to scene layouts, image targets, and real‑world scans.
By Ruchao Bao, Wenzheng Wu, Chucheng Xiang, Zhongyuan Liu, Yuan Liu, Jinxin Dong, Ligang Liu, Ziqi Wang
BinoGen is an automated framework that generates large-scale, embodiment-aware egocentric binocular visual experiences in indoor environments. It models environmental and observer variation through generative scene synthesis, probabilistic object instantiation, appearance randomization, stochastic trajectory generation, and configurable binocular camera setups, producing synchronized videos with dense multimodal supervision such as depth maps, optical flow, surface normals, semantic maps, object coordinates, and camera poses. Using BinoGen, the authors created a dataset of over 20 million annotated images, demonstrating that incorporating this data improves real-world visual perception tasks like depth estimation, object detection, and video object tracking, and that embodiment-specific adaptation enhances performance while joint training enables a single model to perform competitively across different observer embodiments.
By Chunpeng Li, Ya-tang Li
The paper introduces BADA, a Boundary-Aware Data Augmentation technique for offline reinforcement learning. By interpolating neighboring states to create synthetic data that respects the original distribution, BADA improves in-distribution generalization and robustness. Experiments on limited offline datasets show that BADA achieves state-of-the-art performance across diverse benchmarks.
By Gong Gao, Weidong Zhao, Xianhui Liu
PerSeM is a training‑free framework that builds a persistent semantic memory for long‑horizon UAV mapping by associating frame‑wise segmentation results with world‑space voxels and refining them through spatial refinement, trust‑aware replay, and context‑guided verification. Experiments on Forest and UAVScenes benchmarks show that this persistent 3D memory improves semantic correctness and temporal stability compared to frame‑wise predictions, especially in semantically difficult and temporally unstable regions. The method achieves these gains without retraining or additional neural‑network inference.
By Saurbh Singh Jamwal, Ganesh Ramakrishnan
MoSSGate is a plug‑and‑play module for U‑Net that improves skin lesion segmentation by combining boundary‑aware spatial gating, an external memory modulator, and parallel 2D state‑space modeling for efficient global context aggregation. The design limits long‑range propagation to informative regions, adapts dynamically to each sample, and preserves sharp lesion boundaries while keeping computational cost low. Experiments on ISIC 2017 and 2018 show state‑of‑the‑art accuracy (86.3%/85.9% mIoU, 92.6%/90.6% Dice) with fewer FLOPs than most CNN baselines.
By Anum Awan, Mahnoor Buriro, Muhammad Younas Khan, Md Imam Ahasan
The paper introduces a privacy‑preserving approach to open‑vocabulary 3D semantic segmentation that operates solely on depth data, eliminating the use of RGB images to avoid disclosing scene‑specific visual information. It proposes a stricter depth‑only evaluation protocol and presents UTTO, a model‑agnostic uncertainty‑guided test‑time optimization framework that refines predictions from frozen open‑vocabulary 3D backbones using structured predictive uncertainty. Experiments on ScanNet and Matterport3D show consistent improvements, and additional analyses demonstrate the method’s relevance for privacy‑constrained robotic applications.
By Xuying Huang, Sicong Pan, Maren Bennewitz
LinePilot Digitizer is a tool for extracting numerical data from line plots, offering three calibration modes—standard, enhanced, and OCR—that combine continuous color-based curve recovery with flexible calibration. The authors introduce DigitizerBench, a benchmark that systematically evaluates digitizer performance across signal, rendering, and plot-structure factors, using a failure-penalized capped normalized root-mean-square error metric. Results show that LinePilot (OCR) and LinePilot (enhanced) achieve the lowest error rates and highest trusted usability on their respective benchmark subsets, demonstrating the effectiveness of the calibration modes and the benchmark’s factor analysis capabilities.
By Fengbo Ma, Rayan Akhtar, Aakash H. Joshi, Xiaoting Li, Haijian Sun, Zhen Xiang, Xianyan Chen, Yiping Zhao
The paper presents a method for dense univariate time‑series segmentation by treating each time point as a node in a graph and applying a Graph Attention Network (GAT). Using visibility‑based graph constructions, particularly WDPVG, the approach achieves competitive performance on a 59‑dataset benchmark, matching or surpassing traditional point‑wise classifiers while requiring far fewer parameters and less memory. The method’s advantage stems from long‑range edges that provide contextual information beyond local windows.
By Ivana Kesi\'c, Carolina Fortuna, Mihael Mohor\v{c}i\v{c}, Bla\v{z} Bertalani\v{c}
The paper introduces DynaWeightPnP, a real‑time algorithm for correspondence‑free Perspective‑n‑Point (PnP) problems that aligns 3D and 2D shapes without needing point correspondences. It uses a Reproducing Kernel Hilbert Space formulation solved via iterative reweighted least squares, and addresses a numerical ambiguity between rotation and translation with a dynamic weighting sub‑problem and alternative search strategy. Experiments on 3D‑2D vascular centerline registration in endovascular image‑guided interventions show processing rates of 60 Hz (without refinement) and 31 Hz (with refinement) on a single‑core CPU, achieving accuracy comparable to existing methods.
