The paper introduces MAD2, a synthetic benchmark of 1,000 two‑speaker dialogues with about 10 hours of audio and 1,230 check‑worthy sentence annotations for spoken claim verification. It proposes a calibrated multimodal fusion approach that combines a context‑aware audio encoder with a dialogue‑aware text model. Experiments show that adding dialogue context improves verification performance, though the gains differ across scenarios, and that fusion offers the largest advantage when full‑dialogue context is available, though it does not consistently outperform text alone.
By Chaewan Chun, Delvin Ce Zhang, Dongwon Lee
The article surveys brain‑to‑language decoding, covering tasks, neural signals, methods, and evaluation across invasive and non‑invasive modalities. It traces the field’s evolution from constrained recognition to text generation, streaming speech, and facial animation, linking tasks to neural populations and decoder representations. The review highlights complementary decoding targets, shared representations, and the growing importance of calibration, feedback, and user control for online communication, while proposing a five‑level future trajectory toward bidirectional cognitive exchange.
By Yiqian Yang, Yiqun Duan, Chenyu Liu, Yiqi Wang, Xinliang Zhou, Chin-Teng Lin, Yu Zhang
The paper investigates when vision‑language models (VLMs) can independently analyze human‑centered video and when human oversight is still needed. By reviewing 1,702 CHI 2026 papers, the authors develop a five‑dimensional taxonomy of video annotation tasks and build a benchmark of 15 representative tasks. Experiments show that VLMs alone achieve near‑human accuracy (HNS = 97.0), while human verification of VLM outputs yields the highest accuracy (HNS = 121.5) and significantly reduces annotation time and cost.
By Xiyuan Shen, Jiuyang Lyu, Seokhyun Hwang, Huanfen Yao, Shwetak Patel, Zhihan Zhang, Jacob O. Wobbrock
The paper introduces S2A, a semantic-to-spatial alignment framework designed for alignment‑free RGB‑T salient object detection. It employs a global‑guided hierarchical fusion module to refine intra‑modal features, an alignment‑free cross‑modal channel attention module to exchange semantic information, and a spatial deformable cross‑attention module to recover local spatial correspondence. These components collectively reduce misalignment‑induced feature contamination and achieve competitive performance on public benchmarks without additional bells and whistles.
By Qiangqiang Zhou, Yang Luo, Yong Chen, Jiawei Xu
SatUnreal is a synthetic dataset created with Unreal Engine that offers 10,000 high‑resolution (0.3 m GSD) satellite stereo pairs. It addresses key limitations of existing benchmarks by ensuring physical geometry simulation, spatio‑temporal consistency, topographic diversity, and mathematically precise occlusion masks via a two‑step line‑trace algorithm. Models trained solely on SatUnreal outperform those trained on real datasets when transferred to real‑world benchmarks such as US3D and WHU‑Stereo.
By Han-Gyeol Kim, JaeWan Park, Junmin Park, Darongsae Kwon
SynSeq is a video‑based method that directly predicts the SYNTAX score from coronary angiography videos, using targeted preprocessing and a zero‑inflation‑aware loss with linear target scaling. On the CardioSyntax dataset it outperforms prior state‑of‑the‑art approaches, improving $R^2$ by 0.55, reducing prediction bias by 93.1%, and delivering consistent performance across three expert graders. The model also achieves a weighted $F_1$‑score of 0.80 for revascularization treatment recommendations, approaching inter‑expert agreement.
By Christoph Baumann, Ronny Schweitzer, Noemi Pavo, Ulrike Attenberger, Christian Loewe, Philipp Seeb\"ock
DualStabSleepNet (DSSNet) is a dual-domain diffusion stabilization network designed to improve the robustness of automatic sleep staging across heterogeneous recording conditions. It employs a continuous-scale diffusion-based module to suppress noise while preserving physiological signals, then transforms stabilized signals into time-frequency representations for a Vision Transformer backbone. A teacher‑student guided diffusion feature stabilization further reduces feature drift, achieving state‑of‑the‑art accuracy on four public PSG datasets and demonstrating strong performance under cross‑dataset distribution shifts.
By Chongjian Wang, Chen Liu, Junjie Gao, Xiaofang Zhong, Shiyuan Han, Tong Zhang
LiAM‑SAM is a lifecycle‑aware memory framework designed to improve segmentation‑based multi‑object tracking (MOT) with the SAM2 foundation video model. It addresses three common failure modes—faulty track initiation, memory drift during close interactions, and unreliable re‑identification after occlusion—by introducing contrastive track initiation, motion‑ and geometry‑grounded memory correction, and adaptive context memory. The system achieves state‑of‑the‑art HOTA and IDF1 scores, with ablations showing significant gains in association metrics and a 96% reduction in identity switches.
By Gr\'egoire Francisco, Alessandro D'Amico, Samuele Costantini, Gianpiero Francesca, Lorenzo Garattoni
OD3 introduces an optimization‑free dataset distillation framework tailored for object detection. The method first iteratively places object instances in synthesized images, then screens candidates with a pre‑trained observer model to discard low‑confidence objects. Applied to MS COCO and PASCAL VOC, OD3 achieves compression ratios from 0.25% to 5% and surpasses previous detection‑focused distillation methods by over 14% on COCO mAP50 at a 1.0% compression ratio.
By Salwa K. Al Khatib, Ahmed ElHagry, Shitong Shao, Zhiqiang Shen
arXiv:2606.27264v3 Announce Type: replace
Abstract: Reasoning in multimodal large language models (MLLMs) has shown strong promise in medical imaging. However, this reasoning is usually free-form tex...
By Hashmat Shadab Malik, Anees Ur Rehman Hashmi, Numan Saeed, Muzammal Naseer, Salman Khan, Christoph Lippert
arXiv:2605.23137v3 Announce Type: replace-cross
Abstract: Electroencephalography (EEG) visual decoding remains challenging due to the modality gap between low-SNR neural signals and highly structured...
By Jiahe Meng, Weiming Zeng, Yueyang Li, Bo Chai, Hongjie Yan, Zhiguo Zhang, Wai Ting Siok, Nizhuan Wang
arXiv:2609.28371v1 Announce Type: new
Abstract: Generalized Langevin equations describe non-Markovian dynamics in which the evolution of resolved variables depends on their past. We propose a memory-...
By Minglei Yang, Sicheng He
arXiv:2609.27987v1 Announce Type: new
Abstract: Large language models (LLMs) have made substantial progress on medical question-answering, yet effective medical dialogue also requires learning to ask...
By Chenxuan Li, Jiayi Wan, Xinrong Chen, Zhongyu Zhao, Xuecheng Shang, Peixing Wan
arXiv:2609.27822v1 Announce Type: cross
Abstract: A common multi-agent design asks agents to report confidence and lets the highest-scoring agent speak next, implicitly using one scalar both to route...
By Jingyan Jiang, Huihuo Zheng, Rajeev Thakur, Chih-Hsuan Yang
arXiv:2603.08166v2 Announce Type: replace
Abstract: Automated Drug Combination Extraction (DCE) from large-scale biomedical literature is crucial for advancing precision medicine and pharmacological...
By Zhijun Wang, Ling Luo, Dinghao Pan, Huan Zhuang, Lejing Yu, Yuanyuan Sun, Hongfei Lin
The paper introduces Reinforcement Learning with Decomposed Subtasks (RLDS), a method that splits trajectory rewards into per‑subtask shares before policy updates, replacing the scalar advantage used in Group Relative Policy Optimization (GRPO). RLDS employs Subtask‑Decomposed Advantage Estimation (SDAE) to compute group‑relative advantages and distribute credit to tokens based on subtask importance, focusing on steps where a reflection marks a subtask as consequential. Experiments on four benchmarks—FrozenLake, HotpotQA, ScienceWorld, and DeepResearch—show that RLDS improves performance on high‑heterogeneity tasks (ScienceWorld and FrozenLake) and is more compute‑efficient than scalar GRPO for long rollouts.
By Mattie Terzolo, Mikolaj Sacha, Ayan Sinha, Andrew Rabinovich
NV-Reason-CT is a generative vision‑language model designed for chest and abdominal CT analysis that preserves native 3D visual encoding and incorporates radiologist‑guided reasoning. The system couples a 3D vision transformer with a language model, feeding all visual tokens and their 3D coordinates directly into language decoding to maintain volumetric spatial information. Trained on a curated corpus of about 550,000 multimodal instruction examples, the model supports abnormality classification, report generation, and interactive reasoning, achieving strong performance on CT benchmarks and reducing expert interpretation time by 50%.
By Andriy Myronenko, Dong Yang, Yucheng Tang, Baris Turkbey, Benjamin Simon, Stephanie Harmon, Rikhil Makwana, Mariam Aboian, Sena Azamat, Ibrahim Ethem Hamamci, Sezgin Er, Bjoern Menze, Marc Edgar, Yufan He, Pengfei Guo, Daguang Xu
arXiv:2609.28199v1 Announce Type: new
Abstract: Long physiological recordings contain many routine measurements, while predictive information is often concentrated in rare events, sustained burden, a...
By Tian Zhou, Bingqing Peng, Linxiao Yang, Wenwei Wang, Mengni Ye, Beverly Jin, Zuyi Zhu, Jinjie Gu, Liang Sun
The paper introduces RefineICL, an attention‑gated, feed‑forward‑network‑free framework that refines representations in situ for tabular foundation models. By using support labels to guide episode‑specific updates, the method transfers learned corrections to unlabeled queries without altering model parameters, achieving state‑of‑the‑art performance on AMLB29 and TabArena benchmarks. Experiments and internal interventions demonstrate that intermediate support updates are essential for constructing task‑specific predictors in context.
By Tian Zhou, Beverly Jin, Linxiao Yang, Xue Wang, Wenwei Wang, Bingqing Peng, Mengni Ye, Jinjie Gu, Liang Sun
arXiv:2604.08602v2 Announce Type: replace-cross
Abstract: Server-based screening tools impose subscription costs, while open-source alternatives require coding skills, and full-text screening has rem...
By Yuki Kataoka, Masahiro Banno, Michihito Kyo, Shuri Nakao, Tomoo Sato, Shunsuke Taito, Tomohiro Takayama, Takahiro Tsuge, Yasushi Tsujimoto, Ryuhei So, Toshi A. Furukawa