By Jingwei Song, Maani Ghaffari
PointEvent introduces serialized motion evidence accumulation for event-based tiny object detection, treating motion continuity as an ordered evidence propagation process. The method organizes event streams into locality‑preserving spatiotemporal paths and chronology‑preserving temporal paths, alternating serialized scans across complementary orders to consolidate fragmented motion evidence. A lightweight event‑wise state‑space framework with a high‑resolution event branch and compact context modulation achieves state‑of‑the‑art performance with the fewest parameters and fastest inference among compared methods.
By Zongze Wu, Baofeng Jia, Weiqi Yan, Jingyuan Zhang, Yu Zang, Xiaoyu Chen, Jing Han
AgentPProf is a new semantic profiler designed for long‑horizon AI agents that aggregates agent trajectories into pprof‑compatible profiles, enabling flame‑graph visualization and hierarchical attribution of tasks and subtasks. It introduces a semantic operation stack model and recursive operation segmentation to replace traditional call‑stack profiling, addressing the challenge of profiling agent intent rather than code paths. In evaluations, AgentPProf achieves high F1 scores against human annotations and significantly improves problem‑localization metrics, demonstrating its effectiveness in attributing resources, locating issues, and optimizing token cost.
By Yusheng Zheng, Chaokun Chang, Yu Mao, Tianyuan Wu, Yuxi Huang, Tao Ma, Wenan Mao, Shuyi Cheng, Andi Quinn, Wei Wang
The paper introduces Teacher Prediction Refinement Distillation (TPRD), a plug‑in module for Detection Transformers that refines teacher predictions before distillation. TPRD corrects degraded positive predictions and suppresses overconfident negatives, while preserving informative dark knowledge through Maximum Dark Knowledge Preservation. Experiments on MS COCO and PASCAL VOC show that these refinements improve the quality of supervision and the resulting student model’s performance.
By Yitong Xing, Yuhao Cheng, Yanping Li, Yichao Yan
TRACE is a training‑free, agentic retrieval framework that enables accountable source discovery in historical archives, addressing challenges such as OCR degradation and genre heterogeneity. Developed for the DECIDON project on French Third Republic political discourse, it is deployed internally for 24 researchers across six institutions. On the HistoriQA‑ThirdRepublic benchmark, TRACE achieves R@10 of 0.856 and MRR of 0.653, outperforming sparse, dense, graph‑based, and other agentic RAG baselines, especially on multi‑hop and cross‑corpus questions, while costing only about $0.02 per question.
By Donghan Bian (ENC, LRE), Marie Puren (LRE, ENC), Florian Cafiero (LRE, ENC)
FunArt is a framework that builds articulation‑aware functional 3D scene graphs from a single static RGB‑D observation. It reconstructs object instances, converts their geometry into the O‑Voxel representation of TRELLIS.2, and uses a frozen sparse‑compression VAE as a structural prior. A lightweight query‑based decoder jointly segments movable parts and functional interactive elements while estimating motion type, axis, origin, and range, achieving state‑of‑the‑art results on the Articulate3D dataset.
By Dennis Rotondi, Abdelrhman Werby, Kai O. Arras
G^2RA‑Net is a medical image segmentation framework that models cross‑slice relations using a graph-based approach and refines segmentation with a cross‑slice attention gate. It represents each image slice as a graph node, propagates semantic context through graph message passing, and then selects relevant neighboring context to emphasize target anatomical regions. Experiments on brain MRI and abdominal CT datasets show that G^2RA‑Net improves segmentation accuracy and boundary quality compared to representative methods, with ablation studies confirming the effectiveness of its design.
By Shengye Wang, Zonglin Wu, Liang Fan, Yule Xue, Haozhe Zhao
The review surveys 4D millimeter‑wave radar perception algorithms for autonomous driving, covering signal processing, object detection, semantic segmentation, motion estimation, occupancy prediction, and dynamic scene reconstruction. It organizes the field by perception tasks, discusses radar fundamentals, data representations, and quality‑enhancement methods, and compares radar‑only learning, multimodal fusion, and cross‑modal supervision. The paper also summarizes datasets, annotations, evaluation protocols, and outlines common challenges and future research directions.
By Xumin Wu, Jun Zhou, Jilin Mei, Chen Min, Yu Hu
SeetaPsych v1.0 is an open‑source computer vision toolkit that unifies and extends modules for behavior‑based psychological measurement, focusing on facial image and video analysis. It includes four core modules—emotion analysis, camera‑based heart rate estimation, screen point‑of‑gaze estimation, and scene gaze following—alongside preprocessing tools such as face, landmark, and head detection. The toolkit offers a modular Pipeline/Runner architecture, standardized Python APIs, and an interactive WebUI to enable reproducible, large‑scale research across psychology, behavioral science, and human‑computer interaction.
By Jiabei Zeng, Chiqin Li, Kaizhou Li, Fei Chang, Yong Li, Yuanhao Zhao, Dan Han, Wenqiang Yang, Xilin Chen, Shiguang Shan